diff --git a/.gitattributes b/.gitattributes index 9eb4f4c773d76ee39354d593df25f66a600221ee..f4a33fd66ece4ed0b23cf522ac5d58a29beb16b6 100644 --- a/.gitattributes +++ b/.gitattributes @@ -115,3 +115,4 @@ tuning-competition-baseline/.venv/lib/python3.11/site-packages/torch/_inductor/_ .venv/lib/python3.11/site-packages/pycparser/__pycache__/yacctab.cpython-311.pyc filter=lfs diff=lfs merge=lfs -text .venv/lib/python3.11/site-packages/torchvision.libs/libz.5f199d92.so.1 filter=lfs diff=lfs merge=lfs -text .venv/lib/python3.11/site-packages/torchvision.libs/libjpeg.ceea7512.so.62 filter=lfs diff=lfs merge=lfs -text +.venv/lib/python3.11/site-packages/attr/__pycache__/_make.cpython-311.pyc filter=lfs diff=lfs merge=lfs -text diff --git a/.venv/lib/python3.11/site-packages/aiosignal-1.3.2.dist-info/INSTALLER b/.venv/lib/python3.11/site-packages/aiosignal-1.3.2.dist-info/INSTALLER new file mode 100644 index 0000000000000000000000000000000000000000..a1b589e38a32041e49332e5e81c2d363dc418d68 --- /dev/null +++ b/.venv/lib/python3.11/site-packages/aiosignal-1.3.2.dist-info/INSTALLER @@ -0,0 +1 @@ +pip diff --git a/.venv/lib/python3.11/site-packages/aiosignal-1.3.2.dist-info/LICENSE b/.venv/lib/python3.11/site-packages/aiosignal-1.3.2.dist-info/LICENSE new file mode 100644 index 0000000000000000000000000000000000000000..7082a2d5b9047bfc09589f387053e24ea490bc54 --- /dev/null +++ b/.venv/lib/python3.11/site-packages/aiosignal-1.3.2.dist-info/LICENSE @@ -0,0 +1,201 @@ +Apache License + Version 2.0, January 2004 + http://www.apache.org/licenses/ + + TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION + + 1. Definitions. + + "License" shall mean the terms and conditions for use, reproduction, + and distribution as defined by Sections 1 through 9 of this document. + + "Licensor" shall mean the copyright owner or entity authorized by + the copyright owner that is granting the License. + + "Legal Entity" shall mean the union of the acting entity and all + other entities that control, are controlled by, or are under common + control with that entity. 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For the purposes + of this License, Derivative Works shall not include works that remain + separable from, or merely link (or bind by name) to the interfaces of, + the Work and Derivative Works thereof. + + "Contribution" shall mean any work of authorship, including + the original version of the Work and any modifications or additions + to that Work or Derivative Works thereof, that is intentionally + submitted to Licensor for inclusion in the Work by the copyright owner + or by an individual or Legal Entity authorized to submit on behalf of + the copyright owner. For the purposes of this definition, "submitted" + means any form of electronic, verbal, or written communication sent + to the Licensor or its representatives, including but not limited to + communication on electronic mailing lists, source code control systems, + and issue tracking systems that are managed by, or on behalf of, the + Licensor for the purpose of discussing and improving the Work, but + excluding communication that is conspicuously marked or otherwise + designated in writing by the copyright owner as "Not a Contribution." + + "Contributor" shall mean Licensor and any individual or Legal Entity + on behalf of whom a Contribution has been received by Licensor and + subsequently incorporated within the Work. + + 2. Grant of Copyright License. Subject to the terms and conditions of + this License, each Contributor hereby grants to You a perpetual, + worldwide, non-exclusive, no-charge, royalty-free, irrevocable + copyright license to reproduce, prepare Derivative Works of, + publicly display, publicly perform, sublicense, and distribute the + Work and such Derivative Works in Source or Object form. + + 3. Grant of Patent License. Subject to the terms and conditions of + this License, each Contributor hereby grants to You a perpetual, + worldwide, non-exclusive, no-charge, royalty-free, irrevocable + (except as stated in this section) patent license to make, have made, + use, offer to sell, sell, import, and otherwise transfer the Work, + where such license applies only to those patent claims licensable + by such Contributor that are necessarily infringed by their + Contribution(s) alone or by combination of their Contribution(s) + with the Work to which such Contribution(s) was submitted. If You + institute patent litigation against any entity (including a + cross-claim or counterclaim in a lawsuit) alleging that the Work + or a Contribution incorporated within the Work constitutes direct + or contributory patent infringement, then any patent licenses + granted to You under this License for that Work shall terminate + as of the date such litigation is filed. + + 4. Redistribution. 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The contents + of the NOTICE file are for informational purposes only and + do not modify the License. You may add Your own attribution + notices within Derivative Works that You distribute, alongside + or as an addendum to the NOTICE text from the Work, provided + that such additional attribution notices cannot be construed + as modifying the License. + + You may add Your own copyright statement to Your modifications and + may provide additional or different license terms and conditions + for use, reproduction, or distribution of Your modifications, or + for any such Derivative Works as a whole, provided Your use, + reproduction, and distribution of the Work otherwise complies with + the conditions stated in this License. + + 5. Submission of Contributions. Unless You explicitly state otherwise, + any Contribution intentionally submitted for inclusion in the Work + by You to the Licensor shall be under the terms and conditions of + this License, without any additional terms or conditions. + Notwithstanding the above, nothing herein shall supersede or modify + the terms of any separate license agreement you may have executed + with Licensor regarding such Contributions. + + 6. Trademarks. This License does not grant permission to use the trade + names, trademarks, service marks, or product names of the Licensor, + except as required for reasonable and customary use in describing the + origin of the Work and reproducing the content of the NOTICE file. + + 7. Disclaimer of Warranty. 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In no event and under no legal theory, + whether in tort (including negligence), contract, or otherwise, + unless required by applicable law (such as deliberate and grossly + negligent acts) or agreed to in writing, shall any Contributor be + liable to You for damages, including any direct, indirect, special, + incidental, or consequential damages of any character arising as a + result of this License or out of the use or inability to use the + Work (including but not limited to damages for loss of goodwill, + work stoppage, computer failure or malfunction, or any and all + other commercial damages or losses), even if such Contributor + has been advised of the possibility of such damages. + + 9. Accepting Warranty or Additional Liability. While redistributing + the Work or Derivative Works thereof, You may choose to offer, + and charge a fee for, acceptance of support, warranty, indemnity, + or other liability obligations and/or rights consistent with this + License. However, in accepting such obligations, You may act only + on Your own behalf and on Your sole responsibility, not on behalf + of any other Contributor, and only if You agree to indemnify, + defend, and hold each Contributor harmless for any liability + incurred by, or claims asserted against, such Contributor by reason + of your accepting any such warranty or additional liability. + + END OF TERMS AND CONDITIONS + + APPENDIX: How to apply the Apache License to your work. + + To apply the Apache License to your work, attach the following + boilerplate notice, with the fields enclosed by brackets "{}" + replaced with your own identifying information. (Don't include + the brackets!) The text should be enclosed in the appropriate + comment syntax for the file format. We also recommend that a + file or class name and description of purpose be included on the + same "printed page" as the copyright notice for easier + identification within third-party archives. + + Copyright 2013-2019 Nikolay Kim and Andrew Svetlov + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. diff --git a/.venv/lib/python3.11/site-packages/aiosignal-1.3.2.dist-info/METADATA b/.venv/lib/python3.11/site-packages/aiosignal-1.3.2.dist-info/METADATA new file mode 100644 index 0000000000000000000000000000000000000000..6839bf9c50f5862f05e98ce66b7fd96740c3b4e6 --- /dev/null +++ b/.venv/lib/python3.11/site-packages/aiosignal-1.3.2.dist-info/METADATA @@ -0,0 +1,123 @@ +Metadata-Version: 2.1 +Name: aiosignal +Version: 1.3.2 +Summary: aiosignal: a list of registered asynchronous callbacks +Home-page: https://github.com/aio-libs/aiosignal +Maintainer: aiohttp team +Maintainer-email: team@aiohttp.org +License: Apache 2.0 +Project-URL: Chat: Gitter, https://gitter.im/aio-libs/Lobby +Project-URL: CI: GitHub Actions, https://github.com/aio-libs/aiosignal/actions +Project-URL: Coverage: codecov, https://codecov.io/github/aio-libs/aiosignal +Project-URL: Docs: RTD, https://docs.aiosignal.org +Project-URL: GitHub: issues, https://github.com/aio-libs/aiosignal/issues +Project-URL: GitHub: repo, https://github.com/aio-libs/aiosignal +Classifier: License :: OSI Approved :: Apache Software License +Classifier: Intended Audience :: Developers +Classifier: Programming Language :: Python +Classifier: Programming Language :: Python :: 3 +Classifier: Programming Language :: Python :: 3 :: Only +Classifier: Development Status :: 5 - Production/Stable +Classifier: Operating System :: POSIX +Classifier: Operating System :: MacOS :: MacOS X +Classifier: Operating System :: Microsoft :: Windows +Classifier: Framework :: AsyncIO +Requires-Python: >=3.9 +Description-Content-Type: text/x-rst +License-File: LICENSE +Requires-Dist: frozenlist>=1.1.0 + +========= +aiosignal +========= + +.. image:: https://github.com/aio-libs/aiosignal/workflows/CI/badge.svg + :target: https://github.com/aio-libs/aiosignal/actions?query=workflow%3ACI + :alt: GitHub status for master branch + +.. image:: https://codecov.io/gh/aio-libs/aiosignal/branch/master/graph/badge.svg + :target: https://codecov.io/gh/aio-libs/aiosignal + :alt: codecov.io status for master branch + +.. image:: https://badge.fury.io/py/aiosignal.svg + :target: https://pypi.org/project/aiosignal + :alt: Latest PyPI package version + +.. image:: https://readthedocs.org/projects/aiosignal/badge/?version=latest + :target: https://aiosignal.readthedocs.io/ + :alt: Latest Read The Docs + +.. image:: https://img.shields.io/discourse/topics?server=https%3A%2F%2Faio-libs.discourse.group%2F + :target: https://aio-libs.discourse.group/ + :alt: Discourse group for io-libs + +.. image:: https://badges.gitter.im/Join%20Chat.svg + :target: https://gitter.im/aio-libs/Lobby + :alt: Chat on Gitter + +Introduction +============ + +A project to manage callbacks in `asyncio` projects. + +``Signal`` is a list of registered asynchronous callbacks. + +The signal's life-cycle has two stages: after creation its content +could be filled by using standard list operations: ``sig.append()`` +etc. + +After you call ``sig.freeze()`` the signal is *frozen*: adding, removing +and dropping callbacks is forbidden. + +The only available operation is calling the previously registered +callbacks by using ``await sig.send(data)``. + +For concrete usage examples see the `Signals + +section of the `Web Server Advanced +` chapter of the `aiohttp +documentation`_. + + +Installation +------------ + +:: + + $ pip install aiosignal + +The library requires Python 3.8 or newer. + + +Documentation +============= + +https://aiosignal.readthedocs.io/ + +Communication channels +====================== + +*gitter chat* https://gitter.im/aio-libs/Lobby + +Requirements +============ + +- Python >= 3.8 +- frozenlist >= 1.0.0 + +License +======= + +``aiosignal`` is offered under the Apache 2 license. + +Source code +=========== + +The project is hosted on GitHub_ + +Please file an issue in the `bug tracker +`_ if you have found a bug +or have some suggestions to improve the library. + +.. _GitHub: https://github.com/aio-libs/aiosignal +.. _aiohttp documentation: https://docs.aiohttp.org/ diff --git a/.venv/lib/python3.11/site-packages/aiosignal-1.3.2.dist-info/RECORD 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IN NO EVENT SHALL THE FOUNDATION OR +CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, +EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, +PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR +PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF +LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING +NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS +SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + +------------------------------------------------------------------------------ +libsrt is redistributed within opencv-python macOS packages. +This license applies to libsrt binary in the directory cv2/. + +/* + * + * Copyright (c) 2001-2017 Cisco Systems, Inc. + * All rights reserved. + * + * Redistribution and use in source and binary forms, with or without + * modification, are permitted provided that the following conditions + * are met: + * + * Redistributions of source code must retain the above copyright + * notice, this list of conditions and the following disclaimer. + * + * Redistributions in binary form must reproduce the above + * copyright notice, this list of conditions and the following + * disclaimer in the documentation and/or other materials provided + * with the distribution. + * + * Neither the name of the Cisco Systems, Inc. nor the names of its + * contributors may be used to endorse or promote products derived + * from this software without specific prior written permission. + * + * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + * "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + * LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS + * FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE + * COPYRIGHT HOLDERS OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, + * INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES + * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR + * SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) + * HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, + * STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) + * ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED + * OF THE POSSIBILITY OF SUCH DAMAGE. + * + */ + + + Mozilla Public License Version 2.0 +================================== + +1. Definitions +-------------- + +1.1. "Contributor" + means each individual or legal entity that creates, contributes to + the creation of, or owns Covered Software. + +1.2. "Contributor Version" + means the combination of the Contributions of others (if any) used + by a Contributor and that particular Contributor's Contribution. + +1.3. "Contribution" + means Covered Software of a particular Contributor. + +1.4. "Covered Software" + means Source Code Form to which the initial Contributor has attached + the notice in Exhibit A, the Executable Form of such Source Code + Form, and Modifications of such Source Code Form, in each case + including portions thereof. + +1.5. "Incompatible With Secondary Licenses" + means + + (a) that the initial Contributor has attached the notice described + in Exhibit B to the Covered Software; or + + (b) that the Covered Software was made available under the terms of + version 1.1 or earlier of the License, but not also under the + terms of a Secondary License. + +1.6. "Executable Form" + means any form of the work other than Source Code Form. + +1.7. "Larger Work" + means a work that combines Covered Software with other material, in + a separate file or files, that is not Covered Software. + +1.8. "License" + means this document. + +1.9. "Licensable" + means having the right to grant, to the maximum extent possible, + whether at the time of the initial grant or subsequently, any and + all of the rights conveyed by this License. + +1.10. "Modifications" + means any of the following: + + (a) any file in Source Code Form that results from an addition to, + deletion from, or modification of the contents of Covered + Software; or + + (b) any new file in Source Code Form that contains any Covered + Software. + +1.11. "Patent Claims" of a Contributor + means any patent claim(s), including without limitation, method, + process, and apparatus claims, in any patent Licensable by such + Contributor that would be infringed, but for the grant of the + License, by the making, using, selling, offering for sale, having + made, import, or transfer of either its Contributions or its + Contributor Version. + +1.12. "Secondary License" + means either the GNU General Public License, Version 2.0, the GNU + Lesser General Public License, Version 2.1, the GNU Affero General + Public License, Version 3.0, or any later versions of those + licenses. + +1.13. "Source Code Form" + means the form of the work preferred for making modifications. + +1.14. "You" (or "Your") + means an individual or a legal entity exercising rights under this + License. For legal entities, "You" includes any entity that + controls, is controlled by, or is under common control with You. For + purposes of this definition, "control" means (a) the power, direct + or indirect, to cause the direction or management of such entity, + whether by contract or otherwise, or (b) ownership of more than + fifty percent (50%) of the outstanding shares or beneficial + ownership of such entity. + +2. License Grants and Conditions +-------------------------------- + +2.1. Grants + +Each Contributor hereby grants You a world-wide, royalty-free, +non-exclusive license: + +(a) under intellectual property rights (other than patent or trademark) + Licensable by such Contributor to use, reproduce, make available, + modify, display, perform, distribute, and otherwise exploit its + Contributions, either on an unmodified basis, with Modifications, or + as part of a Larger Work; and + +(b) under Patent Claims of such Contributor to make, use, sell, offer + for sale, have made, import, and otherwise transfer either its + Contributions or its Contributor Version. + +2.2. Effective Date + +The licenses granted in Section 2.1 with respect to any Contribution +become effective for each Contribution on the date the Contributor first +distributes such Contribution. + +2.3. Limitations on Grant Scope + +The licenses granted in this Section 2 are the only rights granted under +this License. No additional rights or licenses will be implied from the +distribution or licensing of Covered Software under this License. +Notwithstanding Section 2.1(b) above, no patent license is granted by a +Contributor: + +(a) for any code that a Contributor has removed from Covered Software; + or + +(b) for infringements caused by: (i) Your and any other third party's + modifications of Covered Software, or (ii) the combination of its + Contributions with other software (except as part of its Contributor + Version); or + +(c) under Patent Claims infringed by Covered Software in the absence of + its Contributions. + +This License does not grant any rights in the trademarks, service marks, +or logos of any Contributor (except as may be necessary to comply with +the notice requirements in Section 3.4). + +2.4. Subsequent Licenses + +No Contributor makes additional grants as a result of Your choice to +distribute the Covered Software under a subsequent version of this +License (see Section 10.2) or under the terms of a Secondary License (if +permitted under the terms of Section 3.3). + +2.5. Representation + +Each Contributor represents that the Contributor believes its +Contributions are its original creation(s) or it has sufficient rights +to grant the rights to its Contributions conveyed by this License. + +2.6. Fair Use + +This License is not intended to limit any rights You have under +applicable copyright doctrines of fair use, fair dealing, or other +equivalents. + +2.7. Conditions + +Sections 3.1, 3.2, 3.3, and 3.4 are conditions of the licenses granted +in Section 2.1. + +3. Responsibilities +------------------- + +3.1. Distribution of Source Form + +All distribution of Covered Software in Source Code Form, including any +Modifications that You create or to which You contribute, must be under +the terms of this License. You must inform recipients that the Source +Code Form of the Covered Software is governed by the terms of this +License, and how they can obtain a copy of this License. You may not +attempt to alter or restrict the recipients' rights in the Source Code +Form. + +3.2. Distribution of Executable Form + +If You distribute Covered Software in Executable Form then: + +(a) such Covered Software must also be made available in Source Code + Form, as described in Section 3.1, and You must inform recipients of + the Executable Form how they can obtain a copy of such Source Code + Form by reasonable means in a timely manner, at a charge no more + than the cost of distribution to the recipient; and + +(b) You may distribute such Executable Form under the terms of this + License, or sublicense it under different terms, provided that the + license for the Executable Form does not attempt to limit or alter + the recipients' rights in the Source Code Form under this License. + +3.3. Distribution of a Larger Work + +You may create and distribute a Larger Work under terms of Your choice, +provided that You also comply with the requirements of this License for +the Covered Software. If the Larger Work is a combination of Covered +Software with a work governed by one or more Secondary Licenses, and the +Covered Software is not Incompatible With Secondary Licenses, this +License permits You to additionally distribute such Covered Software +under the terms of such Secondary License(s), so that the recipient of +the Larger Work may, at their option, further distribute the Covered +Software under the terms of either this License or such Secondary +License(s). + +3.4. Notices + +You may not remove or alter the substance of any license notices +(including copyright notices, patent notices, disclaimers of warranty, +or limitations of liability) contained within the Source Code Form of +the Covered Software, except that You may alter any license notices to +the extent required to remedy known factual inaccuracies. + +3.5. Application of Additional Terms + +You may choose to offer, and to charge a fee for, warranty, support, +indemnity or liability obligations to one or more recipients of Covered +Software. However, You may do so only on Your own behalf, and not on +behalf of any Contributor. You must make it absolutely clear that any +such warranty, support, indemnity, or liability obligation is offered by +You alone, and You hereby agree to indemnify every Contributor for any +liability incurred by such Contributor as a result of warranty, support, +indemnity or liability terms You offer. You may include additional +disclaimers of warranty and limitations of liability specific to any +jurisdiction. + +4. Inability to Comply Due to Statute or Regulation +--------------------------------------------------- + +If it is impossible for You to comply with any of the terms of this +License with respect to some or all of the Covered Software due to +statute, judicial order, or regulation then You must: (a) comply with +the terms of this License to the maximum extent possible; and (b) +describe the limitations and the code they affect. Such description must +be placed in a text file included with all distributions of the Covered +Software under this License. Except to the extent prohibited by statute +or regulation, such description must be sufficiently detailed for a +recipient of ordinary skill to be able to understand it. + +5. Termination +-------------- + +5.1. The rights granted under this License will terminate automatically +if You fail to comply with any of its terms. However, if You become +compliant, then the rights granted under this License from a particular +Contributor are reinstated (a) provisionally, unless and until such +Contributor explicitly and finally terminates Your grants, and (b) on an +ongoing basis, if such Contributor fails to notify You of the +non-compliance by some reasonable means prior to 60 days after You have +come back into compliance. Moreover, Your grants from a particular +Contributor are reinstated on an ongoing basis if such Contributor +notifies You of the non-compliance by some reasonable means, this is the +first time You have received notice of non-compliance with this License +from such Contributor, and You become compliant prior to 30 days after +Your receipt of the notice. + +5.2. If You initiate litigation against any entity by asserting a patent +infringement claim (excluding declaratory judgment actions, +counter-claims, and cross-claims) alleging that a Contributor Version +directly or indirectly infringes any patent, then the rights granted to +You by any and all Contributors for the Covered Software under Section +2.1 of this License shall terminate. + +5.3. In the event of termination under Sections 5.1 or 5.2 above, all +end user license agreements (excluding distributors and resellers) which +have been validly granted by You or Your distributors under this License +prior to termination shall survive termination. + +************************************************************************ +* * +* 6. Disclaimer of Warranty * +* ------------------------- * +* * +* Covered Software is provided under this License on an "as is" * +* basis, without warranty of any kind, either expressed, implied, or * +* statutory, including, without limitation, warranties that the * +* Covered Software is free of defects, merchantable, fit for a * +* particular purpose or non-infringing. The entire risk as to the * +* quality and performance of the Covered Software is with You. * +* Should any Covered Software prove defective in any respect, You * +* (not any Contributor) assume the cost of any necessary servicing, * +* repair, or correction. This disclaimer of warranty constitutes an * +* essential part of this License. No use of any Covered Software is * +* authorized under this License except under this disclaimer. * +* * +************************************************************************ + +************************************************************************ +* * +* 7. Limitation of Liability * +* -------------------------- * +* * +* Under no circumstances and under no legal theory, whether tort * +* (including negligence), contract, or otherwise, shall any * +* Contributor, or anyone who distributes Covered Software as * +* permitted above, be liable to You for any direct, indirect, * +* special, incidental, or consequential damages of any character * +* including, without limitation, damages for lost profits, loss of * +* goodwill, work stoppage, computer failure or malfunction, or any * +* and all other commercial damages or losses, even if such party * +* shall have been informed of the possibility of such damages. This * +* limitation of liability shall not apply to liability for death or * +* personal injury resulting from such party's negligence to the * +* extent applicable law prohibits such limitation. Some * +* jurisdictions do not allow the exclusion or limitation of * +* incidental or consequential damages, so this exclusion and * +* limitation may not apply to You. * +* * +************************************************************************ + +8. Litigation +------------- + +Any litigation relating to this License may be brought only in the +courts of a jurisdiction where the defendant maintains its principal +place of business and such litigation shall be governed by laws of that +jurisdiction, without reference to its conflict-of-law provisions. +Nothing in this Section shall prevent a party's ability to bring +cross-claims or counter-claims. + +9. Miscellaneous +---------------- + +This License represents the complete agreement concerning the subject +matter hereof. If any provision of this License is held to be +unenforceable, such provision shall be reformed only to the extent +necessary to make it enforceable. Any law or regulation which provides +that the language of a contract shall be construed against the drafter +shall not be used to construe this License against a Contributor. + +10. Versions of the License +--------------------------- + +10.1. New Versions + +Mozilla Foundation is the license steward. Except as provided in Section +10.3, no one other than the license steward has the right to modify or +publish new versions of this License. Each version will be given a +distinguishing version number. + +10.2. Effect of New Versions + +You may distribute the Covered Software under the terms of the version +of the License under which You originally received the Covered Software, +or under the terms of any subsequent version published by the license +steward. + +10.3. Modified Versions + +If you create software not governed by this License, and you want to +create a new license for such software, you may create and use a +modified version of this License if you rename the license and remove +any references to the name of the license steward (except to note that +such modified license differs from this License). + +10.4. Distributing Source Code Form that is Incompatible With Secondary +Licenses + +If You choose to distribute Source Code Form that is Incompatible With +Secondary Licenses under the terms of this version of the License, the +notice described in Exhibit B of this License must be attached. + +Exhibit A - Source Code Form License Notice +------------------------------------------- + + This Source Code Form is subject to the terms of the Mozilla Public + License, v. 2.0. If a copy of the MPL was not distributed with this + file, You can obtain one at http://mozilla.org/MPL/2.0/. + +If it is not possible or desirable to put the notice in a particular +file, then You may include the notice in a location (such as a LICENSE +file in a relevant directory) where a recipient would be likely to look +for such a notice. + +You may add additional accurate notices of copyright ownership. + +Exhibit B - "Incompatible With Secondary Licenses" Notice +--------------------------------------------------------- + + This Source Code Form is "Incompatible With Secondary Licenses", as + defined by the Mozilla Public License, v. 2.0. + +------------------------------------------------------------------------------ +libtheoradec and libtheoraenc are redistributed within opencv-python macOS packages. +This license applies to libtheoradec and libtheoraenc binaries in the directory cv2/. + + Copyright (C) 2002-2009 Xiph.org Foundation + +Redistribution and use in source and binary forms, with or without +modification, are permitted provided that the following conditions +are met: + +- Redistributions of source code must retain the above copyright +notice, this list of conditions and the following disclaimer. + +- Redistributions in binary form must reproduce the above copyright +notice, this list of conditions and the following disclaimer in the +documentation and/or other materials provided with the distribution. + +- Neither the name of the Xiph.org Foundation nor the names of its +contributors may be used to endorse or promote products derived from +this software without specific prior written permission. + +THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS +``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT +LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR +A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE FOUNDATION +OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, +SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT +LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, +DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY +THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT +(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE +OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + +------------------------------------------------------------------------------ +libwebp and libwebpmux are redistributed within all opencv-python packages. +This license applies to libwebp and libwebpmux binaries in the directory cv2/. + +Copyright (c) 2010, Google Inc. All rights reserved. + +Redistribution and use in source and binary forms, with or without +modification, are permitted provided that the following conditions are +met: + + * Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + * Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in + the documentation and/or other materials provided with the + distribution. + + * Neither the name of Google nor the names of its contributors may + be used to endorse or promote products derived from this software + without specific prior written permission. + +THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS +"AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT +LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR +A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT +HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, +SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT +LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, +DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY +THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT +(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE +OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + +------------------------------------------------------------------------------ +libvorbis and libvorbisenc are redistributed within opencv-python macOS packages. +This license applies to libvorbis and libvorbisenc binaries in the directory cv2/. + +Copyright (c) 2002-2020 Xiph.org Foundation + +Redistribution and use in source and binary forms, with or without +modification, are permitted provided that the following conditions +are met: + +- Redistributions of source code must retain the above copyright +notice, this list of conditions and the following disclaimer. + +- Redistributions in binary form must reproduce the above copyright +notice, this list of conditions and the following disclaimer in the +documentation and/or other materials provided with the distribution. + +- Neither the name of the Xiph.org Foundation nor the names of its +contributors may be used to endorse or promote products derived from +this software without specific prior written permission. + +THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS +``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT +LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR +A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE FOUNDATION +OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, +SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT +LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, +DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY +THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT +(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE +OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + +------------------------------------------------------------------------------ +Libxcb utility libraries are redistributed within opencv-python non-headless Linux packages. +This license applies to libxcb related binaries in the directory cv2/. + +Copyright (C) 2001-2006 Bart Massey, Jamey Sharp, and Josh Triplett. +All Rights Reserved. + +Permission is hereby granted, free of charge, to any person +obtaining a copy of this software and associated +documentation files (the "Software"), to deal in the +Software without restriction, including without limitation +the rights to use, copy, modify, merge, publish, distribute, +sublicense, and/or sell copies of the Software, and to +permit persons to whom the Software is furnished to do so, +subject to the following conditions: + +The above copyright notice and this permission notice shall +be included in all copies or substantial portions of the +Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY +KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE +WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR +PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS +BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER +IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR +OTHER DEALINGS IN THE SOFTWARE. + +Except as contained in this notice, the names of the authors +or their institutions shall not be used in advertising or +otherwise to promote the sale, use or other dealings in this +Software without prior written authorization from the +authors. + +------------------------------------------------------------------------------ +Libxcb-image is redistributed within opencv-python non-headless Linux packages. +This license applies to libxcb-image binary in the directory cv2/. + +Copyright © 2007-2008 Bart Massey +Copyright © 2008 Julien Danjou +Copyright © 2008 Keith Packard + +Permission is hereby granted, free of charge, to any person +obtaining a copy of this software and associated documentation +files (the "Software"), to deal in the Software without +restriction, including without limitation the rights to use, copy, +modify, merge, publish, distribute, sublicense, and/or sell copies +of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be +included in all copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, +EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF +MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND +NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS BE LIABLE FOR ANY +CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF +CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION +WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. + +Except as contained in this notice, the names of the authors or +their institutions shall not be used in advertising or otherwise to +promote the sale, use or other dealings in this Software without +prior written authorization from the authors. + +------------------------------------------------------------------------------ +Libxcb-util is redistributed within opencv-python non-headless Linux packages. +This license applies to libxcb-util binary in the directory cv2/. + +Copyright © 2008 Bart Massey +Copyright © 2008 Ian Osgood +Copyright © 2008 Jamey Sharp +Copyright © 2008 Josh Triplett +Copyright © 2008-2009 Julien Danjou + +Permission is hereby granted, free of charge, to any person +obtaining a copy of this software and associated documentation +files (the "Software"), to deal in the Software without +restriction, including without limitation the rights to use, copy, +modify, merge, publish, distribute, sublicense, and/or sell copies +of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be +included in all copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, +EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF +MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND +NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS BE LIABLE FOR ANY +CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF +CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION +WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. + +Except as contained in this notice, the names of the authors or +their institutions shall not be used in advertising or otherwise to +promote the sale, use or other dealings in this Software without +prior written authorization from the authors. + +------------------------------------------------------------------------------ +Libxcb-render-util is redistributed within opencv-python non-headless Linux packages. +This license applies to libxcb-render-util binary in the directory cv2/. + +Copyright © 2000 Keith Packard + +Permission to use, copy, modify, distribute, and sell this software and its +documentation for any purpose is hereby granted without fee, provided that +the above copyright notice appear in all copies and that both that +copyright notice and this permission notice appear in supporting +documentation, and that the name of Keith Packard not be used in +advertising or publicity pertaining to distribution of the software without +specific, written prior permission. Keith Packard makes no +representations about the suitability of this software for any purpose. It +is provided "as is" without express or implied warranty. + +KEITH PACKARD DISCLAIMS ALL WARRANTIES WITH REGARD TO THIS SOFTWARE, +INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS, IN NO +EVENT SHALL KEITH PACKARD BE LIABLE FOR ANY SPECIAL, INDIRECT OR +CONSEQUENTIAL DAMAGES OR ANY DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, +DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER +TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR +PERFORMANCE OF THIS SOFTWARE. + +Copyright © 2006 Jamey Sharp. + +Permission is hereby granted, free of charge, to any person obtaining a +copy of this software and associated documentation files (the "Software"), +to deal in the Software without restriction, including without limitation +the rights to use, copy, modify, merge, publish, distribute, sublicense, +and/or sell copies of the Software, and to permit persons to whom the +Software is furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in +all copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. 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IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/.venv/lib/python3.11/site-packages/cv2/LICENSE.txt b/.venv/lib/python3.11/site-packages/cv2/LICENSE.txt new file mode 100644 index 0000000000000000000000000000000000000000..328bf50632a988cf1cc494d557936d84fec16335 --- /dev/null +++ b/.venv/lib/python3.11/site-packages/cv2/LICENSE.txt @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) Olli-Pekka Heinisuo + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. \ No newline at end of file diff --git a/.venv/lib/python3.11/site-packages/cv2/__init__.py b/.venv/lib/python3.11/site-packages/cv2/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..7e148fc9f2b93a2b51b3bc6ec49187dbdcdfc5cb --- /dev/null +++ b/.venv/lib/python3.11/site-packages/cv2/__init__.py @@ -0,0 +1,181 @@ +''' +OpenCV Python binary extension loader +''' +import os +import importlib +import sys + +__all__ = [] + +try: + import numpy + import numpy.core.multiarray +except ImportError: + print('OpenCV bindings requires "numpy" package.') + print('Install it via command:') + print(' pip install numpy') + raise + +# TODO +# is_x64 = sys.maxsize > 2**32 + + +def __load_extra_py_code_for_module(base, name, enable_debug_print=False): + module_name = "{}.{}".format(__name__, name) + export_module_name = "{}.{}".format(base, name) + native_module = sys.modules.pop(module_name, None) + try: + py_module = importlib.import_module(module_name) + except ImportError as err: + if enable_debug_print: + print("Can't load Python code for module:", module_name, + ". Reason:", err) + # Extension doesn't contain extra py code + return False + + if base in sys.modules and not hasattr(sys.modules[base], name): + setattr(sys.modules[base], name, py_module) + sys.modules[export_module_name] = py_module + # If it is C extension module it is already loaded by cv2 package + if native_module: + setattr(py_module, "_native", native_module) + for k, v in filter(lambda kv: not hasattr(py_module, kv[0]), + native_module.__dict__.items()): + if enable_debug_print: print(' symbol({}): {} = {}'.format(name, k, v)) + setattr(py_module, k, v) + return True + + +def __collect_extra_submodules(enable_debug_print=False): + def modules_filter(module): + return all(( + # module is not internal + not module.startswith("_"), + not module.startswith("python-"), + # it is not a file + os.path.isdir(os.path.join(_extra_submodules_init_path, module)) + )) + if sys.version_info[0] < 3: + if enable_debug_print: + print("Extra submodules is loaded only for Python 3") + return [] + + __INIT_FILE_PATH = os.path.abspath(__file__) + _extra_submodules_init_path = os.path.dirname(__INIT_FILE_PATH) + return filter(modules_filter, os.listdir(_extra_submodules_init_path)) + + +def bootstrap(): + import sys + + import copy + save_sys_path = copy.copy(sys.path) + + if hasattr(sys, 'OpenCV_LOADER'): + print(sys.path) + raise ImportError('ERROR: recursion is detected during loading of "cv2" binary extensions. Check OpenCV installation.') + sys.OpenCV_LOADER = True + + DEBUG = False + if hasattr(sys, 'OpenCV_LOADER_DEBUG'): + DEBUG = True + + import platform + if DEBUG: print('OpenCV loader: os.name="{}" platform.system()="{}"'.format(os.name, str(platform.system()))) + + LOADER_DIR = os.path.dirname(os.path.abspath(os.path.realpath(__file__))) + + PYTHON_EXTENSIONS_PATHS = [] + BINARIES_PATHS = [] + + g_vars = globals() + l_vars = locals().copy() + + if sys.version_info[:2] < (3, 0): + from . load_config_py2 import exec_file_wrapper + else: + from . load_config_py3 import exec_file_wrapper + + def load_first_config(fnames, required=True): + for fname in fnames: + fpath = os.path.join(LOADER_DIR, fname) + if not os.path.exists(fpath): + if DEBUG: print('OpenCV loader: config not found, skip: {}'.format(fpath)) + continue + if DEBUG: print('OpenCV loader: loading config: {}'.format(fpath)) + exec_file_wrapper(fpath, g_vars, l_vars) + return True + if required: + raise ImportError('OpenCV loader: missing configuration file: {}. Check OpenCV installation.'.format(fnames)) + + load_first_config(['config.py'], True) + load_first_config([ + 'config-{}.{}.py'.format(sys.version_info[0], sys.version_info[1]), + 'config-{}.py'.format(sys.version_info[0]) + ], True) + + if DEBUG: print('OpenCV loader: PYTHON_EXTENSIONS_PATHS={}'.format(str(l_vars['PYTHON_EXTENSIONS_PATHS']))) + if DEBUG: print('OpenCV loader: BINARIES_PATHS={}'.format(str(l_vars['BINARIES_PATHS']))) + + applySysPathWorkaround = False + if hasattr(sys, 'OpenCV_REPLACE_SYS_PATH_0'): + applySysPathWorkaround = True + else: + try: + BASE_DIR = os.path.dirname(LOADER_DIR) + if sys.path[0] == BASE_DIR or os.path.realpath(sys.path[0]) == BASE_DIR: + applySysPathWorkaround = True + except: + if DEBUG: print('OpenCV loader: exception during checking workaround for sys.path[0]') + pass # applySysPathWorkaround is False + + for p in reversed(l_vars['PYTHON_EXTENSIONS_PATHS']): + sys.path.insert(1 if not applySysPathWorkaround else 0, p) + + if os.name == 'nt': + if sys.version_info[:2] >= (3, 8): # https://github.com/python/cpython/pull/12302 + for p in l_vars['BINARIES_PATHS']: + try: + os.add_dll_directory(p) + except Exception as e: + if DEBUG: print('Failed os.add_dll_directory(): '+ str(e)) + pass + os.environ['PATH'] = ';'.join(l_vars['BINARIES_PATHS']) + ';' + os.environ.get('PATH', '') + if DEBUG: print('OpenCV loader: PATH={}'.format(str(os.environ['PATH']))) + else: + # amending of LD_LIBRARY_PATH works for sub-processes only + os.environ['LD_LIBRARY_PATH'] = ':'.join(l_vars['BINARIES_PATHS']) + ':' + os.environ.get('LD_LIBRARY_PATH', '') + + if DEBUG: print("Relink everything from native cv2 module to cv2 package") + + py_module = sys.modules.pop("cv2") + + native_module = importlib.import_module("cv2") + + sys.modules["cv2"] = py_module + setattr(py_module, "_native", native_module) + + for item_name, item in filter(lambda kv: kv[0] not in ("__file__", "__loader__", "__spec__", + "__name__", "__package__"), + native_module.__dict__.items()): + if item_name not in g_vars: + g_vars[item_name] = item + + sys.path = save_sys_path # multiprocessing should start from bootstrap code (https://github.com/opencv/opencv/issues/18502) + + try: + del sys.OpenCV_LOADER + except Exception as e: + if DEBUG: + print("Exception during delete OpenCV_LOADER:", e) + + if DEBUG: print('OpenCV loader: binary extension... OK') + + for submodule in __collect_extra_submodules(DEBUG): + if __load_extra_py_code_for_module("cv2", submodule, DEBUG): + if DEBUG: print("Extra Python code for", submodule, "is loaded") + + if DEBUG: print('OpenCV loader: DONE') + + +bootstrap() diff --git a/.venv/lib/python3.11/site-packages/cv2/__init__.pyi b/.venv/lib/python3.11/site-packages/cv2/__init__.pyi new file mode 100644 index 0000000000000000000000000000000000000000..c99c0f70e9f3307dc6eaa32bb28dce2617878537 --- /dev/null +++ b/.venv/lib/python3.11/site-packages/cv2/__init__.pyi @@ -0,0 +1,6394 @@ +__all__: list[str] = [] + +import cv2.aruco +import cv2.cuda +import cv2.detail +import cv2.dnn +import cv2.gapi +import cv2.gapi.ot +import cv2.gapi.streaming +import cv2.typing +import numpy +import typing as _typing + + +from cv2 import Error as Error +from cv2 import aruco as aruco +from cv2 import barcode as barcode +from cv2 import cuda as cuda +from cv2 import detail as detail +from cv2 import dnn as dnn +from cv2 import fisheye as fisheye +from cv2 import flann as flann +from cv2 import gapi as gapi +from cv2 import ipp as ipp +from cv2 import ml as ml +from cv2 import ocl as ocl +from cv2 import ogl as ogl +from cv2 import parallel as parallel +from cv2 import samples as samples +from cv2 import segmentation as segmentation +from cv2 import typing as typing +from cv2 import utils as utils +from cv2 import videoio_registry as videoio_registry +from cv2.mat_wrapper import Mat as Mat + + +# Enumerations +SORT_EVERY_ROW: int +SORT_EVERY_COLUMN: int +SORT_ASCENDING: int +SORT_DESCENDING: int +SortFlags = int +"""One of [SORT_EVERY_ROW, SORT_EVERY_COLUMN, SORT_ASCENDING, SORT_DESCENDING]""" + +COVAR_SCRAMBLED: int +COVAR_NORMAL: int +COVAR_USE_AVG: int +COVAR_SCALE: int +COVAR_ROWS: int +COVAR_COLS: int +CovarFlags = int +"""One of [COVAR_SCRAMBLED, COVAR_NORMAL, COVAR_USE_AVG, COVAR_SCALE, COVAR_ROWS, COVAR_COLS]""" + +REDUCE_SUM: int +REDUCE_AVG: int +REDUCE_MAX: int +REDUCE_MIN: int +REDUCE_SUM2: int +ReduceTypes = int +"""One of [REDUCE_SUM, REDUCE_AVG, REDUCE_MAX, REDUCE_MIN, REDUCE_SUM2]""" + +ROTATE_90_CLOCKWISE: int +ROTATE_180: int +ROTATE_90_COUNTERCLOCKWISE: int +RotateFlags = int +"""One of [ROTATE_90_CLOCKWISE, ROTATE_180, ROTATE_90_COUNTERCLOCKWISE]""" + +KMEANS_RANDOM_CENTERS: int +KMEANS_PP_CENTERS: int +KMEANS_USE_INITIAL_LABELS: int +KmeansFlags = int +"""One of [KMEANS_RANDOM_CENTERS, KMEANS_PP_CENTERS, KMEANS_USE_INITIAL_LABELS]""" + +Param_INT: int +PARAM_INT: int +Param_BOOLEAN: int +PARAM_BOOLEAN: int +Param_REAL: int +PARAM_REAL: int +Param_STRING: int +PARAM_STRING: int +Param_MAT: int +PARAM_MAT: int +Param_MAT_VECTOR: int +PARAM_MAT_VECTOR: int +Param_ALGORITHM: int +PARAM_ALGORITHM: int +Param_FLOAT: int +PARAM_FLOAT: int +Param_UNSIGNED_INT: int +PARAM_UNSIGNED_INT: int +Param_UINT64: int +PARAM_UINT64: int +Param_UCHAR: int +PARAM_UCHAR: int +Param_SCALAR: int +PARAM_SCALAR: int +Param = int +"""One of [Param_INT, PARAM_INT, Param_BOOLEAN, PARAM_BOOLEAN, Param_REAL, PARAM_REAL, Param_STRING, PARAM_STRING, Param_MAT, PARAM_MAT, Param_MAT_VECTOR, PARAM_MAT_VECTOR, Param_ALGORITHM, PARAM_ALGORITHM, Param_FLOAT, PARAM_FLOAT, Param_UNSIGNED_INT, PARAM_UNSIGNED_INT, Param_UINT64, PARAM_UINT64, Param_UCHAR, PARAM_UCHAR, Param_SCALAR, PARAM_SCALAR]""" + +DECOMP_LU: int +DECOMP_SVD: int +DECOMP_EIG: int +DECOMP_CHOLESKY: int +DECOMP_QR: int +DECOMP_NORMAL: int +DecompTypes = int +"""One of [DECOMP_LU, DECOMP_SVD, DECOMP_EIG, DECOMP_CHOLESKY, DECOMP_QR, DECOMP_NORMAL]""" + +NORM_INF: int +NORM_L1: int +NORM_L2: int +NORM_L2SQR: int +NORM_HAMMING: int +NORM_HAMMING2: int +NORM_TYPE_MASK: int +NORM_RELATIVE: int +NORM_MINMAX: int +NormTypes = int +"""One of [NORM_INF, NORM_L1, NORM_L2, NORM_L2SQR, NORM_HAMMING, NORM_HAMMING2, NORM_TYPE_MASK, NORM_RELATIVE, NORM_MINMAX]""" + +CMP_EQ: int +CMP_GT: int +CMP_GE: int +CMP_LT: int +CMP_LE: int +CMP_NE: int +CmpTypes = int +"""One of [CMP_EQ, CMP_GT, CMP_GE, CMP_LT, CMP_LE, CMP_NE]""" + +GEMM_1_T: int +GEMM_2_T: int +GEMM_3_T: int +GemmFlags = int +"""One of [GEMM_1_T, GEMM_2_T, GEMM_3_T]""" + +DFT_INVERSE: int +DFT_SCALE: int +DFT_ROWS: int +DFT_COMPLEX_OUTPUT: int +DFT_REAL_OUTPUT: int +DFT_COMPLEX_INPUT: int +DCT_INVERSE: int +DCT_ROWS: int +DftFlags = int +"""One of [DFT_INVERSE, DFT_SCALE, DFT_ROWS, DFT_COMPLEX_OUTPUT, DFT_REAL_OUTPUT, DFT_COMPLEX_INPUT, DCT_INVERSE, DCT_ROWS]""" + +BORDER_CONSTANT: int +BORDER_REPLICATE: int +BORDER_REFLECT: int +BORDER_WRAP: int +BORDER_REFLECT_101: int +BORDER_TRANSPARENT: int +BORDER_REFLECT101: int +BORDER_DEFAULT: int +BORDER_ISOLATED: int +BorderTypes = int +"""One of [BORDER_CONSTANT, BORDER_REPLICATE, BORDER_REFLECT, BORDER_WRAP, BORDER_REFLECT_101, BORDER_TRANSPARENT, BORDER_REFLECT101, BORDER_DEFAULT, BORDER_ISOLATED]""" + +ACCESS_READ: int +ACCESS_WRITE: int +ACCESS_RW: int +ACCESS_MASK: int +ACCESS_FAST: int +AccessFlag = int +"""One of [ACCESS_READ, ACCESS_WRITE, ACCESS_RW, ACCESS_MASK, ACCESS_FAST]""" + +USAGE_DEFAULT: int +USAGE_ALLOCATE_HOST_MEMORY: int +USAGE_ALLOCATE_DEVICE_MEMORY: int +USAGE_ALLOCATE_SHARED_MEMORY: int +__UMAT_USAGE_FLAGS_32BIT: int +UMatUsageFlags = int +"""One of [USAGE_DEFAULT, USAGE_ALLOCATE_HOST_MEMORY, USAGE_ALLOCATE_DEVICE_MEMORY, USAGE_ALLOCATE_SHARED_MEMORY, __UMAT_USAGE_FLAGS_32BIT]""" + +SOLVELP_LOST: int +SOLVELP_UNBOUNDED: int +SOLVELP_UNFEASIBLE: int +SOLVELP_SINGLE: int +SOLVELP_MULTI: int +SolveLPResult = int +"""One of [SOLVELP_LOST, SOLVELP_UNBOUNDED, SOLVELP_UNFEASIBLE, SOLVELP_SINGLE, SOLVELP_MULTI]""" + +QUAT_ASSUME_NOT_UNIT: int +QUAT_ASSUME_UNIT: int +QuatAssumeType = int +"""One of [QUAT_ASSUME_NOT_UNIT, QUAT_ASSUME_UNIT]""" + +ALGO_HINT_DEFAULT: int +ALGO_HINT_ACCURATE: int +ALGO_HINT_APPROX: int +AlgorithmHint = int +"""One of [ALGO_HINT_DEFAULT, ALGO_HINT_ACCURATE, ALGO_HINT_APPROX]""" + +FILTER_SCHARR: int +SpecialFilter = int +"""One of [FILTER_SCHARR]""" + +MORPH_ERODE: int +MORPH_DILATE: int +MORPH_OPEN: int +MORPH_CLOSE: int +MORPH_GRADIENT: int +MORPH_TOPHAT: int +MORPH_BLACKHAT: int +MORPH_HITMISS: int +MorphTypes = int +"""One of [MORPH_ERODE, MORPH_DILATE, MORPH_OPEN, MORPH_CLOSE, MORPH_GRADIENT, MORPH_TOPHAT, MORPH_BLACKHAT, MORPH_HITMISS]""" + +MORPH_RECT: int +MORPH_CROSS: int +MORPH_ELLIPSE: int +MorphShapes = int +"""One of [MORPH_RECT, MORPH_CROSS, MORPH_ELLIPSE]""" + +INTER_NEAREST: int +INTER_LINEAR: int +INTER_CUBIC: int +INTER_AREA: int +INTER_LANCZOS4: int +INTER_LINEAR_EXACT: int +INTER_NEAREST_EXACT: int +INTER_MAX: int +WARP_FILL_OUTLIERS: int +WARP_INVERSE_MAP: int +WARP_RELATIVE_MAP: int +InterpolationFlags = int +"""One of [INTER_NEAREST, INTER_LINEAR, INTER_CUBIC, INTER_AREA, INTER_LANCZOS4, INTER_LINEAR_EXACT, INTER_NEAREST_EXACT, INTER_MAX, WARP_FILL_OUTLIERS, WARP_INVERSE_MAP, WARP_RELATIVE_MAP]""" + +WARP_POLAR_LINEAR: int +WARP_POLAR_LOG: int +WarpPolarMode = int +"""One of [WARP_POLAR_LINEAR, WARP_POLAR_LOG]""" + +INTER_BITS: int +INTER_BITS2: int +INTER_TAB_SIZE: int +INTER_TAB_SIZE2: int +InterpolationMasks = int +"""One of [INTER_BITS, INTER_BITS2, INTER_TAB_SIZE, INTER_TAB_SIZE2]""" + +DIST_USER: int +DIST_L1: int +DIST_L2: int +DIST_C: int +DIST_L12: int +DIST_FAIR: int +DIST_WELSCH: int +DIST_HUBER: int +DistanceTypes = int +"""One of [DIST_USER, DIST_L1, DIST_L2, DIST_C, DIST_L12, DIST_FAIR, DIST_WELSCH, DIST_HUBER]""" + +DIST_MASK_3: int +DIST_MASK_5: int +DIST_MASK_PRECISE: int +DistanceTransformMasks = int +"""One of [DIST_MASK_3, DIST_MASK_5, DIST_MASK_PRECISE]""" + +THRESH_BINARY: int +THRESH_BINARY_INV: int +THRESH_TRUNC: int +THRESH_TOZERO: int +THRESH_TOZERO_INV: int +THRESH_MASK: int +THRESH_OTSU: int +THRESH_TRIANGLE: int +ThresholdTypes = int +"""One of [THRESH_BINARY, THRESH_BINARY_INV, THRESH_TRUNC, THRESH_TOZERO, THRESH_TOZERO_INV, THRESH_MASK, THRESH_OTSU, THRESH_TRIANGLE]""" + +ADAPTIVE_THRESH_MEAN_C: int +ADAPTIVE_THRESH_GAUSSIAN_C: int +AdaptiveThresholdTypes = int +"""One of [ADAPTIVE_THRESH_MEAN_C, ADAPTIVE_THRESH_GAUSSIAN_C]""" + +GC_BGD: int +GC_FGD: int +GC_PR_BGD: int +GC_PR_FGD: int +GrabCutClasses = int +"""One of [GC_BGD, GC_FGD, GC_PR_BGD, GC_PR_FGD]""" + +GC_INIT_WITH_RECT: int +GC_INIT_WITH_MASK: int +GC_EVAL: int +GC_EVAL_FREEZE_MODEL: int +GrabCutModes = int +"""One of [GC_INIT_WITH_RECT, GC_INIT_WITH_MASK, GC_EVAL, GC_EVAL_FREEZE_MODEL]""" + +DIST_LABEL_CCOMP: int +DIST_LABEL_PIXEL: int +DistanceTransformLabelTypes = int +"""One of [DIST_LABEL_CCOMP, DIST_LABEL_PIXEL]""" + +FLOODFILL_FIXED_RANGE: int +FLOODFILL_MASK_ONLY: int +FloodFillFlags = int +"""One of [FLOODFILL_FIXED_RANGE, FLOODFILL_MASK_ONLY]""" + +CC_STAT_LEFT: int +CC_STAT_TOP: int +CC_STAT_WIDTH: int +CC_STAT_HEIGHT: int +CC_STAT_AREA: int +CC_STAT_MAX: int +ConnectedComponentsTypes = int +"""One of [CC_STAT_LEFT, CC_STAT_TOP, CC_STAT_WIDTH, CC_STAT_HEIGHT, CC_STAT_AREA, CC_STAT_MAX]""" + +CCL_DEFAULT: int +CCL_WU: int +CCL_GRANA: int +CCL_BOLELLI: int +CCL_SAUF: int +CCL_BBDT: int +CCL_SPAGHETTI: int +ConnectedComponentsAlgorithmsTypes = int +"""One of [CCL_DEFAULT, CCL_WU, CCL_GRANA, CCL_BOLELLI, CCL_SAUF, CCL_BBDT, CCL_SPAGHETTI]""" + +RETR_EXTERNAL: int +RETR_LIST: int +RETR_CCOMP: int +RETR_TREE: int +RETR_FLOODFILL: int +RetrievalModes = int +"""One of [RETR_EXTERNAL, RETR_LIST, RETR_CCOMP, RETR_TREE, RETR_FLOODFILL]""" + +CHAIN_APPROX_NONE: int +CHAIN_APPROX_SIMPLE: int +CHAIN_APPROX_TC89_L1: int +CHAIN_APPROX_TC89_KCOS: int +ContourApproximationModes = int +"""One of [CHAIN_APPROX_NONE, CHAIN_APPROX_SIMPLE, CHAIN_APPROX_TC89_L1, CHAIN_APPROX_TC89_KCOS]""" + +CONTOURS_MATCH_I1: int +CONTOURS_MATCH_I2: int +CONTOURS_MATCH_I3: int +ShapeMatchModes = int +"""One of [CONTOURS_MATCH_I1, CONTOURS_MATCH_I2, CONTOURS_MATCH_I3]""" + +HOUGH_STANDARD: int +HOUGH_PROBABILISTIC: int +HOUGH_MULTI_SCALE: int +HOUGH_GRADIENT: int +HOUGH_GRADIENT_ALT: int +HoughModes = int +"""One of [HOUGH_STANDARD, HOUGH_PROBABILISTIC, HOUGH_MULTI_SCALE, HOUGH_GRADIENT, HOUGH_GRADIENT_ALT]""" + +LSD_REFINE_NONE: int +LSD_REFINE_STD: int +LSD_REFINE_ADV: int +LineSegmentDetectorModes = int +"""One of [LSD_REFINE_NONE, LSD_REFINE_STD, LSD_REFINE_ADV]""" + +HISTCMP_CORREL: int +HISTCMP_CHISQR: int +HISTCMP_INTERSECT: int +HISTCMP_BHATTACHARYYA: int +HISTCMP_HELLINGER: int +HISTCMP_CHISQR_ALT: int +HISTCMP_KL_DIV: int +HistCompMethods = int +"""One of [HISTCMP_CORREL, HISTCMP_CHISQR, HISTCMP_INTERSECT, HISTCMP_BHATTACHARYYA, HISTCMP_HELLINGER, HISTCMP_CHISQR_ALT, HISTCMP_KL_DIV]""" + +COLOR_BGR2BGRA: int +COLOR_RGB2RGBA: int +COLOR_BGRA2BGR: int +COLOR_RGBA2RGB: int +COLOR_BGR2RGBA: int +COLOR_RGB2BGRA: int +COLOR_RGBA2BGR: int +COLOR_BGRA2RGB: int +COLOR_BGR2RGB: int +COLOR_RGB2BGR: int +COLOR_BGRA2RGBA: int +COLOR_RGBA2BGRA: int +COLOR_BGR2GRAY: int +COLOR_RGB2GRAY: int +COLOR_GRAY2BGR: int +COLOR_GRAY2RGB: int +COLOR_GRAY2BGRA: int +COLOR_GRAY2RGBA: int +COLOR_BGRA2GRAY: int +COLOR_RGBA2GRAY: int +COLOR_BGR2BGR565: int +COLOR_RGB2BGR565: int +COLOR_BGR5652BGR: int +COLOR_BGR5652RGB: int +COLOR_BGRA2BGR565: int +COLOR_RGBA2BGR565: int +COLOR_BGR5652BGRA: int +COLOR_BGR5652RGBA: int +COLOR_GRAY2BGR565: int +COLOR_BGR5652GRAY: int +COLOR_BGR2BGR555: int +COLOR_RGB2BGR555: int +COLOR_BGR5552BGR: int +COLOR_BGR5552RGB: int +COLOR_BGRA2BGR555: int +COLOR_RGBA2BGR555: int +COLOR_BGR5552BGRA: int +COLOR_BGR5552RGBA: int +COLOR_GRAY2BGR555: int +COLOR_BGR5552GRAY: int +COLOR_BGR2XYZ: int +COLOR_RGB2XYZ: int +COLOR_XYZ2BGR: int +COLOR_XYZ2RGB: int +COLOR_BGR2YCrCb: int +COLOR_BGR2YCR_CB: int +COLOR_RGB2YCrCb: int +COLOR_RGB2YCR_CB: int +COLOR_YCrCb2BGR: int +COLOR_YCR_CB2BGR: int +COLOR_YCrCb2RGB: int +COLOR_YCR_CB2RGB: int +COLOR_BGR2HSV: int +COLOR_RGB2HSV: int +COLOR_BGR2Lab: int +COLOR_BGR2LAB: int +COLOR_RGB2Lab: int +COLOR_RGB2LAB: int +COLOR_BGR2Luv: int +COLOR_BGR2LUV: int +COLOR_RGB2Luv: int +COLOR_RGB2LUV: int +COLOR_BGR2HLS: int +COLOR_RGB2HLS: int +COLOR_HSV2BGR: int +COLOR_HSV2RGB: int +COLOR_Lab2BGR: int +COLOR_LAB2BGR: int +COLOR_Lab2RGB: int +COLOR_LAB2RGB: int +COLOR_Luv2BGR: int +COLOR_LUV2BGR: int +COLOR_Luv2RGB: int +COLOR_LUV2RGB: int +COLOR_HLS2BGR: int +COLOR_HLS2RGB: int +COLOR_BGR2HSV_FULL: int +COLOR_RGB2HSV_FULL: int +COLOR_BGR2HLS_FULL: int +COLOR_RGB2HLS_FULL: int +COLOR_HSV2BGR_FULL: int +COLOR_HSV2RGB_FULL: int +COLOR_HLS2BGR_FULL: int +COLOR_HLS2RGB_FULL: int +COLOR_LBGR2Lab: int +COLOR_LBGR2LAB: int +COLOR_LRGB2Lab: int +COLOR_LRGB2LAB: int +COLOR_LBGR2Luv: int +COLOR_LBGR2LUV: int +COLOR_LRGB2Luv: int +COLOR_LRGB2LUV: int +COLOR_Lab2LBGR: int +COLOR_LAB2LBGR: int +COLOR_Lab2LRGB: int +COLOR_LAB2LRGB: int +COLOR_Luv2LBGR: int +COLOR_LUV2LBGR: int +COLOR_Luv2LRGB: int +COLOR_LUV2LRGB: int +COLOR_BGR2YUV: int +COLOR_RGB2YUV: int +COLOR_YUV2BGR: int +COLOR_YUV2RGB: int +COLOR_YUV2RGB_NV12: int +COLOR_YUV2BGR_NV12: int +COLOR_YUV2RGB_NV21: int +COLOR_YUV2BGR_NV21: int +COLOR_YUV420sp2RGB: int +COLOR_YUV420SP2RGB: int +COLOR_YUV420sp2BGR: int +COLOR_YUV420SP2BGR: int +COLOR_YUV2RGBA_NV12: int +COLOR_YUV2BGRA_NV12: int +COLOR_YUV2RGBA_NV21: int +COLOR_YUV2BGRA_NV21: int +COLOR_YUV420sp2RGBA: int +COLOR_YUV420SP2RGBA: int +COLOR_YUV420sp2BGRA: int +COLOR_YUV420SP2BGRA: int +COLOR_YUV2RGB_YV12: int +COLOR_YUV2BGR_YV12: int +COLOR_YUV2RGB_IYUV: int +COLOR_YUV2BGR_IYUV: int +COLOR_YUV2RGB_I420: int +COLOR_YUV2BGR_I420: int +COLOR_YUV420p2RGB: int +COLOR_YUV420P2RGB: int +COLOR_YUV420p2BGR: int +COLOR_YUV420P2BGR: int +COLOR_YUV2RGBA_YV12: int +COLOR_YUV2BGRA_YV12: int +COLOR_YUV2RGBA_IYUV: int +COLOR_YUV2BGRA_IYUV: int +COLOR_YUV2RGBA_I420: int +COLOR_YUV2BGRA_I420: int +COLOR_YUV420p2RGBA: int +COLOR_YUV420P2RGBA: int +COLOR_YUV420p2BGRA: int +COLOR_YUV420P2BGRA: int +COLOR_YUV2GRAY_420: int +COLOR_YUV2GRAY_NV21: int +COLOR_YUV2GRAY_NV12: int +COLOR_YUV2GRAY_YV12: int +COLOR_YUV2GRAY_IYUV: int +COLOR_YUV2GRAY_I420: int +COLOR_YUV420sp2GRAY: int +COLOR_YUV420SP2GRAY: int +COLOR_YUV420p2GRAY: int +COLOR_YUV420P2GRAY: int +COLOR_YUV2RGB_UYVY: int +COLOR_YUV2BGR_UYVY: int +COLOR_YUV2RGB_Y422: int +COLOR_YUV2BGR_Y422: int +COLOR_YUV2RGB_UYNV: int +COLOR_YUV2BGR_UYNV: int +COLOR_YUV2RGBA_UYVY: int +COLOR_YUV2BGRA_UYVY: int +COLOR_YUV2RGBA_Y422: int +COLOR_YUV2BGRA_Y422: int +COLOR_YUV2RGBA_UYNV: int +COLOR_YUV2BGRA_UYNV: int +COLOR_YUV2RGB_YUY2: int +COLOR_YUV2BGR_YUY2: int +COLOR_YUV2RGB_YVYU: int +COLOR_YUV2BGR_YVYU: int +COLOR_YUV2RGB_YUYV: int +COLOR_YUV2BGR_YUYV: int +COLOR_YUV2RGB_YUNV: int +COLOR_YUV2BGR_YUNV: int +COLOR_YUV2RGBA_YUY2: int +COLOR_YUV2BGRA_YUY2: int +COLOR_YUV2RGBA_YVYU: int +COLOR_YUV2BGRA_YVYU: int +COLOR_YUV2RGBA_YUYV: int +COLOR_YUV2BGRA_YUYV: int +COLOR_YUV2RGBA_YUNV: int +COLOR_YUV2BGRA_YUNV: int +COLOR_YUV2GRAY_UYVY: int +COLOR_YUV2GRAY_YUY2: int +COLOR_YUV2GRAY_Y422: int +COLOR_YUV2GRAY_UYNV: int +COLOR_YUV2GRAY_YVYU: int +COLOR_YUV2GRAY_YUYV: int +COLOR_YUV2GRAY_YUNV: int +COLOR_RGBA2mRGBA: int +COLOR_RGBA2M_RGBA: int +COLOR_mRGBA2RGBA: int +COLOR_M_RGBA2RGBA: int +COLOR_RGB2YUV_I420: int +COLOR_BGR2YUV_I420: int +COLOR_RGB2YUV_IYUV: int +COLOR_BGR2YUV_IYUV: int +COLOR_RGBA2YUV_I420: int +COLOR_BGRA2YUV_I420: int +COLOR_RGBA2YUV_IYUV: int +COLOR_BGRA2YUV_IYUV: int +COLOR_RGB2YUV_YV12: int +COLOR_BGR2YUV_YV12: int +COLOR_RGBA2YUV_YV12: int +COLOR_BGRA2YUV_YV12: int +COLOR_BayerBG2BGR: int +COLOR_BAYER_BG2BGR: int +COLOR_BayerGB2BGR: int +COLOR_BAYER_GB2BGR: int +COLOR_BayerRG2BGR: int +COLOR_BAYER_RG2BGR: int +COLOR_BayerGR2BGR: int +COLOR_BAYER_GR2BGR: int +COLOR_BayerRGGB2BGR: int +COLOR_BAYER_RGGB2BGR: int +COLOR_BayerGRBG2BGR: int +COLOR_BAYER_GRBG2BGR: int +COLOR_BayerBGGR2BGR: int +COLOR_BAYER_BGGR2BGR: int +COLOR_BayerGBRG2BGR: int +COLOR_BAYER_GBRG2BGR: int +COLOR_BayerRGGB2RGB: int +COLOR_BAYER_RGGB2RGB: int +COLOR_BayerGRBG2RGB: int +COLOR_BAYER_GRBG2RGB: int +COLOR_BayerBGGR2RGB: int +COLOR_BAYER_BGGR2RGB: int +COLOR_BayerGBRG2RGB: int +COLOR_BAYER_GBRG2RGB: int +COLOR_BayerBG2RGB: int +COLOR_BAYER_BG2RGB: int +COLOR_BayerGB2RGB: int +COLOR_BAYER_GB2RGB: int +COLOR_BayerRG2RGB: int +COLOR_BAYER_RG2RGB: int +COLOR_BayerGR2RGB: int +COLOR_BAYER_GR2RGB: int +COLOR_BayerBG2GRAY: int +COLOR_BAYER_BG2GRAY: int +COLOR_BayerGB2GRAY: int +COLOR_BAYER_GB2GRAY: int +COLOR_BayerRG2GRAY: int +COLOR_BAYER_RG2GRAY: int +COLOR_BayerGR2GRAY: int +COLOR_BAYER_GR2GRAY: int +COLOR_BayerRGGB2GRAY: int +COLOR_BAYER_RGGB2GRAY: int +COLOR_BayerGRBG2GRAY: int +COLOR_BAYER_GRBG2GRAY: int +COLOR_BayerBGGR2GRAY: int +COLOR_BAYER_BGGR2GRAY: int +COLOR_BayerGBRG2GRAY: int +COLOR_BAYER_GBRG2GRAY: int +COLOR_BayerBG2BGR_VNG: int +COLOR_BAYER_BG2BGR_VNG: int +COLOR_BayerGB2BGR_VNG: int +COLOR_BAYER_GB2BGR_VNG: int +COLOR_BayerRG2BGR_VNG: int +COLOR_BAYER_RG2BGR_VNG: int +COLOR_BayerGR2BGR_VNG: int +COLOR_BAYER_GR2BGR_VNG: int +COLOR_BayerRGGB2BGR_VNG: int +COLOR_BAYER_RGGB2BGR_VNG: int +COLOR_BayerGRBG2BGR_VNG: int +COLOR_BAYER_GRBG2BGR_VNG: int +COLOR_BayerBGGR2BGR_VNG: int +COLOR_BAYER_BGGR2BGR_VNG: int +COLOR_BayerGBRG2BGR_VNG: int +COLOR_BAYER_GBRG2BGR_VNG: int +COLOR_BayerRGGB2RGB_VNG: int +COLOR_BAYER_RGGB2RGB_VNG: int +COLOR_BayerGRBG2RGB_VNG: int +COLOR_BAYER_GRBG2RGB_VNG: int +COLOR_BayerBGGR2RGB_VNG: int +COLOR_BAYER_BGGR2RGB_VNG: int +COLOR_BayerGBRG2RGB_VNG: int +COLOR_BAYER_GBRG2RGB_VNG: int +COLOR_BayerBG2RGB_VNG: int +COLOR_BAYER_BG2RGB_VNG: int +COLOR_BayerGB2RGB_VNG: int +COLOR_BAYER_GB2RGB_VNG: int +COLOR_BayerRG2RGB_VNG: int +COLOR_BAYER_RG2RGB_VNG: int +COLOR_BayerGR2RGB_VNG: int +COLOR_BAYER_GR2RGB_VNG: int +COLOR_BayerBG2BGR_EA: int +COLOR_BAYER_BG2BGR_EA: int +COLOR_BayerGB2BGR_EA: int +COLOR_BAYER_GB2BGR_EA: int +COLOR_BayerRG2BGR_EA: int +COLOR_BAYER_RG2BGR_EA: int +COLOR_BayerGR2BGR_EA: int +COLOR_BAYER_GR2BGR_EA: int +COLOR_BayerRGGB2BGR_EA: int +COLOR_BAYER_RGGB2BGR_EA: int +COLOR_BayerGRBG2BGR_EA: int +COLOR_BAYER_GRBG2BGR_EA: int +COLOR_BayerBGGR2BGR_EA: int +COLOR_BAYER_BGGR2BGR_EA: int +COLOR_BayerGBRG2BGR_EA: int +COLOR_BAYER_GBRG2BGR_EA: int +COLOR_BayerRGGB2RGB_EA: int +COLOR_BAYER_RGGB2RGB_EA: int +COLOR_BayerGRBG2RGB_EA: int +COLOR_BAYER_GRBG2RGB_EA: int +COLOR_BayerBGGR2RGB_EA: int +COLOR_BAYER_BGGR2RGB_EA: int +COLOR_BayerGBRG2RGB_EA: int +COLOR_BAYER_GBRG2RGB_EA: int +COLOR_BayerBG2RGB_EA: int +COLOR_BAYER_BG2RGB_EA: int +COLOR_BayerGB2RGB_EA: int +COLOR_BAYER_GB2RGB_EA: int +COLOR_BayerRG2RGB_EA: int +COLOR_BAYER_RG2RGB_EA: int +COLOR_BayerGR2RGB_EA: int +COLOR_BAYER_GR2RGB_EA: int +COLOR_BayerBG2BGRA: int +COLOR_BAYER_BG2BGRA: int +COLOR_BayerGB2BGRA: int +COLOR_BAYER_GB2BGRA: int +COLOR_BayerRG2BGRA: int +COLOR_BAYER_RG2BGRA: int +COLOR_BayerGR2BGRA: int +COLOR_BAYER_GR2BGRA: int +COLOR_BayerRGGB2BGRA: int +COLOR_BAYER_RGGB2BGRA: int +COLOR_BayerGRBG2BGRA: int +COLOR_BAYER_GRBG2BGRA: int +COLOR_BayerBGGR2BGRA: int +COLOR_BAYER_BGGR2BGRA: int +COLOR_BayerGBRG2BGRA: int +COLOR_BAYER_GBRG2BGRA: int +COLOR_BayerRGGB2RGBA: int +COLOR_BAYER_RGGB2RGBA: int +COLOR_BayerGRBG2RGBA: int +COLOR_BAYER_GRBG2RGBA: int +COLOR_BayerBGGR2RGBA: int +COLOR_BAYER_BGGR2RGBA: int +COLOR_BayerGBRG2RGBA: int +COLOR_BAYER_GBRG2RGBA: int +COLOR_BayerBG2RGBA: int +COLOR_BAYER_BG2RGBA: int +COLOR_BayerGB2RGBA: int +COLOR_BAYER_GB2RGBA: int +COLOR_BayerRG2RGBA: int +COLOR_BAYER_RG2RGBA: int +COLOR_BayerGR2RGBA: int +COLOR_BAYER_GR2RGBA: int +COLOR_RGB2YUV_UYVY: int +COLOR_BGR2YUV_UYVY: int +COLOR_RGB2YUV_Y422: int +COLOR_BGR2YUV_Y422: int +COLOR_RGB2YUV_UYNV: int +COLOR_BGR2YUV_UYNV: int +COLOR_RGBA2YUV_UYVY: int +COLOR_BGRA2YUV_UYVY: int +COLOR_RGBA2YUV_Y422: int +COLOR_BGRA2YUV_Y422: int +COLOR_RGBA2YUV_UYNV: int +COLOR_BGRA2YUV_UYNV: int +COLOR_RGB2YUV_YUY2: int +COLOR_BGR2YUV_YUY2: int +COLOR_RGB2YUV_YVYU: int +COLOR_BGR2YUV_YVYU: int +COLOR_RGB2YUV_YUYV: int +COLOR_BGR2YUV_YUYV: int +COLOR_RGB2YUV_YUNV: int +COLOR_BGR2YUV_YUNV: int +COLOR_RGBA2YUV_YUY2: int +COLOR_BGRA2YUV_YUY2: int +COLOR_RGBA2YUV_YVYU: int +COLOR_BGRA2YUV_YVYU: int +COLOR_RGBA2YUV_YUYV: int +COLOR_BGRA2YUV_YUYV: int +COLOR_RGBA2YUV_YUNV: int +COLOR_BGRA2YUV_YUNV: int +COLOR_COLORCVT_MAX: int +ColorConversionCodes = int +"""One of [COLOR_BGR2BGRA, COLOR_RGB2RGBA, COLOR_BGRA2BGR, COLOR_RGBA2RGB, COLOR_BGR2RGBA, COLOR_RGB2BGRA, COLOR_RGBA2BGR, COLOR_BGRA2RGB, COLOR_BGR2RGB, COLOR_RGB2BGR, COLOR_BGRA2RGBA, COLOR_RGBA2BGRA, COLOR_BGR2GRAY, COLOR_RGB2GRAY, COLOR_GRAY2BGR, COLOR_GRAY2RGB, COLOR_GRAY2BGRA, COLOR_GRAY2RGBA, COLOR_BGRA2GRAY, COLOR_RGBA2GRAY, COLOR_BGR2BGR565, COLOR_RGB2BGR565, COLOR_BGR5652BGR, COLOR_BGR5652RGB, COLOR_BGRA2BGR565, COLOR_RGBA2BGR565, COLOR_BGR5652BGRA, COLOR_BGR5652RGBA, COLOR_GRAY2BGR565, COLOR_BGR5652GRAY, COLOR_BGR2BGR555, COLOR_RGB2BGR555, COLOR_BGR5552BGR, COLOR_BGR5552RGB, COLOR_BGRA2BGR555, COLOR_RGBA2BGR555, COLOR_BGR5552BGRA, COLOR_BGR5552RGBA, COLOR_GRAY2BGR555, COLOR_BGR5552GRAY, COLOR_BGR2XYZ, COLOR_RGB2XYZ, COLOR_XYZ2BGR, COLOR_XYZ2RGB, COLOR_BGR2YCrCb, COLOR_BGR2YCR_CB, COLOR_RGB2YCrCb, COLOR_RGB2YCR_CB, COLOR_YCrCb2BGR, COLOR_YCR_CB2BGR, COLOR_YCrCb2RGB, COLOR_YCR_CB2RGB, COLOR_BGR2HSV, COLOR_RGB2HSV, COLOR_BGR2Lab, COLOR_BGR2LAB, COLOR_RGB2Lab, COLOR_RGB2LAB, COLOR_BGR2Luv, COLOR_BGR2LUV, COLOR_RGB2Luv, COLOR_RGB2LUV, COLOR_BGR2HLS, COLOR_RGB2HLS, COLOR_HSV2BGR, COLOR_HSV2RGB, COLOR_Lab2BGR, COLOR_LAB2BGR, COLOR_Lab2RGB, COLOR_LAB2RGB, COLOR_Luv2BGR, COLOR_LUV2BGR, COLOR_Luv2RGB, COLOR_LUV2RGB, COLOR_HLS2BGR, COLOR_HLS2RGB, COLOR_BGR2HSV_FULL, COLOR_RGB2HSV_FULL, COLOR_BGR2HLS_FULL, COLOR_RGB2HLS_FULL, COLOR_HSV2BGR_FULL, COLOR_HSV2RGB_FULL, COLOR_HLS2BGR_FULL, COLOR_HLS2RGB_FULL, COLOR_LBGR2Lab, COLOR_LBGR2LAB, COLOR_LRGB2Lab, COLOR_LRGB2LAB, COLOR_LBGR2Luv, COLOR_LBGR2LUV, COLOR_LRGB2Luv, COLOR_LRGB2LUV, COLOR_Lab2LBGR, COLOR_LAB2LBGR, COLOR_Lab2LRGB, COLOR_LAB2LRGB, COLOR_Luv2LBGR, COLOR_LUV2LBGR, COLOR_Luv2LRGB, COLOR_LUV2LRGB, COLOR_BGR2YUV, COLOR_RGB2YUV, COLOR_YUV2BGR, COLOR_YUV2RGB, COLOR_YUV2RGB_NV12, COLOR_YUV2BGR_NV12, COLOR_YUV2RGB_NV21, COLOR_YUV2BGR_NV21, COLOR_YUV420sp2RGB, COLOR_YUV420SP2RGB, COLOR_YUV420sp2BGR, COLOR_YUV420SP2BGR, COLOR_YUV2RGBA_NV12, COLOR_YUV2BGRA_NV12, COLOR_YUV2RGBA_NV21, COLOR_YUV2BGRA_NV21, COLOR_YUV420sp2RGBA, COLOR_YUV420SP2RGBA, COLOR_YUV420sp2BGRA, COLOR_YUV420SP2BGRA, COLOR_YUV2RGB_YV12, COLOR_YUV2BGR_YV12, COLOR_YUV2RGB_IYUV, COLOR_YUV2BGR_IYUV, COLOR_YUV2RGB_I420, COLOR_YUV2BGR_I420, COLOR_YUV420p2RGB, COLOR_YUV420P2RGB, COLOR_YUV420p2BGR, COLOR_YUV420P2BGR, COLOR_YUV2RGBA_YV12, COLOR_YUV2BGRA_YV12, COLOR_YUV2RGBA_IYUV, COLOR_YUV2BGRA_IYUV, COLOR_YUV2RGBA_I420, COLOR_YUV2BGRA_I420, COLOR_YUV420p2RGBA, COLOR_YUV420P2RGBA, COLOR_YUV420p2BGRA, COLOR_YUV420P2BGRA, COLOR_YUV2GRAY_420, COLOR_YUV2GRAY_NV21, COLOR_YUV2GRAY_NV12, COLOR_YUV2GRAY_YV12, COLOR_YUV2GRAY_IYUV, COLOR_YUV2GRAY_I420, COLOR_YUV420sp2GRAY, COLOR_YUV420SP2GRAY, COLOR_YUV420p2GRAY, COLOR_YUV420P2GRAY, COLOR_YUV2RGB_UYVY, COLOR_YUV2BGR_UYVY, COLOR_YUV2RGB_Y422, COLOR_YUV2BGR_Y422, COLOR_YUV2RGB_UYNV, COLOR_YUV2BGR_UYNV, COLOR_YUV2RGBA_UYVY, COLOR_YUV2BGRA_UYVY, COLOR_YUV2RGBA_Y422, COLOR_YUV2BGRA_Y422, COLOR_YUV2RGBA_UYNV, COLOR_YUV2BGRA_UYNV, COLOR_YUV2RGB_YUY2, COLOR_YUV2BGR_YUY2, COLOR_YUV2RGB_YVYU, COLOR_YUV2BGR_YVYU, COLOR_YUV2RGB_YUYV, COLOR_YUV2BGR_YUYV, COLOR_YUV2RGB_YUNV, COLOR_YUV2BGR_YUNV, COLOR_YUV2RGBA_YUY2, COLOR_YUV2BGRA_YUY2, COLOR_YUV2RGBA_YVYU, COLOR_YUV2BGRA_YVYU, COLOR_YUV2RGBA_YUYV, COLOR_YUV2BGRA_YUYV, COLOR_YUV2RGBA_YUNV, COLOR_YUV2BGRA_YUNV, COLOR_YUV2GRAY_UYVY, COLOR_YUV2GRAY_YUY2, COLOR_YUV2GRAY_Y422, COLOR_YUV2GRAY_UYNV, COLOR_YUV2GRAY_YVYU, COLOR_YUV2GRAY_YUYV, COLOR_YUV2GRAY_YUNV, COLOR_RGBA2mRGBA, COLOR_RGBA2M_RGBA, COLOR_mRGBA2RGBA, COLOR_M_RGBA2RGBA, COLOR_RGB2YUV_I420, COLOR_BGR2YUV_I420, COLOR_RGB2YUV_IYUV, COLOR_BGR2YUV_IYUV, COLOR_RGBA2YUV_I420, COLOR_BGRA2YUV_I420, COLOR_RGBA2YUV_IYUV, COLOR_BGRA2YUV_IYUV, COLOR_RGB2YUV_YV12, COLOR_BGR2YUV_YV12, COLOR_RGBA2YUV_YV12, COLOR_BGRA2YUV_YV12, COLOR_BayerBG2BGR, COLOR_BAYER_BG2BGR, COLOR_BayerGB2BGR, COLOR_BAYER_GB2BGR, COLOR_BayerRG2BGR, COLOR_BAYER_RG2BGR, COLOR_BayerGR2BGR, COLOR_BAYER_GR2BGR, COLOR_BayerRGGB2BGR, COLOR_BAYER_RGGB2BGR, COLOR_BayerGRBG2BGR, COLOR_BAYER_GRBG2BGR, COLOR_BayerBGGR2BGR, COLOR_BAYER_BGGR2BGR, COLOR_BayerGBRG2BGR, COLOR_BAYER_GBRG2BGR, COLOR_BayerRGGB2RGB, COLOR_BAYER_RGGB2RGB, COLOR_BayerGRBG2RGB, COLOR_BAYER_GRBG2RGB, COLOR_BayerBGGR2RGB, COLOR_BAYER_BGGR2RGB, COLOR_BayerGBRG2RGB, COLOR_BAYER_GBRG2RGB, COLOR_BayerBG2RGB, COLOR_BAYER_BG2RGB, COLOR_BayerGB2RGB, COLOR_BAYER_GB2RGB, COLOR_BayerRG2RGB, COLOR_BAYER_RG2RGB, COLOR_BayerGR2RGB, COLOR_BAYER_GR2RGB, COLOR_BayerBG2GRAY, COLOR_BAYER_BG2GRAY, COLOR_BayerGB2GRAY, COLOR_BAYER_GB2GRAY, COLOR_BayerRG2GRAY, COLOR_BAYER_RG2GRAY, COLOR_BayerGR2GRAY, COLOR_BAYER_GR2GRAY, COLOR_BayerRGGB2GRAY, COLOR_BAYER_RGGB2GRAY, COLOR_BayerGRBG2GRAY, COLOR_BAYER_GRBG2GRAY, COLOR_BayerBGGR2GRAY, COLOR_BAYER_BGGR2GRAY, COLOR_BayerGBRG2GRAY, COLOR_BAYER_GBRG2GRAY, COLOR_BayerBG2BGR_VNG, COLOR_BAYER_BG2BGR_VNG, COLOR_BayerGB2BGR_VNG, COLOR_BAYER_GB2BGR_VNG, COLOR_BayerRG2BGR_VNG, COLOR_BAYER_RG2BGR_VNG, COLOR_BayerGR2BGR_VNG, COLOR_BAYER_GR2BGR_VNG, COLOR_BayerRGGB2BGR_VNG, COLOR_BAYER_RGGB2BGR_VNG, COLOR_BayerGRBG2BGR_VNG, COLOR_BAYER_GRBG2BGR_VNG, COLOR_BayerBGGR2BGR_VNG, COLOR_BAYER_BGGR2BGR_VNG, COLOR_BayerGBRG2BGR_VNG, COLOR_BAYER_GBRG2BGR_VNG, COLOR_BayerRGGB2RGB_VNG, COLOR_BAYER_RGGB2RGB_VNG, COLOR_BayerGRBG2RGB_VNG, COLOR_BAYER_GRBG2RGB_VNG, COLOR_BayerBGGR2RGB_VNG, COLOR_BAYER_BGGR2RGB_VNG, COLOR_BayerGBRG2RGB_VNG, COLOR_BAYER_GBRG2RGB_VNG, COLOR_BayerBG2RGB_VNG, COLOR_BAYER_BG2RGB_VNG, COLOR_BayerGB2RGB_VNG, COLOR_BAYER_GB2RGB_VNG, COLOR_BayerRG2RGB_VNG, COLOR_BAYER_RG2RGB_VNG, COLOR_BayerGR2RGB_VNG, COLOR_BAYER_GR2RGB_VNG, COLOR_BayerBG2BGR_EA, COLOR_BAYER_BG2BGR_EA, COLOR_BayerGB2BGR_EA, COLOR_BAYER_GB2BGR_EA, COLOR_BayerRG2BGR_EA, COLOR_BAYER_RG2BGR_EA, COLOR_BayerGR2BGR_EA, COLOR_BAYER_GR2BGR_EA, COLOR_BayerRGGB2BGR_EA, COLOR_BAYER_RGGB2BGR_EA, COLOR_BayerGRBG2BGR_EA, COLOR_BAYER_GRBG2BGR_EA, COLOR_BayerBGGR2BGR_EA, COLOR_BAYER_BGGR2BGR_EA, COLOR_BayerGBRG2BGR_EA, COLOR_BAYER_GBRG2BGR_EA, COLOR_BayerRGGB2RGB_EA, COLOR_BAYER_RGGB2RGB_EA, COLOR_BayerGRBG2RGB_EA, COLOR_BAYER_GRBG2RGB_EA, COLOR_BayerBGGR2RGB_EA, COLOR_BAYER_BGGR2RGB_EA, COLOR_BayerGBRG2RGB_EA, COLOR_BAYER_GBRG2RGB_EA, COLOR_BayerBG2RGB_EA, COLOR_BAYER_BG2RGB_EA, COLOR_BayerGB2RGB_EA, COLOR_BAYER_GB2RGB_EA, COLOR_BayerRG2RGB_EA, COLOR_BAYER_RG2RGB_EA, COLOR_BayerGR2RGB_EA, COLOR_BAYER_GR2RGB_EA, COLOR_BayerBG2BGRA, COLOR_BAYER_BG2BGRA, COLOR_BayerGB2BGRA, COLOR_BAYER_GB2BGRA, COLOR_BayerRG2BGRA, COLOR_BAYER_RG2BGRA, COLOR_BayerGR2BGRA, COLOR_BAYER_GR2BGRA, COLOR_BayerRGGB2BGRA, COLOR_BAYER_RGGB2BGRA, COLOR_BayerGRBG2BGRA, COLOR_BAYER_GRBG2BGRA, COLOR_BayerBGGR2BGRA, COLOR_BAYER_BGGR2BGRA, COLOR_BayerGBRG2BGRA, COLOR_BAYER_GBRG2BGRA, COLOR_BayerRGGB2RGBA, COLOR_BAYER_RGGB2RGBA, COLOR_BayerGRBG2RGBA, COLOR_BAYER_GRBG2RGBA, COLOR_BayerBGGR2RGBA, COLOR_BAYER_BGGR2RGBA, COLOR_BayerGBRG2RGBA, COLOR_BAYER_GBRG2RGBA, COLOR_BayerBG2RGBA, COLOR_BAYER_BG2RGBA, COLOR_BayerGB2RGBA, COLOR_BAYER_GB2RGBA, COLOR_BayerRG2RGBA, COLOR_BAYER_RG2RGBA, COLOR_BayerGR2RGBA, COLOR_BAYER_GR2RGBA, COLOR_RGB2YUV_UYVY, COLOR_BGR2YUV_UYVY, COLOR_RGB2YUV_Y422, COLOR_BGR2YUV_Y422, COLOR_RGB2YUV_UYNV, COLOR_BGR2YUV_UYNV, COLOR_RGBA2YUV_UYVY, COLOR_BGRA2YUV_UYVY, COLOR_RGBA2YUV_Y422, COLOR_BGRA2YUV_Y422, COLOR_RGBA2YUV_UYNV, COLOR_BGRA2YUV_UYNV, COLOR_RGB2YUV_YUY2, COLOR_BGR2YUV_YUY2, COLOR_RGB2YUV_YVYU, COLOR_BGR2YUV_YVYU, COLOR_RGB2YUV_YUYV, COLOR_BGR2YUV_YUYV, COLOR_RGB2YUV_YUNV, COLOR_BGR2YUV_YUNV, COLOR_RGBA2YUV_YUY2, COLOR_BGRA2YUV_YUY2, COLOR_RGBA2YUV_YVYU, COLOR_BGRA2YUV_YVYU, COLOR_RGBA2YUV_YUYV, COLOR_BGRA2YUV_YUYV, COLOR_RGBA2YUV_YUNV, COLOR_BGRA2YUV_YUNV, COLOR_COLORCVT_MAX]""" + +INTERSECT_NONE: int +INTERSECT_PARTIAL: int +INTERSECT_FULL: int +RectanglesIntersectTypes = int +"""One of [INTERSECT_NONE, INTERSECT_PARTIAL, INTERSECT_FULL]""" + +FILLED: int +LINE_4: int +LINE_8: int +LINE_AA: int +LineTypes = int +"""One of [FILLED, LINE_4, LINE_8, LINE_AA]""" + +FONT_HERSHEY_SIMPLEX: int +FONT_HERSHEY_PLAIN: int +FONT_HERSHEY_DUPLEX: int +FONT_HERSHEY_COMPLEX: int +FONT_HERSHEY_TRIPLEX: int +FONT_HERSHEY_COMPLEX_SMALL: int +FONT_HERSHEY_SCRIPT_SIMPLEX: int +FONT_HERSHEY_SCRIPT_COMPLEX: int +FONT_ITALIC: int +HersheyFonts = int +"""One of [FONT_HERSHEY_SIMPLEX, FONT_HERSHEY_PLAIN, FONT_HERSHEY_DUPLEX, FONT_HERSHEY_COMPLEX, FONT_HERSHEY_TRIPLEX, FONT_HERSHEY_COMPLEX_SMALL, FONT_HERSHEY_SCRIPT_SIMPLEX, FONT_HERSHEY_SCRIPT_COMPLEX, FONT_ITALIC]""" + +MARKER_CROSS: int +MARKER_TILTED_CROSS: int +MARKER_STAR: int +MARKER_DIAMOND: int +MARKER_SQUARE: int +MARKER_TRIANGLE_UP: int +MARKER_TRIANGLE_DOWN: int +MarkerTypes = int +"""One of [MARKER_CROSS, MARKER_TILTED_CROSS, MARKER_STAR, MARKER_DIAMOND, MARKER_SQUARE, MARKER_TRIANGLE_UP, MARKER_TRIANGLE_DOWN]""" + +TM_SQDIFF: int +TM_SQDIFF_NORMED: int +TM_CCORR: int +TM_CCORR_NORMED: int +TM_CCOEFF: int +TM_CCOEFF_NORMED: int +TemplateMatchModes = int +"""One of [TM_SQDIFF, TM_SQDIFF_NORMED, TM_CCORR, TM_CCORR_NORMED, TM_CCOEFF, TM_CCOEFF_NORMED]""" + +COLORMAP_AUTUMN: int +COLORMAP_BONE: int +COLORMAP_JET: int +COLORMAP_WINTER: int +COLORMAP_RAINBOW: int +COLORMAP_OCEAN: int +COLORMAP_SUMMER: int +COLORMAP_SPRING: int +COLORMAP_COOL: int +COLORMAP_HSV: int +COLORMAP_PINK: int +COLORMAP_HOT: int +COLORMAP_PARULA: int +COLORMAP_MAGMA: int +COLORMAP_INFERNO: int +COLORMAP_PLASMA: int +COLORMAP_VIRIDIS: int +COLORMAP_CIVIDIS: int +COLORMAP_TWILIGHT: int +COLORMAP_TWILIGHT_SHIFTED: int +COLORMAP_TURBO: int +COLORMAP_DEEPGREEN: int +ColormapTypes = int +"""One of [COLORMAP_AUTUMN, COLORMAP_BONE, COLORMAP_JET, COLORMAP_WINTER, COLORMAP_RAINBOW, COLORMAP_OCEAN, COLORMAP_SUMMER, COLORMAP_SPRING, COLORMAP_COOL, COLORMAP_HSV, COLORMAP_PINK, COLORMAP_HOT, COLORMAP_PARULA, COLORMAP_MAGMA, COLORMAP_INFERNO, COLORMAP_PLASMA, COLORMAP_VIRIDIS, COLORMAP_CIVIDIS, COLORMAP_TWILIGHT, COLORMAP_TWILIGHT_SHIFTED, COLORMAP_TURBO, COLORMAP_DEEPGREEN]""" + +INPAINT_NS: int +INPAINT_TELEA: int +LDR_SIZE: int +RECURS_FILTER: int +NORMCONV_FILTER: int +CAP_PROP_DC1394_OFF: int +CAP_PROP_DC1394_MODE_MANUAL: int +CAP_PROP_DC1394_MODE_AUTO: int +CAP_PROP_DC1394_MODE_ONE_PUSH_AUTO: int +CAP_PROP_DC1394_MAX: int +CAP_OPENNI_DEPTH_GENERATOR: int +CAP_OPENNI_IMAGE_GENERATOR: int +CAP_OPENNI_IR_GENERATOR: int +CAP_OPENNI_GENERATORS_MASK: int +CAP_PROP_OPENNI_OUTPUT_MODE: int +CAP_PROP_OPENNI_FRAME_MAX_DEPTH: int +CAP_PROP_OPENNI_BASELINE: int +CAP_PROP_OPENNI_FOCAL_LENGTH: int +CAP_PROP_OPENNI_REGISTRATION: int +CAP_PROP_OPENNI_REGISTRATION_ON: int +CAP_PROP_OPENNI_APPROX_FRAME_SYNC: int +CAP_PROP_OPENNI_MAX_BUFFER_SIZE: int +CAP_PROP_OPENNI_CIRCLE_BUFFER: int +CAP_PROP_OPENNI_MAX_TIME_DURATION: int +CAP_PROP_OPENNI_GENERATOR_PRESENT: int +CAP_PROP_OPENNI2_SYNC: int +CAP_PROP_OPENNI2_MIRROR: int +CAP_OPENNI_IMAGE_GENERATOR_PRESENT: int +CAP_OPENNI_IMAGE_GENERATOR_OUTPUT_MODE: int +CAP_OPENNI_DEPTH_GENERATOR_PRESENT: int +CAP_OPENNI_DEPTH_GENERATOR_BASELINE: int +CAP_OPENNI_DEPTH_GENERATOR_FOCAL_LENGTH: int +CAP_OPENNI_DEPTH_GENERATOR_REGISTRATION: int +CAP_OPENNI_DEPTH_GENERATOR_REGISTRATION_ON: int +CAP_OPENNI_IR_GENERATOR_PRESENT: int +CAP_OPENNI_DEPTH_MAP: int +CAP_OPENNI_POINT_CLOUD_MAP: int +CAP_OPENNI_DISPARITY_MAP: int +CAP_OPENNI_DISPARITY_MAP_32F: int +CAP_OPENNI_VALID_DEPTH_MASK: int +CAP_OPENNI_BGR_IMAGE: int +CAP_OPENNI_GRAY_IMAGE: int +CAP_OPENNI_IR_IMAGE: int +CAP_OPENNI_VGA_30HZ: int +CAP_OPENNI_SXGA_15HZ: int +CAP_OPENNI_SXGA_30HZ: int +CAP_OPENNI_QVGA_30HZ: int +CAP_OPENNI_QVGA_60HZ: int +CAP_PROP_GSTREAMER_QUEUE_LENGTH: int +CAP_PROP_PVAPI_MULTICASTIP: int +CAP_PROP_PVAPI_FRAMESTARTTRIGGERMODE: int +CAP_PROP_PVAPI_DECIMATIONHORIZONTAL: int +CAP_PROP_PVAPI_DECIMATIONVERTICAL: int +CAP_PROP_PVAPI_BINNINGX: int +CAP_PROP_PVAPI_BINNINGY: int +CAP_PROP_PVAPI_PIXELFORMAT: int +CAP_PVAPI_FSTRIGMODE_FREERUN: int +CAP_PVAPI_FSTRIGMODE_SYNCIN1: int +CAP_PVAPI_FSTRIGMODE_SYNCIN2: int +CAP_PVAPI_FSTRIGMODE_FIXEDRATE: int +CAP_PVAPI_FSTRIGMODE_SOFTWARE: int +CAP_PVAPI_DECIMATION_OFF: int +CAP_PVAPI_DECIMATION_2OUTOF4: int +CAP_PVAPI_DECIMATION_2OUTOF8: int +CAP_PVAPI_DECIMATION_2OUTOF16: int +CAP_PVAPI_PIXELFORMAT_MONO8: int +CAP_PVAPI_PIXELFORMAT_MONO16: int +CAP_PVAPI_PIXELFORMAT_BAYER8: int +CAP_PVAPI_PIXELFORMAT_BAYER16: int +CAP_PVAPI_PIXELFORMAT_RGB24: int +CAP_PVAPI_PIXELFORMAT_BGR24: int +CAP_PVAPI_PIXELFORMAT_RGBA32: int +CAP_PVAPI_PIXELFORMAT_BGRA32: int +CAP_PROP_XI_DOWNSAMPLING: int +CAP_PROP_XI_DATA_FORMAT: int +CAP_PROP_XI_OFFSET_X: int +CAP_PROP_XI_OFFSET_Y: int +CAP_PROP_XI_TRG_SOURCE: int +CAP_PROP_XI_TRG_SOFTWARE: int +CAP_PROP_XI_GPI_SELECTOR: int +CAP_PROP_XI_GPI_MODE: int +CAP_PROP_XI_GPI_LEVEL: int +CAP_PROP_XI_GPO_SELECTOR: int +CAP_PROP_XI_GPO_MODE: int +CAP_PROP_XI_LED_SELECTOR: int +CAP_PROP_XI_LED_MODE: int +CAP_PROP_XI_MANUAL_WB: int +CAP_PROP_XI_AUTO_WB: int +CAP_PROP_XI_AEAG: int +CAP_PROP_XI_EXP_PRIORITY: int +CAP_PROP_XI_AE_MAX_LIMIT: int +CAP_PROP_XI_AG_MAX_LIMIT: int +CAP_PROP_XI_AEAG_LEVEL: int +CAP_PROP_XI_TIMEOUT: int +CAP_PROP_XI_EXPOSURE: int +CAP_PROP_XI_EXPOSURE_BURST_COUNT: int +CAP_PROP_XI_GAIN_SELECTOR: int +CAP_PROP_XI_GAIN: int +CAP_PROP_XI_DOWNSAMPLING_TYPE: int +CAP_PROP_XI_BINNING_SELECTOR: int +CAP_PROP_XI_BINNING_VERTICAL: int +CAP_PROP_XI_BINNING_HORIZONTAL: int +CAP_PROP_XI_BINNING_PATTERN: int +CAP_PROP_XI_DECIMATION_SELECTOR: int +CAP_PROP_XI_DECIMATION_VERTICAL: int +CAP_PROP_XI_DECIMATION_HORIZONTAL: int +CAP_PROP_XI_DECIMATION_PATTERN: int +CAP_PROP_XI_TEST_PATTERN_GENERATOR_SELECTOR: int +CAP_PROP_XI_TEST_PATTERN: int +CAP_PROP_XI_IMAGE_DATA_FORMAT: int +CAP_PROP_XI_SHUTTER_TYPE: int +CAP_PROP_XI_SENSOR_TAPS: int +CAP_PROP_XI_AEAG_ROI_OFFSET_X: int +CAP_PROP_XI_AEAG_ROI_OFFSET_Y: int +CAP_PROP_XI_AEAG_ROI_WIDTH: int +CAP_PROP_XI_AEAG_ROI_HEIGHT: int +CAP_PROP_XI_BPC: int +CAP_PROP_XI_WB_KR: int +CAP_PROP_XI_WB_KG: int +CAP_PROP_XI_WB_KB: int +CAP_PROP_XI_WIDTH: int +CAP_PROP_XI_HEIGHT: int +CAP_PROP_XI_REGION_SELECTOR: int +CAP_PROP_XI_REGION_MODE: int +CAP_PROP_XI_LIMIT_BANDWIDTH: int +CAP_PROP_XI_SENSOR_DATA_BIT_DEPTH: int +CAP_PROP_XI_OUTPUT_DATA_BIT_DEPTH: int +CAP_PROP_XI_IMAGE_DATA_BIT_DEPTH: int +CAP_PROP_XI_OUTPUT_DATA_PACKING: int +CAP_PROP_XI_OUTPUT_DATA_PACKING_TYPE: int +CAP_PROP_XI_IS_COOLED: int +CAP_PROP_XI_COOLING: int +CAP_PROP_XI_TARGET_TEMP: int +CAP_PROP_XI_CHIP_TEMP: int +CAP_PROP_XI_HOUS_TEMP: int +CAP_PROP_XI_HOUS_BACK_SIDE_TEMP: int +CAP_PROP_XI_SENSOR_BOARD_TEMP: int +CAP_PROP_XI_CMS: int +CAP_PROP_XI_APPLY_CMS: int +CAP_PROP_XI_IMAGE_IS_COLOR: int +CAP_PROP_XI_COLOR_FILTER_ARRAY: int +CAP_PROP_XI_GAMMAY: int +CAP_PROP_XI_GAMMAC: int +CAP_PROP_XI_SHARPNESS: int +CAP_PROP_XI_CC_MATRIX_00: int +CAP_PROP_XI_CC_MATRIX_01: int +CAP_PROP_XI_CC_MATRIX_02: int +CAP_PROP_XI_CC_MATRIX_03: int +CAP_PROP_XI_CC_MATRIX_10: int +CAP_PROP_XI_CC_MATRIX_11: int +CAP_PROP_XI_CC_MATRIX_12: int +CAP_PROP_XI_CC_MATRIX_13: int +CAP_PROP_XI_CC_MATRIX_20: int +CAP_PROP_XI_CC_MATRIX_21: int +CAP_PROP_XI_CC_MATRIX_22: int +CAP_PROP_XI_CC_MATRIX_23: int +CAP_PROP_XI_CC_MATRIX_30: int +CAP_PROP_XI_CC_MATRIX_31: int +CAP_PROP_XI_CC_MATRIX_32: int +CAP_PROP_XI_CC_MATRIX_33: int +CAP_PROP_XI_DEFAULT_CC_MATRIX: int +CAP_PROP_XI_TRG_SELECTOR: int +CAP_PROP_XI_ACQ_FRAME_BURST_COUNT: int +CAP_PROP_XI_DEBOUNCE_EN: int +CAP_PROP_XI_DEBOUNCE_T0: int +CAP_PROP_XI_DEBOUNCE_T1: int +CAP_PROP_XI_DEBOUNCE_POL: int +CAP_PROP_XI_LENS_MODE: int +CAP_PROP_XI_LENS_APERTURE_VALUE: int +CAP_PROP_XI_LENS_FOCUS_MOVEMENT_VALUE: int +CAP_PROP_XI_LENS_FOCUS_MOVE: int +CAP_PROP_XI_LENS_FOCUS_DISTANCE: int +CAP_PROP_XI_LENS_FOCAL_LENGTH: int +CAP_PROP_XI_LENS_FEATURE_SELECTOR: int +CAP_PROP_XI_LENS_FEATURE: int +CAP_PROP_XI_DEVICE_MODEL_ID: int +CAP_PROP_XI_DEVICE_SN: int +CAP_PROP_XI_IMAGE_DATA_FORMAT_RGB32_ALPHA: int +CAP_PROP_XI_IMAGE_PAYLOAD_SIZE: int +CAP_PROP_XI_TRANSPORT_PIXEL_FORMAT: int +CAP_PROP_XI_SENSOR_CLOCK_FREQ_HZ: int +CAP_PROP_XI_SENSOR_CLOCK_FREQ_INDEX: int +CAP_PROP_XI_SENSOR_OUTPUT_CHANNEL_COUNT: int +CAP_PROP_XI_FRAMERATE: int +CAP_PROP_XI_COUNTER_SELECTOR: int +CAP_PROP_XI_COUNTER_VALUE: int +CAP_PROP_XI_ACQ_TIMING_MODE: int +CAP_PROP_XI_AVAILABLE_BANDWIDTH: int +CAP_PROP_XI_BUFFER_POLICY: int +CAP_PROP_XI_LUT_EN: int +CAP_PROP_XI_LUT_INDEX: int +CAP_PROP_XI_LUT_VALUE: int +CAP_PROP_XI_TRG_DELAY: int +CAP_PROP_XI_TS_RST_MODE: int +CAP_PROP_XI_TS_RST_SOURCE: int +CAP_PROP_XI_IS_DEVICE_EXIST: int +CAP_PROP_XI_ACQ_BUFFER_SIZE: int +CAP_PROP_XI_ACQ_BUFFER_SIZE_UNIT: int +CAP_PROP_XI_ACQ_TRANSPORT_BUFFER_SIZE: int +CAP_PROP_XI_BUFFERS_QUEUE_SIZE: int +CAP_PROP_XI_ACQ_TRANSPORT_BUFFER_COMMIT: int +CAP_PROP_XI_RECENT_FRAME: int +CAP_PROP_XI_DEVICE_RESET: int +CAP_PROP_XI_COLUMN_FPN_CORRECTION: int +CAP_PROP_XI_ROW_FPN_CORRECTION: int +CAP_PROP_XI_SENSOR_MODE: int +CAP_PROP_XI_HDR: int +CAP_PROP_XI_HDR_KNEEPOINT_COUNT: int +CAP_PROP_XI_HDR_T1: int +CAP_PROP_XI_HDR_T2: int +CAP_PROP_XI_KNEEPOINT1: int +CAP_PROP_XI_KNEEPOINT2: int +CAP_PROP_XI_IMAGE_BLACK_LEVEL: int +CAP_PROP_XI_HW_REVISION: int +CAP_PROP_XI_DEBUG_LEVEL: int +CAP_PROP_XI_AUTO_BANDWIDTH_CALCULATION: int +CAP_PROP_XI_FFS_FILE_ID: int +CAP_PROP_XI_FFS_FILE_SIZE: int +CAP_PROP_XI_FREE_FFS_SIZE: int +CAP_PROP_XI_USED_FFS_SIZE: int +CAP_PROP_XI_FFS_ACCESS_KEY: int +CAP_PROP_XI_SENSOR_FEATURE_SELECTOR: int +CAP_PROP_XI_SENSOR_FEATURE_VALUE: int +CAP_PROP_ARAVIS_AUTOTRIGGER: int +CAP_PROP_ANDROID_DEVICE_TORCH: int +CAP_PROP_IOS_DEVICE_FOCUS: int +CAP_PROP_IOS_DEVICE_EXPOSURE: int +CAP_PROP_IOS_DEVICE_FLASH: int +CAP_PROP_IOS_DEVICE_WHITEBALANCE: int +CAP_PROP_IOS_DEVICE_TORCH: int +CAP_PROP_GIGA_FRAME_OFFSET_X: int +CAP_PROP_GIGA_FRAME_OFFSET_Y: int +CAP_PROP_GIGA_FRAME_WIDTH_MAX: int +CAP_PROP_GIGA_FRAME_HEIGH_MAX: int +CAP_PROP_GIGA_FRAME_SENS_WIDTH: int +CAP_PROP_GIGA_FRAME_SENS_HEIGH: int +CAP_PROP_INTELPERC_PROFILE_COUNT: int +CAP_PROP_INTELPERC_PROFILE_IDX: int +CAP_PROP_INTELPERC_DEPTH_LOW_CONFIDENCE_VALUE: int +CAP_PROP_INTELPERC_DEPTH_SATURATION_VALUE: int +CAP_PROP_INTELPERC_DEPTH_CONFIDENCE_THRESHOLD: int +CAP_PROP_INTELPERC_DEPTH_FOCAL_LENGTH_HORZ: int +CAP_PROP_INTELPERC_DEPTH_FOCAL_LENGTH_VERT: int +CAP_INTELPERC_DEPTH_GENERATOR: int +CAP_INTELPERC_IMAGE_GENERATOR: int +CAP_INTELPERC_IR_GENERATOR: int +CAP_INTELPERC_GENERATORS_MASK: int +CAP_INTELPERC_DEPTH_MAP: int +CAP_INTELPERC_UVDEPTH_MAP: int +CAP_INTELPERC_IR_MAP: int +CAP_INTELPERC_IMAGE: int +CAP_PROP_GPHOTO2_PREVIEW: int +CAP_PROP_GPHOTO2_WIDGET_ENUMERATE: int +CAP_PROP_GPHOTO2_RELOAD_CONFIG: int +CAP_PROP_GPHOTO2_RELOAD_ON_CHANGE: int +CAP_PROP_GPHOTO2_COLLECT_MSGS: int +CAP_PROP_GPHOTO2_FLUSH_MSGS: int +CAP_PROP_SPEED: int +CAP_PROP_APERTURE: int +CAP_PROP_EXPOSUREPROGRAM: int +CAP_PROP_VIEWFINDER: int +CAP_PROP_IMAGES_BASE: int +CAP_PROP_IMAGES_LAST: int +LMEDS: int +RANSAC: int +RHO: int +USAC_DEFAULT: int +USAC_PARALLEL: int +USAC_FM_8PTS: int +USAC_FAST: int +USAC_ACCURATE: int +USAC_PROSAC: int +USAC_MAGSAC: int +CALIB_CB_ADAPTIVE_THRESH: int +CALIB_CB_NORMALIZE_IMAGE: int +CALIB_CB_FILTER_QUADS: int +CALIB_CB_FAST_CHECK: int +CALIB_CB_EXHAUSTIVE: int +CALIB_CB_ACCURACY: int +CALIB_CB_LARGER: int +CALIB_CB_MARKER: int +CALIB_CB_PLAIN: int +CALIB_CB_SYMMETRIC_GRID: int +CALIB_CB_ASYMMETRIC_GRID: int +CALIB_CB_CLUSTERING: int +CALIB_NINTRINSIC: int +CALIB_USE_INTRINSIC_GUESS: int +CALIB_FIX_ASPECT_RATIO: int +CALIB_FIX_PRINCIPAL_POINT: int +CALIB_ZERO_TANGENT_DIST: int +CALIB_FIX_FOCAL_LENGTH: int +CALIB_FIX_K1: int +CALIB_FIX_K2: int +CALIB_FIX_K3: int +CALIB_FIX_K4: int +CALIB_FIX_K5: int +CALIB_FIX_K6: int +CALIB_RATIONAL_MODEL: int +CALIB_THIN_PRISM_MODEL: int +CALIB_FIX_S1_S2_S3_S4: int +CALIB_TILTED_MODEL: int +CALIB_FIX_TAUX_TAUY: int +CALIB_USE_QR: int +CALIB_FIX_TANGENT_DIST: int +CALIB_FIX_INTRINSIC: int +CALIB_SAME_FOCAL_LENGTH: int +CALIB_ZERO_DISPARITY: int +CALIB_USE_LU: int +CALIB_USE_EXTRINSIC_GUESS: int +FM_7POINT: int +FM_8POINT: int +FM_LMEDS: int +FM_RANSAC: int +CASCADE_DO_CANNY_PRUNING: int +CASCADE_SCALE_IMAGE: int +CASCADE_FIND_BIGGEST_OBJECT: int +CASCADE_DO_ROUGH_SEARCH: int +OPTFLOW_USE_INITIAL_FLOW: int +OPTFLOW_LK_GET_MIN_EIGENVALS: int +OPTFLOW_FARNEBACK_GAUSSIAN: int +MOTION_TRANSLATION: int +MOTION_EUCLIDEAN: int +MOTION_AFFINE: int +MOTION_HOMOGRAPHY: int + +NORMAL_CLONE: int +MIXED_CLONE: int +MONOCHROME_TRANSFER: int +NORMAL_CLONE_WIDE: int +MIXED_CLONE_WIDE: int +MONOCHROME_TRANSFER_WIDE: int +SeamlessCloneFlags = int +"""One of [NORMAL_CLONE, MIXED_CLONE, MONOCHROME_TRANSFER, NORMAL_CLONE_WIDE, MIXED_CLONE_WIDE, MONOCHROME_TRANSFER_WIDE]""" + +DrawMatchesFlags_DEFAULT: int +DRAW_MATCHES_FLAGS_DEFAULT: int +DrawMatchesFlags_DRAW_OVER_OUTIMG: int +DRAW_MATCHES_FLAGS_DRAW_OVER_OUTIMG: int +DrawMatchesFlags_NOT_DRAW_SINGLE_POINTS: int +DRAW_MATCHES_FLAGS_NOT_DRAW_SINGLE_POINTS: int +DrawMatchesFlags_DRAW_RICH_KEYPOINTS: int +DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS: int +DrawMatchesFlags = int +"""One of [DrawMatchesFlags_DEFAULT, DRAW_MATCHES_FLAGS_DEFAULT, DrawMatchesFlags_DRAW_OVER_OUTIMG, DRAW_MATCHES_FLAGS_DRAW_OVER_OUTIMG, DrawMatchesFlags_NOT_DRAW_SINGLE_POINTS, DRAW_MATCHES_FLAGS_NOT_DRAW_SINGLE_POINTS, DrawMatchesFlags_DRAW_RICH_KEYPOINTS, DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS]""" + +IMREAD_UNCHANGED: int +IMREAD_GRAYSCALE: int +IMREAD_COLOR_BGR: int +IMREAD_COLOR: int +IMREAD_ANYDEPTH: int +IMREAD_ANYCOLOR: int +IMREAD_LOAD_GDAL: int +IMREAD_REDUCED_GRAYSCALE_2: int +IMREAD_REDUCED_COLOR_2: int +IMREAD_REDUCED_GRAYSCALE_4: int +IMREAD_REDUCED_COLOR_4: int +IMREAD_REDUCED_GRAYSCALE_8: int +IMREAD_REDUCED_COLOR_8: int +IMREAD_IGNORE_ORIENTATION: int +IMREAD_COLOR_RGB: int +ImreadModes = int +"""One of [IMREAD_UNCHANGED, IMREAD_GRAYSCALE, IMREAD_COLOR_BGR, IMREAD_COLOR, IMREAD_ANYDEPTH, IMREAD_ANYCOLOR, IMREAD_LOAD_GDAL, IMREAD_REDUCED_GRAYSCALE_2, IMREAD_REDUCED_COLOR_2, IMREAD_REDUCED_GRAYSCALE_4, IMREAD_REDUCED_COLOR_4, IMREAD_REDUCED_GRAYSCALE_8, IMREAD_REDUCED_COLOR_8, IMREAD_IGNORE_ORIENTATION, IMREAD_COLOR_RGB]""" + +IMWRITE_JPEG_QUALITY: int +IMWRITE_JPEG_PROGRESSIVE: int +IMWRITE_JPEG_OPTIMIZE: int +IMWRITE_JPEG_RST_INTERVAL: int +IMWRITE_JPEG_LUMA_QUALITY: int +IMWRITE_JPEG_CHROMA_QUALITY: int +IMWRITE_JPEG_SAMPLING_FACTOR: int +IMWRITE_PNG_COMPRESSION: int +IMWRITE_PNG_STRATEGY: int +IMWRITE_PNG_BILEVEL: int +IMWRITE_PXM_BINARY: int +IMWRITE_EXR_TYPE: int +IMWRITE_EXR_COMPRESSION: int +IMWRITE_EXR_DWA_COMPRESSION_LEVEL: int +IMWRITE_WEBP_QUALITY: int +IMWRITE_HDR_COMPRESSION: int +IMWRITE_PAM_TUPLETYPE: int +IMWRITE_TIFF_RESUNIT: int +IMWRITE_TIFF_XDPI: int +IMWRITE_TIFF_YDPI: int +IMWRITE_TIFF_COMPRESSION: int +IMWRITE_TIFF_ROWSPERSTRIP: int +IMWRITE_TIFF_PREDICTOR: int +IMWRITE_JPEG2000_COMPRESSION_X1000: int +IMWRITE_AVIF_QUALITY: int +IMWRITE_AVIF_DEPTH: int +IMWRITE_AVIF_SPEED: int +IMWRITE_JPEGXL_QUALITY: int +IMWRITE_JPEGXL_EFFORT: int +IMWRITE_JPEGXL_DISTANCE: int +IMWRITE_JPEGXL_DECODING_SPEED: int +IMWRITE_GIF_LOOP: int +IMWRITE_GIF_SPEED: int +IMWRITE_GIF_QUALITY: int +IMWRITE_GIF_DITHER: int +IMWRITE_GIF_TRANSPARENCY: int +IMWRITE_GIF_COLORTABLE: int +ImwriteFlags = int +"""One of [IMWRITE_JPEG_QUALITY, IMWRITE_JPEG_PROGRESSIVE, IMWRITE_JPEG_OPTIMIZE, IMWRITE_JPEG_RST_INTERVAL, IMWRITE_JPEG_LUMA_QUALITY, IMWRITE_JPEG_CHROMA_QUALITY, IMWRITE_JPEG_SAMPLING_FACTOR, IMWRITE_PNG_COMPRESSION, IMWRITE_PNG_STRATEGY, IMWRITE_PNG_BILEVEL, IMWRITE_PXM_BINARY, IMWRITE_EXR_TYPE, IMWRITE_EXR_COMPRESSION, IMWRITE_EXR_DWA_COMPRESSION_LEVEL, IMWRITE_WEBP_QUALITY, IMWRITE_HDR_COMPRESSION, IMWRITE_PAM_TUPLETYPE, IMWRITE_TIFF_RESUNIT, IMWRITE_TIFF_XDPI, IMWRITE_TIFF_YDPI, IMWRITE_TIFF_COMPRESSION, IMWRITE_TIFF_ROWSPERSTRIP, IMWRITE_TIFF_PREDICTOR, IMWRITE_JPEG2000_COMPRESSION_X1000, IMWRITE_AVIF_QUALITY, IMWRITE_AVIF_DEPTH, IMWRITE_AVIF_SPEED, IMWRITE_JPEGXL_QUALITY, IMWRITE_JPEGXL_EFFORT, IMWRITE_JPEGXL_DISTANCE, IMWRITE_JPEGXL_DECODING_SPEED, IMWRITE_GIF_LOOP, IMWRITE_GIF_SPEED, IMWRITE_GIF_QUALITY, IMWRITE_GIF_DITHER, IMWRITE_GIF_TRANSPARENCY, IMWRITE_GIF_COLORTABLE]""" + +IMWRITE_JPEG_SAMPLING_FACTOR_411: int +IMWRITE_JPEG_SAMPLING_FACTOR_420: int +IMWRITE_JPEG_SAMPLING_FACTOR_422: int +IMWRITE_JPEG_SAMPLING_FACTOR_440: int +IMWRITE_JPEG_SAMPLING_FACTOR_444: int +ImwriteJPEGSamplingFactorParams = int +"""One of [IMWRITE_JPEG_SAMPLING_FACTOR_411, IMWRITE_JPEG_SAMPLING_FACTOR_420, IMWRITE_JPEG_SAMPLING_FACTOR_422, IMWRITE_JPEG_SAMPLING_FACTOR_440, IMWRITE_JPEG_SAMPLING_FACTOR_444]""" + +IMWRITE_TIFF_COMPRESSION_NONE: int +IMWRITE_TIFF_COMPRESSION_CCITTRLE: int +IMWRITE_TIFF_COMPRESSION_CCITTFAX3: int +IMWRITE_TIFF_COMPRESSION_CCITT_T4: int +IMWRITE_TIFF_COMPRESSION_CCITTFAX4: int +IMWRITE_TIFF_COMPRESSION_CCITT_T6: int +IMWRITE_TIFF_COMPRESSION_LZW: int +IMWRITE_TIFF_COMPRESSION_OJPEG: int +IMWRITE_TIFF_COMPRESSION_JPEG: int +IMWRITE_TIFF_COMPRESSION_T85: int +IMWRITE_TIFF_COMPRESSION_T43: int +IMWRITE_TIFF_COMPRESSION_NEXT: int +IMWRITE_TIFF_COMPRESSION_CCITTRLEW: int +IMWRITE_TIFF_COMPRESSION_PACKBITS: int +IMWRITE_TIFF_COMPRESSION_THUNDERSCAN: int +IMWRITE_TIFF_COMPRESSION_IT8CTPAD: int +IMWRITE_TIFF_COMPRESSION_IT8LW: int +IMWRITE_TIFF_COMPRESSION_IT8MP: int +IMWRITE_TIFF_COMPRESSION_IT8BL: int +IMWRITE_TIFF_COMPRESSION_PIXARFILM: int +IMWRITE_TIFF_COMPRESSION_PIXARLOG: int +IMWRITE_TIFF_COMPRESSION_DEFLATE: int +IMWRITE_TIFF_COMPRESSION_ADOBE_DEFLATE: int +IMWRITE_TIFF_COMPRESSION_DCS: int +IMWRITE_TIFF_COMPRESSION_JBIG: int +IMWRITE_TIFF_COMPRESSION_SGILOG: int +IMWRITE_TIFF_COMPRESSION_SGILOG24: int +IMWRITE_TIFF_COMPRESSION_JP2000: int +IMWRITE_TIFF_COMPRESSION_LERC: int +IMWRITE_TIFF_COMPRESSION_LZMA: int +IMWRITE_TIFF_COMPRESSION_ZSTD: int +IMWRITE_TIFF_COMPRESSION_WEBP: int +IMWRITE_TIFF_COMPRESSION_JXL: int +ImwriteTiffCompressionFlags = int +"""One of [IMWRITE_TIFF_COMPRESSION_NONE, IMWRITE_TIFF_COMPRESSION_CCITTRLE, IMWRITE_TIFF_COMPRESSION_CCITTFAX3, IMWRITE_TIFF_COMPRESSION_CCITT_T4, IMWRITE_TIFF_COMPRESSION_CCITTFAX4, IMWRITE_TIFF_COMPRESSION_CCITT_T6, IMWRITE_TIFF_COMPRESSION_LZW, IMWRITE_TIFF_COMPRESSION_OJPEG, IMWRITE_TIFF_COMPRESSION_JPEG, IMWRITE_TIFF_COMPRESSION_T85, IMWRITE_TIFF_COMPRESSION_T43, IMWRITE_TIFF_COMPRESSION_NEXT, IMWRITE_TIFF_COMPRESSION_CCITTRLEW, IMWRITE_TIFF_COMPRESSION_PACKBITS, IMWRITE_TIFF_COMPRESSION_THUNDERSCAN, IMWRITE_TIFF_COMPRESSION_IT8CTPAD, IMWRITE_TIFF_COMPRESSION_IT8LW, IMWRITE_TIFF_COMPRESSION_IT8MP, IMWRITE_TIFF_COMPRESSION_IT8BL, IMWRITE_TIFF_COMPRESSION_PIXARFILM, IMWRITE_TIFF_COMPRESSION_PIXARLOG, IMWRITE_TIFF_COMPRESSION_DEFLATE, IMWRITE_TIFF_COMPRESSION_ADOBE_DEFLATE, IMWRITE_TIFF_COMPRESSION_DCS, IMWRITE_TIFF_COMPRESSION_JBIG, IMWRITE_TIFF_COMPRESSION_SGILOG, IMWRITE_TIFF_COMPRESSION_SGILOG24, IMWRITE_TIFF_COMPRESSION_JP2000, IMWRITE_TIFF_COMPRESSION_LERC, IMWRITE_TIFF_COMPRESSION_LZMA, IMWRITE_TIFF_COMPRESSION_ZSTD, IMWRITE_TIFF_COMPRESSION_WEBP, IMWRITE_TIFF_COMPRESSION_JXL]""" + +IMWRITE_TIFF_PREDICTOR_NONE: int +IMWRITE_TIFF_PREDICTOR_HORIZONTAL: int +IMWRITE_TIFF_PREDICTOR_FLOATINGPOINT: int +ImwriteTiffPredictorFlags = int +"""One of [IMWRITE_TIFF_PREDICTOR_NONE, IMWRITE_TIFF_PREDICTOR_HORIZONTAL, IMWRITE_TIFF_PREDICTOR_FLOATINGPOINT]""" + +IMWRITE_EXR_TYPE_HALF: int +IMWRITE_EXR_TYPE_FLOAT: int +ImwriteEXRTypeFlags = int +"""One of [IMWRITE_EXR_TYPE_HALF, IMWRITE_EXR_TYPE_FLOAT]""" + +IMWRITE_EXR_COMPRESSION_NO: int +IMWRITE_EXR_COMPRESSION_RLE: int +IMWRITE_EXR_COMPRESSION_ZIPS: int +IMWRITE_EXR_COMPRESSION_ZIP: int +IMWRITE_EXR_COMPRESSION_PIZ: int +IMWRITE_EXR_COMPRESSION_PXR24: int +IMWRITE_EXR_COMPRESSION_B44: int +IMWRITE_EXR_COMPRESSION_B44A: int +IMWRITE_EXR_COMPRESSION_DWAA: int +IMWRITE_EXR_COMPRESSION_DWAB: int +ImwriteEXRCompressionFlags = int +"""One of [IMWRITE_EXR_COMPRESSION_NO, IMWRITE_EXR_COMPRESSION_RLE, IMWRITE_EXR_COMPRESSION_ZIPS, IMWRITE_EXR_COMPRESSION_ZIP, IMWRITE_EXR_COMPRESSION_PIZ, IMWRITE_EXR_COMPRESSION_PXR24, IMWRITE_EXR_COMPRESSION_B44, IMWRITE_EXR_COMPRESSION_B44A, IMWRITE_EXR_COMPRESSION_DWAA, IMWRITE_EXR_COMPRESSION_DWAB]""" + +IMWRITE_PNG_STRATEGY_DEFAULT: int +IMWRITE_PNG_STRATEGY_FILTERED: int +IMWRITE_PNG_STRATEGY_HUFFMAN_ONLY: int +IMWRITE_PNG_STRATEGY_RLE: int +IMWRITE_PNG_STRATEGY_FIXED: int +ImwritePNGFlags = int +"""One of [IMWRITE_PNG_STRATEGY_DEFAULT, IMWRITE_PNG_STRATEGY_FILTERED, IMWRITE_PNG_STRATEGY_HUFFMAN_ONLY, IMWRITE_PNG_STRATEGY_RLE, IMWRITE_PNG_STRATEGY_FIXED]""" + +IMWRITE_PAM_FORMAT_NULL: int +IMWRITE_PAM_FORMAT_BLACKANDWHITE: int +IMWRITE_PAM_FORMAT_GRAYSCALE: int +IMWRITE_PAM_FORMAT_GRAYSCALE_ALPHA: int +IMWRITE_PAM_FORMAT_RGB: int +IMWRITE_PAM_FORMAT_RGB_ALPHA: int +ImwritePAMFlags = int +"""One of [IMWRITE_PAM_FORMAT_NULL, IMWRITE_PAM_FORMAT_BLACKANDWHITE, IMWRITE_PAM_FORMAT_GRAYSCALE, IMWRITE_PAM_FORMAT_GRAYSCALE_ALPHA, IMWRITE_PAM_FORMAT_RGB, IMWRITE_PAM_FORMAT_RGB_ALPHA]""" + +IMWRITE_HDR_COMPRESSION_NONE: int +IMWRITE_HDR_COMPRESSION_RLE: int +ImwriteHDRCompressionFlags = int +"""One of [IMWRITE_HDR_COMPRESSION_NONE, IMWRITE_HDR_COMPRESSION_RLE]""" + +IMWRITE_GIF_FAST_NO_DITHER: int +IMWRITE_GIF_FAST_FLOYD_DITHER: int +IMWRITE_GIF_COLORTABLE_SIZE_8: int +IMWRITE_GIF_COLORTABLE_SIZE_16: int +IMWRITE_GIF_COLORTABLE_SIZE_32: int +IMWRITE_GIF_COLORTABLE_SIZE_64: int +IMWRITE_GIF_COLORTABLE_SIZE_128: int +IMWRITE_GIF_COLORTABLE_SIZE_256: int +ImwriteGIFCompressionFlags = int +"""One of [IMWRITE_GIF_FAST_NO_DITHER, IMWRITE_GIF_FAST_FLOYD_DITHER, IMWRITE_GIF_COLORTABLE_SIZE_8, IMWRITE_GIF_COLORTABLE_SIZE_16, IMWRITE_GIF_COLORTABLE_SIZE_32, IMWRITE_GIF_COLORTABLE_SIZE_64, IMWRITE_GIF_COLORTABLE_SIZE_128, IMWRITE_GIF_COLORTABLE_SIZE_256]""" + +CAP_ANY: int +CAP_VFW: int +CAP_V4L: int +CAP_V4L2: int +CAP_FIREWIRE: int +CAP_FIREWARE: int +CAP_IEEE1394: int +CAP_DC1394: int +CAP_CMU1394: int +CAP_QT: int +CAP_UNICAP: int +CAP_DSHOW: int +CAP_PVAPI: int +CAP_OPENNI: int +CAP_OPENNI_ASUS: int +CAP_ANDROID: int +CAP_XIAPI: int +CAP_AVFOUNDATION: int +CAP_GIGANETIX: int +CAP_MSMF: int +CAP_WINRT: int +CAP_INTELPERC: int +CAP_REALSENSE: int +CAP_OPENNI2: int +CAP_OPENNI2_ASUS: int +CAP_OPENNI2_ASTRA: int +CAP_GPHOTO2: int +CAP_GSTREAMER: int +CAP_FFMPEG: int +CAP_IMAGES: int +CAP_ARAVIS: int +CAP_OPENCV_MJPEG: int +CAP_INTEL_MFX: int +CAP_XINE: int +CAP_UEYE: int +CAP_OBSENSOR: int +VideoCaptureAPIs = int +"""One of [CAP_ANY, CAP_VFW, CAP_V4L, CAP_V4L2, CAP_FIREWIRE, CAP_FIREWARE, CAP_IEEE1394, CAP_DC1394, CAP_CMU1394, CAP_QT, CAP_UNICAP, CAP_DSHOW, CAP_PVAPI, CAP_OPENNI, CAP_OPENNI_ASUS, CAP_ANDROID, CAP_XIAPI, CAP_AVFOUNDATION, CAP_GIGANETIX, CAP_MSMF, CAP_WINRT, CAP_INTELPERC, CAP_REALSENSE, CAP_OPENNI2, CAP_OPENNI2_ASUS, CAP_OPENNI2_ASTRA, CAP_GPHOTO2, CAP_GSTREAMER, CAP_FFMPEG, CAP_IMAGES, CAP_ARAVIS, CAP_OPENCV_MJPEG, CAP_INTEL_MFX, CAP_XINE, CAP_UEYE, CAP_OBSENSOR]""" + +CAP_PROP_POS_MSEC: int +CAP_PROP_POS_FRAMES: int +CAP_PROP_POS_AVI_RATIO: int +CAP_PROP_FRAME_WIDTH: int +CAP_PROP_FRAME_HEIGHT: int +CAP_PROP_FPS: int +CAP_PROP_FOURCC: int +CAP_PROP_FRAME_COUNT: int +CAP_PROP_FORMAT: int +CAP_PROP_MODE: int +CAP_PROP_BRIGHTNESS: int +CAP_PROP_CONTRAST: int +CAP_PROP_SATURATION: int +CAP_PROP_HUE: int +CAP_PROP_GAIN: int +CAP_PROP_EXPOSURE: int +CAP_PROP_CONVERT_RGB: int +CAP_PROP_WHITE_BALANCE_BLUE_U: int +CAP_PROP_RECTIFICATION: int +CAP_PROP_MONOCHROME: int +CAP_PROP_SHARPNESS: int +CAP_PROP_AUTO_EXPOSURE: int +CAP_PROP_GAMMA: int +CAP_PROP_TEMPERATURE: int +CAP_PROP_TRIGGER: int +CAP_PROP_TRIGGER_DELAY: int +CAP_PROP_WHITE_BALANCE_RED_V: int +CAP_PROP_ZOOM: int +CAP_PROP_FOCUS: int +CAP_PROP_GUID: int +CAP_PROP_ISO_SPEED: int +CAP_PROP_BACKLIGHT: int +CAP_PROP_PAN: int +CAP_PROP_TILT: int +CAP_PROP_ROLL: int +CAP_PROP_IRIS: int +CAP_PROP_SETTINGS: int +CAP_PROP_BUFFERSIZE: int +CAP_PROP_AUTOFOCUS: int +CAP_PROP_SAR_NUM: int +CAP_PROP_SAR_DEN: int +CAP_PROP_BACKEND: int +CAP_PROP_CHANNEL: int +CAP_PROP_AUTO_WB: int +CAP_PROP_WB_TEMPERATURE: int +CAP_PROP_CODEC_PIXEL_FORMAT: int +CAP_PROP_BITRATE: int +CAP_PROP_ORIENTATION_META: int +CAP_PROP_ORIENTATION_AUTO: int +CAP_PROP_HW_ACCELERATION: int +CAP_PROP_HW_DEVICE: int +CAP_PROP_HW_ACCELERATION_USE_OPENCL: int +CAP_PROP_OPEN_TIMEOUT_MSEC: int +CAP_PROP_READ_TIMEOUT_MSEC: int +CAP_PROP_STREAM_OPEN_TIME_USEC: int +CAP_PROP_VIDEO_TOTAL_CHANNELS: int +CAP_PROP_VIDEO_STREAM: int +CAP_PROP_AUDIO_STREAM: int +CAP_PROP_AUDIO_POS: int +CAP_PROP_AUDIO_SHIFT_NSEC: int +CAP_PROP_AUDIO_DATA_DEPTH: int +CAP_PROP_AUDIO_SAMPLES_PER_SECOND: int +CAP_PROP_AUDIO_BASE_INDEX: int +CAP_PROP_AUDIO_TOTAL_CHANNELS: int +CAP_PROP_AUDIO_TOTAL_STREAMS: int +CAP_PROP_AUDIO_SYNCHRONIZE: int +CAP_PROP_LRF_HAS_KEY_FRAME: int +CAP_PROP_CODEC_EXTRADATA_INDEX: int +CAP_PROP_FRAME_TYPE: int +CAP_PROP_N_THREADS: int +CAP_PROP_PTS: int +CAP_PROP_DTS_DELAY: int +VideoCaptureProperties = int +"""One of [CAP_PROP_POS_MSEC, CAP_PROP_POS_FRAMES, CAP_PROP_POS_AVI_RATIO, CAP_PROP_FRAME_WIDTH, CAP_PROP_FRAME_HEIGHT, CAP_PROP_FPS, CAP_PROP_FOURCC, CAP_PROP_FRAME_COUNT, CAP_PROP_FORMAT, CAP_PROP_MODE, CAP_PROP_BRIGHTNESS, CAP_PROP_CONTRAST, CAP_PROP_SATURATION, CAP_PROP_HUE, CAP_PROP_GAIN, CAP_PROP_EXPOSURE, CAP_PROP_CONVERT_RGB, CAP_PROP_WHITE_BALANCE_BLUE_U, CAP_PROP_RECTIFICATION, CAP_PROP_MONOCHROME, CAP_PROP_SHARPNESS, CAP_PROP_AUTO_EXPOSURE, CAP_PROP_GAMMA, CAP_PROP_TEMPERATURE, CAP_PROP_TRIGGER, CAP_PROP_TRIGGER_DELAY, CAP_PROP_WHITE_BALANCE_RED_V, CAP_PROP_ZOOM, CAP_PROP_FOCUS, CAP_PROP_GUID, CAP_PROP_ISO_SPEED, CAP_PROP_BACKLIGHT, CAP_PROP_PAN, CAP_PROP_TILT, CAP_PROP_ROLL, CAP_PROP_IRIS, CAP_PROP_SETTINGS, CAP_PROP_BUFFERSIZE, CAP_PROP_AUTOFOCUS, CAP_PROP_SAR_NUM, CAP_PROP_SAR_DEN, CAP_PROP_BACKEND, CAP_PROP_CHANNEL, CAP_PROP_AUTO_WB, CAP_PROP_WB_TEMPERATURE, CAP_PROP_CODEC_PIXEL_FORMAT, CAP_PROP_BITRATE, CAP_PROP_ORIENTATION_META, CAP_PROP_ORIENTATION_AUTO, CAP_PROP_HW_ACCELERATION, CAP_PROP_HW_DEVICE, CAP_PROP_HW_ACCELERATION_USE_OPENCL, CAP_PROP_OPEN_TIMEOUT_MSEC, CAP_PROP_READ_TIMEOUT_MSEC, CAP_PROP_STREAM_OPEN_TIME_USEC, CAP_PROP_VIDEO_TOTAL_CHANNELS, CAP_PROP_VIDEO_STREAM, CAP_PROP_AUDIO_STREAM, CAP_PROP_AUDIO_POS, CAP_PROP_AUDIO_SHIFT_NSEC, CAP_PROP_AUDIO_DATA_DEPTH, CAP_PROP_AUDIO_SAMPLES_PER_SECOND, CAP_PROP_AUDIO_BASE_INDEX, CAP_PROP_AUDIO_TOTAL_CHANNELS, CAP_PROP_AUDIO_TOTAL_STREAMS, CAP_PROP_AUDIO_SYNCHRONIZE, CAP_PROP_LRF_HAS_KEY_FRAME, CAP_PROP_CODEC_EXTRADATA_INDEX, CAP_PROP_FRAME_TYPE, CAP_PROP_N_THREADS, CAP_PROP_PTS, CAP_PROP_DTS_DELAY]""" + +VIDEOWRITER_PROP_QUALITY: int +VIDEOWRITER_PROP_FRAMEBYTES: int +VIDEOWRITER_PROP_NSTRIPES: int +VIDEOWRITER_PROP_IS_COLOR: int +VIDEOWRITER_PROP_DEPTH: int +VIDEOWRITER_PROP_HW_ACCELERATION: int +VIDEOWRITER_PROP_HW_DEVICE: int +VIDEOWRITER_PROP_HW_ACCELERATION_USE_OPENCL: int +VIDEOWRITER_PROP_RAW_VIDEO: int +VIDEOWRITER_PROP_KEY_INTERVAL: int +VIDEOWRITER_PROP_KEY_FLAG: int +VIDEOWRITER_PROP_PTS: int +VIDEOWRITER_PROP_DTS_DELAY: int +VideoWriterProperties = int +"""One of [VIDEOWRITER_PROP_QUALITY, VIDEOWRITER_PROP_FRAMEBYTES, VIDEOWRITER_PROP_NSTRIPES, VIDEOWRITER_PROP_IS_COLOR, VIDEOWRITER_PROP_DEPTH, VIDEOWRITER_PROP_HW_ACCELERATION, VIDEOWRITER_PROP_HW_DEVICE, VIDEOWRITER_PROP_HW_ACCELERATION_USE_OPENCL, VIDEOWRITER_PROP_RAW_VIDEO, VIDEOWRITER_PROP_KEY_INTERVAL, VIDEOWRITER_PROP_KEY_FLAG, VIDEOWRITER_PROP_PTS, VIDEOWRITER_PROP_DTS_DELAY]""" + +VIDEO_ACCELERATION_NONE: int +VIDEO_ACCELERATION_ANY: int +VIDEO_ACCELERATION_D3D11: int +VIDEO_ACCELERATION_VAAPI: int +VIDEO_ACCELERATION_MFX: int +VideoAccelerationType = int +"""One of [VIDEO_ACCELERATION_NONE, VIDEO_ACCELERATION_ANY, VIDEO_ACCELERATION_D3D11, VIDEO_ACCELERATION_VAAPI, VIDEO_ACCELERATION_MFX]""" + +CAP_OBSENSOR_DEPTH_MAP: int +CAP_OBSENSOR_BGR_IMAGE: int +CAP_OBSENSOR_IR_IMAGE: int +VideoCaptureOBSensorDataType = int +"""One of [CAP_OBSENSOR_DEPTH_MAP, CAP_OBSENSOR_BGR_IMAGE, CAP_OBSENSOR_IR_IMAGE]""" + +CAP_OBSENSOR_DEPTH_GENERATOR: int +CAP_OBSENSOR_IMAGE_GENERATOR: int +CAP_OBSENSOR_IR_GENERATOR: int +CAP_OBSENSOR_GENERATORS_MASK: int +VideoCaptureOBSensorGenerators = int +"""One of [CAP_OBSENSOR_DEPTH_GENERATOR, CAP_OBSENSOR_IMAGE_GENERATOR, CAP_OBSENSOR_IR_GENERATOR, CAP_OBSENSOR_GENERATORS_MASK]""" + +CAP_PROP_OBSENSOR_INTRINSIC_FX: int +CAP_PROP_OBSENSOR_INTRINSIC_FY: int +CAP_PROP_OBSENSOR_INTRINSIC_CX: int +CAP_PROP_OBSENSOR_INTRINSIC_CY: int +VideoCaptureOBSensorProperties = int +"""One of [CAP_PROP_OBSENSOR_INTRINSIC_FX, CAP_PROP_OBSENSOR_INTRINSIC_FY, CAP_PROP_OBSENSOR_INTRINSIC_CX, CAP_PROP_OBSENSOR_INTRINSIC_CY]""" + +SOLVEPNP_ITERATIVE: int +SOLVEPNP_EPNP: int +SOLVEPNP_P3P: int +SOLVEPNP_DLS: int +SOLVEPNP_UPNP: int +SOLVEPNP_AP3P: int +SOLVEPNP_IPPE: int +SOLVEPNP_IPPE_SQUARE: int +SOLVEPNP_SQPNP: int +SOLVEPNP_MAX_COUNT: int +SolvePnPMethod = int +"""One of [SOLVEPNP_ITERATIVE, SOLVEPNP_EPNP, SOLVEPNP_P3P, SOLVEPNP_DLS, SOLVEPNP_UPNP, SOLVEPNP_AP3P, SOLVEPNP_IPPE, SOLVEPNP_IPPE_SQUARE, SOLVEPNP_SQPNP, SOLVEPNP_MAX_COUNT]""" + +CALIB_HAND_EYE_TSAI: int +CALIB_HAND_EYE_PARK: int +CALIB_HAND_EYE_HORAUD: int +CALIB_HAND_EYE_ANDREFF: int +CALIB_HAND_EYE_DANIILIDIS: int +HandEyeCalibrationMethod = int +"""One of [CALIB_HAND_EYE_TSAI, CALIB_HAND_EYE_PARK, CALIB_HAND_EYE_HORAUD, CALIB_HAND_EYE_ANDREFF, CALIB_HAND_EYE_DANIILIDIS]""" + +CALIB_ROBOT_WORLD_HAND_EYE_SHAH: int +CALIB_ROBOT_WORLD_HAND_EYE_LI: int +RobotWorldHandEyeCalibrationMethod = int +"""One of [CALIB_ROBOT_WORLD_HAND_EYE_SHAH, CALIB_ROBOT_WORLD_HAND_EYE_LI]""" + +SAMPLING_UNIFORM: int +SAMPLING_PROGRESSIVE_NAPSAC: int +SAMPLING_NAPSAC: int +SAMPLING_PROSAC: int +SamplingMethod = int +"""One of [SAMPLING_UNIFORM, SAMPLING_PROGRESSIVE_NAPSAC, SAMPLING_NAPSAC, SAMPLING_PROSAC]""" + +LOCAL_OPTIM_NULL: int +LOCAL_OPTIM_INNER_LO: int +LOCAL_OPTIM_INNER_AND_ITER_LO: int +LOCAL_OPTIM_GC: int +LOCAL_OPTIM_SIGMA: int +LocalOptimMethod = int +"""One of [LOCAL_OPTIM_NULL, LOCAL_OPTIM_INNER_LO, LOCAL_OPTIM_INNER_AND_ITER_LO, LOCAL_OPTIM_GC, LOCAL_OPTIM_SIGMA]""" + +SCORE_METHOD_RANSAC: int +SCORE_METHOD_MSAC: int +SCORE_METHOD_MAGSAC: int +SCORE_METHOD_LMEDS: int +ScoreMethod = int +"""One of [SCORE_METHOD_RANSAC, SCORE_METHOD_MSAC, SCORE_METHOD_MAGSAC, SCORE_METHOD_LMEDS]""" + +NEIGH_FLANN_KNN: int +NEIGH_GRID: int +NEIGH_FLANN_RADIUS: int +NeighborSearchMethod = int +"""One of [NEIGH_FLANN_KNN, NEIGH_GRID, NEIGH_FLANN_RADIUS]""" + +NONE_POLISHER: int +LSQ_POLISHER: int +MAGSAC: int +COV_POLISHER: int +PolishingMethod = int +"""One of [NONE_POLISHER, LSQ_POLISHER, MAGSAC, COV_POLISHER]""" + +PROJ_SPHERICAL_ORTHO: int +PROJ_SPHERICAL_EQRECT: int +UndistortTypes = int +"""One of [PROJ_SPHERICAL_ORTHO, PROJ_SPHERICAL_EQRECT]""" + +WINDOW_NORMAL: int +WINDOW_AUTOSIZE: int +WINDOW_OPENGL: int +WINDOW_FULLSCREEN: int +WINDOW_FREERATIO: int +WINDOW_KEEPRATIO: int +WINDOW_GUI_EXPANDED: int +WINDOW_GUI_NORMAL: int +WindowFlags = int +"""One of [WINDOW_NORMAL, WINDOW_AUTOSIZE, WINDOW_OPENGL, WINDOW_FULLSCREEN, WINDOW_FREERATIO, WINDOW_KEEPRATIO, WINDOW_GUI_EXPANDED, WINDOW_GUI_NORMAL]""" + +WND_PROP_FULLSCREEN: int +WND_PROP_AUTOSIZE: int +WND_PROP_ASPECT_RATIO: int +WND_PROP_OPENGL: int +WND_PROP_VISIBLE: int +WND_PROP_TOPMOST: int +WND_PROP_VSYNC: int +WindowPropertyFlags = int +"""One of [WND_PROP_FULLSCREEN, WND_PROP_AUTOSIZE, WND_PROP_ASPECT_RATIO, WND_PROP_OPENGL, WND_PROP_VISIBLE, WND_PROP_TOPMOST, WND_PROP_VSYNC]""" + +EVENT_MOUSEMOVE: int +EVENT_LBUTTONDOWN: int +EVENT_RBUTTONDOWN: int +EVENT_MBUTTONDOWN: int +EVENT_LBUTTONUP: int +EVENT_RBUTTONUP: int +EVENT_MBUTTONUP: int +EVENT_LBUTTONDBLCLK: int +EVENT_RBUTTONDBLCLK: int +EVENT_MBUTTONDBLCLK: int +EVENT_MOUSEWHEEL: int +EVENT_MOUSEHWHEEL: int +MouseEventTypes = int +"""One of [EVENT_MOUSEMOVE, EVENT_LBUTTONDOWN, EVENT_RBUTTONDOWN, EVENT_MBUTTONDOWN, EVENT_LBUTTONUP, EVENT_RBUTTONUP, EVENT_MBUTTONUP, EVENT_LBUTTONDBLCLK, EVENT_RBUTTONDBLCLK, EVENT_MBUTTONDBLCLK, EVENT_MOUSEWHEEL, EVENT_MOUSEHWHEEL]""" + +EVENT_FLAG_LBUTTON: int +EVENT_FLAG_RBUTTON: int +EVENT_FLAG_MBUTTON: int +EVENT_FLAG_CTRLKEY: int +EVENT_FLAG_SHIFTKEY: int +EVENT_FLAG_ALTKEY: int +MouseEventFlags = int +"""One of [EVENT_FLAG_LBUTTON, EVENT_FLAG_RBUTTON, EVENT_FLAG_MBUTTON, EVENT_FLAG_CTRLKEY, EVENT_FLAG_SHIFTKEY, EVENT_FLAG_ALTKEY]""" + +QT_FONT_LIGHT: int +QT_FONT_NORMAL: int +QT_FONT_DEMIBOLD: int +QT_FONT_BOLD: int +QT_FONT_BLACK: int +QtFontWeights = int +"""One of [QT_FONT_LIGHT, QT_FONT_NORMAL, QT_FONT_DEMIBOLD, QT_FONT_BOLD, QT_FONT_BLACK]""" + +QT_STYLE_NORMAL: int +QT_STYLE_ITALIC: int +QT_STYLE_OBLIQUE: int +QtFontStyles = int +"""One of [QT_STYLE_NORMAL, QT_STYLE_ITALIC, QT_STYLE_OBLIQUE]""" + +QT_PUSH_BUTTON: int +QT_CHECKBOX: int +QT_RADIOBOX: int +QT_NEW_BUTTONBAR: int +QtButtonTypes = int +"""One of [QT_PUSH_BUTTON, QT_CHECKBOX, QT_RADIOBOX, QT_NEW_BUTTONBAR]""" + +GShape_GMAT: int +GSHAPE_GMAT: int +GShape_GSCALAR: int +GSHAPE_GSCALAR: int +GShape_GARRAY: int +GSHAPE_GARRAY: int +GShape_GOPAQUE: int +GSHAPE_GOPAQUE: int +GShape_GFRAME: int +GSHAPE_GFRAME: int +GShape = int +"""One of [GShape_GMAT, GSHAPE_GMAT, GShape_GSCALAR, GSHAPE_GSCALAR, GShape_GARRAY, GSHAPE_GARRAY, GShape_GOPAQUE, GSHAPE_GOPAQUE, GShape_GFRAME, GSHAPE_GFRAME]""" + +MediaFormat_BGR: int +MEDIA_FORMAT_BGR: int +MediaFormat_NV12: int +MEDIA_FORMAT_NV12: int +MediaFormat_GRAY: int +MEDIA_FORMAT_GRAY: int +MediaFormat = int +"""One of [MediaFormat_BGR, MEDIA_FORMAT_BGR, MediaFormat_NV12, MEDIA_FORMAT_NV12, MediaFormat_GRAY, MEDIA_FORMAT_GRAY]""" + + +FileStorage_READ: int +FILE_STORAGE_READ: int +FileStorage_WRITE: int +FILE_STORAGE_WRITE: int +FileStorage_APPEND: int +FILE_STORAGE_APPEND: int +FileStorage_MEMORY: int +FILE_STORAGE_MEMORY: int +FileStorage_FORMAT_MASK: int +FILE_STORAGE_FORMAT_MASK: int +FileStorage_FORMAT_AUTO: int +FILE_STORAGE_FORMAT_AUTO: int +FileStorage_FORMAT_XML: int +FILE_STORAGE_FORMAT_XML: int +FileStorage_FORMAT_YAML: int +FILE_STORAGE_FORMAT_YAML: int +FileStorage_FORMAT_JSON: int +FILE_STORAGE_FORMAT_JSON: int +FileStorage_BASE64: int +FILE_STORAGE_BASE64: int +FileStorage_WRITE_BASE64: int +FILE_STORAGE_WRITE_BASE64: int +FileStorage_Mode = int +"""One of [FileStorage_READ, FILE_STORAGE_READ, FileStorage_WRITE, FILE_STORAGE_WRITE, FileStorage_APPEND, FILE_STORAGE_APPEND, FileStorage_MEMORY, FILE_STORAGE_MEMORY, FileStorage_FORMAT_MASK, FILE_STORAGE_FORMAT_MASK, FileStorage_FORMAT_AUTO, FILE_STORAGE_FORMAT_AUTO, FileStorage_FORMAT_XML, FILE_STORAGE_FORMAT_XML, FileStorage_FORMAT_YAML, FILE_STORAGE_FORMAT_YAML, FileStorage_FORMAT_JSON, FILE_STORAGE_FORMAT_JSON, FileStorage_BASE64, FILE_STORAGE_BASE64, FileStorage_WRITE_BASE64, FILE_STORAGE_WRITE_BASE64]""" + +FileStorage_UNDEFINED: int +FILE_STORAGE_UNDEFINED: int +FileStorage_VALUE_EXPECTED: int +FILE_STORAGE_VALUE_EXPECTED: int +FileStorage_NAME_EXPECTED: int +FILE_STORAGE_NAME_EXPECTED: int +FileStorage_INSIDE_MAP: int +FILE_STORAGE_INSIDE_MAP: int +FileStorage_State = int +"""One of [FileStorage_UNDEFINED, FILE_STORAGE_UNDEFINED, FileStorage_VALUE_EXPECTED, FILE_STORAGE_VALUE_EXPECTED, FileStorage_NAME_EXPECTED, FILE_STORAGE_NAME_EXPECTED, FileStorage_INSIDE_MAP, FILE_STORAGE_INSIDE_MAP]""" + +FileNode_NONE: int +FILE_NODE_NONE: int +FileNode_INT: int +FILE_NODE_INT: int +FileNode_REAL: int +FILE_NODE_REAL: int +FileNode_FLOAT: int +FILE_NODE_FLOAT: int +FileNode_STR: int +FILE_NODE_STR: int +FileNode_STRING: int +FILE_NODE_STRING: int +FileNode_SEQ: int +FILE_NODE_SEQ: int +FileNode_MAP: int +FILE_NODE_MAP: int +FileNode_TYPE_MASK: int +FILE_NODE_TYPE_MASK: int +FileNode_FLOW: int +FILE_NODE_FLOW: int +FileNode_UNIFORM: int +FILE_NODE_UNIFORM: int +FileNode_EMPTY: int +FILE_NODE_EMPTY: int +FileNode_NAMED: int +FILE_NODE_NAMED: int + +UMat_MAGIC_VAL: int +UMAT_MAGIC_VAL: int +UMat_AUTO_STEP: int +UMAT_AUTO_STEP: int +UMat_CONTINUOUS_FLAG: int +UMAT_CONTINUOUS_FLAG: int +UMat_SUBMATRIX_FLAG: int +UMAT_SUBMATRIX_FLAG: int +UMat_MAGIC_MASK: int +UMAT_MAGIC_MASK: int +UMat_TYPE_MASK: int +UMAT_TYPE_MASK: int +UMat_DEPTH_MASK: int +UMAT_DEPTH_MASK: int + +Subdiv2D_PTLOC_ERROR: int +SUBDIV2D_PTLOC_ERROR: int +Subdiv2D_PTLOC_OUTSIDE_RECT: int +SUBDIV2D_PTLOC_OUTSIDE_RECT: int +Subdiv2D_PTLOC_INSIDE: int +SUBDIV2D_PTLOC_INSIDE: int +Subdiv2D_PTLOC_VERTEX: int +SUBDIV2D_PTLOC_VERTEX: int +Subdiv2D_PTLOC_ON_EDGE: int +SUBDIV2D_PTLOC_ON_EDGE: int +Subdiv2D_NEXT_AROUND_ORG: int +SUBDIV2D_NEXT_AROUND_ORG: int +Subdiv2D_NEXT_AROUND_DST: int +SUBDIV2D_NEXT_AROUND_DST: int +Subdiv2D_PREV_AROUND_ORG: int +SUBDIV2D_PREV_AROUND_ORG: int +Subdiv2D_PREV_AROUND_DST: int +SUBDIV2D_PREV_AROUND_DST: int +Subdiv2D_NEXT_AROUND_LEFT: int +SUBDIV2D_NEXT_AROUND_LEFT: int +Subdiv2D_NEXT_AROUND_RIGHT: int +SUBDIV2D_NEXT_AROUND_RIGHT: int +Subdiv2D_PREV_AROUND_LEFT: int +SUBDIV2D_PREV_AROUND_LEFT: int +Subdiv2D_PREV_AROUND_RIGHT: int +SUBDIV2D_PREV_AROUND_RIGHT: int + +ORB_HARRIS_SCORE: int +ORB_FAST_SCORE: int +ORB_ScoreType = int +"""One of [ORB_HARRIS_SCORE, ORB_FAST_SCORE]""" + +FastFeatureDetector_TYPE_5_8: int +FAST_FEATURE_DETECTOR_TYPE_5_8: int +FastFeatureDetector_TYPE_7_12: int +FAST_FEATURE_DETECTOR_TYPE_7_12: int +FastFeatureDetector_TYPE_9_16: int +FAST_FEATURE_DETECTOR_TYPE_9_16: int +FastFeatureDetector_DetectorType = int +"""One of [FastFeatureDetector_TYPE_5_8, FAST_FEATURE_DETECTOR_TYPE_5_8, FastFeatureDetector_TYPE_7_12, FAST_FEATURE_DETECTOR_TYPE_7_12, FastFeatureDetector_TYPE_9_16, FAST_FEATURE_DETECTOR_TYPE_9_16]""" + +FastFeatureDetector_THRESHOLD: int +FAST_FEATURE_DETECTOR_THRESHOLD: int +FastFeatureDetector_NONMAX_SUPPRESSION: int +FAST_FEATURE_DETECTOR_NONMAX_SUPPRESSION: int +FastFeatureDetector_FAST_N: int +FAST_FEATURE_DETECTOR_FAST_N: int + +AgastFeatureDetector_AGAST_5_8: int +AGAST_FEATURE_DETECTOR_AGAST_5_8: int +AgastFeatureDetector_AGAST_7_12d: int +AGAST_FEATURE_DETECTOR_AGAST_7_12D: int +AgastFeatureDetector_AGAST_7_12s: int +AGAST_FEATURE_DETECTOR_AGAST_7_12S: int +AgastFeatureDetector_OAST_9_16: int +AGAST_FEATURE_DETECTOR_OAST_9_16: int +AgastFeatureDetector_DetectorType = int +"""One of [AgastFeatureDetector_AGAST_5_8, AGAST_FEATURE_DETECTOR_AGAST_5_8, AgastFeatureDetector_AGAST_7_12d, AGAST_FEATURE_DETECTOR_AGAST_7_12D, AgastFeatureDetector_AGAST_7_12s, AGAST_FEATURE_DETECTOR_AGAST_7_12S, AgastFeatureDetector_OAST_9_16, AGAST_FEATURE_DETECTOR_OAST_9_16]""" + +AgastFeatureDetector_THRESHOLD: int +AGAST_FEATURE_DETECTOR_THRESHOLD: int +AgastFeatureDetector_NONMAX_SUPPRESSION: int +AGAST_FEATURE_DETECTOR_NONMAX_SUPPRESSION: int + +KAZE_DIFF_PM_G1: int +KAZE_DIFF_PM_G2: int +KAZE_DIFF_WEICKERT: int +KAZE_DIFF_CHARBONNIER: int +KAZE_DiffusivityType = int +"""One of [KAZE_DIFF_PM_G1, KAZE_DIFF_PM_G2, KAZE_DIFF_WEICKERT, KAZE_DIFF_CHARBONNIER]""" + +AKAZE_DESCRIPTOR_KAZE_UPRIGHT: int +AKAZE_DESCRIPTOR_KAZE: int +AKAZE_DESCRIPTOR_MLDB_UPRIGHT: int +AKAZE_DESCRIPTOR_MLDB: int +AKAZE_DescriptorType = int +"""One of [AKAZE_DESCRIPTOR_KAZE_UPRIGHT, AKAZE_DESCRIPTOR_KAZE, AKAZE_DESCRIPTOR_MLDB_UPRIGHT, AKAZE_DESCRIPTOR_MLDB]""" + +DescriptorMatcher_FLANNBASED: int +DESCRIPTOR_MATCHER_FLANNBASED: int +DescriptorMatcher_BRUTEFORCE: int +DESCRIPTOR_MATCHER_BRUTEFORCE: int +DescriptorMatcher_BRUTEFORCE_L1: int +DESCRIPTOR_MATCHER_BRUTEFORCE_L1: int +DescriptorMatcher_BRUTEFORCE_HAMMING: int +DESCRIPTOR_MATCHER_BRUTEFORCE_HAMMING: int +DescriptorMatcher_BRUTEFORCE_HAMMINGLUT: int +DESCRIPTOR_MATCHER_BRUTEFORCE_HAMMINGLUT: int +DescriptorMatcher_BRUTEFORCE_SL2: int +DESCRIPTOR_MATCHER_BRUTEFORCE_SL2: int +DescriptorMatcher_MatcherType = int +"""One of [DescriptorMatcher_FLANNBASED, DESCRIPTOR_MATCHER_FLANNBASED, DescriptorMatcher_BRUTEFORCE, DESCRIPTOR_MATCHER_BRUTEFORCE, DescriptorMatcher_BRUTEFORCE_L1, DESCRIPTOR_MATCHER_BRUTEFORCE_L1, DescriptorMatcher_BRUTEFORCE_HAMMING, DESCRIPTOR_MATCHER_BRUTEFORCE_HAMMING, DescriptorMatcher_BRUTEFORCE_HAMMINGLUT, DESCRIPTOR_MATCHER_BRUTEFORCE_HAMMINGLUT, DescriptorMatcher_BRUTEFORCE_SL2, DESCRIPTOR_MATCHER_BRUTEFORCE_SL2]""" + +CirclesGridFinderParameters_SYMMETRIC_GRID: int +CIRCLES_GRID_FINDER_PARAMETERS_SYMMETRIC_GRID: int +CirclesGridFinderParameters_ASYMMETRIC_GRID: int +CIRCLES_GRID_FINDER_PARAMETERS_ASYMMETRIC_GRID: int +CirclesGridFinderParameters_GridType = int +"""One of [CirclesGridFinderParameters_SYMMETRIC_GRID, CIRCLES_GRID_FINDER_PARAMETERS_SYMMETRIC_GRID, CirclesGridFinderParameters_ASYMMETRIC_GRID, CIRCLES_GRID_FINDER_PARAMETERS_ASYMMETRIC_GRID]""" + +StereoMatcher_DISP_SHIFT: int +STEREO_MATCHER_DISP_SHIFT: int +StereoMatcher_DISP_SCALE: int +STEREO_MATCHER_DISP_SCALE: int + +StereoBM_PREFILTER_NORMALIZED_RESPONSE: int +STEREO_BM_PREFILTER_NORMALIZED_RESPONSE: int +StereoBM_PREFILTER_XSOBEL: int +STEREO_BM_PREFILTER_XSOBEL: int + +StereoSGBM_MODE_SGBM: int +STEREO_SGBM_MODE_SGBM: int +StereoSGBM_MODE_HH: int +STEREO_SGBM_MODE_HH: int +StereoSGBM_MODE_SGBM_3WAY: int +STEREO_SGBM_MODE_SGBM_3WAY: int +StereoSGBM_MODE_HH4: int +STEREO_SGBM_MODE_HH4: int + +HOGDescriptor_L2Hys: int +HOGDESCRIPTOR_L2HYS: int +HOGDescriptor_HistogramNormType = int +"""One of [HOGDescriptor_L2Hys, HOGDESCRIPTOR_L2HYS]""" + +HOGDescriptor_DEFAULT_NLEVELS: int +HOGDESCRIPTOR_DEFAULT_NLEVELS: int + +HOGDescriptor_DESCR_FORMAT_COL_BY_COL: int +HOGDESCRIPTOR_DESCR_FORMAT_COL_BY_COL: int +HOGDescriptor_DESCR_FORMAT_ROW_BY_ROW: int +HOGDESCRIPTOR_DESCR_FORMAT_ROW_BY_ROW: int +HOGDescriptor_DescriptorStorageFormat = int +"""One of [HOGDescriptor_DESCR_FORMAT_COL_BY_COL, HOGDESCRIPTOR_DESCR_FORMAT_COL_BY_COL, HOGDescriptor_DESCR_FORMAT_ROW_BY_ROW, HOGDESCRIPTOR_DESCR_FORMAT_ROW_BY_ROW]""" + +QRCodeEncoder_MODE_AUTO: int +QRCODE_ENCODER_MODE_AUTO: int +QRCodeEncoder_MODE_NUMERIC: int +QRCODE_ENCODER_MODE_NUMERIC: int +QRCodeEncoder_MODE_ALPHANUMERIC: int +QRCODE_ENCODER_MODE_ALPHANUMERIC: int +QRCodeEncoder_MODE_BYTE: int +QRCODE_ENCODER_MODE_BYTE: int +QRCodeEncoder_MODE_ECI: int +QRCODE_ENCODER_MODE_ECI: int +QRCodeEncoder_MODE_KANJI: int +QRCODE_ENCODER_MODE_KANJI: int +QRCodeEncoder_MODE_STRUCTURED_APPEND: int +QRCODE_ENCODER_MODE_STRUCTURED_APPEND: int +QRCodeEncoder_EncodeMode = int +"""One of [QRCodeEncoder_MODE_AUTO, QRCODE_ENCODER_MODE_AUTO, QRCodeEncoder_MODE_NUMERIC, QRCODE_ENCODER_MODE_NUMERIC, QRCodeEncoder_MODE_ALPHANUMERIC, QRCODE_ENCODER_MODE_ALPHANUMERIC, QRCodeEncoder_MODE_BYTE, QRCODE_ENCODER_MODE_BYTE, QRCodeEncoder_MODE_ECI, QRCODE_ENCODER_MODE_ECI, QRCodeEncoder_MODE_KANJI, QRCODE_ENCODER_MODE_KANJI, QRCodeEncoder_MODE_STRUCTURED_APPEND, QRCODE_ENCODER_MODE_STRUCTURED_APPEND]""" + +QRCodeEncoder_CORRECT_LEVEL_L: int +QRCODE_ENCODER_CORRECT_LEVEL_L: int +QRCodeEncoder_CORRECT_LEVEL_M: int +QRCODE_ENCODER_CORRECT_LEVEL_M: int +QRCodeEncoder_CORRECT_LEVEL_Q: int +QRCODE_ENCODER_CORRECT_LEVEL_Q: int +QRCodeEncoder_CORRECT_LEVEL_H: int +QRCODE_ENCODER_CORRECT_LEVEL_H: int +QRCodeEncoder_CorrectionLevel = int +"""One of [QRCodeEncoder_CORRECT_LEVEL_L, QRCODE_ENCODER_CORRECT_LEVEL_L, QRCodeEncoder_CORRECT_LEVEL_M, QRCODE_ENCODER_CORRECT_LEVEL_M, QRCodeEncoder_CORRECT_LEVEL_Q, QRCODE_ENCODER_CORRECT_LEVEL_Q, QRCodeEncoder_CORRECT_LEVEL_H, QRCODE_ENCODER_CORRECT_LEVEL_H]""" + +QRCodeEncoder_ECI_UTF8: int +QRCODE_ENCODER_ECI_UTF8: int +QRCodeEncoder_ECIEncodings = int +"""One of [QRCodeEncoder_ECI_UTF8, QRCODE_ENCODER_ECI_UTF8]""" + +FaceRecognizerSF_FR_COSINE: int +FACE_RECOGNIZER_SF_FR_COSINE: int +FaceRecognizerSF_FR_NORM_L2: int +FACE_RECOGNIZER_SF_FR_NORM_L2: int +FaceRecognizerSF_DisType = int +"""One of [FaceRecognizerSF_FR_COSINE, FACE_RECOGNIZER_SF_FR_COSINE, FaceRecognizerSF_FR_NORM_L2, FACE_RECOGNIZER_SF_FR_NORM_L2]""" + +Stitcher_OK: int +STITCHER_OK: int +Stitcher_ERR_NEED_MORE_IMGS: int +STITCHER_ERR_NEED_MORE_IMGS: int +Stitcher_ERR_HOMOGRAPHY_EST_FAIL: int +STITCHER_ERR_HOMOGRAPHY_EST_FAIL: int +Stitcher_ERR_CAMERA_PARAMS_ADJUST_FAIL: int +STITCHER_ERR_CAMERA_PARAMS_ADJUST_FAIL: int +Stitcher_Status = int +"""One of [Stitcher_OK, STITCHER_OK, Stitcher_ERR_NEED_MORE_IMGS, STITCHER_ERR_NEED_MORE_IMGS, Stitcher_ERR_HOMOGRAPHY_EST_FAIL, STITCHER_ERR_HOMOGRAPHY_EST_FAIL, Stitcher_ERR_CAMERA_PARAMS_ADJUST_FAIL, STITCHER_ERR_CAMERA_PARAMS_ADJUST_FAIL]""" + +Stitcher_PANORAMA: int +STITCHER_PANORAMA: int +Stitcher_SCANS: int +STITCHER_SCANS: int +Stitcher_Mode = int +"""One of [Stitcher_PANORAMA, STITCHER_PANORAMA, Stitcher_SCANS, STITCHER_SCANS]""" + +DISOpticalFlow_PRESET_ULTRAFAST: int +DISOPTICAL_FLOW_PRESET_ULTRAFAST: int +DISOpticalFlow_PRESET_FAST: int +DISOPTICAL_FLOW_PRESET_FAST: int +DISOpticalFlow_PRESET_MEDIUM: int +DISOPTICAL_FLOW_PRESET_MEDIUM: int + +PCA_DATA_AS_ROW: int +PCA_DATA_AS_COL: int +PCA_USE_AVG: int +PCA_Flags = int +"""One of [PCA_DATA_AS_ROW, PCA_DATA_AS_COL, PCA_USE_AVG]""" + +SVD_MODIFY_A: int +SVD_NO_UV: int +SVD_FULL_UV: int +SVD_Flags = int +"""One of [SVD_MODIFY_A, SVD_NO_UV, SVD_FULL_UV]""" + +RNG_UNIFORM: int +RNG_NORMAL: int + +Formatter_FMT_DEFAULT: int +FORMATTER_FMT_DEFAULT: int +Formatter_FMT_MATLAB: int +FORMATTER_FMT_MATLAB: int +Formatter_FMT_CSV: int +FORMATTER_FMT_CSV: int +Formatter_FMT_PYTHON: int +FORMATTER_FMT_PYTHON: int +Formatter_FMT_NUMPY: int +FORMATTER_FMT_NUMPY: int +Formatter_FMT_C: int +FORMATTER_FMT_C: int +Formatter_FormatType = int +"""One of [Formatter_FMT_DEFAULT, FORMATTER_FMT_DEFAULT, Formatter_FMT_MATLAB, FORMATTER_FMT_MATLAB, Formatter_FMT_CSV, FORMATTER_FMT_CSV, Formatter_FMT_PYTHON, FORMATTER_FMT_PYTHON, Formatter_FMT_NUMPY, FORMATTER_FMT_NUMPY, Formatter_FMT_C, FORMATTER_FMT_C]""" + +_InputArray_KIND_SHIFT: int +_INPUT_ARRAY_KIND_SHIFT: int +_InputArray_FIXED_TYPE: int +_INPUT_ARRAY_FIXED_TYPE: int +_InputArray_FIXED_SIZE: int +_INPUT_ARRAY_FIXED_SIZE: int +_InputArray_KIND_MASK: int +_INPUT_ARRAY_KIND_MASK: int +_InputArray_NONE: int +_INPUT_ARRAY_NONE: int +_InputArray_MAT: int +_INPUT_ARRAY_MAT: int +_InputArray_MATX: int +_INPUT_ARRAY_MATX: int +_InputArray_STD_VECTOR: int +_INPUT_ARRAY_STD_VECTOR: int +_InputArray_STD_VECTOR_VECTOR: int +_INPUT_ARRAY_STD_VECTOR_VECTOR: int +_InputArray_STD_VECTOR_MAT: int +_INPUT_ARRAY_STD_VECTOR_MAT: int +_InputArray_EXPR: int +_INPUT_ARRAY_EXPR: int +_InputArray_OPENGL_BUFFER: int +_INPUT_ARRAY_OPENGL_BUFFER: int +_InputArray_CUDA_HOST_MEM: int +_INPUT_ARRAY_CUDA_HOST_MEM: int +_InputArray_CUDA_GPU_MAT: int +_INPUT_ARRAY_CUDA_GPU_MAT: int +_InputArray_UMAT: int +_INPUT_ARRAY_UMAT: int +_InputArray_STD_VECTOR_UMAT: int +_INPUT_ARRAY_STD_VECTOR_UMAT: int +_InputArray_STD_BOOL_VECTOR: int +_INPUT_ARRAY_STD_BOOL_VECTOR: int +_InputArray_STD_VECTOR_CUDA_GPU_MAT: int +_INPUT_ARRAY_STD_VECTOR_CUDA_GPU_MAT: int +_InputArray_STD_ARRAY: int +_INPUT_ARRAY_STD_ARRAY: int +_InputArray_STD_ARRAY_MAT: int +_INPUT_ARRAY_STD_ARRAY_MAT: int +_InputArray_KindFlag = int +"""One of [_InputArray_KIND_SHIFT, _INPUT_ARRAY_KIND_SHIFT, _InputArray_FIXED_TYPE, _INPUT_ARRAY_FIXED_TYPE, _InputArray_FIXED_SIZE, _INPUT_ARRAY_FIXED_SIZE, _InputArray_KIND_MASK, _INPUT_ARRAY_KIND_MASK, _InputArray_NONE, _INPUT_ARRAY_NONE, _InputArray_MAT, _INPUT_ARRAY_MAT, _InputArray_MATX, _INPUT_ARRAY_MATX, _InputArray_STD_VECTOR, _INPUT_ARRAY_STD_VECTOR, _InputArray_STD_VECTOR_VECTOR, _INPUT_ARRAY_STD_VECTOR_VECTOR, _InputArray_STD_VECTOR_MAT, _INPUT_ARRAY_STD_VECTOR_MAT, _InputArray_EXPR, _INPUT_ARRAY_EXPR, _InputArray_OPENGL_BUFFER, _INPUT_ARRAY_OPENGL_BUFFER, _InputArray_CUDA_HOST_MEM, _INPUT_ARRAY_CUDA_HOST_MEM, _InputArray_CUDA_GPU_MAT, _INPUT_ARRAY_CUDA_GPU_MAT, _InputArray_UMAT, _INPUT_ARRAY_UMAT, _InputArray_STD_VECTOR_UMAT, _INPUT_ARRAY_STD_VECTOR_UMAT, _InputArray_STD_BOOL_VECTOR, _INPUT_ARRAY_STD_BOOL_VECTOR, _InputArray_STD_VECTOR_CUDA_GPU_MAT, _INPUT_ARRAY_STD_VECTOR_CUDA_GPU_MAT, _InputArray_STD_ARRAY, _INPUT_ARRAY_STD_ARRAY, _InputArray_STD_ARRAY_MAT, _INPUT_ARRAY_STD_ARRAY_MAT]""" + +_OutputArray_DEPTH_MASK_8U: int +_OUTPUT_ARRAY_DEPTH_MASK_8U: int +_OutputArray_DEPTH_MASK_8S: int +_OUTPUT_ARRAY_DEPTH_MASK_8S: int +_OutputArray_DEPTH_MASK_16U: int +_OUTPUT_ARRAY_DEPTH_MASK_16U: int +_OutputArray_DEPTH_MASK_16S: int +_OUTPUT_ARRAY_DEPTH_MASK_16S: int +_OutputArray_DEPTH_MASK_32S: int +_OUTPUT_ARRAY_DEPTH_MASK_32S: int +_OutputArray_DEPTH_MASK_32F: int +_OUTPUT_ARRAY_DEPTH_MASK_32F: int +_OutputArray_DEPTH_MASK_64F: int +_OUTPUT_ARRAY_DEPTH_MASK_64F: int +_OutputArray_DEPTH_MASK_16F: int +_OUTPUT_ARRAY_DEPTH_MASK_16F: int +_OutputArray_DEPTH_MASK_ALL: int +_OUTPUT_ARRAY_DEPTH_MASK_ALL: int +_OutputArray_DEPTH_MASK_ALL_BUT_8S: int +_OUTPUT_ARRAY_DEPTH_MASK_ALL_BUT_8S: int +_OutputArray_DEPTH_MASK_ALL_16F: int +_OUTPUT_ARRAY_DEPTH_MASK_ALL_16F: int +_OutputArray_DEPTH_MASK_FLT: int +_OUTPUT_ARRAY_DEPTH_MASK_FLT: int +_OutputArray_DepthMask = int +"""One of [_OutputArray_DEPTH_MASK_8U, _OUTPUT_ARRAY_DEPTH_MASK_8U, _OutputArray_DEPTH_MASK_8S, _OUTPUT_ARRAY_DEPTH_MASK_8S, _OutputArray_DEPTH_MASK_16U, _OUTPUT_ARRAY_DEPTH_MASK_16U, _OutputArray_DEPTH_MASK_16S, _OUTPUT_ARRAY_DEPTH_MASK_16S, _OutputArray_DEPTH_MASK_32S, _OUTPUT_ARRAY_DEPTH_MASK_32S, _OutputArray_DEPTH_MASK_32F, _OUTPUT_ARRAY_DEPTH_MASK_32F, _OutputArray_DEPTH_MASK_64F, _OUTPUT_ARRAY_DEPTH_MASK_64F, _OutputArray_DEPTH_MASK_16F, _OUTPUT_ARRAY_DEPTH_MASK_16F, _OutputArray_DEPTH_MASK_ALL, _OUTPUT_ARRAY_DEPTH_MASK_ALL, _OutputArray_DEPTH_MASK_ALL_BUT_8S, _OUTPUT_ARRAY_DEPTH_MASK_ALL_BUT_8S, _OutputArray_DEPTH_MASK_ALL_16F, _OUTPUT_ARRAY_DEPTH_MASK_ALL_16F, _OutputArray_DEPTH_MASK_FLT, _OUTPUT_ARRAY_DEPTH_MASK_FLT]""" + +UMatData_COPY_ON_MAP: int +UMAT_DATA_COPY_ON_MAP: int +UMatData_HOST_COPY_OBSOLETE: int +UMAT_DATA_HOST_COPY_OBSOLETE: int +UMatData_DEVICE_COPY_OBSOLETE: int +UMAT_DATA_DEVICE_COPY_OBSOLETE: int +UMatData_TEMP_UMAT: int +UMAT_DATA_TEMP_UMAT: int +UMatData_TEMP_COPIED_UMAT: int +UMAT_DATA_TEMP_COPIED_UMAT: int +UMatData_USER_ALLOCATED: int +UMAT_DATA_USER_ALLOCATED: int +UMatData_DEVICE_MEM_MAPPED: int +UMAT_DATA_DEVICE_MEM_MAPPED: int +UMatData_ASYNC_CLEANUP: int +UMAT_DATA_ASYNC_CLEANUP: int +UMatData_MemoryFlag = int +"""One of [UMatData_COPY_ON_MAP, UMAT_DATA_COPY_ON_MAP, UMatData_HOST_COPY_OBSOLETE, UMAT_DATA_HOST_COPY_OBSOLETE, UMatData_DEVICE_COPY_OBSOLETE, UMAT_DATA_DEVICE_COPY_OBSOLETE, UMatData_TEMP_UMAT, UMAT_DATA_TEMP_UMAT, UMatData_TEMP_COPIED_UMAT, UMAT_DATA_TEMP_COPIED_UMAT, UMatData_USER_ALLOCATED, UMAT_DATA_USER_ALLOCATED, UMatData_DEVICE_MEM_MAPPED, UMAT_DATA_DEVICE_MEM_MAPPED, UMatData_ASYNC_CLEANUP, UMAT_DATA_ASYNC_CLEANUP]""" + +Mat_MAGIC_VAL: int +MAT_MAGIC_VAL: int +Mat_AUTO_STEP: int +MAT_AUTO_STEP: int +Mat_CONTINUOUS_FLAG: int +MAT_CONTINUOUS_FLAG: int +Mat_SUBMATRIX_FLAG: int +MAT_SUBMATRIX_FLAG: int +Mat_MAGIC_MASK: int +MAT_MAGIC_MASK: int +Mat_TYPE_MASK: int +MAT_TYPE_MASK: int +Mat_DEPTH_MASK: int +MAT_DEPTH_MASK: int + +SparseMat_MAGIC_VAL: int +SPARSE_MAT_MAGIC_VAL: int +SparseMat_MAX_DIM: int +SPARSE_MAT_MAX_DIM: int +SparseMat_HASH_SCALE: int +SPARSE_MAT_HASH_SCALE: int +SparseMat_HASH_BIT: int +SPARSE_MAT_HASH_BIT: int + +QuatEnum_INT_XYZ: int +QUAT_ENUM_INT_XYZ: int +QuatEnum_INT_XZY: int +QUAT_ENUM_INT_XZY: int +QuatEnum_INT_YXZ: int +QUAT_ENUM_INT_YXZ: int +QuatEnum_INT_YZX: int +QUAT_ENUM_INT_YZX: int +QuatEnum_INT_ZXY: int +QUAT_ENUM_INT_ZXY: int +QuatEnum_INT_ZYX: int +QUAT_ENUM_INT_ZYX: int +QuatEnum_INT_XYX: int +QUAT_ENUM_INT_XYX: int +QuatEnum_INT_XZX: int +QUAT_ENUM_INT_XZX: int +QuatEnum_INT_YXY: int +QUAT_ENUM_INT_YXY: int +QuatEnum_INT_YZY: int +QUAT_ENUM_INT_YZY: int +QuatEnum_INT_ZXZ: int +QUAT_ENUM_INT_ZXZ: int +QuatEnum_INT_ZYZ: int +QUAT_ENUM_INT_ZYZ: int +QuatEnum_EXT_XYZ: int +QUAT_ENUM_EXT_XYZ: int +QuatEnum_EXT_XZY: int +QUAT_ENUM_EXT_XZY: int +QuatEnum_EXT_YXZ: int +QUAT_ENUM_EXT_YXZ: int +QuatEnum_EXT_YZX: int +QUAT_ENUM_EXT_YZX: int +QuatEnum_EXT_ZXY: int +QUAT_ENUM_EXT_ZXY: int +QuatEnum_EXT_ZYX: int +QUAT_ENUM_EXT_ZYX: int +QuatEnum_EXT_XYX: int +QUAT_ENUM_EXT_XYX: int +QuatEnum_EXT_XZX: int +QUAT_ENUM_EXT_XZX: int +QuatEnum_EXT_YXY: int +QUAT_ENUM_EXT_YXY: int +QuatEnum_EXT_YZY: int +QUAT_ENUM_EXT_YZY: int +QuatEnum_EXT_ZXZ: int +QUAT_ENUM_EXT_ZXZ: int +QuatEnum_EXT_ZYZ: int +QUAT_ENUM_EXT_ZYZ: int +QuatEnum_EULER_ANGLES_MAX_VALUE: int +QUAT_ENUM_EULER_ANGLES_MAX_VALUE: int +QuatEnum_EulerAnglesType = int +"""One of [QuatEnum_INT_XYZ, QUAT_ENUM_INT_XYZ, QuatEnum_INT_XZY, QUAT_ENUM_INT_XZY, QuatEnum_INT_YXZ, QUAT_ENUM_INT_YXZ, QuatEnum_INT_YZX, QUAT_ENUM_INT_YZX, QuatEnum_INT_ZXY, QUAT_ENUM_INT_ZXY, QuatEnum_INT_ZYX, QUAT_ENUM_INT_ZYX, QuatEnum_INT_XYX, QUAT_ENUM_INT_XYX, QuatEnum_INT_XZX, QUAT_ENUM_INT_XZX, QuatEnum_INT_YXY, QUAT_ENUM_INT_YXY, QuatEnum_INT_YZY, QUAT_ENUM_INT_YZY, QuatEnum_INT_ZXZ, QUAT_ENUM_INT_ZXZ, QuatEnum_INT_ZYZ, QUAT_ENUM_INT_ZYZ, QuatEnum_EXT_XYZ, QUAT_ENUM_EXT_XYZ, QuatEnum_EXT_XZY, QUAT_ENUM_EXT_XZY, QuatEnum_EXT_YXZ, QUAT_ENUM_EXT_YXZ, QuatEnum_EXT_YZX, QUAT_ENUM_EXT_YZX, QuatEnum_EXT_ZXY, QUAT_ENUM_EXT_ZXY, QuatEnum_EXT_ZYX, QUAT_ENUM_EXT_ZYX, QuatEnum_EXT_XYX, QUAT_ENUM_EXT_XYX, QuatEnum_EXT_XZX, QUAT_ENUM_EXT_XZX, QuatEnum_EXT_YXY, QUAT_ENUM_EXT_YXY, QuatEnum_EXT_YZY, QUAT_ENUM_EXT_YZY, QuatEnum_EXT_ZXZ, QUAT_ENUM_EXT_ZXZ, QuatEnum_EXT_ZYZ, QUAT_ENUM_EXT_ZYZ, QuatEnum_EULER_ANGLES_MAX_VALUE, QUAT_ENUM_EULER_ANGLES_MAX_VALUE]""" + +TermCriteria_COUNT: int +TERM_CRITERIA_COUNT: int +TermCriteria_MAX_ITER: int +TERM_CRITERIA_MAX_ITER: int +TermCriteria_EPS: int +TERM_CRITERIA_EPS: int +TermCriteria_Type = int +"""One of [TermCriteria_COUNT, TERM_CRITERIA_COUNT, TermCriteria_MAX_ITER, TERM_CRITERIA_MAX_ITER, TermCriteria_EPS, TERM_CRITERIA_EPS]""" + +GFluidKernel_Kind_Filter: int +GFLUID_KERNEL_KIND_FILTER: int +GFluidKernel_Kind_Resize: int +GFLUID_KERNEL_KIND_RESIZE: int +GFluidKernel_Kind_YUV420toRGB: int +GFLUID_KERNEL_KIND_YUV420TO_RGB: int +GFluidKernel_Kind = int +"""One of [GFluidKernel_Kind_Filter, GFLUID_KERNEL_KIND_FILTER, GFluidKernel_Kind_Resize, GFLUID_KERNEL_KIND_RESIZE, GFluidKernel_Kind_YUV420toRGB, GFLUID_KERNEL_KIND_YUV420TO_RGB]""" + +MediaFrame_Access_R: int +MEDIA_FRAME_ACCESS_R: int +MediaFrame_Access_W: int +MEDIA_FRAME_ACCESS_W: int +MediaFrame_Access = int +"""One of [MediaFrame_Access_R, MEDIA_FRAME_ACCESS_R, MediaFrame_Access_W, MEDIA_FRAME_ACCESS_W]""" + +RMat_Access_R: int +RMAT_ACCESS_R: int +RMat_Access_W: int +RMAT_ACCESS_W: int +RMat_Access = int +"""One of [RMat_Access_R, RMAT_ACCESS_R, RMat_Access_W, RMAT_ACCESS_W]""" + + +# Constants +CV_8U: int +CV_8UC1: int +CV_8UC2: int +CV_8UC3: int +CV_8UC4: int +CV_8S: int +CV_8SC1: int +CV_8SC2: int +CV_8SC3: int +CV_8SC4: int +CV_16U: int +CV_16UC1: int +CV_16UC2: int +CV_16UC3: int +CV_16UC4: int +CV_16S: int +CV_16SC1: int +CV_16SC2: int +CV_16SC3: int +CV_16SC4: int +CV_32S: int +CV_32SC1: int +CV_32SC2: int +CV_32SC3: int +CV_32SC4: int +CV_32F: int +CV_32FC1: int +CV_32FC2: int +CV_32FC3: int +CV_32FC4: int +CV_64F: int +CV_64FC1: int +CV_64FC2: int +CV_64FC3: int +CV_64FC4: int +CV_16F: int +CV_16FC1: int +CV_16FC2: int +CV_16FC3: int +CV_16FC4: int +__version__: str + +# Classes +class Algorithm: + # Functions + def clear(self) -> None: ... + + @_typing.overload + def write(self, fs: FileStorage) -> None: ... + @_typing.overload + def write(self, fs: FileStorage, name: str) -> None: ... + + def read(self, fn: FileNode) -> None: ... + + def empty(self) -> bool: ... + + def save(self, filename: str) -> None: ... + + def getDefaultName(self) -> str: ... + + +class AsyncArray: + # Functions + def __init__(self) -> None: ... + + def release(self) -> None: ... + + @_typing.overload + def get(self, dst: cv2.typing.MatLike | None = ...) -> cv2.typing.MatLike: ... + @_typing.overload + def get(self, dst: UMat | None = ...) -> UMat: ... + @_typing.overload + def get(self, timeoutNs: float, dst: cv2.typing.MatLike | None = ...) -> tuple[bool, cv2.typing.MatLike]: ... + @_typing.overload + def get(self, timeoutNs: float, dst: UMat | None = ...) -> tuple[bool, UMat]: ... + + def wait_for(self, timeoutNs: float) -> bool: ... + + def valid(self) -> bool: ... + + +class FileStorage: + # Functions + @_typing.overload + def __init__(self) -> None: ... + @_typing.overload + def __init__(self, filename: str, flags: int, encoding: str = ...) -> None: ... + + def open(self, filename: str, flags: int, encoding: str = ...) -> bool: ... + + def isOpened(self) -> bool: ... + + def release(self) -> None: ... + + def releaseAndGetString(self) -> str: ... + + def getFirstTopLevelNode(self) -> FileNode: ... + + def root(self, streamidx: int = ...) -> FileNode: ... + + def getNode(self, nodename: str) -> FileNode: ... + + @_typing.overload + def write(self, name: str, val: int) -> None: ... + @_typing.overload + def write(self, name: str, val: int) -> None: ... + @_typing.overload + def write(self, name: str, val: float) -> None: ... + @_typing.overload + def write(self, name: str, val: str) -> None: ... + @_typing.overload + def write(self, name: str, val: cv2.typing.MatLike) -> None: ... + @_typing.overload + def write(self, name: str, val: _typing.Sequence[str]) -> None: ... + + def writeComment(self, comment: str, append: bool = ...) -> None: ... + + def startWriteStruct(self, name: str, flags: int, typeName: str = ...) -> None: ... + + def endWriteStruct(self) -> None: ... + + def getFormat(self) -> int: ... + + +class FileNode: + # Functions + def __init__(self) -> None: ... + + def getNode(self, nodename: str) -> FileNode: ... + + def at(self, i: int) -> FileNode: ... + + def keys(self) -> _typing.Sequence[str]: ... + + def type(self) -> int: ... + + def empty(self) -> bool: ... + + def isNone(self) -> bool: ... + + def isSeq(self) -> bool: ... + + def isMap(self) -> bool: ... + + def isInt(self) -> bool: ... + + def isReal(self) -> bool: ... + + def isString(self) -> bool: ... + + def isNamed(self) -> bool: ... + + def name(self) -> str: ... + + def size(self) -> int: ... + + def rawSize(self) -> int: ... + + def real(self) -> float: ... + + def string(self) -> str: ... + + def mat(self) -> cv2.typing.MatLike: ... + + +class RotatedRect: + center: cv2.typing.Point2f + size: cv2.typing.Size2f + angle: float + + # Functions + @_typing.overload + def __init__(self) -> None: ... + @_typing.overload + def __init__(self, center: cv2.typing.Point2f, size: cv2.typing.Size2f, angle: float) -> None: ... + @_typing.overload + def __init__(self, point1: cv2.typing.Point2f, point2: cv2.typing.Point2f, point3: cv2.typing.Point2f) -> None: ... + + def points(self) -> _typing.Sequence[cv2.typing.Point2f]: ... + + def boundingRect(self) -> cv2.typing.Rect: ... + + def boundingRect2f(self) -> cv2.typing.Rect2f: ... + + +class KeyPoint: + pt: cv2.typing.Point2f + size: float + angle: float + response: float + octave: int + class_id: int + + # Functions + @_typing.overload + def __init__(self) -> None: ... + @_typing.overload + def __init__(self, x: float, y: float, size: float, angle: float = ..., response: float = ..., octave: int = ..., class_id: int = ...) -> None: ... + + @staticmethod + @_typing.overload + def convert(keypoints: _typing.Sequence[KeyPoint], keypointIndexes: _typing.Sequence[int] = ...) -> _typing.Sequence[cv2.typing.Point2f]: ... + @staticmethod + @_typing.overload + def convert(points2f: _typing.Sequence[cv2.typing.Point2f], size: float = ..., response: float = ..., octave: int = ..., class_id: int = ...) -> _typing.Sequence[KeyPoint]: ... + + @staticmethod + def overlap(kp1: KeyPoint, kp2: KeyPoint) -> float: ... + + +class DMatch: + queryIdx: int + trainIdx: int + imgIdx: int + distance: float + + # Functions + @_typing.overload + def __init__(self) -> None: ... + @_typing.overload + def __init__(self, _queryIdx: int, _trainIdx: int, _distance: float) -> None: ... + @_typing.overload + def __init__(self, _queryIdx: int, _trainIdx: int, _imgIdx: int, _distance: float) -> None: ... + + +class TickMeter: + # Functions + def __init__(self) -> None: ... + + def start(self) -> None: ... + + def stop(self) -> None: ... + + def getTimeTicks(self) -> int: ... + + def getTimeMicro(self) -> float: ... + + def getTimeMilli(self) -> float: ... + + def getTimeSec(self) -> float: ... + + def getLastTimeTicks(self) -> int: ... + + def getLastTimeMicro(self) -> float: ... + + def getLastTimeMilli(self) -> float: ... + + def getLastTimeSec(self) -> float: ... + + def getCounter(self) -> int: ... + + def getFPS(self) -> float: ... + + def getAvgTimeSec(self) -> float: ... + + def getAvgTimeMilli(self) -> float: ... + + def reset(self) -> None: ... + + +class UMat: + offset: int + + # Functions + @_typing.overload + def __init__(self, usageFlags: UMatUsageFlags = ...) -> None: ... + @_typing.overload + def __init__(self, rows: int, cols: int, type: int, usageFlags: UMatUsageFlags = ...) -> None: ... + @_typing.overload + def __init__(self, size: cv2.typing.Size, type: int, usageFlags: UMatUsageFlags = ...) -> None: ... + @_typing.overload + def __init__(self, rows: int, cols: int, type: int, s: cv2.typing.Scalar, usageFlags: UMatUsageFlags = ...) -> None: ... + @_typing.overload + def __init__(self, size: cv2.typing.Size, type: int, s: cv2.typing.Scalar, usageFlags: UMatUsageFlags = ...) -> None: ... + @_typing.overload + def __init__(self, m: UMat) -> None: ... + @_typing.overload + def __init__(self, m: UMat, rowRange: cv2.typing.Range, colRange: cv2.typing.Range = ...) -> None: ... + @_typing.overload + def __init__(self, m: UMat, roi: cv2.typing.Rect) -> None: ... + @_typing.overload + def __init__(self, m: UMat, ranges: _typing.Sequence[cv2.typing.Range]) -> None: ... + + @staticmethod + def queue() -> cv2.typing.IntPointer: ... + + @staticmethod + def context() -> cv2.typing.IntPointer: ... + + def get(self) -> cv2.typing.MatLike: ... + + def isContinuous(self) -> bool: ... + + def isSubmatrix(self) -> bool: ... + + def handle(self, accessFlags: AccessFlag) -> cv2.typing.IntPointer: ... + + +class GeneralizedHough(Algorithm): + # Functions + @_typing.overload + def setTemplate(self, templ: cv2.typing.MatLike, templCenter: cv2.typing.Point = ...) -> None: ... + @_typing.overload + def setTemplate(self, templ: UMat, templCenter: cv2.typing.Point = ...) -> None: ... + @_typing.overload + def setTemplate(self, edges: cv2.typing.MatLike, dx: cv2.typing.MatLike, dy: cv2.typing.MatLike, templCenter: cv2.typing.Point = ...) -> None: ... + @_typing.overload + def setTemplate(self, edges: UMat, dx: UMat, dy: UMat, templCenter: cv2.typing.Point = ...) -> None: ... + + @_typing.overload + def detect(self, image: cv2.typing.MatLike, positions: cv2.typing.MatLike | None = ..., votes: cv2.typing.MatLike | None = ...) -> tuple[cv2.typing.MatLike, cv2.typing.MatLike]: ... + @_typing.overload + def detect(self, image: UMat, positions: UMat | None = ..., votes: UMat | None = ...) -> tuple[UMat, UMat]: ... + @_typing.overload + def detect(self, edges: cv2.typing.MatLike, dx: cv2.typing.MatLike, dy: cv2.typing.MatLike, positions: cv2.typing.MatLike | None = ..., votes: cv2.typing.MatLike | None = ...) -> tuple[cv2.typing.MatLike, cv2.typing.MatLike]: ... + @_typing.overload + def detect(self, edges: UMat, dx: UMat, dy: UMat, positions: UMat | None = ..., votes: UMat | None = ...) -> tuple[UMat, UMat]: ... + + def setCannyLowThresh(self, cannyLowThresh: int) -> None: ... + + def getCannyLowThresh(self) -> int: ... + + def setCannyHighThresh(self, cannyHighThresh: int) -> None: ... + + def getCannyHighThresh(self) -> int: ... + + def setMinDist(self, minDist: float) -> None: ... + + def getMinDist(self) -> float: ... + + def setDp(self, dp: float) -> None: ... + + def getDp(self) -> float: ... + + def setMaxBufferSize(self, maxBufferSize: int) -> None: ... + + def getMaxBufferSize(self) -> int: ... + + +class GeneralizedHoughBallard(GeneralizedHough): + # Functions + def setLevels(self, levels: int) -> None: ... + + def getLevels(self) -> int: ... + + def setVotesThreshold(self, votesThreshold: int) -> None: ... + + def getVotesThreshold(self) -> int: ... + + +class GeneralizedHoughGuil(GeneralizedHough): + # Functions + def setXi(self, xi: float) -> None: ... + + def getXi(self) -> float: ... + + def setLevels(self, levels: int) -> None: ... + + def getLevels(self) -> int: ... + + def setAngleEpsilon(self, angleEpsilon: float) -> None: ... + + def getAngleEpsilon(self) -> float: ... + + def setMinAngle(self, minAngle: float) -> None: ... + + def getMinAngle(self) -> float: ... + + def setMaxAngle(self, maxAngle: float) -> None: ... + + def getMaxAngle(self) -> float: ... + + def setAngleStep(self, angleStep: float) -> None: ... + + def getAngleStep(self) -> float: ... + + def setAngleThresh(self, angleThresh: int) -> None: ... + + def getAngleThresh(self) -> int: ... + + def setMinScale(self, minScale: float) -> None: ... + + def getMinScale(self) -> float: ... + + def setMaxScale(self, maxScale: float) -> None: ... + + def getMaxScale(self) -> float: ... + + def setScaleStep(self, scaleStep: float) -> None: ... + + def getScaleStep(self) -> float: ... + + def setScaleThresh(self, scaleThresh: int) -> None: ... + + def getScaleThresh(self) -> int: ... + + def setPosThresh(self, posThresh: int) -> None: ... + + def getPosThresh(self) -> int: ... + + +class CLAHE(Algorithm): + # Functions + @_typing.overload + def apply(self, src: cv2.typing.MatLike, dst: cv2.typing.MatLike | None = ...) -> cv2.typing.MatLike: ... + @_typing.overload + def apply(self, src: UMat, dst: UMat | None = ...) -> UMat: ... + + def setClipLimit(self, clipLimit: float) -> None: ... + + def getClipLimit(self) -> float: ... + + def setTilesGridSize(self, tileGridSize: cv2.typing.Size) -> None: ... + + def getTilesGridSize(self) -> cv2.typing.Size: ... + + def collectGarbage(self) -> None: ... + + +class Subdiv2D: + # Functions + @_typing.overload + def __init__(self) -> None: ... + @_typing.overload + def __init__(self, rect: cv2.typing.Rect) -> None: ... + + def initDelaunay(self, rect: cv2.typing.Rect) -> None: ... + + @_typing.overload + def insert(self, pt: cv2.typing.Point2f) -> int: ... + @_typing.overload + def insert(self, ptvec: _typing.Sequence[cv2.typing.Point2f]) -> None: ... + + def locate(self, pt: cv2.typing.Point2f) -> tuple[int, int, int]: ... + + def findNearest(self, pt: cv2.typing.Point2f) -> tuple[int, cv2.typing.Point2f]: ... + + def getEdgeList(self) -> _typing.Sequence[cv2.typing.Vec4f]: ... + + def getLeadingEdgeList(self) -> _typing.Sequence[int]: ... + + def getTriangleList(self) -> _typing.Sequence[cv2.typing.Vec6f]: ... + + def getVoronoiFacetList(self, idx: _typing.Sequence[int]) -> tuple[_typing.Sequence[_typing.Sequence[cv2.typing.Point2f]], _typing.Sequence[cv2.typing.Point2f]]: ... + + def getVertex(self, vertex: int) -> tuple[cv2.typing.Point2f, int]: ... + + def getEdge(self, edge: int, nextEdgeType: int) -> int: ... + + def nextEdge(self, edge: int) -> int: ... + + def rotateEdge(self, edge: int, rotate: int) -> int: ... + + def symEdge(self, edge: int) -> int: ... + + def edgeOrg(self, edge: int) -> tuple[int, cv2.typing.Point2f]: ... + + def edgeDst(self, edge: int) -> tuple[int, cv2.typing.Point2f]: ... + + +class LineSegmentDetector(Algorithm): + # Functions + @_typing.overload + def detect(self, image: cv2.typing.MatLike, lines: cv2.typing.MatLike | None = ..., width: cv2.typing.MatLike | None = ..., prec: cv2.typing.MatLike | None = ..., nfa: cv2.typing.MatLike | None = ...) -> tuple[cv2.typing.MatLike, cv2.typing.MatLike, cv2.typing.MatLike, cv2.typing.MatLike]: ... + @_typing.overload + def detect(self, image: UMat, lines: UMat | None = ..., width: UMat | None = ..., prec: UMat | None = ..., nfa: UMat | None = ...) -> tuple[UMat, UMat, UMat, UMat]: ... + + @_typing.overload + def drawSegments(self, image: cv2.typing.MatLike, lines: cv2.typing.MatLike) -> cv2.typing.MatLike: ... + @_typing.overload + def drawSegments(self, image: UMat, lines: UMat) -> UMat: ... + + @_typing.overload + def compareSegments(self, size: cv2.typing.Size, lines1: cv2.typing.MatLike, lines2: cv2.typing.MatLike, image: cv2.typing.MatLike | None = ...) -> tuple[int, cv2.typing.MatLike]: ... + @_typing.overload + def compareSegments(self, size: cv2.typing.Size, lines1: UMat, lines2: UMat, image: UMat | None = ...) -> tuple[int, UMat]: ... + + +class Tonemap(Algorithm): + # Functions + @_typing.overload + def process(self, src: cv2.typing.MatLike, dst: cv2.typing.MatLike | None = ...) -> cv2.typing.MatLike: ... + @_typing.overload + def process(self, src: UMat, dst: UMat | None = ...) -> UMat: ... + + def getGamma(self) -> float: ... + + def setGamma(self, gamma: float) -> None: ... + + +class TonemapDrago(Tonemap): + # Functions + def getSaturation(self) -> float: ... + + def setSaturation(self, saturation: float) -> None: ... + + def getBias(self) -> float: ... + + def setBias(self, bias: float) -> None: ... + + +class TonemapReinhard(Tonemap): + # Functions + def getIntensity(self) -> float: ... + + def setIntensity(self, intensity: float) -> None: ... + + def getLightAdaptation(self) -> float: ... + + def setLightAdaptation(self, light_adapt: float) -> None: ... + + def getColorAdaptation(self) -> float: ... + + def setColorAdaptation(self, color_adapt: float) -> None: ... + + +class TonemapMantiuk(Tonemap): + # Functions + def getScale(self) -> float: ... + + def setScale(self, scale: float) -> None: ... + + def getSaturation(self) -> float: ... + + def setSaturation(self, saturation: float) -> None: ... + + +class AlignExposures(Algorithm): + # Functions + @_typing.overload + def process(self, src: _typing.Sequence[cv2.typing.MatLike], dst: _typing.Sequence[cv2.typing.MatLike], times: cv2.typing.MatLike, response: cv2.typing.MatLike) -> None: ... + @_typing.overload + def process(self, src: _typing.Sequence[UMat], dst: _typing.Sequence[cv2.typing.MatLike], times: UMat, response: UMat) -> None: ... + + +class AlignMTB(AlignExposures): + # Functions + @_typing.overload + def process(self, src: _typing.Sequence[cv2.typing.MatLike], dst: _typing.Sequence[cv2.typing.MatLike], times: cv2.typing.MatLike, response: cv2.typing.MatLike) -> None: ... + @_typing.overload + def process(self, src: _typing.Sequence[UMat], dst: _typing.Sequence[cv2.typing.MatLike], times: UMat, response: UMat) -> None: ... + @_typing.overload + def process(self, src: _typing.Sequence[cv2.typing.MatLike], dst: _typing.Sequence[cv2.typing.MatLike]) -> None: ... + @_typing.overload + def process(self, src: _typing.Sequence[UMat], dst: _typing.Sequence[cv2.typing.MatLike]) -> None: ... + + @_typing.overload + def calculateShift(self, img0: cv2.typing.MatLike, img1: cv2.typing.MatLike) -> cv2.typing.Point: ... + @_typing.overload + def calculateShift(self, img0: UMat, img1: UMat) -> cv2.typing.Point: ... + + @_typing.overload + def shiftMat(self, src: cv2.typing.MatLike, shift: cv2.typing.Point, dst: cv2.typing.MatLike | None = ...) -> cv2.typing.MatLike: ... + @_typing.overload + def shiftMat(self, src: UMat, shift: cv2.typing.Point, dst: UMat | None = ...) -> UMat: ... + + @_typing.overload + def computeBitmaps(self, img: cv2.typing.MatLike, tb: cv2.typing.MatLike | None = ..., eb: cv2.typing.MatLike | None = ...) -> tuple[cv2.typing.MatLike, cv2.typing.MatLike]: ... + @_typing.overload + def computeBitmaps(self, img: UMat, tb: UMat | None = ..., eb: UMat | None = ...) -> tuple[UMat, UMat]: ... + + def getMaxBits(self) -> int: ... + + def setMaxBits(self, max_bits: int) -> None: ... + + def getExcludeRange(self) -> int: ... + + def setExcludeRange(self, exclude_range: int) -> None: ... + + def getCut(self) -> bool: ... + + def setCut(self, value: bool) -> None: ... + + +class CalibrateCRF(Algorithm): + # Functions + @_typing.overload + def process(self, src: _typing.Sequence[cv2.typing.MatLike], times: cv2.typing.MatLike, dst: cv2.typing.MatLike | None = ...) -> cv2.typing.MatLike: ... + @_typing.overload + def process(self, src: _typing.Sequence[UMat], times: UMat, dst: UMat | None = ...) -> UMat: ... + + +class CalibrateDebevec(CalibrateCRF): + # Functions + def getLambda(self) -> float: ... + + def setLambda(self, lambda_: float) -> None: ... + + def getSamples(self) -> int: ... + + def setSamples(self, samples: int) -> None: ... + + def getRandom(self) -> bool: ... + + def setRandom(self, random: bool) -> None: ... + + +class CalibrateRobertson(CalibrateCRF): + # Functions + def getMaxIter(self) -> int: ... + + def setMaxIter(self, max_iter: int) -> None: ... + + def getThreshold(self) -> float: ... + + def setThreshold(self, threshold: float) -> None: ... + + def getRadiance(self) -> cv2.typing.MatLike: ... + + +class MergeExposures(Algorithm): + # Functions + @_typing.overload + def process(self, src: _typing.Sequence[cv2.typing.MatLike], times: cv2.typing.MatLike, response: cv2.typing.MatLike, dst: cv2.typing.MatLike | None = ...) -> cv2.typing.MatLike: ... + @_typing.overload + def process(self, src: _typing.Sequence[UMat], times: UMat, response: UMat, dst: UMat | None = ...) -> UMat: ... + + +class MergeDebevec(MergeExposures): + # Functions + @_typing.overload + def process(self, src: _typing.Sequence[cv2.typing.MatLike], times: cv2.typing.MatLike, response: cv2.typing.MatLike, dst: cv2.typing.MatLike | None = ...) -> cv2.typing.MatLike: ... + @_typing.overload + def process(self, src: _typing.Sequence[UMat], times: UMat, response: UMat, dst: UMat | None = ...) -> UMat: ... + @_typing.overload + def process(self, src: _typing.Sequence[cv2.typing.MatLike], times: cv2.typing.MatLike, dst: cv2.typing.MatLike | None = ...) -> cv2.typing.MatLike: ... + @_typing.overload + def process(self, src: _typing.Sequence[UMat], times: UMat, dst: UMat | None = ...) -> UMat: ... + + +class MergeMertens(MergeExposures): + # Functions + @_typing.overload + def process(self, src: _typing.Sequence[cv2.typing.MatLike], times: cv2.typing.MatLike, response: cv2.typing.MatLike, dst: cv2.typing.MatLike | None = ...) -> cv2.typing.MatLike: ... + @_typing.overload + def process(self, src: _typing.Sequence[UMat], times: UMat, response: UMat, dst: UMat | None = ...) -> UMat: ... + @_typing.overload + def process(self, src: _typing.Sequence[cv2.typing.MatLike], dst: cv2.typing.MatLike | None = ...) -> cv2.typing.MatLike: ... + @_typing.overload + def process(self, src: _typing.Sequence[UMat], dst: UMat | None = ...) -> UMat: ... + + def getContrastWeight(self) -> float: ... + + def setContrastWeight(self, contrast_weiht: float) -> None: ... + + def getSaturationWeight(self) -> float: ... + + def setSaturationWeight(self, saturation_weight: float) -> None: ... + + def getExposureWeight(self) -> float: ... + + def setExposureWeight(self, exposure_weight: float) -> None: ... + + +class MergeRobertson(MergeExposures): + # Functions + @_typing.overload + def process(self, src: _typing.Sequence[cv2.typing.MatLike], times: cv2.typing.MatLike, response: cv2.typing.MatLike, dst: cv2.typing.MatLike | None = ...) -> cv2.typing.MatLike: ... + @_typing.overload + def process(self, src: _typing.Sequence[UMat], times: UMat, response: UMat, dst: UMat | None = ...) -> UMat: ... + @_typing.overload + def process(self, src: _typing.Sequence[cv2.typing.MatLike], times: cv2.typing.MatLike, dst: cv2.typing.MatLike | None = ...) -> cv2.typing.MatLike: ... + @_typing.overload + def process(self, src: _typing.Sequence[UMat], times: UMat, dst: UMat | None = ...) -> UMat: ... + + +class Feature2D: + # Functions + @_typing.overload + def detect(self, image: cv2.typing.MatLike, mask: cv2.typing.MatLike | None = ...) -> _typing.Sequence[KeyPoint]: ... + @_typing.overload + def detect(self, image: UMat, mask: UMat | None = ...) -> _typing.Sequence[KeyPoint]: ... + @_typing.overload + def detect(self, images: _typing.Sequence[cv2.typing.MatLike], masks: _typing.Sequence[cv2.typing.MatLike] | None = ...) -> _typing.Sequence[_typing.Sequence[KeyPoint]]: ... + @_typing.overload + def detect(self, images: _typing.Sequence[UMat], masks: _typing.Sequence[UMat] | None = ...) -> _typing.Sequence[_typing.Sequence[KeyPoint]]: ... + + @_typing.overload + def compute(self, image: cv2.typing.MatLike, keypoints: _typing.Sequence[KeyPoint], descriptors: cv2.typing.MatLike | None = ...) -> tuple[_typing.Sequence[KeyPoint], cv2.typing.MatLike]: ... + @_typing.overload + def compute(self, image: UMat, keypoints: _typing.Sequence[KeyPoint], descriptors: UMat | None = ...) -> tuple[_typing.Sequence[KeyPoint], UMat]: ... + @_typing.overload + def compute(self, images: _typing.Sequence[cv2.typing.MatLike], keypoints: _typing.Sequence[_typing.Sequence[KeyPoint]], descriptors: _typing.Sequence[cv2.typing.MatLike] | None = ...) -> tuple[_typing.Sequence[_typing.Sequence[KeyPoint]], _typing.Sequence[cv2.typing.MatLike]]: ... + @_typing.overload + def compute(self, images: _typing.Sequence[UMat], keypoints: _typing.Sequence[_typing.Sequence[KeyPoint]], descriptors: _typing.Sequence[UMat] | None = ...) -> tuple[_typing.Sequence[_typing.Sequence[KeyPoint]], _typing.Sequence[UMat]]: ... + + @_typing.overload + def detectAndCompute(self, image: cv2.typing.MatLike, mask: cv2.typing.MatLike, descriptors: cv2.typing.MatLike | None = ..., useProvidedKeypoints: bool = ...) -> tuple[_typing.Sequence[KeyPoint], cv2.typing.MatLike]: ... + @_typing.overload + def detectAndCompute(self, image: UMat, mask: UMat, descriptors: UMat | None = ..., useProvidedKeypoints: bool = ...) -> tuple[_typing.Sequence[KeyPoint], UMat]: ... + + def descriptorSize(self) -> int: ... + + def descriptorType(self) -> int: ... + + def defaultNorm(self) -> int: ... + + @_typing.overload + def write(self, fileName: str) -> None: ... + @_typing.overload + def write(self, fs: FileStorage, name: str) -> None: ... + + @_typing.overload + def read(self, fileName: str) -> None: ... + @_typing.overload + def read(self, arg1: FileNode) -> None: ... + + def empty(self) -> bool: ... + + def getDefaultName(self) -> str: ... + + +class AffineFeature(Feature2D): + # Functions + @classmethod + def create(cls, backend: Feature2D, maxTilt: int = ..., minTilt: int = ..., tiltStep: float = ..., rotateStepBase: float = ...) -> AffineFeature: ... + + def setViewParams(self, tilts: _typing.Sequence[float], rolls: _typing.Sequence[float]) -> None: ... + + def getViewParams(self, tilts: _typing.Sequence[float], rolls: _typing.Sequence[float]) -> None: ... + + def getDefaultName(self) -> str: ... + + +class SIFT(Feature2D): + # Functions + @classmethod + @_typing.overload + def create(cls, nfeatures: int = ..., nOctaveLayers: int = ..., contrastThreshold: float = ..., edgeThreshold: float = ..., sigma: float = ..., enable_precise_upscale: bool = ...) -> SIFT: ... + @classmethod + @_typing.overload + def create(cls, nfeatures: int, nOctaveLayers: int, contrastThreshold: float, edgeThreshold: float, sigma: float, descriptorType: int, enable_precise_upscale: bool = ...) -> SIFT: ... + + def getDefaultName(self) -> str: ... + + def setNFeatures(self, maxFeatures: int) -> None: ... + + def getNFeatures(self) -> int: ... + + def setNOctaveLayers(self, nOctaveLayers: int) -> None: ... + + def getNOctaveLayers(self) -> int: ... + + def setContrastThreshold(self, contrastThreshold: float) -> None: ... + + def getContrastThreshold(self) -> float: ... + + def setEdgeThreshold(self, edgeThreshold: float) -> None: ... + + def getEdgeThreshold(self) -> float: ... + + def setSigma(self, sigma: float) -> None: ... + + def getSigma(self) -> float: ... + + +class BRISK(Feature2D): + # Functions + @classmethod + @_typing.overload + def create(cls, thresh: int = ..., octaves: int = ..., patternScale: float = ...) -> BRISK: ... + @classmethod + @_typing.overload + def create(cls, radiusList: _typing.Sequence[float], numberList: _typing.Sequence[int], dMax: float = ..., dMin: float = ..., indexChange: _typing.Sequence[int] = ...) -> BRISK: ... + @classmethod + @_typing.overload + def create(cls, thresh: int, octaves: int, radiusList: _typing.Sequence[float], numberList: _typing.Sequence[int], dMax: float = ..., dMin: float = ..., indexChange: _typing.Sequence[int] = ...) -> BRISK: ... + + def getDefaultName(self) -> str: ... + + def setThreshold(self, threshold: int) -> None: ... + + def getThreshold(self) -> int: ... + + def setOctaves(self, octaves: int) -> None: ... + + def getOctaves(self) -> int: ... + + def setPatternScale(self, patternScale: float) -> None: ... + + def getPatternScale(self) -> float: ... + + +class ORB(Feature2D): + # Functions + @classmethod + def create(cls, nfeatures: int = ..., scaleFactor: float = ..., nlevels: int = ..., edgeThreshold: int = ..., firstLevel: int = ..., WTA_K: int = ..., scoreType: ORB_ScoreType = ..., patchSize: int = ..., fastThreshold: int = ...) -> ORB: ... + + def setMaxFeatures(self, maxFeatures: int) -> None: ... + + def getMaxFeatures(self) -> int: ... + + def setScaleFactor(self, scaleFactor: float) -> None: ... + + def getScaleFactor(self) -> float: ... + + def setNLevels(self, nlevels: int) -> None: ... + + def getNLevels(self) -> int: ... + + def setEdgeThreshold(self, edgeThreshold: int) -> None: ... + + def getEdgeThreshold(self) -> int: ... + + def setFirstLevel(self, firstLevel: int) -> None: ... + + def getFirstLevel(self) -> int: ... + + def setWTA_K(self, wta_k: int) -> None: ... + + def getWTA_K(self) -> int: ... + + def setScoreType(self, scoreType: ORB_ScoreType) -> None: ... + + def getScoreType(self) -> ORB_ScoreType: ... + + def setPatchSize(self, patchSize: int) -> None: ... + + def getPatchSize(self) -> int: ... + + def setFastThreshold(self, fastThreshold: int) -> None: ... + + def getFastThreshold(self) -> int: ... + + def getDefaultName(self) -> str: ... + + +class MSER(Feature2D): + # Functions + @classmethod + def create(cls, delta: int = ..., min_area: int = ..., max_area: int = ..., max_variation: float = ..., min_diversity: float = ..., max_evolution: int = ..., area_threshold: float = ..., min_margin: float = ..., edge_blur_size: int = ...) -> MSER: ... + + @_typing.overload + def detectRegions(self, image: cv2.typing.MatLike) -> tuple[_typing.Sequence[_typing.Sequence[cv2.typing.Point]], _typing.Sequence[cv2.typing.Rect]]: ... + @_typing.overload + def detectRegions(self, image: UMat) -> tuple[_typing.Sequence[_typing.Sequence[cv2.typing.Point]], _typing.Sequence[cv2.typing.Rect]]: ... + + def setDelta(self, delta: int) -> None: ... + + def getDelta(self) -> int: ... + + def setMinArea(self, minArea: int) -> None: ... + + def getMinArea(self) -> int: ... + + def setMaxArea(self, maxArea: int) -> None: ... + + def getMaxArea(self) -> int: ... + + def setMaxVariation(self, maxVariation: float) -> None: ... + + def getMaxVariation(self) -> float: ... + + def setMinDiversity(self, minDiversity: float) -> None: ... + + def getMinDiversity(self) -> float: ... + + def setMaxEvolution(self, maxEvolution: int) -> None: ... + + def getMaxEvolution(self) -> int: ... + + def setAreaThreshold(self, areaThreshold: float) -> None: ... + + def getAreaThreshold(self) -> float: ... + + def setMinMargin(self, min_margin: float) -> None: ... + + def getMinMargin(self) -> float: ... + + def setEdgeBlurSize(self, edge_blur_size: int) -> None: ... + + def getEdgeBlurSize(self) -> int: ... + + def setPass2Only(self, f: bool) -> None: ... + + def getPass2Only(self) -> bool: ... + + def getDefaultName(self) -> str: ... + + +class FastFeatureDetector(Feature2D): + # Functions + @classmethod + def create(cls, threshold: int = ..., nonmaxSuppression: bool = ..., type: FastFeatureDetector_DetectorType = ...) -> FastFeatureDetector: ... + + def setThreshold(self, threshold: int) -> None: ... + + def getThreshold(self) -> int: ... + + def setNonmaxSuppression(self, f: bool) -> None: ... + + def getNonmaxSuppression(self) -> bool: ... + + def setType(self, type: FastFeatureDetector_DetectorType) -> None: ... + + def getType(self) -> FastFeatureDetector_DetectorType: ... + + def getDefaultName(self) -> str: ... + + +class AgastFeatureDetector(Feature2D): + # Functions + @classmethod + def create(cls, threshold: int = ..., nonmaxSuppression: bool = ..., type: AgastFeatureDetector_DetectorType = ...) -> AgastFeatureDetector: ... + + def setThreshold(self, threshold: int) -> None: ... + + def getThreshold(self) -> int: ... + + def setNonmaxSuppression(self, f: bool) -> None: ... + + def getNonmaxSuppression(self) -> bool: ... + + def setType(self, type: AgastFeatureDetector_DetectorType) -> None: ... + + def getType(self) -> AgastFeatureDetector_DetectorType: ... + + def getDefaultName(self) -> str: ... + + +class GFTTDetector(Feature2D): + # Functions + @classmethod + @_typing.overload + def create(cls, maxCorners: int = ..., qualityLevel: float = ..., minDistance: float = ..., blockSize: int = ..., useHarrisDetector: bool = ..., k: float = ...) -> GFTTDetector: ... + @classmethod + @_typing.overload + def create(cls, maxCorners: int, qualityLevel: float, minDistance: float, blockSize: int, gradiantSize: int, useHarrisDetector: bool = ..., k: float = ...) -> GFTTDetector: ... + + def setMaxFeatures(self, maxFeatures: int) -> None: ... + + def getMaxFeatures(self) -> int: ... + + def setQualityLevel(self, qlevel: float) -> None: ... + + def getQualityLevel(self) -> float: ... + + def setMinDistance(self, minDistance: float) -> None: ... + + def getMinDistance(self) -> float: ... + + def setBlockSize(self, blockSize: int) -> None: ... + + def getBlockSize(self) -> int: ... + + def setGradientSize(self, gradientSize_: int) -> None: ... + + def getGradientSize(self) -> int: ... + + def setHarrisDetector(self, val: bool) -> None: ... + + def getHarrisDetector(self) -> bool: ... + + def setK(self, k: float) -> None: ... + + def getK(self) -> float: ... + + def getDefaultName(self) -> str: ... + + +class SimpleBlobDetector(Feature2D): + # Classes + class Params: + thresholdStep: float + minThreshold: float + maxThreshold: float + minRepeatability: int + minDistBetweenBlobs: float + filterByColor: bool + blobColor: int + filterByArea: bool + minArea: float + maxArea: float + filterByCircularity: bool + minCircularity: float + maxCircularity: float + filterByInertia: bool + minInertiaRatio: float + maxInertiaRatio: float + filterByConvexity: bool + minConvexity: float + maxConvexity: float + collectContours: bool + + # Functions + def __init__(self) -> None: ... + + + + # Functions + @classmethod + def create(cls, parameters: SimpleBlobDetector.Params = ...) -> SimpleBlobDetector: ... + + def setParams(self, params: SimpleBlobDetector.Params) -> None: ... + + def getParams(self) -> SimpleBlobDetector.Params: ... + + def getDefaultName(self) -> str: ... + + def getBlobContours(self) -> _typing.Sequence[_typing.Sequence[cv2.typing.Point]]: ... + + +class KAZE(Feature2D): + # Functions + @classmethod + def create(cls, extended: bool = ..., upright: bool = ..., threshold: float = ..., nOctaves: int = ..., nOctaveLayers: int = ..., diffusivity: KAZE_DiffusivityType = ...) -> KAZE: ... + + def setExtended(self, extended: bool) -> None: ... + + def getExtended(self) -> bool: ... + + def setUpright(self, upright: bool) -> None: ... + + def getUpright(self) -> bool: ... + + def setThreshold(self, threshold: float) -> None: ... + + def getThreshold(self) -> float: ... + + def setNOctaves(self, octaves: int) -> None: ... + + def getNOctaves(self) -> int: ... + + def setNOctaveLayers(self, octaveLayers: int) -> None: ... + + def getNOctaveLayers(self) -> int: ... + + def setDiffusivity(self, diff: KAZE_DiffusivityType) -> None: ... + + def getDiffusivity(self) -> KAZE_DiffusivityType: ... + + def getDefaultName(self) -> str: ... + + +class AKAZE(Feature2D): + # Functions + @classmethod + def create(cls, descriptor_type: AKAZE_DescriptorType = ..., descriptor_size: int = ..., descriptor_channels: int = ..., threshold: float = ..., nOctaves: int = ..., nOctaveLayers: int = ..., diffusivity: KAZE_DiffusivityType = ..., max_points: int = ...) -> AKAZE: ... + + def setDescriptorType(self, dtype: AKAZE_DescriptorType) -> None: ... + + def getDescriptorType(self) -> AKAZE_DescriptorType: ... + + def setDescriptorSize(self, dsize: int) -> None: ... + + def getDescriptorSize(self) -> int: ... + + def setDescriptorChannels(self, dch: int) -> None: ... + + def getDescriptorChannels(self) -> int: ... + + def setThreshold(self, threshold: float) -> None: ... + + def getThreshold(self) -> float: ... + + def setNOctaves(self, octaves: int) -> None: ... + + def getNOctaves(self) -> int: ... + + def setNOctaveLayers(self, octaveLayers: int) -> None: ... + + def getNOctaveLayers(self) -> int: ... + + def setDiffusivity(self, diff: KAZE_DiffusivityType) -> None: ... + + def getDiffusivity(self) -> KAZE_DiffusivityType: ... + + def getDefaultName(self) -> str: ... + + def setMaxPoints(self, max_points: int) -> None: ... + + def getMaxPoints(self) -> int: ... + + +class DescriptorMatcher(Algorithm): + # Functions + @_typing.overload + def add(self, descriptors: _typing.Sequence[cv2.typing.MatLike]) -> None: ... + @_typing.overload + def add(self, descriptors: _typing.Sequence[UMat]) -> None: ... + + def getTrainDescriptors(self) -> _typing.Sequence[cv2.typing.MatLike]: ... + + def clear(self) -> None: ... + + def empty(self) -> bool: ... + + def isMaskSupported(self) -> bool: ... + + def train(self) -> None: ... + + @_typing.overload + def match(self, queryDescriptors: cv2.typing.MatLike, trainDescriptors: cv2.typing.MatLike, mask: cv2.typing.MatLike | None = ...) -> _typing.Sequence[DMatch]: ... + @_typing.overload + def match(self, queryDescriptors: UMat, trainDescriptors: UMat, mask: UMat | None = ...) -> _typing.Sequence[DMatch]: ... + @_typing.overload + def match(self, queryDescriptors: cv2.typing.MatLike, masks: _typing.Sequence[cv2.typing.MatLike] | None = ...) -> _typing.Sequence[DMatch]: ... + @_typing.overload + def match(self, queryDescriptors: UMat, masks: _typing.Sequence[UMat] | None = ...) -> _typing.Sequence[DMatch]: ... + + @_typing.overload + def knnMatch(self, queryDescriptors: cv2.typing.MatLike, trainDescriptors: cv2.typing.MatLike, k: int, mask: cv2.typing.MatLike | None = ..., compactResult: bool = ...) -> _typing.Sequence[_typing.Sequence[DMatch]]: ... + @_typing.overload + def knnMatch(self, queryDescriptors: UMat, trainDescriptors: UMat, k: int, mask: UMat | None = ..., compactResult: bool = ...) -> _typing.Sequence[_typing.Sequence[DMatch]]: ... + @_typing.overload + def knnMatch(self, queryDescriptors: cv2.typing.MatLike, k: int, masks: _typing.Sequence[cv2.typing.MatLike] | None = ..., compactResult: bool = ...) -> _typing.Sequence[_typing.Sequence[DMatch]]: ... + @_typing.overload + def knnMatch(self, queryDescriptors: UMat, k: int, masks: _typing.Sequence[UMat] | None = ..., compactResult: bool = ...) -> _typing.Sequence[_typing.Sequence[DMatch]]: ... + + @_typing.overload + def radiusMatch(self, queryDescriptors: cv2.typing.MatLike, trainDescriptors: cv2.typing.MatLike, maxDistance: float, mask: cv2.typing.MatLike | None = ..., compactResult: bool = ...) -> _typing.Sequence[_typing.Sequence[DMatch]]: ... + @_typing.overload + def radiusMatch(self, queryDescriptors: UMat, trainDescriptors: UMat, maxDistance: float, mask: UMat | None = ..., compactResult: bool = ...) -> _typing.Sequence[_typing.Sequence[DMatch]]: ... + @_typing.overload + def radiusMatch(self, queryDescriptors: cv2.typing.MatLike, maxDistance: float, masks: _typing.Sequence[cv2.typing.MatLike] | None = ..., compactResult: bool = ...) -> _typing.Sequence[_typing.Sequence[DMatch]]: ... + @_typing.overload + def radiusMatch(self, queryDescriptors: UMat, maxDistance: float, masks: _typing.Sequence[UMat] | None = ..., compactResult: bool = ...) -> _typing.Sequence[_typing.Sequence[DMatch]]: ... + + @_typing.overload + def write(self, fileName: str) -> None: ... + @_typing.overload + def write(self, fs: FileStorage, name: str) -> None: ... + + @_typing.overload + def read(self, fileName: str) -> None: ... + @_typing.overload + def read(self, arg1: FileNode) -> None: ... + + def clone(self, emptyTrainData: bool = ...) -> DescriptorMatcher: ... + + @classmethod + @_typing.overload + def create(cls, descriptorMatcherType: str) -> DescriptorMatcher: ... + @classmethod + @_typing.overload + def create(cls, matcherType: DescriptorMatcher_MatcherType) -> DescriptorMatcher: ... + + +class BFMatcher(DescriptorMatcher): + # Functions + def __init__(self, normType: int = ..., crossCheck: bool = ...) -> None: ... + + @classmethod + def create(cls, normType: int = ..., crossCheck: bool = ...) -> BFMatcher: ... + + +class FlannBasedMatcher(DescriptorMatcher): + # Functions + def __init__(self, indexParams: cv2.typing.IndexParams = ..., searchParams: cv2.typing.SearchParams = ...) -> None: ... + + @classmethod + def create(cls) -> FlannBasedMatcher: ... + + +class BOWTrainer: + # Functions + def add(self, descriptors: cv2.typing.MatLike) -> None: ... + + def getDescriptors(self) -> _typing.Sequence[cv2.typing.MatLike]: ... + + def descriptorsCount(self) -> int: ... + + def clear(self) -> None: ... + + @_typing.overload + def cluster(self) -> cv2.typing.MatLike: ... + @_typing.overload + def cluster(self, descriptors: cv2.typing.MatLike) -> cv2.typing.MatLike: ... + + +class BOWKMeansTrainer(BOWTrainer): + # Functions + def __init__(self, clusterCount: int, termcrit: cv2.typing.TermCriteria = ..., attempts: int = ..., flags: int = ...) -> None: ... + + @_typing.overload + def cluster(self) -> cv2.typing.MatLike: ... + @_typing.overload + def cluster(self, descriptors: cv2.typing.MatLike) -> cv2.typing.MatLike: ... + + +class BOWImgDescriptorExtractor: + # Functions + def __init__(self, dextractor: Feature2D, dmatcher: DescriptorMatcher) -> None: ... + + def setVocabulary(self, vocabulary: cv2.typing.MatLike) -> None: ... + + def getVocabulary(self) -> cv2.typing.MatLike: ... + + def compute(self, image: cv2.typing.MatLike, keypoints: _typing.Sequence[KeyPoint], imgDescriptor: cv2.typing.MatLike | None = ...) -> cv2.typing.MatLike: ... + + def descriptorSize(self) -> int: ... + + def descriptorType(self) -> int: ... + + +class Animation: + loop_count: int + bgcolor: cv2.typing.Scalar + durations: _typing.Sequence[int] + frames: _typing.Sequence[cv2.typing.MatLike] + +class IStreamReader: + ... + +class VideoCapture: + # Functions + @_typing.overload + def __init__(self) -> None: ... + @_typing.overload + def __init__(self, filename: str, apiPreference: int = ...) -> None: ... + @_typing.overload + def __init__(self, filename: str, apiPreference: int, params: _typing.Sequence[int]) -> None: ... + @_typing.overload + def __init__(self, index: int, apiPreference: int = ...) -> None: ... + @_typing.overload + def __init__(self, index: int, apiPreference: int, params: _typing.Sequence[int]) -> None: ... + @_typing.overload + def __init__(self, source: IStreamReader, apiPreference: int, params: _typing.Sequence[int]) -> None: ... + + @_typing.overload + def open(self, filename: str, apiPreference: int = ...) -> bool: ... + @_typing.overload + def open(self, filename: str, apiPreference: int, params: _typing.Sequence[int]) -> bool: ... + @_typing.overload + def open(self, index: int, apiPreference: int = ...) -> bool: ... + @_typing.overload + def open(self, index: int, apiPreference: int, params: _typing.Sequence[int]) -> bool: ... + @_typing.overload + def open(self, source: IStreamReader, apiPreference: int, params: _typing.Sequence[int]) -> bool: ... + + def isOpened(self) -> bool: ... + + def release(self) -> None: ... + + def grab(self) -> bool: ... + + @_typing.overload + def retrieve(self, image: cv2.typing.MatLike | None = ..., flag: int = ...) -> tuple[bool, cv2.typing.MatLike]: ... + @_typing.overload + def retrieve(self, image: UMat | None = ..., flag: int = ...) -> tuple[bool, UMat]: ... + + @_typing.overload + def read(self, image: cv2.typing.MatLike | None = ...) -> tuple[bool, cv2.typing.MatLike]: ... + @_typing.overload + def read(self, image: UMat | None = ...) -> tuple[bool, UMat]: ... + + def set(self, propId: int, value: float) -> bool: ... + + def get(self, propId: int) -> float: ... + + def getBackendName(self) -> str: ... + + def setExceptionMode(self, enable: bool) -> None: ... + + def getExceptionMode(self) -> bool: ... + + @staticmethod + def waitAny(streams: _typing.Sequence[VideoCapture], timeoutNs: int = ...) -> tuple[bool, _typing.Sequence[int]]: ... + + +class VideoWriter: + # Functions + @_typing.overload + def __init__(self) -> None: ... + @_typing.overload + def __init__(self, filename: str, fourcc: int, fps: float, frameSize: cv2.typing.Size, isColor: bool = ...) -> None: ... + @_typing.overload + def __init__(self, filename: str, apiPreference: int, fourcc: int, fps: float, frameSize: cv2.typing.Size, isColor: bool = ...) -> None: ... + @_typing.overload + def __init__(self, filename: str, fourcc: int, fps: float, frameSize: cv2.typing.Size, params: _typing.Sequence[int]) -> None: ... + @_typing.overload + def __init__(self, filename: str, apiPreference: int, fourcc: int, fps: float, frameSize: cv2.typing.Size, params: _typing.Sequence[int]) -> None: ... + + @_typing.overload + def open(self, filename: str, fourcc: int, fps: float, frameSize: cv2.typing.Size, isColor: bool = ...) -> bool: ... + @_typing.overload + def open(self, filename: str, apiPreference: int, fourcc: int, fps: float, frameSize: cv2.typing.Size, isColor: bool = ...) -> bool: ... + @_typing.overload + def open(self, filename: str, fourcc: int, fps: float, frameSize: cv2.typing.Size, params: _typing.Sequence[int]) -> bool: ... + @_typing.overload + def open(self, filename: str, apiPreference: int, fourcc: int, fps: float, frameSize: cv2.typing.Size, params: _typing.Sequence[int]) -> bool: ... + + def isOpened(self) -> bool: ... + + def release(self) -> None: ... + + @_typing.overload + def write(self, image: cv2.typing.MatLike) -> None: ... + @_typing.overload + def write(self, image: UMat) -> None: ... + + def set(self, propId: int, value: float) -> bool: ... + + def get(self, propId: int) -> float: ... + + @staticmethod + def fourcc(c1: str, c2: str, c3: str, c4: str) -> int: ... + + def getBackendName(self) -> str: ... + + +class UsacParams: + confidence: float + isParallel: bool + loIterations: int + loMethod: LocalOptimMethod + loSampleSize: int + maxIterations: int + neighborsSearch: NeighborSearchMethod + randomGeneratorState: int + sampler: SamplingMethod + score: ScoreMethod + threshold: float + final_polisher: PolishingMethod + final_polisher_iterations: int + + # Functions + def __init__(self) -> None: ... + + +class CirclesGridFinderParameters: + densityNeighborhoodSize: cv2.typing.Size2f + minDensity: float + kmeansAttempts: int + minDistanceToAddKeypoint: int + keypointScale: int + minGraphConfidence: float + vertexGain: float + vertexPenalty: float + existingVertexGain: float + edgeGain: float + edgePenalty: float + convexHullFactor: float + minRNGEdgeSwitchDist: float + squareSize: float + maxRectifiedDistance: float + + # Functions + def __init__(self) -> None: ... + + +class StereoMatcher(Algorithm): + # Functions + @_typing.overload + def compute(self, left: cv2.typing.MatLike, right: cv2.typing.MatLike, disparity: cv2.typing.MatLike | None = ...) -> cv2.typing.MatLike: ... + @_typing.overload + def compute(self, left: UMat, right: UMat, disparity: UMat | None = ...) -> UMat: ... + + def getMinDisparity(self) -> int: ... + + def setMinDisparity(self, minDisparity: int) -> None: ... + + def getNumDisparities(self) -> int: ... + + def setNumDisparities(self, numDisparities: int) -> None: ... + + def getBlockSize(self) -> int: ... + + def setBlockSize(self, blockSize: int) -> None: ... + + def getSpeckleWindowSize(self) -> int: ... + + def setSpeckleWindowSize(self, speckleWindowSize: int) -> None: ... + + def getSpeckleRange(self) -> int: ... + + def setSpeckleRange(self, speckleRange: int) -> None: ... + + def getDisp12MaxDiff(self) -> int: ... + + def setDisp12MaxDiff(self, disp12MaxDiff: int) -> None: ... + + +class StereoBM(StereoMatcher): + # Functions + def getPreFilterType(self) -> int: ... + + def setPreFilterType(self, preFilterType: int) -> None: ... + + def getPreFilterSize(self) -> int: ... + + def setPreFilterSize(self, preFilterSize: int) -> None: ... + + def getPreFilterCap(self) -> int: ... + + def setPreFilterCap(self, preFilterCap: int) -> None: ... + + def getTextureThreshold(self) -> int: ... + + def setTextureThreshold(self, textureThreshold: int) -> None: ... + + def getUniquenessRatio(self) -> int: ... + + def setUniquenessRatio(self, uniquenessRatio: int) -> None: ... + + def getSmallerBlockSize(self) -> int: ... + + def setSmallerBlockSize(self, blockSize: int) -> None: ... + + def getROI1(self) -> cv2.typing.Rect: ... + + def setROI1(self, roi1: cv2.typing.Rect) -> None: ... + + def getROI2(self) -> cv2.typing.Rect: ... + + def setROI2(self, roi2: cv2.typing.Rect) -> None: ... + + @classmethod + def create(cls, numDisparities: int = ..., blockSize: int = ...) -> StereoBM: ... + + +class StereoSGBM(StereoMatcher): + # Functions + def getPreFilterCap(self) -> int: ... + + def setPreFilterCap(self, preFilterCap: int) -> None: ... + + def getUniquenessRatio(self) -> int: ... + + def setUniquenessRatio(self, uniquenessRatio: int) -> None: ... + + def getP1(self) -> int: ... + + def setP1(self, P1: int) -> None: ... + + def getP2(self) -> int: ... + + def setP2(self, P2: int) -> None: ... + + def getMode(self) -> int: ... + + def setMode(self, mode: int) -> None: ... + + @classmethod + def create(cls, minDisparity: int = ..., numDisparities: int = ..., blockSize: int = ..., P1: int = ..., P2: int = ..., disp12MaxDiff: int = ..., preFilterCap: int = ..., uniquenessRatio: int = ..., speckleWindowSize: int = ..., speckleRange: int = ..., mode: int = ...) -> StereoSGBM: ... + + +class BaseCascadeClassifier(Algorithm): + ... + +class CascadeClassifier: + # Functions + @_typing.overload + def __init__(self) -> None: ... + @_typing.overload + def __init__(self, filename: str) -> None: ... + + def empty(self) -> bool: ... + + def load(self, filename: str) -> bool: ... + + def read(self, node: FileNode) -> bool: ... + + @_typing.overload + def detectMultiScale(self, image: cv2.typing.MatLike, scaleFactor: float = ..., minNeighbors: int = ..., flags: int = ..., minSize: cv2.typing.Size = ..., maxSize: cv2.typing.Size = ...) -> _typing.Sequence[cv2.typing.Rect]: ... + @_typing.overload + def detectMultiScale(self, image: UMat, scaleFactor: float = ..., minNeighbors: int = ..., flags: int = ..., minSize: cv2.typing.Size = ..., maxSize: cv2.typing.Size = ...) -> _typing.Sequence[cv2.typing.Rect]: ... + + @_typing.overload + def detectMultiScale2(self, image: cv2.typing.MatLike, scaleFactor: float = ..., minNeighbors: int = ..., flags: int = ..., minSize: cv2.typing.Size = ..., maxSize: cv2.typing.Size = ...) -> tuple[_typing.Sequence[cv2.typing.Rect], _typing.Sequence[int]]: ... + @_typing.overload + def detectMultiScale2(self, image: UMat, scaleFactor: float = ..., minNeighbors: int = ..., flags: int = ..., minSize: cv2.typing.Size = ..., maxSize: cv2.typing.Size = ...) -> tuple[_typing.Sequence[cv2.typing.Rect], _typing.Sequence[int]]: ... + + @_typing.overload + def detectMultiScale3(self, image: cv2.typing.MatLike, scaleFactor: float = ..., minNeighbors: int = ..., flags: int = ..., minSize: cv2.typing.Size = ..., maxSize: cv2.typing.Size = ..., outputRejectLevels: bool = ...) -> tuple[_typing.Sequence[cv2.typing.Rect], _typing.Sequence[int], _typing.Sequence[float]]: ... + @_typing.overload + def detectMultiScale3(self, image: UMat, scaleFactor: float = ..., minNeighbors: int = ..., flags: int = ..., minSize: cv2.typing.Size = ..., maxSize: cv2.typing.Size = ..., outputRejectLevels: bool = ...) -> tuple[_typing.Sequence[cv2.typing.Rect], _typing.Sequence[int], _typing.Sequence[float]]: ... + + def isOldFormatCascade(self) -> bool: ... + + def getOriginalWindowSize(self) -> cv2.typing.Size: ... + + def getFeatureType(self) -> int: ... + + @staticmethod + def convert(oldcascade: str, newcascade: str) -> bool: ... + + +class HOGDescriptor: + @property + def winSize(self) -> cv2.typing.Size: ... + @property + def blockSize(self) -> cv2.typing.Size: ... + @property + def blockStride(self) -> cv2.typing.Size: ... + @property + def cellSize(self) -> cv2.typing.Size: ... + @property + def nbins(self) -> int: ... + @property + def derivAperture(self) -> int: ... + @property + def winSigma(self) -> float: ... + @property + def histogramNormType(self) -> HOGDescriptor_HistogramNormType: ... + @property + def L2HysThreshold(self) -> float: ... + @property + def gammaCorrection(self) -> bool: ... + @property + def svmDetector(self) -> _typing.Sequence[float]: ... + @property + def nlevels(self) -> int: ... + @property + def signedGradient(self) -> bool: ... + + # Functions + @_typing.overload + def __init__(self) -> None: ... + @_typing.overload + def __init__(self, _winSize: cv2.typing.Size, _blockSize: cv2.typing.Size, _blockStride: cv2.typing.Size, _cellSize: cv2.typing.Size, _nbins: int, _derivAperture: int = ..., _winSigma: float = ..., _histogramNormType: HOGDescriptor_HistogramNormType = ..., _L2HysThreshold: float = ..., _gammaCorrection: bool = ..., _nlevels: int = ..., _signedGradient: bool = ...) -> None: ... + @_typing.overload + def __init__(self, filename: str) -> None: ... + + def getDescriptorSize(self) -> int: ... + + def checkDetectorSize(self) -> bool: ... + + def getWinSigma(self) -> float: ... + + @_typing.overload + def setSVMDetector(self, svmdetector: cv2.typing.MatLike) -> None: ... + @_typing.overload + def setSVMDetector(self, svmdetector: UMat) -> None: ... + + def load(self, filename: str, objname: str = ...) -> bool: ... + + def save(self, filename: str, objname: str = ...) -> None: ... + + @_typing.overload + def compute(self, img: cv2.typing.MatLike, winStride: cv2.typing.Size = ..., padding: cv2.typing.Size = ..., locations: _typing.Sequence[cv2.typing.Point] = ...) -> _typing.Sequence[float]: ... + @_typing.overload + def compute(self, img: UMat, winStride: cv2.typing.Size = ..., padding: cv2.typing.Size = ..., locations: _typing.Sequence[cv2.typing.Point] = ...) -> _typing.Sequence[float]: ... + + @_typing.overload + def detect(self, img: cv2.typing.MatLike, hitThreshold: float = ..., winStride: cv2.typing.Size = ..., padding: cv2.typing.Size = ..., searchLocations: _typing.Sequence[cv2.typing.Point] = ...) -> tuple[_typing.Sequence[cv2.typing.Point], _typing.Sequence[float]]: ... + @_typing.overload + def detect(self, img: UMat, hitThreshold: float = ..., winStride: cv2.typing.Size = ..., padding: cv2.typing.Size = ..., searchLocations: _typing.Sequence[cv2.typing.Point] = ...) -> tuple[_typing.Sequence[cv2.typing.Point], _typing.Sequence[float]]: ... + + @_typing.overload + def detectMultiScale(self, img: cv2.typing.MatLike, hitThreshold: float = ..., winStride: cv2.typing.Size = ..., padding: cv2.typing.Size = ..., scale: float = ..., groupThreshold: float = ..., useMeanshiftGrouping: bool = ...) -> tuple[_typing.Sequence[cv2.typing.Rect], _typing.Sequence[float]]: ... + @_typing.overload + def detectMultiScale(self, img: UMat, hitThreshold: float = ..., winStride: cv2.typing.Size = ..., padding: cv2.typing.Size = ..., scale: float = ..., groupThreshold: float = ..., useMeanshiftGrouping: bool = ...) -> tuple[_typing.Sequence[cv2.typing.Rect], _typing.Sequence[float]]: ... + + @_typing.overload + def computeGradient(self, img: cv2.typing.MatLike, grad: cv2.typing.MatLike, angleOfs: cv2.typing.MatLike, paddingTL: cv2.typing.Size = ..., paddingBR: cv2.typing.Size = ...) -> tuple[cv2.typing.MatLike, cv2.typing.MatLike]: ... + @_typing.overload + def computeGradient(self, img: UMat, grad: UMat, angleOfs: UMat, paddingTL: cv2.typing.Size = ..., paddingBR: cv2.typing.Size = ...) -> tuple[UMat, UMat]: ... + + @staticmethod + def getDefaultPeopleDetector() -> _typing.Sequence[float]: ... + + @staticmethod + def getDaimlerPeopleDetector() -> _typing.Sequence[float]: ... + + +class QRCodeEncoder: + # Classes + class Params: + version: int + correction_level: QRCodeEncoder_CorrectionLevel + mode: QRCodeEncoder_EncodeMode + structure_number: int + + # Functions + def __init__(self) -> None: ... + + + + # Functions + @classmethod + def create(cls, parameters: QRCodeEncoder.Params = ...) -> QRCodeEncoder: ... + + @_typing.overload + def encode(self, encoded_info: str, qrcode: cv2.typing.MatLike | None = ...) -> cv2.typing.MatLike: ... + @_typing.overload + def encode(self, encoded_info: str, qrcode: UMat | None = ...) -> UMat: ... + + @_typing.overload + def encodeStructuredAppend(self, encoded_info: str, qrcodes: _typing.Sequence[cv2.typing.MatLike] | None = ...) -> _typing.Sequence[cv2.typing.MatLike]: ... + @_typing.overload + def encodeStructuredAppend(self, encoded_info: str, qrcodes: _typing.Sequence[UMat] | None = ...) -> _typing.Sequence[UMat]: ... + + +class QRCodeDetector(GraphicalCodeDetector): + # Functions + def __init__(self) -> None: ... + + def setEpsX(self, epsX: float) -> QRCodeDetector: ... + + def setEpsY(self, epsY: float) -> QRCodeDetector: ... + + def setUseAlignmentMarkers(self, useAlignmentMarkers: bool) -> QRCodeDetector: ... + + @_typing.overload + def decodeCurved(self, img: cv2.typing.MatLike, points: cv2.typing.MatLike, straight_qrcode: cv2.typing.MatLike | None = ...) -> tuple[str, cv2.typing.MatLike]: ... + @_typing.overload + def decodeCurved(self, img: UMat, points: UMat, straight_qrcode: UMat | None = ...) -> tuple[str, UMat]: ... + + @_typing.overload + def detectAndDecodeCurved(self, img: cv2.typing.MatLike, points: cv2.typing.MatLike | None = ..., straight_qrcode: cv2.typing.MatLike | None = ...) -> tuple[str, cv2.typing.MatLike, cv2.typing.MatLike]: ... + @_typing.overload + def detectAndDecodeCurved(self, img: UMat, points: UMat | None = ..., straight_qrcode: UMat | None = ...) -> tuple[str, UMat, UMat]: ... + + +class GraphicalCodeDetector: + # Functions + @_typing.overload + def detect(self, img: cv2.typing.MatLike, points: cv2.typing.MatLike | None = ...) -> tuple[bool, cv2.typing.MatLike]: ... + @_typing.overload + def detect(self, img: UMat, points: UMat | None = ...) -> tuple[bool, UMat]: ... + + @_typing.overload + def decode(self, img: cv2.typing.MatLike, points: cv2.typing.MatLike, straight_code: cv2.typing.MatLike | None = ...) -> tuple[str, cv2.typing.MatLike]: ... + @_typing.overload + def decode(self, img: UMat, points: UMat, straight_code: UMat | None = ...) -> tuple[str, UMat]: ... + + @_typing.overload + def detectAndDecode(self, img: cv2.typing.MatLike, points: cv2.typing.MatLike | None = ..., straight_code: cv2.typing.MatLike | None = ...) -> tuple[str, cv2.typing.MatLike, cv2.typing.MatLike]: ... + @_typing.overload + def detectAndDecode(self, img: UMat, points: UMat | None = ..., straight_code: UMat | None = ...) -> tuple[str, UMat, UMat]: ... + + @_typing.overload + def detectMulti(self, img: cv2.typing.MatLike, points: cv2.typing.MatLike | None = ...) -> tuple[bool, cv2.typing.MatLike]: ... + @_typing.overload + def detectMulti(self, img: UMat, points: UMat | None = ...) -> tuple[bool, UMat]: ... + + @_typing.overload + def decodeMulti(self, img: cv2.typing.MatLike, points: cv2.typing.MatLike, straight_code: _typing.Sequence[cv2.typing.MatLike] | None = ...) -> tuple[bool, _typing.Sequence[str], _typing.Sequence[cv2.typing.MatLike]]: ... + @_typing.overload + def decodeMulti(self, img: UMat, points: UMat, straight_code: _typing.Sequence[UMat] | None = ...) -> tuple[bool, _typing.Sequence[str], _typing.Sequence[UMat]]: ... + + @_typing.overload + def detectAndDecodeMulti(self, img: cv2.typing.MatLike, points: cv2.typing.MatLike | None = ..., straight_code: _typing.Sequence[cv2.typing.MatLike] | None = ...) -> tuple[bool, _typing.Sequence[str], cv2.typing.MatLike, _typing.Sequence[cv2.typing.MatLike]]: ... + @_typing.overload + def detectAndDecodeMulti(self, img: UMat, points: UMat | None = ..., straight_code: _typing.Sequence[UMat] | None = ...) -> tuple[bool, _typing.Sequence[str], UMat, _typing.Sequence[UMat]]: ... + + +class QRCodeDetectorAruco(GraphicalCodeDetector): + # Classes + class Params: + minModuleSizeInPyramid: float + maxRotation: float + maxModuleSizeMismatch: float + maxTimingPatternMismatch: float + maxPenalties: float + maxColorsMismatch: float + scaleTimingPatternScore: float + + # Functions + def __init__(self) -> None: ... + + + + # Functions + @_typing.overload + def __init__(self) -> None: ... + @_typing.overload + def __init__(self, params: QRCodeDetectorAruco.Params) -> None: ... + + def getDetectorParameters(self) -> QRCodeDetectorAruco.Params: ... + + def setDetectorParameters(self, params: QRCodeDetectorAruco.Params) -> QRCodeDetectorAruco: ... + + def getArucoParameters(self) -> cv2.aruco.DetectorParameters: ... + + def setArucoParameters(self, params: cv2.aruco.DetectorParameters) -> None: ... + + +class FaceDetectorYN: + # Functions + def setInputSize(self, input_size: cv2.typing.Size) -> None: ... + + def getInputSize(self) -> cv2.typing.Size: ... + + def setScoreThreshold(self, score_threshold: float) -> None: ... + + def getScoreThreshold(self) -> float: ... + + def setNMSThreshold(self, nms_threshold: float) -> None: ... + + def getNMSThreshold(self) -> float: ... + + def setTopK(self, top_k: int) -> None: ... + + def getTopK(self) -> int: ... + + @_typing.overload + def detect(self, image: cv2.typing.MatLike, faces: cv2.typing.MatLike | None = ...) -> tuple[int, cv2.typing.MatLike]: ... + @_typing.overload + def detect(self, image: UMat, faces: UMat | None = ...) -> tuple[int, UMat]: ... + + @classmethod + @_typing.overload + def create(cls, model: str, config: str, input_size: cv2.typing.Size, score_threshold: float = ..., nms_threshold: float = ..., top_k: int = ..., backend_id: int = ..., target_id: int = ...) -> FaceDetectorYN: ... + @classmethod + @_typing.overload + def create(cls, framework: str, bufferModel: numpy.ndarray[_typing.Any, numpy.dtype[numpy.uint8]], bufferConfig: numpy.ndarray[_typing.Any, numpy.dtype[numpy.uint8]], input_size: cv2.typing.Size, score_threshold: float = ..., nms_threshold: float = ..., top_k: int = ..., backend_id: int = ..., target_id: int = ...) -> FaceDetectorYN: ... + + +class FaceRecognizerSF: + # Functions + @_typing.overload + def alignCrop(self, src_img: cv2.typing.MatLike, face_box: cv2.typing.MatLike, aligned_img: cv2.typing.MatLike | None = ...) -> cv2.typing.MatLike: ... + @_typing.overload + def alignCrop(self, src_img: UMat, face_box: UMat, aligned_img: UMat | None = ...) -> UMat: ... + + @_typing.overload + def feature(self, aligned_img: cv2.typing.MatLike, face_feature: cv2.typing.MatLike | None = ...) -> cv2.typing.MatLike: ... + @_typing.overload + def feature(self, aligned_img: UMat, face_feature: UMat | None = ...) -> UMat: ... + + @_typing.overload + def match(self, face_feature1: cv2.typing.MatLike, face_feature2: cv2.typing.MatLike, dis_type: int = ...) -> float: ... + @_typing.overload + def match(self, face_feature1: UMat, face_feature2: UMat, dis_type: int = ...) -> float: ... + + @classmethod + @_typing.overload + def create(cls, model: str, config: str, backend_id: int = ..., target_id: int = ...) -> FaceRecognizerSF: ... + @classmethod + @_typing.overload + def create(cls, framework: str, bufferModel: numpy.ndarray[_typing.Any, numpy.dtype[numpy.uint8]], bufferConfig: numpy.ndarray[_typing.Any, numpy.dtype[numpy.uint8]], backend_id: int = ..., target_id: int = ...) -> FaceRecognizerSF: ... + + +class Stitcher: + # Functions + @classmethod + def create(cls, mode: Stitcher_Mode = ...) -> Stitcher: ... + + def registrationResol(self) -> float: ... + + def setRegistrationResol(self, resol_mpx: float) -> None: ... + + def seamEstimationResol(self) -> float: ... + + def setSeamEstimationResol(self, resol_mpx: float) -> None: ... + + def compositingResol(self) -> float: ... + + def setCompositingResol(self, resol_mpx: float) -> None: ... + + def panoConfidenceThresh(self) -> float: ... + + def setPanoConfidenceThresh(self, conf_thresh: float) -> None: ... + + def waveCorrection(self) -> bool: ... + + def setWaveCorrection(self, flag: bool) -> None: ... + + def interpolationFlags(self) -> InterpolationFlags: ... + + def setInterpolationFlags(self, interp_flags: InterpolationFlags) -> None: ... + + @_typing.overload + def estimateTransform(self, images: _typing.Sequence[cv2.typing.MatLike], masks: _typing.Sequence[cv2.typing.MatLike] | None = ...) -> Stitcher_Status: ... + @_typing.overload + def estimateTransform(self, images: _typing.Sequence[UMat], masks: _typing.Sequence[UMat] | None = ...) -> Stitcher_Status: ... + + @_typing.overload + def composePanorama(self, pano: cv2.typing.MatLike | None = ...) -> tuple[Stitcher_Status, cv2.typing.MatLike]: ... + @_typing.overload + def composePanorama(self, pano: UMat | None = ...) -> tuple[Stitcher_Status, UMat]: ... + @_typing.overload + def composePanorama(self, images: _typing.Sequence[cv2.typing.MatLike], pano: cv2.typing.MatLike | None = ...) -> tuple[Stitcher_Status, cv2.typing.MatLike]: ... + @_typing.overload + def composePanorama(self, images: _typing.Sequence[UMat], pano: UMat | None = ...) -> tuple[Stitcher_Status, UMat]: ... + + @_typing.overload + def stitch(self, images: _typing.Sequence[cv2.typing.MatLike], pano: cv2.typing.MatLike | None = ...) -> tuple[Stitcher_Status, cv2.typing.MatLike]: ... + @_typing.overload + def stitch(self, images: _typing.Sequence[UMat], pano: UMat | None = ...) -> tuple[Stitcher_Status, UMat]: ... + @_typing.overload + def stitch(self, images: _typing.Sequence[cv2.typing.MatLike], masks: _typing.Sequence[cv2.typing.MatLike], pano: cv2.typing.MatLike | None = ...) -> tuple[Stitcher_Status, cv2.typing.MatLike]: ... + @_typing.overload + def stitch(self, images: _typing.Sequence[UMat], masks: _typing.Sequence[UMat], pano: UMat | None = ...) -> tuple[Stitcher_Status, UMat]: ... + + def component(self) -> _typing.Sequence[int]: ... + + def cameras(self) -> _typing.Sequence[cv2.detail.CameraParams]: ... + + def workScale(self) -> float: ... + + +class PyRotationWarper: + # Functions + @_typing.overload + def __init__(self, type: str, scale: float) -> None: ... + @_typing.overload + def __init__(self) -> None: ... + + @_typing.overload + def warpPoint(self, pt: cv2.typing.Point2f, K: cv2.typing.MatLike, R: cv2.typing.MatLike) -> cv2.typing.Point2f: ... + @_typing.overload + def warpPoint(self, pt: cv2.typing.Point2f, K: UMat, R: UMat) -> cv2.typing.Point2f: ... + + @_typing.overload + def warpPointBackward(self, pt: cv2.typing.Point2f, K: cv2.typing.MatLike, R: cv2.typing.MatLike) -> cv2.typing.Point2f: ... + @_typing.overload + def warpPointBackward(self, pt: cv2.typing.Point2f, K: UMat, R: UMat) -> cv2.typing.Point2f: ... + @_typing.overload + def warpPointBackward(self, pt: cv2.typing.Point2f, K: cv2.typing.MatLike, R: cv2.typing.MatLike) -> cv2.typing.Point2f: ... + @_typing.overload + def warpPointBackward(self, pt: cv2.typing.Point2f, K: UMat, R: UMat) -> cv2.typing.Point2f: ... + + @_typing.overload + def buildMaps(self, src_size: cv2.typing.Size, K: cv2.typing.MatLike, R: cv2.typing.MatLike, xmap: cv2.typing.MatLike | None = ..., ymap: cv2.typing.MatLike | None = ...) -> tuple[cv2.typing.Rect, cv2.typing.MatLike, cv2.typing.MatLike]: ... + @_typing.overload + def buildMaps(self, src_size: cv2.typing.Size, K: UMat, R: UMat, xmap: UMat | None = ..., ymap: UMat | None = ...) -> tuple[cv2.typing.Rect, UMat, UMat]: ... + + @_typing.overload + def warp(self, src: cv2.typing.MatLike, K: cv2.typing.MatLike, R: cv2.typing.MatLike, interp_mode: int, border_mode: int, dst: cv2.typing.MatLike | None = ...) -> tuple[cv2.typing.Point, cv2.typing.MatLike]: ... + @_typing.overload + def warp(self, src: UMat, K: UMat, R: UMat, interp_mode: int, border_mode: int, dst: UMat | None = ...) -> tuple[cv2.typing.Point, UMat]: ... + + @_typing.overload + def warpBackward(self, src: cv2.typing.MatLike, K: cv2.typing.MatLike, R: cv2.typing.MatLike, interp_mode: int, border_mode: int, dst_size: cv2.typing.Size, dst: cv2.typing.MatLike | None = ...) -> cv2.typing.MatLike: ... + @_typing.overload + def warpBackward(self, src: UMat, K: UMat, R: UMat, interp_mode: int, border_mode: int, dst_size: cv2.typing.Size, dst: UMat | None = ...) -> UMat: ... + + @_typing.overload + def warpRoi(self, src_size: cv2.typing.Size, K: cv2.typing.MatLike, R: cv2.typing.MatLike) -> cv2.typing.Rect: ... + @_typing.overload + def warpRoi(self, src_size: cv2.typing.Size, K: UMat, R: UMat) -> cv2.typing.Rect: ... + + def getScale(self) -> float: ... + + def setScale(self, arg1: float) -> None: ... + + +class WarperCreator: + ... + +class BackgroundSubtractor(Algorithm): + # Functions + @_typing.overload + def apply(self, image: cv2.typing.MatLike, fgmask: cv2.typing.MatLike | None = ..., learningRate: float = ...) -> cv2.typing.MatLike: ... + @_typing.overload + def apply(self, image: UMat, fgmask: UMat | None = ..., learningRate: float = ...) -> UMat: ... + + @_typing.overload + def getBackgroundImage(self, backgroundImage: cv2.typing.MatLike | None = ...) -> cv2.typing.MatLike: ... + @_typing.overload + def getBackgroundImage(self, backgroundImage: UMat | None = ...) -> UMat: ... + + +class BackgroundSubtractorMOG2(BackgroundSubtractor): + # Functions + def getHistory(self) -> int: ... + + def setHistory(self, history: int) -> None: ... + + def getNMixtures(self) -> int: ... + + def setNMixtures(self, nmixtures: int) -> None: ... + + def getBackgroundRatio(self) -> float: ... + + def setBackgroundRatio(self, ratio: float) -> None: ... + + def getVarThreshold(self) -> float: ... + + def setVarThreshold(self, varThreshold: float) -> None: ... + + def getVarThresholdGen(self) -> float: ... + + def setVarThresholdGen(self, varThresholdGen: float) -> None: ... + + def getVarInit(self) -> float: ... + + def setVarInit(self, varInit: float) -> None: ... + + def getVarMin(self) -> float: ... + + def setVarMin(self, varMin: float) -> None: ... + + def getVarMax(self) -> float: ... + + def setVarMax(self, varMax: float) -> None: ... + + def getComplexityReductionThreshold(self) -> float: ... + + def setComplexityReductionThreshold(self, ct: float) -> None: ... + + def getDetectShadows(self) -> bool: ... + + def setDetectShadows(self, detectShadows: bool) -> None: ... + + def getShadowValue(self) -> int: ... + + def setShadowValue(self, value: int) -> None: ... + + def getShadowThreshold(self) -> float: ... + + def setShadowThreshold(self, threshold: float) -> None: ... + + @_typing.overload + def apply(self, image: cv2.typing.MatLike, fgmask: cv2.typing.MatLike | None = ..., learningRate: float = ...) -> cv2.typing.MatLike: ... + @_typing.overload + def apply(self, image: UMat, fgmask: UMat | None = ..., learningRate: float = ...) -> UMat: ... + + +class BackgroundSubtractorKNN(BackgroundSubtractor): + # Functions + def getHistory(self) -> int: ... + + def setHistory(self, history: int) -> None: ... + + def getNSamples(self) -> int: ... + + def setNSamples(self, _nN: int) -> None: ... + + def getDist2Threshold(self) -> float: ... + + def setDist2Threshold(self, _dist2Threshold: float) -> None: ... + + def getkNNSamples(self) -> int: ... + + def setkNNSamples(self, _nkNN: int) -> None: ... + + def getDetectShadows(self) -> bool: ... + + def setDetectShadows(self, detectShadows: bool) -> None: ... + + def getShadowValue(self) -> int: ... + + def setShadowValue(self, value: int) -> None: ... + + def getShadowThreshold(self) -> float: ... + + def setShadowThreshold(self, threshold: float) -> None: ... + + +class KalmanFilter: + statePre: cv2.typing.MatLike + statePost: cv2.typing.MatLike + transitionMatrix: cv2.typing.MatLike + controlMatrix: cv2.typing.MatLike + measurementMatrix: cv2.typing.MatLike + processNoiseCov: cv2.typing.MatLike + measurementNoiseCov: cv2.typing.MatLike + errorCovPre: cv2.typing.MatLike + gain: cv2.typing.MatLike + errorCovPost: cv2.typing.MatLike + + # Functions + @_typing.overload + def __init__(self) -> None: ... + @_typing.overload + def __init__(self, dynamParams: int, measureParams: int, controlParams: int = ..., type: int = ...) -> None: ... + + def predict(self, control: cv2.typing.MatLike | None = ...) -> cv2.typing.MatLike: ... + + def correct(self, measurement: cv2.typing.MatLike) -> cv2.typing.MatLike: ... + + +class DenseOpticalFlow(Algorithm): + # Functions + @_typing.overload + def calc(self, I0: cv2.typing.MatLike, I1: cv2.typing.MatLike, flow: cv2.typing.MatLike) -> cv2.typing.MatLike: ... + @_typing.overload + def calc(self, I0: UMat, I1: UMat, flow: UMat) -> UMat: ... + + def collectGarbage(self) -> None: ... + + +class SparseOpticalFlow(Algorithm): + # Functions + @_typing.overload + def calc(self, prevImg: cv2.typing.MatLike, nextImg: cv2.typing.MatLike, prevPts: cv2.typing.MatLike, nextPts: cv2.typing.MatLike, status: cv2.typing.MatLike | None = ..., err: cv2.typing.MatLike | None = ...) -> tuple[cv2.typing.MatLike, cv2.typing.MatLike, cv2.typing.MatLike]: ... + @_typing.overload + def calc(self, prevImg: UMat, nextImg: UMat, prevPts: UMat, nextPts: UMat, status: UMat | None = ..., err: UMat | None = ...) -> tuple[UMat, UMat, UMat]: ... + + +class FarnebackOpticalFlow(DenseOpticalFlow): + # Functions + def getNumLevels(self) -> int: ... + + def setNumLevels(self, numLevels: int) -> None: ... + + def getPyrScale(self) -> float: ... + + def setPyrScale(self, pyrScale: float) -> None: ... + + def getFastPyramids(self) -> bool: ... + + def setFastPyramids(self, fastPyramids: bool) -> None: ... + + def getWinSize(self) -> int: ... + + def setWinSize(self, winSize: int) -> None: ... + + def getNumIters(self) -> int: ... + + def setNumIters(self, numIters: int) -> None: ... + + def getPolyN(self) -> int: ... + + def setPolyN(self, polyN: int) -> None: ... + + def getPolySigma(self) -> float: ... + + def setPolySigma(self, polySigma: float) -> None: ... + + def getFlags(self) -> int: ... + + def setFlags(self, flags: int) -> None: ... + + @classmethod + def create(cls, numLevels: int = ..., pyrScale: float = ..., fastPyramids: bool = ..., winSize: int = ..., numIters: int = ..., polyN: int = ..., polySigma: float = ..., flags: int = ...) -> FarnebackOpticalFlow: ... + + +class VariationalRefinement(DenseOpticalFlow): + # Functions + @_typing.overload + def calcUV(self, I0: cv2.typing.MatLike, I1: cv2.typing.MatLike, flow_u: cv2.typing.MatLike, flow_v: cv2.typing.MatLike) -> tuple[cv2.typing.MatLike, cv2.typing.MatLike]: ... + @_typing.overload + def calcUV(self, I0: UMat, I1: UMat, flow_u: UMat, flow_v: UMat) -> tuple[UMat, UMat]: ... + + def getFixedPointIterations(self) -> int: ... + + def setFixedPointIterations(self, val: int) -> None: ... + + def getSorIterations(self) -> int: ... + + def setSorIterations(self, val: int) -> None: ... + + def getOmega(self) -> float: ... + + def setOmega(self, val: float) -> None: ... + + def getAlpha(self) -> float: ... + + def setAlpha(self, val: float) -> None: ... + + def getDelta(self) -> float: ... + + def setDelta(self, val: float) -> None: ... + + def getGamma(self) -> float: ... + + def setGamma(self, val: float) -> None: ... + + def getEpsilon(self) -> float: ... + + def setEpsilon(self, val: float) -> None: ... + + @classmethod + def create(cls) -> VariationalRefinement: ... + + +class DISOpticalFlow(DenseOpticalFlow): + # Functions + def getFinestScale(self) -> int: ... + + def setFinestScale(self, val: int) -> None: ... + + def getPatchSize(self) -> int: ... + + def setPatchSize(self, val: int) -> None: ... + + def getPatchStride(self) -> int: ... + + def setPatchStride(self, val: int) -> None: ... + + def getGradientDescentIterations(self) -> int: ... + + def setGradientDescentIterations(self, val: int) -> None: ... + + def getVariationalRefinementIterations(self) -> int: ... + + def setVariationalRefinementIterations(self, val: int) -> None: ... + + def getVariationalRefinementAlpha(self) -> float: ... + + def setVariationalRefinementAlpha(self, val: float) -> None: ... + + def getVariationalRefinementDelta(self) -> float: ... + + def setVariationalRefinementDelta(self, val: float) -> None: ... + + def getVariationalRefinementGamma(self) -> float: ... + + def setVariationalRefinementGamma(self, val: float) -> None: ... + + def getVariationalRefinementEpsilon(self) -> float: ... + + def setVariationalRefinementEpsilon(self, val: float) -> None: ... + + def getUseMeanNormalization(self) -> bool: ... + + def setUseMeanNormalization(self, val: bool) -> None: ... + + def getUseSpatialPropagation(self) -> bool: ... + + def setUseSpatialPropagation(self, val: bool) -> None: ... + + @classmethod + def create(cls, preset: int = ...) -> DISOpticalFlow: ... + + +class SparsePyrLKOpticalFlow(SparseOpticalFlow): + # Functions + def getWinSize(self) -> cv2.typing.Size: ... + + def setWinSize(self, winSize: cv2.typing.Size) -> None: ... + + def getMaxLevel(self) -> int: ... + + def setMaxLevel(self, maxLevel: int) -> None: ... + + def getTermCriteria(self) -> cv2.typing.TermCriteria: ... + + def setTermCriteria(self, crit: cv2.typing.TermCriteria) -> None: ... + + def getFlags(self) -> int: ... + + def setFlags(self, flags: int) -> None: ... + + def getMinEigThreshold(self) -> float: ... + + def setMinEigThreshold(self, minEigThreshold: float) -> None: ... + + @classmethod + def create(cls, winSize: cv2.typing.Size = ..., maxLevel: int = ..., crit: cv2.typing.TermCriteria = ..., flags: int = ..., minEigThreshold: float = ...) -> SparsePyrLKOpticalFlow: ... + + +class Tracker: + # Functions + @_typing.overload + def init(self, image: cv2.typing.MatLike, boundingBox: cv2.typing.Rect) -> None: ... + @_typing.overload + def init(self, image: UMat, boundingBox: cv2.typing.Rect) -> None: ... + + @_typing.overload + def update(self, image: cv2.typing.MatLike) -> tuple[bool, cv2.typing.Rect]: ... + @_typing.overload + def update(self, image: UMat) -> tuple[bool, cv2.typing.Rect]: ... + + +class TrackerMIL(Tracker): + # Classes + class Params: + samplerInitInRadius: float + samplerInitMaxNegNum: int + samplerSearchWinSize: float + samplerTrackInRadius: float + samplerTrackMaxPosNum: int + samplerTrackMaxNegNum: int + featureSetNumFeatures: int + + # Functions + def __init__(self) -> None: ... + + + + # Functions + @classmethod + def create(cls, parameters: TrackerMIL.Params = ...) -> TrackerMIL: ... + + +class TrackerGOTURN(Tracker): + # Classes + class Params: + modelTxt: str + modelBin: str + + # Functions + def __init__(self) -> None: ... + + + + # Functions + @classmethod + def create(cls, parameters: TrackerGOTURN.Params = ...) -> TrackerGOTURN: ... + + +class TrackerDaSiamRPN(Tracker): + # Classes + class Params: + model: str + kernel_cls1: str + kernel_r1: str + backend: int + target: int + + # Functions + def __init__(self) -> None: ... + + + + # Functions + @classmethod + def create(cls, parameters: TrackerDaSiamRPN.Params = ...) -> TrackerDaSiamRPN: ... + + def getTrackingScore(self) -> float: ... + + +class TrackerNano(Tracker): + # Classes + class Params: + backbone: str + neckhead: str + backend: int + target: int + + # Functions + def __init__(self) -> None: ... + + + + # Functions + @classmethod + def create(cls, parameters: TrackerNano.Params = ...) -> TrackerNano: ... + + def getTrackingScore(self) -> float: ... + + +class TrackerVit(Tracker): + # Classes + class Params: + net: str + backend: int + target: int + meanvalue: cv2.typing.Scalar + stdvalue: cv2.typing.Scalar + tracking_score_threshold: float + + # Functions + def __init__(self) -> None: ... + + + + # Functions + @classmethod + def create(cls, parameters: TrackerVit.Params = ...) -> TrackerVit: ... + + def getTrackingScore(self) -> float: ... + + +class GArrayDesc: + ... + +class GComputation: + # Functions + @_typing.overload + def __init__(self, ins: cv2.typing.GProtoInputArgs, outs: cv2.typing.GProtoOutputArgs) -> None: ... + @_typing.overload + def __init__(self, in_: GMat, out: GMat) -> None: ... + @_typing.overload + def __init__(self, in_: GMat, out: GScalar) -> None: ... + @_typing.overload + def __init__(self, in1: GMat, in2: GMat, out: GMat) -> None: ... + + def apply(self, callback: cv2.typing.ExtractArgsCallback, args: _typing.Sequence[GCompileArg] = ...) -> _typing.Sequence[cv2.typing.GRunArg]: ... + + @_typing.overload + def compileStreaming(self, in_metas: _typing.Sequence[cv2.typing.GMetaArg], args: _typing.Sequence[GCompileArg] = ...) -> GStreamingCompiled: ... + @_typing.overload + def compileStreaming(self, args: _typing.Sequence[GCompileArg] = ...) -> GStreamingCompiled: ... + @_typing.overload + def compileStreaming(self, callback: cv2.typing.ExtractMetaCallback, args: _typing.Sequence[GCompileArg] = ...) -> GStreamingCompiled: ... + + +class GFrame: + # Functions + def __init__(self) -> None: ... + + +class GKernelPackage: + # Functions + def size(self) -> int: ... + + +class GMat: + # Functions + @_typing.overload + def __init__(self) -> None: ... + @_typing.overload + def __init__(self, m: cv2.typing.MatLike) -> None: ... + + +class GMatDesc: + @property + def depth(self) -> int: ... + @property + def chan(self) -> int: ... + @property + def size(self) -> cv2.typing.Size: ... + @property + def planar(self) -> bool: ... + @property + def dims(self) -> _typing.Sequence[int]: ... + + # Functions + @_typing.overload + def __init__(self, d: int, c: int, s: cv2.typing.Size, p: bool = ...) -> None: ... + @_typing.overload + def __init__(self, d: int, dd: _typing.Sequence[int]) -> None: ... + @_typing.overload + def __init__(self, d: int, dd: _typing.Sequence[int]) -> None: ... + @_typing.overload + def __init__(self) -> None: ... + + @_typing.overload + def withSizeDelta(self, delta: cv2.typing.Size) -> GMatDesc: ... + @_typing.overload + def withSizeDelta(self, dx: int, dy: int) -> GMatDesc: ... + + def withSize(self, sz: cv2.typing.Size) -> GMatDesc: ... + + def withDepth(self, ddepth: int) -> GMatDesc: ... + + def withType(self, ddepth: int, dchan: int) -> GMatDesc: ... + + @_typing.overload + def asPlanar(self) -> GMatDesc: ... + @_typing.overload + def asPlanar(self, planes: int) -> GMatDesc: ... + + def asInterleaved(self) -> GMatDesc: ... + + +class GOpaqueDesc: + ... + +class GScalar: + # Functions + @_typing.overload + def __init__(self) -> None: ... + @_typing.overload + def __init__(self, s: cv2.typing.Scalar) -> None: ... + + +class GScalarDesc: + ... + +class GStreamingCompiled: + # Functions + def __init__(self) -> None: ... + + def setSource(self, callback: cv2.typing.ExtractArgsCallback) -> None: ... + + def start(self) -> None: ... + + def pull(self) -> tuple[bool, _typing.Sequence[cv2.typing.GRunArg] | _typing.Sequence[cv2.typing.GOptRunArg]]: ... + + def stop(self) -> None: ... + + def running(self) -> bool: ... + + +class GOpaqueT: + # Functions + def __init__(self, type: cv2.gapi.ArgType) -> None: ... + + def type(self) -> cv2.gapi.ArgType: ... + + +class GArrayT: + # Functions + def __init__(self, type: cv2.gapi.ArgType) -> None: ... + + def type(self) -> cv2.gapi.ArgType: ... + + +class GCompileArg: + # Functions + @_typing.overload + def __init__(self, arg: GKernelPackage) -> None: ... + @_typing.overload + def __init__(self, arg: cv2.gapi.GNetPackage) -> None: ... + @_typing.overload + def __init__(self, arg: cv2.gapi.streaming.queue_capacity) -> None: ... + @_typing.overload + def __init__(self, arg: cv2.gapi.ot.ObjectTrackerParams) -> None: ... + + +class GInferInputs: + # Functions + def __init__(self) -> None: ... + + @_typing.overload + def setInput(self, name: str, value: GMat) -> GInferInputs: ... + @_typing.overload + def setInput(self, name: str, value: GFrame) -> GInferInputs: ... + + +class GInferListInputs: + # Functions + def __init__(self) -> None: ... + + @_typing.overload + def setInput(self, name: str, value: GArrayT) -> GInferListInputs: ... + @_typing.overload + def setInput(self, name: str, value: GArrayT) -> GInferListInputs: ... + + +class GInferOutputs: + # Functions + def __init__(self) -> None: ... + + def at(self, name: str) -> GMat: ... + + +class GInferListOutputs: + # Functions + def __init__(self) -> None: ... + + def at(self, name: str) -> GArrayT: ... + + +class error(Exception): + code: int + err: str + file: str + func: str + line: int + msg: str + + +# Functions +@_typing.overload +def CamShift(probImage: cv2.typing.MatLike, window: cv2.typing.Rect, criteria: cv2.typing.TermCriteria) -> tuple[cv2.typing.RotatedRect, cv2.typing.Rect]: ... +@_typing.overload +def CamShift(probImage: UMat, window: cv2.typing.Rect, criteria: cv2.typing.TermCriteria) -> tuple[cv2.typing.RotatedRect, cv2.typing.Rect]: ... + +@_typing.overload +def Canny(image: cv2.typing.MatLike, threshold1: float, threshold2: float, edges: cv2.typing.MatLike | None = ..., apertureSize: int = ..., L2gradient: bool = ...) -> cv2.typing.MatLike: ... +@_typing.overload +def Canny(image: UMat, threshold1: float, threshold2: float, edges: UMat | None = ..., apertureSize: int = ..., L2gradient: bool = ...) -> UMat: ... +@_typing.overload +def Canny(dx: cv2.typing.MatLike, dy: cv2.typing.MatLike, threshold1: float, threshold2: float, edges: cv2.typing.MatLike | None = ..., L2gradient: bool = ...) -> cv2.typing.MatLike: ... +@_typing.overload +def Canny(dx: UMat, dy: UMat, threshold1: float, threshold2: float, edges: UMat | None = ..., L2gradient: bool = ...) -> UMat: ... + +@_typing.overload +def EMD(signature1: cv2.typing.MatLike, signature2: cv2.typing.MatLike, distType: int, cost: cv2.typing.MatLike | None = ..., lowerBound: float | None = ..., flow: cv2.typing.MatLike | None = ...) -> tuple[float, float, cv2.typing.MatLike]: ... +@_typing.overload +def EMD(signature1: UMat, signature2: UMat, distType: int, cost: UMat | None = ..., lowerBound: float | None = ..., flow: UMat | None = ...) -> tuple[float, float, UMat]: ... + +@_typing.overload +def GaussianBlur(src: cv2.typing.MatLike, ksize: cv2.typing.Size, sigmaX: float, dst: cv2.typing.MatLike | None = ..., sigmaY: float = ..., borderType: int = ..., hint: AlgorithmHint = ...) -> cv2.typing.MatLike: ... +@_typing.overload +def GaussianBlur(src: UMat, ksize: cv2.typing.Size, sigmaX: float, dst: UMat | None = ..., sigmaY: float = ..., borderType: int = ..., hint: AlgorithmHint = ...) -> UMat: ... + +@_typing.overload +def HoughCircles(image: cv2.typing.MatLike, method: int, dp: float, minDist: float, circles: cv2.typing.MatLike | None = ..., param1: float = ..., param2: float = ..., minRadius: int = ..., maxRadius: int = ...) -> cv2.typing.MatLike: ... +@_typing.overload +def HoughCircles(image: UMat, method: int, dp: float, minDist: float, circles: UMat | None = ..., param1: float = ..., param2: float = ..., minRadius: int = ..., maxRadius: int = ...) -> UMat: ... + +@_typing.overload +def HoughLines(image: cv2.typing.MatLike, rho: float, theta: float, threshold: int, lines: cv2.typing.MatLike | None = ..., srn: float = ..., stn: float = ..., min_theta: float = ..., max_theta: float = ..., use_edgeval: bool = ...) -> cv2.typing.MatLike: ... +@_typing.overload +def HoughLines(image: UMat, rho: float, theta: float, threshold: int, lines: UMat | None = ..., srn: float = ..., stn: float = ..., min_theta: float = ..., max_theta: float = ..., use_edgeval: bool = ...) -> UMat: ... + +@_typing.overload +def HoughLinesP(image: cv2.typing.MatLike, rho: float, theta: float, threshold: int, lines: cv2.typing.MatLike | None = ..., minLineLength: float = ..., maxLineGap: float = ...) -> cv2.typing.MatLike: ... +@_typing.overload +def HoughLinesP(image: UMat, rho: float, theta: float, threshold: int, lines: UMat | None = ..., minLineLength: float = ..., maxLineGap: float = ...) -> UMat: ... + +@_typing.overload +def HoughLinesPointSet(point: cv2.typing.MatLike, lines_max: int, threshold: int, min_rho: float, max_rho: float, rho_step: float, min_theta: float, max_theta: float, theta_step: float, lines: cv2.typing.MatLike | None = ...) -> cv2.typing.MatLike: ... +@_typing.overload +def HoughLinesPointSet(point: UMat, lines_max: int, threshold: int, min_rho: float, max_rho: float, rho_step: float, min_theta: float, max_theta: float, theta_step: float, lines: UMat | None = ...) -> UMat: ... + +@_typing.overload +def HoughLinesWithAccumulator(image: cv2.typing.MatLike, rho: float, theta: float, threshold: int, lines: cv2.typing.MatLike | None = ..., srn: float = ..., stn: float = ..., min_theta: float = ..., max_theta: float = ...) -> cv2.typing.MatLike: ... +@_typing.overload +def HoughLinesWithAccumulator(image: UMat, rho: float, theta: float, threshold: int, lines: UMat | None = ..., srn: float = ..., stn: float = ..., min_theta: float = ..., max_theta: float = ...) -> UMat: ... + +@_typing.overload +def HuMoments(m: cv2.typing.Moments, hu: cv2.typing.MatLike | None = ...) -> cv2.typing.MatLike: ... +@_typing.overload +def HuMoments(m: cv2.typing.Moments, hu: UMat | None = ...) -> UMat: ... + +@_typing.overload +def LUT(src: cv2.typing.MatLike, lut: cv2.typing.MatLike, dst: cv2.typing.MatLike | None = ...) -> cv2.typing.MatLike: ... +@_typing.overload +def LUT(src: UMat, lut: UMat, dst: UMat | None = ...) -> UMat: ... + +@_typing.overload +def Laplacian(src: cv2.typing.MatLike, ddepth: int, dst: cv2.typing.MatLike | None = ..., ksize: int = ..., scale: float = ..., delta: float = ..., borderType: int = ...) -> cv2.typing.MatLike: ... +@_typing.overload +def Laplacian(src: UMat, ddepth: int, dst: UMat | None = ..., ksize: int = ..., scale: float = ..., delta: float = ..., borderType: int = ...) -> UMat: ... + +@_typing.overload +def Mahalanobis(v1: cv2.typing.MatLike, v2: cv2.typing.MatLike, icovar: cv2.typing.MatLike) -> float: ... +@_typing.overload +def Mahalanobis(v1: UMat, v2: UMat, icovar: UMat) -> float: ... + +@_typing.overload +def PCABackProject(data: cv2.typing.MatLike, mean: cv2.typing.MatLike, eigenvectors: cv2.typing.MatLike, result: cv2.typing.MatLike | None = ...) -> cv2.typing.MatLike: ... +@_typing.overload +def PCABackProject(data: UMat, mean: UMat, eigenvectors: UMat, result: UMat | None = ...) -> UMat: ... + +@_typing.overload +def PCACompute(data: cv2.typing.MatLike, mean: cv2.typing.MatLike, eigenvectors: cv2.typing.MatLike | None = ..., maxComponents: int = ...) -> tuple[cv2.typing.MatLike, cv2.typing.MatLike]: ... +@_typing.overload +def PCACompute(data: UMat, mean: UMat, eigenvectors: UMat | None = ..., maxComponents: int = ...) -> tuple[UMat, UMat]: ... +@_typing.overload +def PCACompute(data: cv2.typing.MatLike, mean: cv2.typing.MatLike, retainedVariance: float, eigenvectors: cv2.typing.MatLike | None = ...) -> tuple[cv2.typing.MatLike, cv2.typing.MatLike]: ... +@_typing.overload +def PCACompute(data: UMat, mean: UMat, retainedVariance: float, eigenvectors: UMat | None = ...) -> tuple[UMat, UMat]: ... + +@_typing.overload +def PCACompute2(data: cv2.typing.MatLike, mean: cv2.typing.MatLike, eigenvectors: cv2.typing.MatLike | None = ..., eigenvalues: cv2.typing.MatLike | None = ..., maxComponents: int = ...) -> tuple[cv2.typing.MatLike, cv2.typing.MatLike, cv2.typing.MatLike]: ... +@_typing.overload +def PCACompute2(data: UMat, mean: UMat, eigenvectors: UMat | None = ..., eigenvalues: UMat | None = ..., maxComponents: int = ...) -> tuple[UMat, UMat, UMat]: ... +@_typing.overload +def PCACompute2(data: cv2.typing.MatLike, mean: cv2.typing.MatLike, retainedVariance: float, eigenvectors: cv2.typing.MatLike | None = ..., eigenvalues: cv2.typing.MatLike | None = ...) -> tuple[cv2.typing.MatLike, cv2.typing.MatLike, cv2.typing.MatLike]: ... +@_typing.overload +def PCACompute2(data: UMat, mean: UMat, retainedVariance: float, eigenvectors: UMat | None = ..., eigenvalues: UMat | None = ...) -> tuple[UMat, UMat, UMat]: ... + +@_typing.overload +def PCAProject(data: cv2.typing.MatLike, mean: cv2.typing.MatLike, eigenvectors: cv2.typing.MatLike, result: cv2.typing.MatLike | None = ...) -> cv2.typing.MatLike: ... +@_typing.overload +def PCAProject(data: UMat, mean: UMat, eigenvectors: UMat, result: UMat | None = ...) -> UMat: ... + +@_typing.overload +def PSNR(src1: cv2.typing.MatLike, src2: cv2.typing.MatLike, R: float = ...) -> float: ... +@_typing.overload +def PSNR(src1: UMat, src2: UMat, R: float = ...) -> float: ... + +@_typing.overload +def RQDecomp3x3(src: cv2.typing.MatLike, mtxR: cv2.typing.MatLike | None = ..., mtxQ: cv2.typing.MatLike | None = ..., Qx: cv2.typing.MatLike | None = ..., Qy: cv2.typing.MatLike | None = ..., Qz: cv2.typing.MatLike | None = ...) -> tuple[cv2.typing.Vec3d, cv2.typing.MatLike, cv2.typing.MatLike, cv2.typing.MatLike, cv2.typing.MatLike, cv2.typing.MatLike]: ... +@_typing.overload +def RQDecomp3x3(src: UMat, mtxR: UMat | None = ..., mtxQ: UMat | None = ..., Qx: UMat | None = ..., Qy: UMat | None = ..., Qz: UMat | None = ...) -> tuple[cv2.typing.Vec3d, UMat, UMat, UMat, UMat, UMat]: ... + +@_typing.overload +def Rodrigues(src: cv2.typing.MatLike, dst: cv2.typing.MatLike | None = ..., jacobian: cv2.typing.MatLike | None = ...) -> tuple[cv2.typing.MatLike, cv2.typing.MatLike]: ... +@_typing.overload +def Rodrigues(src: UMat, dst: UMat | None = ..., jacobian: UMat | None = ...) -> tuple[UMat, UMat]: ... + +@_typing.overload +def SVBackSubst(w: cv2.typing.MatLike, u: cv2.typing.MatLike, vt: cv2.typing.MatLike, rhs: cv2.typing.MatLike, dst: cv2.typing.MatLike | None = ...) -> cv2.typing.MatLike: ... +@_typing.overload +def SVBackSubst(w: UMat, u: UMat, vt: UMat, rhs: UMat, dst: UMat | None = ...) -> UMat: ... + +@_typing.overload +def SVDecomp(src: cv2.typing.MatLike, w: cv2.typing.MatLike | None = ..., u: cv2.typing.MatLike | None = ..., vt: cv2.typing.MatLike | None = ..., flags: int = ...) -> tuple[cv2.typing.MatLike, cv2.typing.MatLike, cv2.typing.MatLike]: ... +@_typing.overload +def SVDecomp(src: UMat, w: UMat | None = ..., u: UMat | None = ..., vt: UMat | None = ..., flags: int = ...) -> tuple[UMat, UMat, UMat]: ... + +@_typing.overload +def Scharr(src: cv2.typing.MatLike, ddepth: int, dx: int, dy: int, dst: cv2.typing.MatLike | None = ..., scale: float = ..., delta: float = ..., borderType: int = ...) -> cv2.typing.MatLike: ... +@_typing.overload +def Scharr(src: UMat, ddepth: int, dx: int, dy: int, dst: UMat | None = ..., scale: float = ..., delta: float = ..., borderType: int = ...) -> UMat: ... + +@_typing.overload +def Sobel(src: cv2.typing.MatLike, ddepth: int, dx: int, dy: int, dst: cv2.typing.MatLike | None = ..., ksize: int = ..., scale: float = ..., delta: float = ..., borderType: int = ...) -> cv2.typing.MatLike: ... +@_typing.overload +def Sobel(src: UMat, ddepth: int, dx: int, dy: int, dst: UMat | None = ..., ksize: int = ..., scale: float = ..., delta: float = ..., borderType: int = ...) -> UMat: ... + +@_typing.overload +def absdiff(src1: cv2.typing.MatLike, src2: cv2.typing.MatLike, dst: cv2.typing.MatLike | None = ...) -> cv2.typing.MatLike: ... +@_typing.overload +def absdiff(src1: UMat, src2: UMat, dst: UMat | None = ...) -> UMat: ... + +@_typing.overload +def accumulate(src: cv2.typing.MatLike, dst: cv2.typing.MatLike, mask: cv2.typing.MatLike | None = ...) -> cv2.typing.MatLike: ... +@_typing.overload +def accumulate(src: UMat, dst: UMat, mask: UMat | None = ...) -> UMat: ... + +@_typing.overload +def accumulateProduct(src1: cv2.typing.MatLike, src2: cv2.typing.MatLike, dst: cv2.typing.MatLike, mask: cv2.typing.MatLike | None = ...) -> cv2.typing.MatLike: ... +@_typing.overload +def accumulateProduct(src1: UMat, src2: UMat, dst: UMat, mask: UMat | None = ...) -> UMat: ... + +@_typing.overload +def accumulateSquare(src: cv2.typing.MatLike, dst: cv2.typing.MatLike, mask: cv2.typing.MatLike | None = ...) -> cv2.typing.MatLike: ... +@_typing.overload +def accumulateSquare(src: UMat, dst: UMat, mask: UMat | None = ...) -> UMat: ... + +@_typing.overload +def accumulateWeighted(src: cv2.typing.MatLike, dst: cv2.typing.MatLike, alpha: float, mask: cv2.typing.MatLike | None = ...) -> cv2.typing.MatLike: ... +@_typing.overload +def accumulateWeighted(src: UMat, dst: UMat, alpha: float, mask: UMat | None = ...) -> UMat: ... + +@_typing.overload +def adaptiveThreshold(src: cv2.typing.MatLike, maxValue: float, adaptiveMethod: int, thresholdType: int, blockSize: int, C: float, dst: cv2.typing.MatLike | None = ...) -> cv2.typing.MatLike: ... +@_typing.overload +def adaptiveThreshold(src: UMat, maxValue: float, adaptiveMethod: int, thresholdType: int, blockSize: int, C: float, dst: UMat | None = ...) -> UMat: ... + +@_typing.overload +def add(src1: cv2.typing.MatLike, src2: cv2.typing.MatLike, dst: cv2.typing.MatLike | None = ..., mask: cv2.typing.MatLike | None = ..., dtype: int = ...) -> cv2.typing.MatLike: ... +@_typing.overload +def add(src1: UMat, src2: UMat, dst: UMat | None = ..., mask: UMat | None = ..., dtype: int = ...) -> UMat: ... + +def addText(img: cv2.typing.MatLike, text: str, org: cv2.typing.Point, nameFont: str, pointSize: int = ..., color: cv2.typing.Scalar = ..., weight: int = ..., style: int = ..., spacing: int = ...) -> None: ... + +@_typing.overload +def addWeighted(src1: cv2.typing.MatLike, alpha: float, src2: cv2.typing.MatLike, beta: float, gamma: float, dst: cv2.typing.MatLike | None = ..., dtype: int = ...) -> cv2.typing.MatLike: ... +@_typing.overload +def addWeighted(src1: UMat, alpha: float, src2: UMat, beta: float, gamma: float, dst: UMat | None = ..., dtype: int = ...) -> UMat: ... + +@_typing.overload +def applyColorMap(src: cv2.typing.MatLike, colormap: int, dst: cv2.typing.MatLike | None = ...) -> cv2.typing.MatLike: ... +@_typing.overload +def applyColorMap(src: UMat, colormap: int, dst: UMat | None = ...) -> UMat: ... +@_typing.overload +def applyColorMap(src: cv2.typing.MatLike, userColor: cv2.typing.MatLike, dst: cv2.typing.MatLike | None = ...) -> cv2.typing.MatLike: ... +@_typing.overload +def applyColorMap(src: UMat, userColor: UMat, dst: UMat | None = ...) -> UMat: ... + +@_typing.overload +def approxPolyDP(curve: cv2.typing.MatLike, epsilon: float, closed: bool, approxCurve: cv2.typing.MatLike | None = ...) -> cv2.typing.MatLike: ... +@_typing.overload +def approxPolyDP(curve: UMat, epsilon: float, closed: bool, approxCurve: UMat | None = ...) -> UMat: ... + +@_typing.overload +def approxPolyN(curve: cv2.typing.MatLike, nsides: int, approxCurve: cv2.typing.MatLike | None = ..., epsilon_percentage: float = ..., ensure_convex: bool = ...) -> cv2.typing.MatLike: ... +@_typing.overload +def approxPolyN(curve: UMat, nsides: int, approxCurve: UMat | None = ..., epsilon_percentage: float = ..., ensure_convex: bool = ...) -> UMat: ... + +@_typing.overload +def arcLength(curve: cv2.typing.MatLike, closed: bool) -> float: ... +@_typing.overload +def arcLength(curve: UMat, closed: bool) -> float: ... + +@_typing.overload +def arrowedLine(img: cv2.typing.MatLike, pt1: cv2.typing.Point, pt2: cv2.typing.Point, color: cv2.typing.Scalar, thickness: int = ..., line_type: int = ..., shift: int = ..., tipLength: float = ...) -> cv2.typing.MatLike: ... +@_typing.overload +def arrowedLine(img: UMat, pt1: cv2.typing.Point, pt2: cv2.typing.Point, color: cv2.typing.Scalar, thickness: int = ..., line_type: int = ..., shift: int = ..., tipLength: float = ...) -> UMat: ... + +@_typing.overload +def batchDistance(src1: cv2.typing.MatLike, src2: cv2.typing.MatLike, dtype: int, dist: cv2.typing.MatLike | None = ..., nidx: cv2.typing.MatLike | None = ..., normType: int = ..., K: int = ..., mask: cv2.typing.MatLike | None = ..., update: int = ..., crosscheck: bool = ...) -> tuple[cv2.typing.MatLike, cv2.typing.MatLike]: ... +@_typing.overload +def batchDistance(src1: UMat, src2: UMat, dtype: int, dist: UMat | None = ..., nidx: UMat | None = ..., normType: int = ..., K: int = ..., mask: UMat | None = ..., update: int = ..., crosscheck: bool = ...) -> tuple[UMat, UMat]: ... + +@_typing.overload +def bilateralFilter(src: cv2.typing.MatLike, d: int, sigmaColor: float, sigmaSpace: float, dst: cv2.typing.MatLike | None = ..., borderType: int = ...) -> cv2.typing.MatLike: ... +@_typing.overload +def bilateralFilter(src: UMat, d: int, sigmaColor: float, sigmaSpace: float, dst: UMat | None = ..., borderType: int = ...) -> UMat: ... + +@_typing.overload +def bitwise_and(src1: cv2.typing.MatLike, src2: cv2.typing.MatLike, dst: cv2.typing.MatLike | None = ..., mask: cv2.typing.MatLike | None = ...) -> cv2.typing.MatLike: ... +@_typing.overload +def bitwise_and(src1: UMat, src2: UMat, dst: UMat | None = ..., mask: UMat | None = ...) -> UMat: ... + +@_typing.overload +def bitwise_not(src: cv2.typing.MatLike, dst: cv2.typing.MatLike | None = ..., mask: cv2.typing.MatLike | None = ...) -> cv2.typing.MatLike: ... +@_typing.overload +def bitwise_not(src: UMat, dst: UMat | None = ..., mask: UMat | None = ...) -> UMat: ... + +@_typing.overload +def bitwise_or(src1: cv2.typing.MatLike, src2: cv2.typing.MatLike, dst: cv2.typing.MatLike | None = ..., mask: cv2.typing.MatLike | None = ...) -> cv2.typing.MatLike: ... +@_typing.overload +def bitwise_or(src1: UMat, src2: UMat, dst: UMat | None = ..., mask: UMat | None = ...) -> UMat: ... + +@_typing.overload +def bitwise_xor(src1: cv2.typing.MatLike, src2: cv2.typing.MatLike, dst: cv2.typing.MatLike | None = ..., mask: cv2.typing.MatLike | None = ...) -> cv2.typing.MatLike: ... +@_typing.overload +def bitwise_xor(src1: UMat, src2: UMat, dst: UMat | None = ..., mask: UMat | None = ...) -> UMat: ... + +@_typing.overload +def blendLinear(src1: cv2.typing.MatLike, src2: cv2.typing.MatLike, weights1: cv2.typing.MatLike, weights2: cv2.typing.MatLike, dst: cv2.typing.MatLike | None = ...) -> cv2.typing.MatLike: ... +@_typing.overload +def blendLinear(src1: UMat, src2: UMat, weights1: UMat, weights2: UMat, dst: UMat | None = ...) -> UMat: ... + +@_typing.overload +def blur(src: cv2.typing.MatLike, ksize: cv2.typing.Size, dst: cv2.typing.MatLike | None = ..., anchor: cv2.typing.Point = ..., borderType: int = ...) -> cv2.typing.MatLike: ... +@_typing.overload +def blur(src: UMat, ksize: cv2.typing.Size, dst: UMat | None = ..., anchor: cv2.typing.Point = ..., borderType: int = ...) -> UMat: ... + +def borderInterpolate(p: int, len: int, borderType: int) -> int: ... + +@_typing.overload +def boundingRect(array: cv2.typing.MatLike) -> cv2.typing.Rect: ... +@_typing.overload +def boundingRect(array: UMat) -> cv2.typing.Rect: ... + +@_typing.overload +def boxFilter(src: cv2.typing.MatLike, ddepth: int, ksize: cv2.typing.Size, dst: cv2.typing.MatLike | None = ..., anchor: cv2.typing.Point = ..., normalize: bool = ..., borderType: int = ...) -> cv2.typing.MatLike: ... +@_typing.overload +def boxFilter(src: UMat, ddepth: int, ksize: cv2.typing.Size, dst: UMat | None = ..., anchor: cv2.typing.Point = ..., normalize: bool = ..., borderType: int = ...) -> UMat: ... + +@_typing.overload +def boxPoints(box: cv2.typing.RotatedRect, points: cv2.typing.MatLike | None = ...) -> cv2.typing.MatLike: ... +@_typing.overload +def boxPoints(box: cv2.typing.RotatedRect, points: UMat | None = ...) -> UMat: ... + +@_typing.overload +def broadcast(src: cv2.typing.MatLike, shape: cv2.typing.MatLike, dst: cv2.typing.MatLike | None = ...) -> cv2.typing.MatLike: ... +@_typing.overload +def broadcast(src: UMat, shape: UMat, dst: UMat | None = ...) -> UMat: ... + +@_typing.overload +def buildOpticalFlowPyramid(img: cv2.typing.MatLike, winSize: cv2.typing.Size, maxLevel: int, pyramid: _typing.Sequence[cv2.typing.MatLike] | None = ..., withDerivatives: bool = ..., pyrBorder: int = ..., derivBorder: int = ..., tryReuseInputImage: bool = ...) -> tuple[int, _typing.Sequence[cv2.typing.MatLike]]: ... +@_typing.overload +def buildOpticalFlowPyramid(img: UMat, winSize: cv2.typing.Size, maxLevel: int, pyramid: _typing.Sequence[UMat] | None = ..., withDerivatives: bool = ..., pyrBorder: int = ..., derivBorder: int = ..., tryReuseInputImage: bool = ...) -> tuple[int, _typing.Sequence[UMat]]: ... + +@_typing.overload +def calcBackProject(images: _typing.Sequence[cv2.typing.MatLike], channels: _typing.Sequence[int], hist: cv2.typing.MatLike, ranges: _typing.Sequence[float], scale: float, dst: cv2.typing.MatLike | None = ...) -> cv2.typing.MatLike: ... +@_typing.overload +def calcBackProject(images: _typing.Sequence[UMat], channels: _typing.Sequence[int], hist: UMat, ranges: _typing.Sequence[float], scale: float, dst: UMat | None = ...) -> UMat: ... + +@_typing.overload +def calcCovarMatrix(samples: cv2.typing.MatLike, mean: cv2.typing.MatLike, flags: int, covar: cv2.typing.MatLike | None = ..., ctype: int = ...) -> tuple[cv2.typing.MatLike, cv2.typing.MatLike]: ... +@_typing.overload +def calcCovarMatrix(samples: UMat, mean: UMat, flags: int, covar: UMat | None = ..., ctype: int = ...) -> tuple[UMat, UMat]: ... + +@_typing.overload +def calcHist(images: _typing.Sequence[cv2.typing.MatLike], channels: _typing.Sequence[int], mask: cv2.typing.MatLike | None, histSize: _typing.Sequence[int], ranges: _typing.Sequence[float], hist: cv2.typing.MatLike | None = ..., accumulate: bool = ...) -> cv2.typing.MatLike: ... +@_typing.overload +def calcHist(images: _typing.Sequence[UMat], channels: _typing.Sequence[int], mask: UMat | None, histSize: _typing.Sequence[int], ranges: _typing.Sequence[float], hist: UMat | None = ..., accumulate: bool = ...) -> UMat: ... + +@_typing.overload +def calcOpticalFlowFarneback(prev: cv2.typing.MatLike, next: cv2.typing.MatLike, flow: cv2.typing.MatLike, pyr_scale: float, levels: int, winsize: int, iterations: int, poly_n: int, poly_sigma: float, flags: int) -> cv2.typing.MatLike: ... +@_typing.overload +def calcOpticalFlowFarneback(prev: UMat, next: UMat, flow: UMat, pyr_scale: float, levels: int, winsize: int, iterations: int, poly_n: int, poly_sigma: float, flags: int) -> UMat: ... + +@_typing.overload +def calcOpticalFlowPyrLK(prevImg: cv2.typing.MatLike, nextImg: cv2.typing.MatLike, prevPts: cv2.typing.MatLike, nextPts: cv2.typing.MatLike, status: cv2.typing.MatLike | None = ..., err: cv2.typing.MatLike | None = ..., winSize: cv2.typing.Size = ..., maxLevel: int = ..., criteria: cv2.typing.TermCriteria = ..., flags: int = ..., minEigThreshold: float = ...) -> tuple[cv2.typing.MatLike, cv2.typing.MatLike, cv2.typing.MatLike]: ... +@_typing.overload +def calcOpticalFlowPyrLK(prevImg: UMat, nextImg: UMat, prevPts: UMat, nextPts: UMat, status: UMat | None = ..., err: UMat | None = ..., winSize: cv2.typing.Size = ..., maxLevel: int = ..., criteria: cv2.typing.TermCriteria = ..., flags: int = ..., minEigThreshold: float = ...) -> tuple[UMat, UMat, UMat]: ... + +@_typing.overload +def calibrateCamera(objectPoints: _typing.Sequence[cv2.typing.MatLike], imagePoints: _typing.Sequence[cv2.typing.MatLike], imageSize: cv2.typing.Size, cameraMatrix: cv2.typing.MatLike, distCoeffs: cv2.typing.MatLike, rvecs: _typing.Sequence[cv2.typing.MatLike] | None = ..., tvecs: _typing.Sequence[cv2.typing.MatLike] | None = ..., flags: int = ..., criteria: cv2.typing.TermCriteria = ...) -> tuple[float, cv2.typing.MatLike, cv2.typing.MatLike, _typing.Sequence[cv2.typing.MatLike], _typing.Sequence[cv2.typing.MatLike]]: ... +@_typing.overload +def calibrateCamera(objectPoints: _typing.Sequence[UMat], imagePoints: _typing.Sequence[UMat], imageSize: cv2.typing.Size, cameraMatrix: UMat, distCoeffs: UMat, rvecs: _typing.Sequence[UMat] | None = ..., tvecs: _typing.Sequence[UMat] | None = ..., flags: int = ..., criteria: cv2.typing.TermCriteria = ...) -> tuple[float, UMat, UMat, _typing.Sequence[UMat], _typing.Sequence[UMat]]: ... + +@_typing.overload +def calibrateCameraExtended(objectPoints: _typing.Sequence[cv2.typing.MatLike], imagePoints: _typing.Sequence[cv2.typing.MatLike], imageSize: cv2.typing.Size, cameraMatrix: cv2.typing.MatLike, distCoeffs: cv2.typing.MatLike, rvecs: _typing.Sequence[cv2.typing.MatLike] | None = ..., tvecs: _typing.Sequence[cv2.typing.MatLike] | None = ..., stdDeviationsIntrinsics: cv2.typing.MatLike | None = ..., stdDeviationsExtrinsics: cv2.typing.MatLike | None = ..., perViewErrors: cv2.typing.MatLike | None = ..., flags: int = ..., criteria: cv2.typing.TermCriteria = ...) -> tuple[float, cv2.typing.MatLike, cv2.typing.MatLike, _typing.Sequence[cv2.typing.MatLike], _typing.Sequence[cv2.typing.MatLike], cv2.typing.MatLike, cv2.typing.MatLike, cv2.typing.MatLike]: ... +@_typing.overload +def calibrateCameraExtended(objectPoints: _typing.Sequence[UMat], imagePoints: _typing.Sequence[UMat], imageSize: cv2.typing.Size, cameraMatrix: UMat, distCoeffs: UMat, rvecs: _typing.Sequence[UMat] | None = ..., tvecs: _typing.Sequence[UMat] | None = ..., stdDeviationsIntrinsics: UMat | None = ..., stdDeviationsExtrinsics: UMat | None = ..., perViewErrors: UMat | None = ..., flags: int = ..., criteria: cv2.typing.TermCriteria = ...) -> tuple[float, UMat, UMat, _typing.Sequence[UMat], _typing.Sequence[UMat], UMat, UMat, UMat]: ... + +@_typing.overload +def calibrateCameraRO(objectPoints: _typing.Sequence[cv2.typing.MatLike], imagePoints: _typing.Sequence[cv2.typing.MatLike], imageSize: cv2.typing.Size, iFixedPoint: int, cameraMatrix: cv2.typing.MatLike, distCoeffs: cv2.typing.MatLike, rvecs: _typing.Sequence[cv2.typing.MatLike] | None = ..., tvecs: _typing.Sequence[cv2.typing.MatLike] | None = ..., newObjPoints: cv2.typing.MatLike | None = ..., flags: int = ..., criteria: cv2.typing.TermCriteria = ...) -> tuple[float, cv2.typing.MatLike, cv2.typing.MatLike, _typing.Sequence[cv2.typing.MatLike], _typing.Sequence[cv2.typing.MatLike], cv2.typing.MatLike]: ... +@_typing.overload +def calibrateCameraRO(objectPoints: _typing.Sequence[UMat], imagePoints: _typing.Sequence[UMat], imageSize: cv2.typing.Size, iFixedPoint: int, cameraMatrix: UMat, distCoeffs: UMat, rvecs: _typing.Sequence[UMat] | None = ..., tvecs: _typing.Sequence[UMat] | None = ..., newObjPoints: UMat | None = ..., flags: int = ..., criteria: cv2.typing.TermCriteria = ...) -> tuple[float, UMat, UMat, _typing.Sequence[UMat], _typing.Sequence[UMat], UMat]: ... + +@_typing.overload +def calibrateCameraROExtended(objectPoints: _typing.Sequence[cv2.typing.MatLike], imagePoints: _typing.Sequence[cv2.typing.MatLike], imageSize: cv2.typing.Size, iFixedPoint: int, cameraMatrix: cv2.typing.MatLike, distCoeffs: cv2.typing.MatLike, rvecs: _typing.Sequence[cv2.typing.MatLike] | None = ..., tvecs: _typing.Sequence[cv2.typing.MatLike] | None = ..., newObjPoints: cv2.typing.MatLike | None = ..., stdDeviationsIntrinsics: cv2.typing.MatLike | None = ..., stdDeviationsExtrinsics: cv2.typing.MatLike | None = ..., stdDeviationsObjPoints: cv2.typing.MatLike | None = ..., perViewErrors: cv2.typing.MatLike | None = ..., flags: int = ..., criteria: cv2.typing.TermCriteria = ...) -> tuple[float, cv2.typing.MatLike, cv2.typing.MatLike, _typing.Sequence[cv2.typing.MatLike], _typing.Sequence[cv2.typing.MatLike], cv2.typing.MatLike, cv2.typing.MatLike, cv2.typing.MatLike, cv2.typing.MatLike, cv2.typing.MatLike]: ... +@_typing.overload +def calibrateCameraROExtended(objectPoints: _typing.Sequence[UMat], imagePoints: _typing.Sequence[UMat], imageSize: cv2.typing.Size, iFixedPoint: int, cameraMatrix: UMat, distCoeffs: UMat, rvecs: _typing.Sequence[UMat] | None = ..., tvecs: _typing.Sequence[UMat] | None = ..., newObjPoints: UMat | None = ..., stdDeviationsIntrinsics: UMat | None = ..., stdDeviationsExtrinsics: UMat | None = ..., stdDeviationsObjPoints: UMat | None = ..., perViewErrors: UMat | None = ..., flags: int = ..., criteria: cv2.typing.TermCriteria = ...) -> tuple[float, UMat, UMat, _typing.Sequence[UMat], _typing.Sequence[UMat], UMat, UMat, UMat, UMat, UMat]: ... + +@_typing.overload +def calibrateHandEye(R_gripper2base: _typing.Sequence[cv2.typing.MatLike], t_gripper2base: _typing.Sequence[cv2.typing.MatLike], R_target2cam: _typing.Sequence[cv2.typing.MatLike], t_target2cam: _typing.Sequence[cv2.typing.MatLike], R_cam2gripper: cv2.typing.MatLike | None = ..., t_cam2gripper: cv2.typing.MatLike | None = ..., method: HandEyeCalibrationMethod = ...) -> tuple[cv2.typing.MatLike, cv2.typing.MatLike]: ... +@_typing.overload +def calibrateHandEye(R_gripper2base: _typing.Sequence[UMat], t_gripper2base: _typing.Sequence[UMat], R_target2cam: _typing.Sequence[UMat], t_target2cam: _typing.Sequence[UMat], R_cam2gripper: UMat | None = ..., t_cam2gripper: UMat | None = ..., method: HandEyeCalibrationMethod = ...) -> tuple[UMat, UMat]: ... + +@_typing.overload +def calibrateRobotWorldHandEye(R_world2cam: _typing.Sequence[cv2.typing.MatLike], t_world2cam: _typing.Sequence[cv2.typing.MatLike], R_base2gripper: _typing.Sequence[cv2.typing.MatLike], t_base2gripper: _typing.Sequence[cv2.typing.MatLike], R_base2world: cv2.typing.MatLike | None = ..., t_base2world: cv2.typing.MatLike | None = ..., R_gripper2cam: cv2.typing.MatLike | None = ..., t_gripper2cam: cv2.typing.MatLike | None = ..., method: RobotWorldHandEyeCalibrationMethod = ...) -> tuple[cv2.typing.MatLike, cv2.typing.MatLike, cv2.typing.MatLike, cv2.typing.MatLike]: ... +@_typing.overload +def calibrateRobotWorldHandEye(R_world2cam: _typing.Sequence[UMat], t_world2cam: _typing.Sequence[UMat], R_base2gripper: _typing.Sequence[UMat], t_base2gripper: _typing.Sequence[UMat], R_base2world: UMat | None = ..., t_base2world: UMat | None = ..., R_gripper2cam: UMat | None = ..., t_gripper2cam: UMat | None = ..., method: RobotWorldHandEyeCalibrationMethod = ...) -> tuple[UMat, UMat, UMat, UMat]: ... + +@_typing.overload +def calibrationMatrixValues(cameraMatrix: cv2.typing.MatLike, imageSize: cv2.typing.Size, apertureWidth: float, apertureHeight: float) -> tuple[float, float, float, cv2.typing.Point2d, float]: ... +@_typing.overload +def calibrationMatrixValues(cameraMatrix: UMat, imageSize: cv2.typing.Size, apertureWidth: float, apertureHeight: float) -> tuple[float, float, float, cv2.typing.Point2d, float]: ... + +@_typing.overload +def cartToPolar(x: cv2.typing.MatLike, y: cv2.typing.MatLike, magnitude: cv2.typing.MatLike | None = ..., angle: cv2.typing.MatLike | None = ..., angleInDegrees: bool = ...) -> tuple[cv2.typing.MatLike, cv2.typing.MatLike]: ... +@_typing.overload +def cartToPolar(x: UMat, y: UMat, magnitude: UMat | None = ..., angle: UMat | None = ..., angleInDegrees: bool = ...) -> tuple[UMat, UMat]: ... + +@_typing.overload +def checkChessboard(img: cv2.typing.MatLike, size: cv2.typing.Size) -> bool: ... +@_typing.overload +def checkChessboard(img: UMat, size: cv2.typing.Size) -> bool: ... + +def checkHardwareSupport(feature: int) -> bool: ... + +@_typing.overload +def checkRange(a: cv2.typing.MatLike, quiet: bool = ..., minVal: float = ..., maxVal: float = ...) -> tuple[bool, cv2.typing.Point]: ... +@_typing.overload +def checkRange(a: UMat, quiet: bool = ..., minVal: float = ..., maxVal: float = ...) -> tuple[bool, cv2.typing.Point]: ... + +@_typing.overload +def circle(img: cv2.typing.MatLike, center: cv2.typing.Point, radius: int, color: cv2.typing.Scalar, thickness: int = ..., lineType: int = ..., shift: int = ...) -> cv2.typing.MatLike: ... +@_typing.overload +def circle(img: UMat, center: cv2.typing.Point, radius: int, color: cv2.typing.Scalar, thickness: int = ..., lineType: int = ..., shift: int = ...) -> UMat: ... + +def clipLine(imgRect: cv2.typing.Rect, pt1: cv2.typing.Point, pt2: cv2.typing.Point) -> tuple[bool, cv2.typing.Point, cv2.typing.Point]: ... + +@_typing.overload +def colorChange(src: cv2.typing.MatLike, mask: cv2.typing.MatLike, dst: cv2.typing.MatLike | None = ..., red_mul: float = ..., green_mul: float = ..., blue_mul: float = ...) -> cv2.typing.MatLike: ... +@_typing.overload +def colorChange(src: UMat, mask: UMat, dst: UMat | None = ..., red_mul: float = ..., green_mul: float = ..., blue_mul: float = ...) -> UMat: ... + +@_typing.overload +def compare(src1: cv2.typing.MatLike, src2: cv2.typing.MatLike, cmpop: int, dst: cv2.typing.MatLike | None = ...) -> cv2.typing.MatLike: ... +@_typing.overload +def compare(src1: UMat, src2: UMat, cmpop: int, dst: UMat | None = ...) -> UMat: ... + +@_typing.overload +def compareHist(H1: cv2.typing.MatLike, H2: cv2.typing.MatLike, method: int) -> float: ... +@_typing.overload +def compareHist(H1: UMat, H2: UMat, method: int) -> float: ... + +@_typing.overload +def completeSymm(m: cv2.typing.MatLike, lowerToUpper: bool = ...) -> cv2.typing.MatLike: ... +@_typing.overload +def completeSymm(m: UMat, lowerToUpper: bool = ...) -> UMat: ... + +@_typing.overload +def composeRT(rvec1: cv2.typing.MatLike, tvec1: cv2.typing.MatLike, rvec2: cv2.typing.MatLike, tvec2: cv2.typing.MatLike, rvec3: cv2.typing.MatLike | None = ..., tvec3: cv2.typing.MatLike | None = ..., dr3dr1: cv2.typing.MatLike | None = ..., dr3dt1: cv2.typing.MatLike | None = ..., dr3dr2: cv2.typing.MatLike | None = ..., dr3dt2: cv2.typing.MatLike | None = ..., dt3dr1: cv2.typing.MatLike | None = ..., dt3dt1: cv2.typing.MatLike | None = ..., dt3dr2: cv2.typing.MatLike | None = ..., dt3dt2: cv2.typing.MatLike | None = ...) -> tuple[cv2.typing.MatLike, cv2.typing.MatLike, cv2.typing.MatLike, cv2.typing.MatLike, cv2.typing.MatLike, cv2.typing.MatLike, cv2.typing.MatLike, cv2.typing.MatLike, cv2.typing.MatLike, cv2.typing.MatLike]: ... +@_typing.overload +def composeRT(rvec1: UMat, tvec1: UMat, rvec2: UMat, tvec2: UMat, rvec3: UMat | None = ..., tvec3: UMat | None = ..., dr3dr1: UMat | None = ..., dr3dt1: UMat | None = ..., dr3dr2: UMat | None = ..., dr3dt2: UMat | None = ..., dt3dr1: UMat | None = ..., dt3dt1: UMat | None = ..., dt3dr2: UMat | None = ..., dt3dt2: UMat | None = ...) -> tuple[UMat, UMat, UMat, UMat, UMat, UMat, UMat, UMat, UMat, UMat]: ... + +@_typing.overload +def computeCorrespondEpilines(points: cv2.typing.MatLike, whichImage: int, F: cv2.typing.MatLike, lines: cv2.typing.MatLike | None = ...) -> cv2.typing.MatLike: ... +@_typing.overload +def computeCorrespondEpilines(points: UMat, whichImage: int, F: UMat, lines: UMat | None = ...) -> UMat: ... + +@_typing.overload +def computeECC(templateImage: cv2.typing.MatLike, inputImage: cv2.typing.MatLike, inputMask: cv2.typing.MatLike | None = ...) -> float: ... +@_typing.overload +def computeECC(templateImage: UMat, inputImage: UMat, inputMask: UMat | None = ...) -> float: ... + +@_typing.overload +def connectedComponents(image: cv2.typing.MatLike, labels: cv2.typing.MatLike | None = ..., connectivity: int = ..., ltype: int = ...) -> tuple[int, cv2.typing.MatLike]: ... +@_typing.overload +def connectedComponents(image: UMat, labels: UMat | None = ..., connectivity: int = ..., ltype: int = ...) -> tuple[int, UMat]: ... + +@_typing.overload +def connectedComponentsWithAlgorithm(image: cv2.typing.MatLike, connectivity: int, ltype: int, ccltype: int, labels: cv2.typing.MatLike | None = ...) -> tuple[int, cv2.typing.MatLike]: ... +@_typing.overload +def connectedComponentsWithAlgorithm(image: UMat, connectivity: int, ltype: int, ccltype: int, labels: UMat | None = ...) -> tuple[int, UMat]: ... + +@_typing.overload +def connectedComponentsWithStats(image: cv2.typing.MatLike, labels: cv2.typing.MatLike | None = ..., stats: cv2.typing.MatLike | None = ..., centroids: cv2.typing.MatLike | None = ..., connectivity: int = ..., ltype: int = ...) -> tuple[int, cv2.typing.MatLike, cv2.typing.MatLike, cv2.typing.MatLike]: ... +@_typing.overload +def connectedComponentsWithStats(image: UMat, labels: UMat | None = ..., stats: UMat | None = ..., centroids: UMat | None = ..., connectivity: int = ..., ltype: int = ...) -> tuple[int, UMat, UMat, UMat]: ... + +@_typing.overload +def connectedComponentsWithStatsWithAlgorithm(image: cv2.typing.MatLike, connectivity: int, ltype: int, ccltype: int, labels: cv2.typing.MatLike | None = ..., stats: cv2.typing.MatLike | None = ..., centroids: cv2.typing.MatLike | None = ...) -> tuple[int, cv2.typing.MatLike, cv2.typing.MatLike, cv2.typing.MatLike]: ... +@_typing.overload +def connectedComponentsWithStatsWithAlgorithm(image: UMat, connectivity: int, ltype: int, ccltype: int, labels: UMat | None = ..., stats: UMat | None = ..., centroids: UMat | None = ...) -> tuple[int, UMat, UMat, UMat]: ... + +@_typing.overload +def contourArea(contour: cv2.typing.MatLike, oriented: bool = ...) -> float: ... +@_typing.overload +def contourArea(contour: UMat, oriented: bool = ...) -> float: ... + +@_typing.overload +def convertFp16(src: cv2.typing.MatLike, dst: cv2.typing.MatLike | None = ...) -> cv2.typing.MatLike: ... +@_typing.overload +def convertFp16(src: UMat, dst: UMat | None = ...) -> UMat: ... + +@_typing.overload +def convertMaps(map1: cv2.typing.MatLike, map2: cv2.typing.MatLike, dstmap1type: int, dstmap1: cv2.typing.MatLike | None = ..., dstmap2: cv2.typing.MatLike | None = ..., nninterpolation: bool = ...) -> tuple[cv2.typing.MatLike, cv2.typing.MatLike]: ... +@_typing.overload +def convertMaps(map1: UMat, map2: UMat, dstmap1type: int, dstmap1: UMat | None = ..., dstmap2: UMat | None = ..., nninterpolation: bool = ...) -> tuple[UMat, UMat]: ... + +@_typing.overload +def convertPointsFromHomogeneous(src: cv2.typing.MatLike, dst: cv2.typing.MatLike | None = ...) -> cv2.typing.MatLike: ... +@_typing.overload +def convertPointsFromHomogeneous(src: UMat, dst: UMat | None = ...) -> UMat: ... + +@_typing.overload +def convertPointsToHomogeneous(src: cv2.typing.MatLike, dst: cv2.typing.MatLike | None = ...) -> cv2.typing.MatLike: ... +@_typing.overload +def convertPointsToHomogeneous(src: UMat, dst: UMat | None = ...) -> UMat: ... + +@_typing.overload +def convertScaleAbs(src: cv2.typing.MatLike, dst: cv2.typing.MatLike | None = ..., alpha: float = ..., beta: float = ...) -> cv2.typing.MatLike: ... +@_typing.overload +def convertScaleAbs(src: UMat, dst: UMat | None = ..., alpha: float = ..., beta: float = ...) -> UMat: ... + +@_typing.overload +def convexHull(points: cv2.typing.MatLike, hull: cv2.typing.MatLike | None = ..., clockwise: bool = ..., returnPoints: bool = ...) -> cv2.typing.MatLike: ... +@_typing.overload +def convexHull(points: UMat, hull: UMat | None = ..., clockwise: bool = ..., returnPoints: bool = ...) -> UMat: ... + +@_typing.overload +def convexityDefects(contour: cv2.typing.MatLike, convexhull: cv2.typing.MatLike, convexityDefects: cv2.typing.MatLike | None = ...) -> cv2.typing.MatLike: ... +@_typing.overload +def convexityDefects(contour: UMat, convexhull: UMat, convexityDefects: UMat | None = ...) -> UMat: ... + +@_typing.overload +def copyMakeBorder(src: cv2.typing.MatLike, top: int, bottom: int, left: int, right: int, borderType: int, dst: cv2.typing.MatLike | None = ..., value: cv2.typing.Scalar = ...) -> cv2.typing.MatLike: ... +@_typing.overload +def copyMakeBorder(src: UMat, top: int, bottom: int, left: int, right: int, borderType: int, dst: UMat | None = ..., value: cv2.typing.Scalar = ...) -> UMat: ... + +@_typing.overload +def copyTo(src: cv2.typing.MatLike, mask: cv2.typing.MatLike, dst: cv2.typing.MatLike | None = ...) -> cv2.typing.MatLike: ... +@_typing.overload +def copyTo(src: UMat, mask: UMat, dst: UMat | None = ...) -> UMat: ... + +@_typing.overload +def cornerEigenValsAndVecs(src: cv2.typing.MatLike, blockSize: int, ksize: int, dst: cv2.typing.MatLike | None = ..., borderType: int = ...) -> cv2.typing.MatLike: ... +@_typing.overload +def cornerEigenValsAndVecs(src: UMat, blockSize: int, ksize: int, dst: UMat | None = ..., borderType: int = ...) -> UMat: ... + +@_typing.overload +def cornerHarris(src: cv2.typing.MatLike, blockSize: int, ksize: int, k: float, dst: cv2.typing.MatLike | None = ..., borderType: int = ...) -> cv2.typing.MatLike: ... +@_typing.overload +def cornerHarris(src: UMat, blockSize: int, ksize: int, k: float, dst: UMat | None = ..., borderType: int = ...) -> UMat: ... + +@_typing.overload +def cornerMinEigenVal(src: cv2.typing.MatLike, blockSize: int, dst: cv2.typing.MatLike | None = ..., ksize: int = ..., borderType: int = ...) -> cv2.typing.MatLike: ... +@_typing.overload +def cornerMinEigenVal(src: UMat, blockSize: int, dst: UMat | None = ..., ksize: int = ..., borderType: int = ...) -> UMat: ... + +@_typing.overload +def cornerSubPix(image: cv2.typing.MatLike, corners: cv2.typing.MatLike, winSize: cv2.typing.Size, zeroZone: cv2.typing.Size, criteria: cv2.typing.TermCriteria) -> cv2.typing.MatLike: ... +@_typing.overload +def cornerSubPix(image: UMat, corners: UMat, winSize: cv2.typing.Size, zeroZone: cv2.typing.Size, criteria: cv2.typing.TermCriteria) -> UMat: ... + +@_typing.overload +def correctMatches(F: cv2.typing.MatLike, points1: cv2.typing.MatLike, points2: cv2.typing.MatLike, newPoints1: cv2.typing.MatLike | None = ..., newPoints2: cv2.typing.MatLike | None = ...) -> tuple[cv2.typing.MatLike, cv2.typing.MatLike]: ... +@_typing.overload +def correctMatches(F: UMat, points1: UMat, points2: UMat, newPoints1: UMat | None = ..., newPoints2: UMat | None = ...) -> tuple[UMat, UMat]: ... + +@_typing.overload +def countNonZero(src: cv2.typing.MatLike) -> int: ... +@_typing.overload +def countNonZero(src: UMat) -> int: ... + +def createAlignMTB(max_bits: int = ..., exclude_range: int = ..., cut: bool = ...) -> AlignMTB: ... + +def createBackgroundSubtractorKNN(history: int = ..., dist2Threshold: float = ..., detectShadows: bool = ...) -> BackgroundSubtractorKNN: ... + +def createBackgroundSubtractorMOG2(history: int = ..., varThreshold: float = ..., detectShadows: bool = ...) -> BackgroundSubtractorMOG2: ... + +def createCLAHE(clipLimit: float = ..., tileGridSize: cv2.typing.Size = ...) -> CLAHE: ... + +def createCalibrateDebevec(samples: int = ..., lambda_: float = ..., random: bool = ...) -> CalibrateDebevec: ... + +def createCalibrateRobertson(max_iter: int = ..., threshold: float = ...) -> CalibrateRobertson: ... + +def createGeneralizedHoughBallard() -> GeneralizedHoughBallard: ... + +def createGeneralizedHoughGuil() -> GeneralizedHoughGuil: ... + +@_typing.overload +def createHanningWindow(winSize: cv2.typing.Size, type: int, dst: cv2.typing.MatLike | None = ...) -> cv2.typing.MatLike: ... +@_typing.overload +def createHanningWindow(winSize: cv2.typing.Size, type: int, dst: UMat | None = ...) -> UMat: ... + +def createLineSegmentDetector(refine: int = ..., scale: float = ..., sigma_scale: float = ..., quant: float = ..., ang_th: float = ..., log_eps: float = ..., density_th: float = ..., n_bins: int = ...) -> LineSegmentDetector: ... + +def createMergeDebevec() -> MergeDebevec: ... + +def createMergeMertens(contrast_weight: float = ..., saturation_weight: float = ..., exposure_weight: float = ...) -> MergeMertens: ... + +def createMergeRobertson() -> MergeRobertson: ... + +def createTonemap(gamma: float = ...) -> Tonemap: ... + +def createTonemapDrago(gamma: float = ..., saturation: float = ..., bias: float = ...) -> TonemapDrago: ... + +def createTonemapMantiuk(gamma: float = ..., scale: float = ..., saturation: float = ...) -> TonemapMantiuk: ... + +def createTonemapReinhard(gamma: float = ..., intensity: float = ..., light_adapt: float = ..., color_adapt: float = ...) -> TonemapReinhard: ... + +def cubeRoot(val: float) -> float: ... + +def currentUIFramework() -> str: ... + +@_typing.overload +def cvtColor(src: cv2.typing.MatLike, code: int, dst: cv2.typing.MatLike | None = ..., dstCn: int = ..., hint: AlgorithmHint = ...) -> cv2.typing.MatLike: ... +@_typing.overload +def cvtColor(src: UMat, code: int, dst: UMat | None = ..., dstCn: int = ..., hint: AlgorithmHint = ...) -> UMat: ... + +@_typing.overload +def cvtColorTwoPlane(src1: cv2.typing.MatLike, src2: cv2.typing.MatLike, code: int, dst: cv2.typing.MatLike | None = ..., hint: AlgorithmHint = ...) -> cv2.typing.MatLike: ... +@_typing.overload +def cvtColorTwoPlane(src1: UMat, src2: UMat, code: int, dst: UMat | None = ..., hint: AlgorithmHint = ...) -> UMat: ... + +@_typing.overload +def dct(src: cv2.typing.MatLike, dst: cv2.typing.MatLike | None = ..., flags: int = ...) -> cv2.typing.MatLike: ... +@_typing.overload +def dct(src: UMat, dst: UMat | None = ..., flags: int = ...) -> UMat: ... + +@_typing.overload +def decolor(src: cv2.typing.MatLike, grayscale: cv2.typing.MatLike | None = ..., color_boost: cv2.typing.MatLike | None = ...) -> tuple[cv2.typing.MatLike, cv2.typing.MatLike]: ... +@_typing.overload +def decolor(src: UMat, grayscale: UMat | None = ..., color_boost: UMat | None = ...) -> tuple[UMat, UMat]: ... + +@_typing.overload +def decomposeEssentialMat(E: cv2.typing.MatLike, R1: cv2.typing.MatLike | None = ..., R2: cv2.typing.MatLike | None = ..., t: cv2.typing.MatLike | None = ...) -> tuple[cv2.typing.MatLike, cv2.typing.MatLike, cv2.typing.MatLike]: ... +@_typing.overload +def decomposeEssentialMat(E: UMat, R1: UMat | None = ..., R2: UMat | None = ..., t: UMat | None = ...) -> tuple[UMat, UMat, UMat]: ... + +@_typing.overload +def decomposeHomographyMat(H: cv2.typing.MatLike, K: cv2.typing.MatLike, rotations: _typing.Sequence[cv2.typing.MatLike] | None = ..., translations: _typing.Sequence[cv2.typing.MatLike] | None = ..., normals: _typing.Sequence[cv2.typing.MatLike] | None = ...) -> tuple[int, _typing.Sequence[cv2.typing.MatLike], _typing.Sequence[cv2.typing.MatLike], _typing.Sequence[cv2.typing.MatLike]]: ... +@_typing.overload +def decomposeHomographyMat(H: UMat, K: UMat, rotations: _typing.Sequence[UMat] | None = ..., translations: _typing.Sequence[UMat] | None = ..., normals: _typing.Sequence[UMat] | None = ...) -> tuple[int, _typing.Sequence[UMat], _typing.Sequence[UMat], _typing.Sequence[UMat]]: ... + +@_typing.overload +def decomposeProjectionMatrix(projMatrix: cv2.typing.MatLike, cameraMatrix: cv2.typing.MatLike | None = ..., rotMatrix: cv2.typing.MatLike | None = ..., transVect: cv2.typing.MatLike | None = ..., rotMatrixX: cv2.typing.MatLike | None = ..., rotMatrixY: cv2.typing.MatLike | None = ..., rotMatrixZ: cv2.typing.MatLike | None = ..., eulerAngles: cv2.typing.MatLike | None = ...) -> tuple[cv2.typing.MatLike, cv2.typing.MatLike, cv2.typing.MatLike, cv2.typing.MatLike, cv2.typing.MatLike, cv2.typing.MatLike, cv2.typing.MatLike]: ... +@_typing.overload +def decomposeProjectionMatrix(projMatrix: UMat, cameraMatrix: UMat | None = ..., rotMatrix: UMat | None = ..., transVect: UMat | None = ..., rotMatrixX: UMat | None = ..., rotMatrixY: UMat | None = ..., rotMatrixZ: UMat | None = ..., eulerAngles: UMat | None = ...) -> tuple[UMat, UMat, UMat, UMat, UMat, UMat, UMat]: ... + +@_typing.overload +def demosaicing(src: cv2.typing.MatLike, code: int, dst: cv2.typing.MatLike | None = ..., dstCn: int = ...) -> cv2.typing.MatLike: ... +@_typing.overload +def demosaicing(src: UMat, code: int, dst: UMat | None = ..., dstCn: int = ...) -> UMat: ... + +def denoise_TVL1(observations: _typing.Sequence[cv2.typing.MatLike], result: cv2.typing.MatLike, lambda_: float = ..., niters: int = ...) -> None: ... + +def destroyAllWindows() -> None: ... + +def destroyWindow(winname: str) -> None: ... + +@_typing.overload +def detailEnhance(src: cv2.typing.MatLike, dst: cv2.typing.MatLike | None = ..., sigma_s: float = ..., sigma_r: float = ...) -> cv2.typing.MatLike: ... +@_typing.overload +def detailEnhance(src: UMat, dst: UMat | None = ..., sigma_s: float = ..., sigma_r: float = ...) -> UMat: ... + +@_typing.overload +def determinant(mtx: cv2.typing.MatLike) -> float: ... +@_typing.overload +def determinant(mtx: UMat) -> float: ... + +@_typing.overload +def dft(src: cv2.typing.MatLike, dst: cv2.typing.MatLike | None = ..., flags: int = ..., nonzeroRows: int = ...) -> cv2.typing.MatLike: ... +@_typing.overload +def dft(src: UMat, dst: UMat | None = ..., flags: int = ..., nonzeroRows: int = ...) -> UMat: ... + +@_typing.overload +def dilate(src: cv2.typing.MatLike, kernel: cv2.typing.MatLike, dst: cv2.typing.MatLike | None = ..., anchor: cv2.typing.Point = ..., iterations: int = ..., borderType: int = ..., borderValue: cv2.typing.Scalar = ...) -> cv2.typing.MatLike: ... +@_typing.overload +def dilate(src: UMat, kernel: UMat, dst: UMat | None = ..., anchor: cv2.typing.Point = ..., iterations: int = ..., borderType: int = ..., borderValue: cv2.typing.Scalar = ...) -> UMat: ... + +def displayOverlay(winname: str, text: str, delayms: int = ...) -> None: ... + +def displayStatusBar(winname: str, text: str, delayms: int = ...) -> None: ... + +@_typing.overload +def distanceTransform(src: cv2.typing.MatLike, distanceType: int, maskSize: int, dst: cv2.typing.MatLike | None = ..., dstType: int = ...) -> cv2.typing.MatLike: ... +@_typing.overload +def distanceTransform(src: UMat, distanceType: int, maskSize: int, dst: UMat | None = ..., dstType: int = ...) -> UMat: ... + +@_typing.overload +def distanceTransformWithLabels(src: cv2.typing.MatLike, distanceType: int, maskSize: int, dst: cv2.typing.MatLike | None = ..., labels: cv2.typing.MatLike | None = ..., labelType: int = ...) -> tuple[cv2.typing.MatLike, cv2.typing.MatLike]: ... +@_typing.overload +def distanceTransformWithLabels(src: UMat, distanceType: int, maskSize: int, dst: UMat | None = ..., labels: UMat | None = ..., labelType: int = ...) -> tuple[UMat, UMat]: ... + +@_typing.overload +def divSpectrums(a: cv2.typing.MatLike, b: cv2.typing.MatLike, flags: int, c: cv2.typing.MatLike | None = ..., conjB: bool = ...) -> cv2.typing.MatLike: ... +@_typing.overload +def divSpectrums(a: UMat, b: UMat, flags: int, c: UMat | None = ..., conjB: bool = ...) -> UMat: ... + +@_typing.overload +def divide(src1: cv2.typing.MatLike, src2: cv2.typing.MatLike, dst: cv2.typing.MatLike | None = ..., scale: float = ..., dtype: int = ...) -> cv2.typing.MatLike: ... +@_typing.overload +def divide(src1: UMat, src2: UMat, dst: UMat | None = ..., scale: float = ..., dtype: int = ...) -> UMat: ... +@_typing.overload +def divide(scale: float, src2: cv2.typing.MatLike, dst: cv2.typing.MatLike | None = ..., dtype: int = ...) -> cv2.typing.MatLike: ... +@_typing.overload +def divide(scale: float, src2: UMat, dst: UMat | None = ..., dtype: int = ...) -> UMat: ... + +@_typing.overload +def drawChessboardCorners(image: cv2.typing.MatLike, patternSize: cv2.typing.Size, corners: cv2.typing.MatLike, patternWasFound: bool) -> cv2.typing.MatLike: ... +@_typing.overload +def drawChessboardCorners(image: UMat, patternSize: cv2.typing.Size, corners: UMat, patternWasFound: bool) -> UMat: ... + +@_typing.overload +def drawContours(image: cv2.typing.MatLike, contours: _typing.Sequence[cv2.typing.MatLike], contourIdx: int, color: cv2.typing.Scalar, thickness: int = ..., lineType: int = ..., hierarchy: cv2.typing.MatLike | None = ..., maxLevel: int = ..., offset: cv2.typing.Point = ...) -> cv2.typing.MatLike: ... +@_typing.overload +def drawContours(image: UMat, contours: _typing.Sequence[UMat], contourIdx: int, color: cv2.typing.Scalar, thickness: int = ..., lineType: int = ..., hierarchy: UMat | None = ..., maxLevel: int = ..., offset: cv2.typing.Point = ...) -> UMat: ... + +@_typing.overload +def drawFrameAxes(image: cv2.typing.MatLike, cameraMatrix: cv2.typing.MatLike, distCoeffs: cv2.typing.MatLike, rvec: cv2.typing.MatLike, tvec: cv2.typing.MatLike, length: float, thickness: int = ...) -> cv2.typing.MatLike: ... +@_typing.overload +def drawFrameAxes(image: UMat, cameraMatrix: UMat, distCoeffs: UMat, rvec: UMat, tvec: UMat, length: float, thickness: int = ...) -> UMat: ... + +@_typing.overload +def drawKeypoints(image: cv2.typing.MatLike, keypoints: _typing.Sequence[KeyPoint], outImage: cv2.typing.MatLike, color: cv2.typing.Scalar = ..., flags: DrawMatchesFlags = ...) -> cv2.typing.MatLike: ... +@_typing.overload +def drawKeypoints(image: UMat, keypoints: _typing.Sequence[KeyPoint], outImage: UMat, color: cv2.typing.Scalar = ..., flags: DrawMatchesFlags = ...) -> UMat: ... + +@_typing.overload +def drawMarker(img: cv2.typing.MatLike, position: cv2.typing.Point, color: cv2.typing.Scalar, markerType: int = ..., markerSize: int = ..., thickness: int = ..., line_type: int = ...) -> cv2.typing.MatLike: ... +@_typing.overload +def drawMarker(img: UMat, position: cv2.typing.Point, color: cv2.typing.Scalar, markerType: int = ..., markerSize: int = ..., thickness: int = ..., line_type: int = ...) -> UMat: ... + +@_typing.overload +def drawMatches(img1: cv2.typing.MatLike, keypoints1: _typing.Sequence[KeyPoint], img2: cv2.typing.MatLike, keypoints2: _typing.Sequence[KeyPoint], matches1to2: _typing.Sequence[DMatch], outImg: cv2.typing.MatLike, matchColor: cv2.typing.Scalar = ..., singlePointColor: cv2.typing.Scalar = ..., matchesMask: _typing.Sequence[str] = ..., flags: DrawMatchesFlags = ...) -> cv2.typing.MatLike: ... +@_typing.overload +def drawMatches(img1: UMat, keypoints1: _typing.Sequence[KeyPoint], img2: UMat, keypoints2: _typing.Sequence[KeyPoint], matches1to2: _typing.Sequence[DMatch], outImg: UMat, matchColor: cv2.typing.Scalar = ..., singlePointColor: cv2.typing.Scalar = ..., matchesMask: _typing.Sequence[str] = ..., flags: DrawMatchesFlags = ...) -> UMat: ... +@_typing.overload +def drawMatches(img1: cv2.typing.MatLike, keypoints1: _typing.Sequence[KeyPoint], img2: cv2.typing.MatLike, keypoints2: _typing.Sequence[KeyPoint], matches1to2: _typing.Sequence[DMatch], outImg: cv2.typing.MatLike, matchesThickness: int, matchColor: cv2.typing.Scalar = ..., singlePointColor: cv2.typing.Scalar = ..., matchesMask: _typing.Sequence[str] = ..., flags: DrawMatchesFlags = ...) -> cv2.typing.MatLike: ... +@_typing.overload +def drawMatches(img1: UMat, keypoints1: _typing.Sequence[KeyPoint], img2: UMat, keypoints2: _typing.Sequence[KeyPoint], matches1to2: _typing.Sequence[DMatch], outImg: UMat, matchesThickness: int, matchColor: cv2.typing.Scalar = ..., singlePointColor: cv2.typing.Scalar = ..., matchesMask: _typing.Sequence[str] = ..., flags: DrawMatchesFlags = ...) -> UMat: ... + +@_typing.overload +def drawMatchesKnn(img1: cv2.typing.MatLike, keypoints1: _typing.Sequence[KeyPoint], img2: cv2.typing.MatLike, keypoints2: _typing.Sequence[KeyPoint], matches1to2: _typing.Sequence[_typing.Sequence[DMatch]], outImg: cv2.typing.MatLike, matchColor: cv2.typing.Scalar = ..., singlePointColor: cv2.typing.Scalar = ..., matchesMask: _typing.Sequence[_typing.Sequence[str]] = ..., flags: DrawMatchesFlags = ...) -> cv2.typing.MatLike: ... +@_typing.overload +def drawMatchesKnn(img1: UMat, keypoints1: _typing.Sequence[KeyPoint], img2: UMat, keypoints2: _typing.Sequence[KeyPoint], matches1to2: _typing.Sequence[_typing.Sequence[DMatch]], outImg: UMat, matchColor: cv2.typing.Scalar = ..., singlePointColor: cv2.typing.Scalar = ..., matchesMask: _typing.Sequence[_typing.Sequence[str]] = ..., flags: DrawMatchesFlags = ...) -> UMat: ... + +@_typing.overload +def edgePreservingFilter(src: cv2.typing.MatLike, dst: cv2.typing.MatLike | None = ..., flags: int = ..., sigma_s: float = ..., sigma_r: float = ...) -> cv2.typing.MatLike: ... +@_typing.overload +def edgePreservingFilter(src: UMat, dst: UMat | None = ..., flags: int = ..., sigma_s: float = ..., sigma_r: float = ...) -> UMat: ... + +@_typing.overload +def eigen(src: cv2.typing.MatLike, eigenvalues: cv2.typing.MatLike | None = ..., eigenvectors: cv2.typing.MatLike | None = ...) -> tuple[bool, cv2.typing.MatLike, cv2.typing.MatLike]: ... +@_typing.overload +def eigen(src: UMat, eigenvalues: UMat | None = ..., eigenvectors: UMat | None = ...) -> tuple[bool, UMat, UMat]: ... + +@_typing.overload +def eigenNonSymmetric(src: cv2.typing.MatLike, eigenvalues: cv2.typing.MatLike | None = ..., eigenvectors: cv2.typing.MatLike | None = ...) -> tuple[cv2.typing.MatLike, cv2.typing.MatLike]: ... +@_typing.overload +def eigenNonSymmetric(src: UMat, eigenvalues: UMat | None = ..., eigenvectors: UMat | None = ...) -> tuple[UMat, UMat]: ... + +@_typing.overload +def ellipse(img: cv2.typing.MatLike, center: cv2.typing.Point, axes: cv2.typing.Size, angle: float, startAngle: float, endAngle: float, color: cv2.typing.Scalar, thickness: int = ..., lineType: int = ..., shift: int = ...) -> cv2.typing.MatLike: ... +@_typing.overload +def ellipse(img: UMat, center: cv2.typing.Point, axes: cv2.typing.Size, angle: float, startAngle: float, endAngle: float, color: cv2.typing.Scalar, thickness: int = ..., lineType: int = ..., shift: int = ...) -> UMat: ... +@_typing.overload +def ellipse(img: cv2.typing.MatLike, box: cv2.typing.RotatedRect, color: cv2.typing.Scalar, thickness: int = ..., lineType: int = ...) -> cv2.typing.MatLike: ... +@_typing.overload +def ellipse(img: UMat, box: cv2.typing.RotatedRect, color: cv2.typing.Scalar, thickness: int = ..., lineType: int = ...) -> UMat: ... + +def ellipse2Poly(center: cv2.typing.Point, axes: cv2.typing.Size, angle: int, arcStart: int, arcEnd: int, delta: int) -> _typing.Sequence[cv2.typing.Point]: ... + +def empty_array_desc() -> GArrayDesc: ... + +def empty_gopaque_desc() -> GOpaqueDesc: ... + +def empty_scalar_desc() -> GScalarDesc: ... + +@_typing.overload +def equalizeHist(src: cv2.typing.MatLike, dst: cv2.typing.MatLike | None = ...) -> cv2.typing.MatLike: ... +@_typing.overload +def equalizeHist(src: UMat, dst: UMat | None = ...) -> UMat: ... + +@_typing.overload +def erode(src: cv2.typing.MatLike, kernel: cv2.typing.MatLike, dst: cv2.typing.MatLike | None = ..., anchor: cv2.typing.Point = ..., iterations: int = ..., borderType: int = ..., borderValue: cv2.typing.Scalar = ...) -> cv2.typing.MatLike: ... +@_typing.overload +def erode(src: UMat, kernel: UMat, dst: UMat | None = ..., anchor: cv2.typing.Point = ..., iterations: int = ..., borderType: int = ..., borderValue: cv2.typing.Scalar = ...) -> UMat: ... + +@_typing.overload +def estimateAffine2D(from_: cv2.typing.MatLike, to: cv2.typing.MatLike, inliers: cv2.typing.MatLike | None = ..., method: int = ..., ransacReprojThreshold: float = ..., maxIters: int = ..., confidence: float = ..., refineIters: int = ...) -> tuple[cv2.typing.MatLike, cv2.typing.MatLike]: ... +@_typing.overload +def estimateAffine2D(from_: UMat, to: UMat, inliers: UMat | None = ..., method: int = ..., ransacReprojThreshold: float = ..., maxIters: int = ..., confidence: float = ..., refineIters: int = ...) -> tuple[cv2.typing.MatLike, UMat]: ... +@_typing.overload +def estimateAffine2D(pts1: cv2.typing.MatLike, pts2: cv2.typing.MatLike, params: UsacParams, inliers: cv2.typing.MatLike | None = ...) -> tuple[cv2.typing.MatLike, cv2.typing.MatLike]: ... +@_typing.overload +def estimateAffine2D(pts1: UMat, pts2: UMat, params: UsacParams, inliers: UMat | None = ...) -> tuple[cv2.typing.MatLike, UMat]: ... + +@_typing.overload +def estimateAffine3D(src: cv2.typing.MatLike, dst: cv2.typing.MatLike, out: cv2.typing.MatLike | None = ..., inliers: cv2.typing.MatLike | None = ..., ransacThreshold: float = ..., confidence: float = ...) -> tuple[int, cv2.typing.MatLike, cv2.typing.MatLike]: ... +@_typing.overload +def estimateAffine3D(src: UMat, dst: UMat, out: UMat | None = ..., inliers: UMat | None = ..., ransacThreshold: float = ..., confidence: float = ...) -> tuple[int, UMat, UMat]: ... +@_typing.overload +def estimateAffine3D(src: cv2.typing.MatLike, dst: cv2.typing.MatLike, force_rotation: bool = ...) -> tuple[cv2.typing.MatLike, float]: ... +@_typing.overload +def estimateAffine3D(src: UMat, dst: UMat, force_rotation: bool = ...) -> tuple[cv2.typing.MatLike, float]: ... + +@_typing.overload +def estimateAffinePartial2D(from_: cv2.typing.MatLike, to: cv2.typing.MatLike, inliers: cv2.typing.MatLike | None = ..., method: int = ..., ransacReprojThreshold: float = ..., maxIters: int = ..., confidence: float = ..., refineIters: int = ...) -> tuple[cv2.typing.MatLike, cv2.typing.MatLike]: ... +@_typing.overload +def estimateAffinePartial2D(from_: UMat, to: UMat, inliers: UMat | None = ..., method: int = ..., ransacReprojThreshold: float = ..., maxIters: int = ..., confidence: float = ..., refineIters: int = ...) -> tuple[cv2.typing.MatLike, UMat]: ... + +@_typing.overload +def estimateChessboardSharpness(image: cv2.typing.MatLike, patternSize: cv2.typing.Size, corners: cv2.typing.MatLike, rise_distance: float = ..., vertical: bool = ..., sharpness: cv2.typing.MatLike | None = ...) -> tuple[cv2.typing.Scalar, cv2.typing.MatLike]: ... +@_typing.overload +def estimateChessboardSharpness(image: UMat, patternSize: cv2.typing.Size, corners: UMat, rise_distance: float = ..., vertical: bool = ..., sharpness: UMat | None = ...) -> tuple[cv2.typing.Scalar, UMat]: ... + +@_typing.overload +def estimateTranslation3D(src: cv2.typing.MatLike, dst: cv2.typing.MatLike, out: cv2.typing.MatLike | None = ..., inliers: cv2.typing.MatLike | None = ..., ransacThreshold: float = ..., confidence: float = ...) -> tuple[int, cv2.typing.MatLike, cv2.typing.MatLike]: ... +@_typing.overload +def estimateTranslation3D(src: UMat, dst: UMat, out: UMat | None = ..., inliers: UMat | None = ..., ransacThreshold: float = ..., confidence: float = ...) -> tuple[int, UMat, UMat]: ... + +@_typing.overload +def exp(src: cv2.typing.MatLike, dst: cv2.typing.MatLike | None = ...) -> cv2.typing.MatLike: ... +@_typing.overload +def exp(src: UMat, dst: UMat | None = ...) -> UMat: ... + +@_typing.overload +def extractChannel(src: cv2.typing.MatLike, coi: int, dst: cv2.typing.MatLike | None = ...) -> cv2.typing.MatLike: ... +@_typing.overload +def extractChannel(src: UMat, coi: int, dst: UMat | None = ...) -> UMat: ... + +def fastAtan2(y: float, x: float) -> float: ... + +@_typing.overload +def fastNlMeansDenoising(src: cv2.typing.MatLike, dst: cv2.typing.MatLike | None = ..., h: float = ..., templateWindowSize: int = ..., searchWindowSize: int = ...) -> cv2.typing.MatLike: ... +@_typing.overload +def fastNlMeansDenoising(src: UMat, dst: UMat | None = ..., h: float = ..., templateWindowSize: int = ..., searchWindowSize: int = ...) -> UMat: ... +@_typing.overload +def fastNlMeansDenoising(src: cv2.typing.MatLike, h: _typing.Sequence[float], dst: cv2.typing.MatLike | None = ..., templateWindowSize: int = ..., searchWindowSize: int = ..., normType: int = ...) -> cv2.typing.MatLike: ... +@_typing.overload +def fastNlMeansDenoising(src: UMat, h: _typing.Sequence[float], dst: UMat | None = ..., templateWindowSize: int = ..., searchWindowSize: int = ..., normType: int = ...) -> UMat: ... + +@_typing.overload +def fastNlMeansDenoisingColored(src: cv2.typing.MatLike, dst: cv2.typing.MatLike | None = ..., h: float = ..., hColor: float = ..., templateWindowSize: int = ..., searchWindowSize: int = ...) -> cv2.typing.MatLike: ... +@_typing.overload +def fastNlMeansDenoisingColored(src: UMat, dst: UMat | None = ..., h: float = ..., hColor: float = ..., templateWindowSize: int = ..., searchWindowSize: int = ...) -> UMat: ... + +@_typing.overload +def fastNlMeansDenoisingColoredMulti(srcImgs: _typing.Sequence[cv2.typing.MatLike], imgToDenoiseIndex: int, temporalWindowSize: int, dst: cv2.typing.MatLike | None = ..., h: float = ..., hColor: float = ..., templateWindowSize: int = ..., searchWindowSize: int = ...) -> cv2.typing.MatLike: ... +@_typing.overload +def fastNlMeansDenoisingColoredMulti(srcImgs: _typing.Sequence[UMat], imgToDenoiseIndex: int, temporalWindowSize: int, dst: UMat | None = ..., h: float = ..., hColor: float = ..., templateWindowSize: int = ..., searchWindowSize: int = ...) -> UMat: ... + +@_typing.overload +def fastNlMeansDenoisingMulti(srcImgs: _typing.Sequence[cv2.typing.MatLike], imgToDenoiseIndex: int, temporalWindowSize: int, dst: cv2.typing.MatLike | None = ..., h: float = ..., templateWindowSize: int = ..., searchWindowSize: int = ...) -> cv2.typing.MatLike: ... +@_typing.overload +def fastNlMeansDenoisingMulti(srcImgs: _typing.Sequence[UMat], imgToDenoiseIndex: int, temporalWindowSize: int, dst: UMat | None = ..., h: float = ..., templateWindowSize: int = ..., searchWindowSize: int = ...) -> UMat: ... +@_typing.overload +def fastNlMeansDenoisingMulti(srcImgs: _typing.Sequence[cv2.typing.MatLike], imgToDenoiseIndex: int, temporalWindowSize: int, h: _typing.Sequence[float], dst: cv2.typing.MatLike | None = ..., templateWindowSize: int = ..., searchWindowSize: int = ..., normType: int = ...) -> cv2.typing.MatLike: ... +@_typing.overload +def fastNlMeansDenoisingMulti(srcImgs: _typing.Sequence[UMat], imgToDenoiseIndex: int, temporalWindowSize: int, h: _typing.Sequence[float], dst: UMat | None = ..., templateWindowSize: int = ..., searchWindowSize: int = ..., normType: int = ...) -> UMat: ... + +@_typing.overload +def fillConvexPoly(img: cv2.typing.MatLike, points: cv2.typing.MatLike, color: cv2.typing.Scalar, lineType: int = ..., shift: int = ...) -> cv2.typing.MatLike: ... +@_typing.overload +def fillConvexPoly(img: UMat, points: UMat, color: cv2.typing.Scalar, lineType: int = ..., shift: int = ...) -> UMat: ... + +@_typing.overload +def fillPoly(img: cv2.typing.MatLike, pts: _typing.Sequence[cv2.typing.MatLike], color: cv2.typing.Scalar, lineType: int = ..., shift: int = ..., offset: cv2.typing.Point = ...) -> cv2.typing.MatLike: ... +@_typing.overload +def fillPoly(img: UMat, pts: _typing.Sequence[UMat], color: cv2.typing.Scalar, lineType: int = ..., shift: int = ..., offset: cv2.typing.Point = ...) -> UMat: ... + +@_typing.overload +def filter2D(src: cv2.typing.MatLike, ddepth: int, kernel: cv2.typing.MatLike, dst: cv2.typing.MatLike | None = ..., anchor: cv2.typing.Point = ..., delta: float = ..., borderType: int = ...) -> cv2.typing.MatLike: ... +@_typing.overload +def filter2D(src: UMat, ddepth: int, kernel: UMat, dst: UMat | None = ..., anchor: cv2.typing.Point = ..., delta: float = ..., borderType: int = ...) -> UMat: ... + +@_typing.overload +def filterHomographyDecompByVisibleRefpoints(rotations: _typing.Sequence[cv2.typing.MatLike], normals: _typing.Sequence[cv2.typing.MatLike], beforePoints: cv2.typing.MatLike, afterPoints: cv2.typing.MatLike, possibleSolutions: cv2.typing.MatLike | None = ..., pointsMask: cv2.typing.MatLike | None = ...) -> cv2.typing.MatLike: ... +@_typing.overload +def filterHomographyDecompByVisibleRefpoints(rotations: _typing.Sequence[UMat], normals: _typing.Sequence[UMat], beforePoints: UMat, afterPoints: UMat, possibleSolutions: UMat | None = ..., pointsMask: UMat | None = ...) -> UMat: ... + +@_typing.overload +def filterSpeckles(img: cv2.typing.MatLike, newVal: float, maxSpeckleSize: int, maxDiff: float, buf: cv2.typing.MatLike | None = ...) -> tuple[cv2.typing.MatLike, cv2.typing.MatLike]: ... +@_typing.overload +def filterSpeckles(img: UMat, newVal: float, maxSpeckleSize: int, maxDiff: float, buf: UMat | None = ...) -> tuple[UMat, UMat]: ... + +@_typing.overload +def find4QuadCornerSubpix(img: cv2.typing.MatLike, corners: cv2.typing.MatLike, region_size: cv2.typing.Size) -> tuple[bool, cv2.typing.MatLike]: ... +@_typing.overload +def find4QuadCornerSubpix(img: UMat, corners: UMat, region_size: cv2.typing.Size) -> tuple[bool, UMat]: ... + +@_typing.overload +def findChessboardCorners(image: cv2.typing.MatLike, patternSize: cv2.typing.Size, corners: cv2.typing.MatLike | None = ..., flags: int = ...) -> tuple[bool, cv2.typing.MatLike]: ... +@_typing.overload +def findChessboardCorners(image: UMat, patternSize: cv2.typing.Size, corners: UMat | None = ..., flags: int = ...) -> tuple[bool, UMat]: ... + +@_typing.overload +def findChessboardCornersSB(image: cv2.typing.MatLike, patternSize: cv2.typing.Size, corners: cv2.typing.MatLike | None = ..., flags: int = ...) -> tuple[bool, cv2.typing.MatLike]: ... +@_typing.overload +def findChessboardCornersSB(image: UMat, patternSize: cv2.typing.Size, corners: UMat | None = ..., flags: int = ...) -> tuple[bool, UMat]: ... + +@_typing.overload +def findChessboardCornersSBWithMeta(image: cv2.typing.MatLike, patternSize: cv2.typing.Size, flags: int, corners: cv2.typing.MatLike | None = ..., meta: cv2.typing.MatLike | None = ...) -> tuple[bool, cv2.typing.MatLike, cv2.typing.MatLike]: ... +@_typing.overload +def findChessboardCornersSBWithMeta(image: UMat, patternSize: cv2.typing.Size, flags: int, corners: UMat | None = ..., meta: UMat | None = ...) -> tuple[bool, UMat, UMat]: ... + +@_typing.overload +def findCirclesGrid(image: cv2.typing.MatLike, patternSize: cv2.typing.Size, flags: int, blobDetector: cv2.typing.FeatureDetector, parameters: CirclesGridFinderParameters, centers: cv2.typing.MatLike | None = ...) -> tuple[bool, cv2.typing.MatLike]: ... +@_typing.overload +def findCirclesGrid(image: UMat, patternSize: cv2.typing.Size, flags: int, blobDetector: cv2.typing.FeatureDetector, parameters: CirclesGridFinderParameters, centers: UMat | None = ...) -> tuple[bool, UMat]: ... +@_typing.overload +def findCirclesGrid(image: cv2.typing.MatLike, patternSize: cv2.typing.Size, centers: cv2.typing.MatLike | None = ..., flags: int = ..., blobDetector: cv2.typing.FeatureDetector = ...) -> tuple[bool, cv2.typing.MatLike]: ... +@_typing.overload +def findCirclesGrid(image: UMat, patternSize: cv2.typing.Size, centers: UMat | None = ..., flags: int = ..., blobDetector: cv2.typing.FeatureDetector = ...) -> tuple[bool, UMat]: ... + +@_typing.overload +def findContours(image: cv2.typing.MatLike, mode: int, method: int, contours: _typing.Sequence[cv2.typing.MatLike] | None = ..., hierarchy: cv2.typing.MatLike | None = ..., offset: cv2.typing.Point = ...) -> tuple[_typing.Sequence[cv2.typing.MatLike], cv2.typing.MatLike]: ... +@_typing.overload +def findContours(image: UMat, mode: int, method: int, contours: _typing.Sequence[UMat] | None = ..., hierarchy: UMat | None = ..., offset: cv2.typing.Point = ...) -> tuple[_typing.Sequence[UMat], UMat]: ... + +@_typing.overload +def findContoursLinkRuns(image: cv2.typing.MatLike, contours: _typing.Sequence[cv2.typing.MatLike] | None = ..., hierarchy: cv2.typing.MatLike | None = ...) -> tuple[_typing.Sequence[cv2.typing.MatLike], cv2.typing.MatLike]: ... +@_typing.overload +def findContoursLinkRuns(image: UMat, contours: _typing.Sequence[UMat] | None = ..., hierarchy: UMat | None = ...) -> tuple[_typing.Sequence[UMat], UMat]: ... +@_typing.overload +def findContoursLinkRuns(image: cv2.typing.MatLike, contours: _typing.Sequence[cv2.typing.MatLike] | None = ...) -> _typing.Sequence[cv2.typing.MatLike]: ... +@_typing.overload +def findContoursLinkRuns(image: UMat, contours: _typing.Sequence[UMat] | None = ...) -> _typing.Sequence[UMat]: ... + +@_typing.overload +def findEssentialMat(points1: cv2.typing.MatLike, points2: cv2.typing.MatLike, cameraMatrix: cv2.typing.MatLike, method: int = ..., prob: float = ..., threshold: float = ..., maxIters: int = ..., mask: cv2.typing.MatLike | None = ...) -> tuple[cv2.typing.MatLike, cv2.typing.MatLike]: ... +@_typing.overload +def findEssentialMat(points1: UMat, points2: UMat, cameraMatrix: UMat, method: int = ..., prob: float = ..., threshold: float = ..., maxIters: int = ..., mask: UMat | None = ...) -> tuple[cv2.typing.MatLike, UMat]: ... +@_typing.overload +def findEssentialMat(points1: cv2.typing.MatLike, points2: cv2.typing.MatLike, focal: float = ..., pp: cv2.typing.Point2d = ..., method: int = ..., prob: float = ..., threshold: float = ..., maxIters: int = ..., mask: cv2.typing.MatLike | None = ...) -> tuple[cv2.typing.MatLike, cv2.typing.MatLike]: ... +@_typing.overload +def findEssentialMat(points1: UMat, points2: UMat, focal: float = ..., pp: cv2.typing.Point2d = ..., method: int = ..., prob: float = ..., threshold: float = ..., maxIters: int = ..., mask: UMat | None = ...) -> tuple[cv2.typing.MatLike, UMat]: ... +@_typing.overload +def findEssentialMat(points1: cv2.typing.MatLike, points2: cv2.typing.MatLike, cameraMatrix1: cv2.typing.MatLike, distCoeffs1: cv2.typing.MatLike, cameraMatrix2: cv2.typing.MatLike, distCoeffs2: cv2.typing.MatLike, method: int = ..., prob: float = ..., threshold: float = ..., mask: cv2.typing.MatLike | None = ...) -> tuple[cv2.typing.MatLike, cv2.typing.MatLike]: ... +@_typing.overload +def findEssentialMat(points1: UMat, points2: UMat, cameraMatrix1: UMat, distCoeffs1: UMat, cameraMatrix2: UMat, distCoeffs2: UMat, method: int = ..., prob: float = ..., threshold: float = ..., mask: UMat | None = ...) -> tuple[cv2.typing.MatLike, UMat]: ... +@_typing.overload +def findEssentialMat(points1: cv2.typing.MatLike, points2: cv2.typing.MatLike, cameraMatrix1: cv2.typing.MatLike, cameraMatrix2: cv2.typing.MatLike, dist_coeff1: cv2.typing.MatLike, dist_coeff2: cv2.typing.MatLike, params: UsacParams, mask: cv2.typing.MatLike | None = ...) -> tuple[cv2.typing.MatLike, cv2.typing.MatLike]: ... +@_typing.overload +def findEssentialMat(points1: UMat, points2: UMat, cameraMatrix1: UMat, cameraMatrix2: UMat, dist_coeff1: UMat, dist_coeff2: UMat, params: UsacParams, mask: UMat | None = ...) -> tuple[cv2.typing.MatLike, UMat]: ... + +@_typing.overload +def findFundamentalMat(points1: cv2.typing.MatLike, points2: cv2.typing.MatLike, method: int, ransacReprojThreshold: float, confidence: float, maxIters: int, mask: cv2.typing.MatLike | None = ...) -> tuple[cv2.typing.MatLike, cv2.typing.MatLike]: ... +@_typing.overload +def findFundamentalMat(points1: UMat, points2: UMat, method: int, ransacReprojThreshold: float, confidence: float, maxIters: int, mask: UMat | None = ...) -> tuple[cv2.typing.MatLike, UMat]: ... +@_typing.overload +def findFundamentalMat(points1: cv2.typing.MatLike, points2: cv2.typing.MatLike, method: int = ..., ransacReprojThreshold: float = ..., confidence: float = ..., mask: cv2.typing.MatLike | None = ...) -> tuple[cv2.typing.MatLike, cv2.typing.MatLike]: ... +@_typing.overload +def findFundamentalMat(points1: UMat, points2: UMat, method: int = ..., ransacReprojThreshold: float = ..., confidence: float = ..., mask: UMat | None = ...) -> tuple[cv2.typing.MatLike, UMat]: ... +@_typing.overload +def findFundamentalMat(points1: cv2.typing.MatLike, points2: cv2.typing.MatLike, params: UsacParams, mask: cv2.typing.MatLike | None = ...) -> tuple[cv2.typing.MatLike, cv2.typing.MatLike]: ... +@_typing.overload +def findFundamentalMat(points1: UMat, points2: UMat, params: UsacParams, mask: UMat | None = ...) -> tuple[cv2.typing.MatLike, UMat]: ... + +@_typing.overload +def findHomography(srcPoints: cv2.typing.MatLike, dstPoints: cv2.typing.MatLike, method: int = ..., ransacReprojThreshold: float = ..., mask: cv2.typing.MatLike | None = ..., maxIters: int = ..., confidence: float = ...) -> tuple[cv2.typing.MatLike, cv2.typing.MatLike]: ... +@_typing.overload +def findHomography(srcPoints: UMat, dstPoints: UMat, method: int = ..., ransacReprojThreshold: float = ..., mask: UMat | None = ..., maxIters: int = ..., confidence: float = ...) -> tuple[cv2.typing.MatLike, UMat]: ... +@_typing.overload +def findHomography(srcPoints: cv2.typing.MatLike, dstPoints: cv2.typing.MatLike, params: UsacParams, mask: cv2.typing.MatLike | None = ...) -> tuple[cv2.typing.MatLike, cv2.typing.MatLike]: ... +@_typing.overload +def findHomography(srcPoints: UMat, dstPoints: UMat, params: UsacParams, mask: UMat | None = ...) -> tuple[cv2.typing.MatLike, UMat]: ... + +@_typing.overload +def findNonZero(src: cv2.typing.MatLike, idx: cv2.typing.MatLike | None = ...) -> cv2.typing.MatLike: ... +@_typing.overload +def findNonZero(src: UMat, idx: UMat | None = ...) -> UMat: ... + +@_typing.overload +def findTransformECC(templateImage: cv2.typing.MatLike, inputImage: cv2.typing.MatLike, warpMatrix: cv2.typing.MatLike, motionType: int, criteria: cv2.typing.TermCriteria, inputMask: cv2.typing.MatLike, gaussFiltSize: int) -> tuple[float, cv2.typing.MatLike]: ... +@_typing.overload +def findTransformECC(templateImage: UMat, inputImage: UMat, warpMatrix: UMat, motionType: int, criteria: cv2.typing.TermCriteria, inputMask: UMat, gaussFiltSize: int) -> tuple[float, UMat]: ... +@_typing.overload +def findTransformECC(templateImage: cv2.typing.MatLike, inputImage: cv2.typing.MatLike, warpMatrix: cv2.typing.MatLike, motionType: int = ..., criteria: cv2.typing.TermCriteria = ..., inputMask: cv2.typing.MatLike | None = ...) -> tuple[float, cv2.typing.MatLike]: ... +@_typing.overload +def findTransformECC(templateImage: UMat, inputImage: UMat, warpMatrix: UMat, motionType: int = ..., criteria: cv2.typing.TermCriteria = ..., inputMask: UMat | None = ...) -> tuple[float, UMat]: ... + +@_typing.overload +def fitEllipse(points: cv2.typing.MatLike) -> cv2.typing.RotatedRect: ... +@_typing.overload +def fitEllipse(points: UMat) -> cv2.typing.RotatedRect: ... + +@_typing.overload +def fitEllipseAMS(points: cv2.typing.MatLike) -> cv2.typing.RotatedRect: ... +@_typing.overload +def fitEllipseAMS(points: UMat) -> cv2.typing.RotatedRect: ... + +@_typing.overload +def fitEllipseDirect(points: cv2.typing.MatLike) -> cv2.typing.RotatedRect: ... +@_typing.overload +def fitEllipseDirect(points: UMat) -> cv2.typing.RotatedRect: ... + +@_typing.overload +def fitLine(points: cv2.typing.MatLike, distType: int, param: float, reps: float, aeps: float, line: cv2.typing.MatLike | None = ...) -> cv2.typing.MatLike: ... +@_typing.overload +def fitLine(points: UMat, distType: int, param: float, reps: float, aeps: float, line: UMat | None = ...) -> UMat: ... + +@_typing.overload +def flip(src: cv2.typing.MatLike, flipCode: int, dst: cv2.typing.MatLike | None = ...) -> cv2.typing.MatLike: ... +@_typing.overload +def flip(src: UMat, flipCode: int, dst: UMat | None = ...) -> UMat: ... + +@_typing.overload +def flipND(src: cv2.typing.MatLike, axis: int, dst: cv2.typing.MatLike | None = ...) -> cv2.typing.MatLike: ... +@_typing.overload +def flipND(src: UMat, axis: int, dst: UMat | None = ...) -> UMat: ... + +@_typing.overload +def floodFill(image: cv2.typing.MatLike, mask: cv2.typing.MatLike | None, seedPoint: cv2.typing.Point, newVal: cv2.typing.Scalar, loDiff: cv2.typing.Scalar = ..., upDiff: cv2.typing.Scalar = ..., flags: int = ...) -> tuple[int, cv2.typing.MatLike, cv2.typing.MatLike, cv2.typing.Rect]: ... +@_typing.overload +def floodFill(image: UMat, mask: UMat | None, seedPoint: cv2.typing.Point, newVal: cv2.typing.Scalar, loDiff: cv2.typing.Scalar = ..., upDiff: cv2.typing.Scalar = ..., flags: int = ...) -> tuple[int, UMat, UMat, cv2.typing.Rect]: ... + +@_typing.overload +def gemm(src1: cv2.typing.MatLike, src2: cv2.typing.MatLike, alpha: float, src3: cv2.typing.MatLike, beta: float, dst: cv2.typing.MatLike | None = ..., flags: int = ...) -> cv2.typing.MatLike: ... +@_typing.overload +def gemm(src1: UMat, src2: UMat, alpha: float, src3: UMat, beta: float, dst: UMat | None = ..., flags: int = ...) -> UMat: ... + +@_typing.overload +def getAffineTransform(src: cv2.typing.MatLike, dst: cv2.typing.MatLike) -> cv2.typing.MatLike: ... +@_typing.overload +def getAffineTransform(src: UMat, dst: UMat) -> cv2.typing.MatLike: ... + +def getBuildInformation() -> str: ... + +def getCPUFeaturesLine() -> str: ... + +def getCPUTickCount() -> int: ... + +def getDefaultAlgorithmHint() -> AlgorithmHint: ... + +@_typing.overload +def getDefaultNewCameraMatrix(cameraMatrix: cv2.typing.MatLike, imgsize: cv2.typing.Size = ..., centerPrincipalPoint: bool = ...) -> cv2.typing.MatLike: ... +@_typing.overload +def getDefaultNewCameraMatrix(cameraMatrix: UMat, imgsize: cv2.typing.Size = ..., centerPrincipalPoint: bool = ...) -> cv2.typing.MatLike: ... + +@_typing.overload +def getDerivKernels(dx: int, dy: int, ksize: int, kx: cv2.typing.MatLike | None = ..., ky: cv2.typing.MatLike | None = ..., normalize: bool = ..., ktype: int = ...) -> tuple[cv2.typing.MatLike, cv2.typing.MatLike]: ... +@_typing.overload +def getDerivKernels(dx: int, dy: int, ksize: int, kx: UMat | None = ..., ky: UMat | None = ..., normalize: bool = ..., ktype: int = ...) -> tuple[UMat, UMat]: ... + +def getFontScaleFromHeight(fontFace: int, pixelHeight: int, thickness: int = ...) -> float: ... + +def getGaborKernel(ksize: cv2.typing.Size, sigma: float, theta: float, lambd: float, gamma: float, psi: float = ..., ktype: int = ...) -> cv2.typing.MatLike: ... + +def getGaussianKernel(ksize: int, sigma: float, ktype: int = ...) -> cv2.typing.MatLike: ... + +def getHardwareFeatureName(feature: int) -> str: ... + +def getLogLevel() -> int: ... + +def getNumThreads() -> int: ... + +def getNumberOfCPUs() -> int: ... + +def getOptimalDFTSize(vecsize: int) -> int: ... + +@_typing.overload +def getOptimalNewCameraMatrix(cameraMatrix: cv2.typing.MatLike, distCoeffs: cv2.typing.MatLike, imageSize: cv2.typing.Size, alpha: float, newImgSize: cv2.typing.Size = ..., centerPrincipalPoint: bool = ...) -> tuple[cv2.typing.MatLike, cv2.typing.Rect]: ... +@_typing.overload +def getOptimalNewCameraMatrix(cameraMatrix: UMat, distCoeffs: UMat, imageSize: cv2.typing.Size, alpha: float, newImgSize: cv2.typing.Size = ..., centerPrincipalPoint: bool = ...) -> tuple[cv2.typing.MatLike, cv2.typing.Rect]: ... + +@_typing.overload +def getPerspectiveTransform(src: cv2.typing.MatLike, dst: cv2.typing.MatLike, solveMethod: int = ...) -> cv2.typing.MatLike: ... +@_typing.overload +def getPerspectiveTransform(src: UMat, dst: UMat, solveMethod: int = ...) -> cv2.typing.MatLike: ... + +@_typing.overload +def getRectSubPix(image: cv2.typing.MatLike, patchSize: cv2.typing.Size, center: cv2.typing.Point2f, patch: cv2.typing.MatLike | None = ..., patchType: int = ...) -> cv2.typing.MatLike: ... +@_typing.overload +def getRectSubPix(image: UMat, patchSize: cv2.typing.Size, center: cv2.typing.Point2f, patch: UMat | None = ..., patchType: int = ...) -> UMat: ... + +def getRotationMatrix2D(center: cv2.typing.Point2f, angle: float, scale: float) -> cv2.typing.MatLike: ... + +def getStructuringElement(shape: int, ksize: cv2.typing.Size, anchor: cv2.typing.Point = ...) -> cv2.typing.MatLike: ... + +def getTextSize(text: str, fontFace: int, fontScale: float, thickness: int) -> tuple[cv2.typing.Size, int]: ... + +def getThreadNum() -> int: ... + +def getTickCount() -> int: ... + +def getTickFrequency() -> float: ... + +def getTrackbarPos(trackbarname: str, winname: str) -> int: ... + +def getValidDisparityROI(roi1: cv2.typing.Rect, roi2: cv2.typing.Rect, minDisparity: int, numberOfDisparities: int, blockSize: int) -> cv2.typing.Rect: ... + +def getVersionMajor() -> int: ... + +def getVersionMinor() -> int: ... + +def getVersionRevision() -> int: ... + +def getVersionString() -> str: ... + +def getWindowImageRect(winname: str) -> cv2.typing.Rect: ... + +def getWindowProperty(winname: str, prop_id: int) -> float: ... + +@_typing.overload +def goodFeaturesToTrack(image: cv2.typing.MatLike, maxCorners: int, qualityLevel: float, minDistance: float, corners: cv2.typing.MatLike | None = ..., mask: cv2.typing.MatLike | None = ..., blockSize: int = ..., useHarrisDetector: bool = ..., k: float = ...) -> cv2.typing.MatLike: ... +@_typing.overload +def goodFeaturesToTrack(image: UMat, maxCorners: int, qualityLevel: float, minDistance: float, corners: UMat | None = ..., mask: UMat | None = ..., blockSize: int = ..., useHarrisDetector: bool = ..., k: float = ...) -> UMat: ... +@_typing.overload +def goodFeaturesToTrack(image: cv2.typing.MatLike, maxCorners: int, qualityLevel: float, minDistance: float, mask: cv2.typing.MatLike, blockSize: int, gradientSize: int, corners: cv2.typing.MatLike | None = ..., useHarrisDetector: bool = ..., k: float = ...) -> cv2.typing.MatLike: ... +@_typing.overload +def goodFeaturesToTrack(image: UMat, maxCorners: int, qualityLevel: float, minDistance: float, mask: UMat, blockSize: int, gradientSize: int, corners: UMat | None = ..., useHarrisDetector: bool = ..., k: float = ...) -> UMat: ... + +@_typing.overload +def goodFeaturesToTrackWithQuality(image: cv2.typing.MatLike, maxCorners: int, qualityLevel: float, minDistance: float, mask: cv2.typing.MatLike, corners: cv2.typing.MatLike | None = ..., cornersQuality: cv2.typing.MatLike | None = ..., blockSize: int = ..., gradientSize: int = ..., useHarrisDetector: bool = ..., k: float = ...) -> tuple[cv2.typing.MatLike, cv2.typing.MatLike]: ... +@_typing.overload +def goodFeaturesToTrackWithQuality(image: UMat, maxCorners: int, qualityLevel: float, minDistance: float, mask: UMat, corners: UMat | None = ..., cornersQuality: UMat | None = ..., blockSize: int = ..., gradientSize: int = ..., useHarrisDetector: bool = ..., k: float = ...) -> tuple[UMat, UMat]: ... + +@_typing.overload +def grabCut(img: cv2.typing.MatLike, mask: cv2.typing.MatLike, rect: cv2.typing.Rect, bgdModel: cv2.typing.MatLike, fgdModel: cv2.typing.MatLike, iterCount: int, mode: int = ...) -> tuple[cv2.typing.MatLike, cv2.typing.MatLike, cv2.typing.MatLike]: ... +@_typing.overload +def grabCut(img: UMat, mask: UMat, rect: cv2.typing.Rect, bgdModel: UMat, fgdModel: UMat, iterCount: int, mode: int = ...) -> tuple[UMat, UMat, UMat]: ... + +def groupRectangles(rectList: _typing.Sequence[cv2.typing.Rect], groupThreshold: int, eps: float = ...) -> tuple[_typing.Sequence[cv2.typing.Rect], _typing.Sequence[int]]: ... + +@_typing.overload +def hasNonZero(src: cv2.typing.MatLike) -> bool: ... +@_typing.overload +def hasNonZero(src: UMat) -> bool: ... + +def haveImageReader(filename: str) -> bool: ... + +def haveImageWriter(filename: str) -> bool: ... + +def haveOpenVX() -> bool: ... + +@_typing.overload +def hconcat(src: _typing.Sequence[cv2.typing.MatLike], dst: cv2.typing.MatLike | None = ...) -> cv2.typing.MatLike: ... +@_typing.overload +def hconcat(src: _typing.Sequence[UMat], dst: UMat | None = ...) -> UMat: ... + +@_typing.overload +def idct(src: cv2.typing.MatLike, dst: cv2.typing.MatLike | None = ..., flags: int = ...) -> cv2.typing.MatLike: ... +@_typing.overload +def idct(src: UMat, dst: UMat | None = ..., flags: int = ...) -> UMat: ... + +@_typing.overload +def idft(src: cv2.typing.MatLike, dst: cv2.typing.MatLike | None = ..., flags: int = ..., nonzeroRows: int = ...) -> cv2.typing.MatLike: ... +@_typing.overload +def idft(src: UMat, dst: UMat | None = ..., flags: int = ..., nonzeroRows: int = ...) -> UMat: ... + +@_typing.overload +def illuminationChange(src: cv2.typing.MatLike, mask: cv2.typing.MatLike, dst: cv2.typing.MatLike | None = ..., alpha: float = ..., beta: float = ...) -> cv2.typing.MatLike: ... +@_typing.overload +def illuminationChange(src: UMat, mask: UMat, dst: UMat | None = ..., alpha: float = ..., beta: float = ...) -> UMat: ... + +def imcount(filename: str, flags: int = ...) -> int: ... + +@_typing.overload +def imdecode(buf: cv2.typing.MatLike, flags: int) -> cv2.typing.MatLike: ... +@_typing.overload +def imdecode(buf: UMat, flags: int) -> cv2.typing.MatLike: ... + +@_typing.overload +def imdecodemulti(buf: cv2.typing.MatLike, flags: int, mats: _typing.Sequence[cv2.typing.MatLike] | None = ..., range: cv2.typing.Range = ...) -> tuple[bool, _typing.Sequence[cv2.typing.MatLike]]: ... +@_typing.overload +def imdecodemulti(buf: UMat, flags: int, mats: _typing.Sequence[cv2.typing.MatLike] | None = ..., range: cv2.typing.Range = ...) -> tuple[bool, _typing.Sequence[cv2.typing.MatLike]]: ... + +@_typing.overload +def imencode(ext: str, img: cv2.typing.MatLike, params: _typing.Sequence[int] = ...) -> tuple[bool, numpy.ndarray[_typing.Any, numpy.dtype[numpy.uint8]]]: ... +@_typing.overload +def imencode(ext: str, img: UMat, params: _typing.Sequence[int] = ...) -> tuple[bool, numpy.ndarray[_typing.Any, numpy.dtype[numpy.uint8]]]: ... + +@_typing.overload +def imencodemulti(ext: str, imgs: _typing.Sequence[cv2.typing.MatLike], params: _typing.Sequence[int] = ...) -> tuple[bool, numpy.ndarray[_typing.Any, numpy.dtype[numpy.uint8]]]: ... +@_typing.overload +def imencodemulti(ext: str, imgs: _typing.Sequence[UMat], params: _typing.Sequence[int] = ...) -> tuple[bool, numpy.ndarray[_typing.Any, numpy.dtype[numpy.uint8]]]: ... + +@_typing.overload +def imread(filename: str, flags: int = ...) -> cv2.typing.MatLike: ... +@_typing.overload +def imread(filename: str, dst: cv2.typing.MatLike | None = ..., flags: int = ...) -> cv2.typing.MatLike: ... +@_typing.overload +def imread(filename: str, dst: UMat | None = ..., flags: int = ...) -> UMat: ... + +def imreadanimation(filename: str, start: int = ..., count: int = ...) -> tuple[bool, Animation]: ... + +@_typing.overload +def imreadmulti(filename: str, mats: _typing.Sequence[cv2.typing.MatLike] | None = ..., flags: int = ...) -> tuple[bool, _typing.Sequence[cv2.typing.MatLike]]: ... +@_typing.overload +def imreadmulti(filename: str, start: int, count: int, mats: _typing.Sequence[cv2.typing.MatLike] | None = ..., flags: int = ...) -> tuple[bool, _typing.Sequence[cv2.typing.MatLike]]: ... + +@_typing.overload +def imshow(winname: str, mat: cv2.typing.MatLike) -> None: ... +@_typing.overload +def imshow(winname: str, mat: cv2.cuda.GpuMat) -> None: ... +@_typing.overload +def imshow(winname: str, mat: UMat) -> None: ... + +@_typing.overload +def imwrite(filename: str, img: cv2.typing.MatLike, params: _typing.Sequence[int] = ...) -> bool: ... +@_typing.overload +def imwrite(filename: str, img: UMat, params: _typing.Sequence[int] = ...) -> bool: ... + +def imwriteanimation(filename: str, animation: Animation, params: _typing.Sequence[int] = ...) -> bool: ... + +@_typing.overload +def imwritemulti(filename: str, img: _typing.Sequence[cv2.typing.MatLike], params: _typing.Sequence[int] = ...) -> bool: ... +@_typing.overload +def imwritemulti(filename: str, img: _typing.Sequence[UMat], params: _typing.Sequence[int] = ...) -> bool: ... + +@_typing.overload +def inRange(src: cv2.typing.MatLike, lowerb: cv2.typing.MatLike, upperb: cv2.typing.MatLike, dst: cv2.typing.MatLike | None = ...) -> cv2.typing.MatLike: ... +@_typing.overload +def inRange(src: UMat, lowerb: UMat, upperb: UMat, dst: UMat | None = ...) -> UMat: ... + +@_typing.overload +def initCameraMatrix2D(objectPoints: _typing.Sequence[cv2.typing.MatLike], imagePoints: _typing.Sequence[cv2.typing.MatLike], imageSize: cv2.typing.Size, aspectRatio: float = ...) -> cv2.typing.MatLike: ... +@_typing.overload +def initCameraMatrix2D(objectPoints: _typing.Sequence[UMat], imagePoints: _typing.Sequence[UMat], imageSize: cv2.typing.Size, aspectRatio: float = ...) -> cv2.typing.MatLike: ... + +@_typing.overload +def initInverseRectificationMap(cameraMatrix: cv2.typing.MatLike, distCoeffs: cv2.typing.MatLike, R: cv2.typing.MatLike, newCameraMatrix: cv2.typing.MatLike, size: cv2.typing.Size, m1type: int, map1: cv2.typing.MatLike | None = ..., map2: cv2.typing.MatLike | None = ...) -> tuple[cv2.typing.MatLike, cv2.typing.MatLike]: ... +@_typing.overload +def initInverseRectificationMap(cameraMatrix: UMat, distCoeffs: UMat, R: UMat, newCameraMatrix: UMat, size: cv2.typing.Size, m1type: int, map1: UMat | None = ..., map2: UMat | None = ...) -> tuple[UMat, UMat]: ... + +@_typing.overload +def initUndistortRectifyMap(cameraMatrix: cv2.typing.MatLike, distCoeffs: cv2.typing.MatLike, R: cv2.typing.MatLike, newCameraMatrix: cv2.typing.MatLike, size: cv2.typing.Size, m1type: int, map1: cv2.typing.MatLike | None = ..., map2: cv2.typing.MatLike | None = ...) -> tuple[cv2.typing.MatLike, cv2.typing.MatLike]: ... +@_typing.overload +def initUndistortRectifyMap(cameraMatrix: UMat, distCoeffs: UMat, R: UMat, newCameraMatrix: UMat, size: cv2.typing.Size, m1type: int, map1: UMat | None = ..., map2: UMat | None = ...) -> tuple[UMat, UMat]: ... + +@_typing.overload +def inpaint(src: cv2.typing.MatLike, inpaintMask: cv2.typing.MatLike, inpaintRadius: float, flags: int, dst: cv2.typing.MatLike | None = ...) -> cv2.typing.MatLike: ... +@_typing.overload +def inpaint(src: UMat, inpaintMask: UMat, inpaintRadius: float, flags: int, dst: UMat | None = ...) -> UMat: ... + +@_typing.overload +def insertChannel(src: cv2.typing.MatLike, dst: cv2.typing.MatLike, coi: int) -> cv2.typing.MatLike: ... +@_typing.overload +def insertChannel(src: UMat, dst: UMat, coi: int) -> UMat: ... + +@_typing.overload +def integral(src: cv2.typing.MatLike, sum: cv2.typing.MatLike | None = ..., sdepth: int = ...) -> cv2.typing.MatLike: ... +@_typing.overload +def integral(src: UMat, sum: UMat | None = ..., sdepth: int = ...) -> UMat: ... + +@_typing.overload +def integral2(src: cv2.typing.MatLike, sum: cv2.typing.MatLike | None = ..., sqsum: cv2.typing.MatLike | None = ..., sdepth: int = ..., sqdepth: int = ...) -> tuple[cv2.typing.MatLike, cv2.typing.MatLike]: ... +@_typing.overload +def integral2(src: UMat, sum: UMat | None = ..., sqsum: UMat | None = ..., sdepth: int = ..., sqdepth: int = ...) -> tuple[UMat, UMat]: ... + +@_typing.overload +def integral3(src: cv2.typing.MatLike, sum: cv2.typing.MatLike | None = ..., sqsum: cv2.typing.MatLike | None = ..., tilted: cv2.typing.MatLike | None = ..., sdepth: int = ..., sqdepth: int = ...) -> tuple[cv2.typing.MatLike, cv2.typing.MatLike, cv2.typing.MatLike]: ... +@_typing.overload +def integral3(src: UMat, sum: UMat | None = ..., sqsum: UMat | None = ..., tilted: UMat | None = ..., sdepth: int = ..., sqdepth: int = ...) -> tuple[UMat, UMat, UMat]: ... + +@_typing.overload +def intersectConvexConvex(p1: cv2.typing.MatLike, p2: cv2.typing.MatLike, p12: cv2.typing.MatLike | None = ..., handleNested: bool = ...) -> tuple[float, cv2.typing.MatLike]: ... +@_typing.overload +def intersectConvexConvex(p1: UMat, p2: UMat, p12: UMat | None = ..., handleNested: bool = ...) -> tuple[float, UMat]: ... + +@_typing.overload +def invert(src: cv2.typing.MatLike, dst: cv2.typing.MatLike | None = ..., flags: int = ...) -> tuple[float, cv2.typing.MatLike]: ... +@_typing.overload +def invert(src: UMat, dst: UMat | None = ..., flags: int = ...) -> tuple[float, UMat]: ... + +@_typing.overload +def invertAffineTransform(M: cv2.typing.MatLike, iM: cv2.typing.MatLike | None = ...) -> cv2.typing.MatLike: ... +@_typing.overload +def invertAffineTransform(M: UMat, iM: UMat | None = ...) -> UMat: ... + +@_typing.overload +def isContourConvex(contour: cv2.typing.MatLike) -> bool: ... +@_typing.overload +def isContourConvex(contour: UMat) -> bool: ... + +@_typing.overload +def kmeans(data: cv2.typing.MatLike, K: int, bestLabels: cv2.typing.MatLike, criteria: cv2.typing.TermCriteria, attempts: int, flags: int, centers: cv2.typing.MatLike | None = ...) -> tuple[float, cv2.typing.MatLike, cv2.typing.MatLike]: ... +@_typing.overload +def kmeans(data: UMat, K: int, bestLabels: UMat, criteria: cv2.typing.TermCriteria, attempts: int, flags: int, centers: UMat | None = ...) -> tuple[float, UMat, UMat]: ... + +@_typing.overload +def line(img: cv2.typing.MatLike, pt1: cv2.typing.Point, pt2: cv2.typing.Point, color: cv2.typing.Scalar, thickness: int = ..., lineType: int = ..., shift: int = ...) -> cv2.typing.MatLike: ... +@_typing.overload +def line(img: UMat, pt1: cv2.typing.Point, pt2: cv2.typing.Point, color: cv2.typing.Scalar, thickness: int = ..., lineType: int = ..., shift: int = ...) -> UMat: ... + +@_typing.overload +def linearPolar(src: cv2.typing.MatLike, center: cv2.typing.Point2f, maxRadius: float, flags: int, dst: cv2.typing.MatLike | None = ...) -> cv2.typing.MatLike: ... +@_typing.overload +def linearPolar(src: UMat, center: cv2.typing.Point2f, maxRadius: float, flags: int, dst: UMat | None = ...) -> UMat: ... + +@_typing.overload +def log(src: cv2.typing.MatLike, dst: cv2.typing.MatLike | None = ...) -> cv2.typing.MatLike: ... +@_typing.overload +def log(src: UMat, dst: UMat | None = ...) -> UMat: ... + +@_typing.overload +def logPolar(src: cv2.typing.MatLike, center: cv2.typing.Point2f, M: float, flags: int, dst: cv2.typing.MatLike | None = ...) -> cv2.typing.MatLike: ... +@_typing.overload +def logPolar(src: UMat, center: cv2.typing.Point2f, M: float, flags: int, dst: UMat | None = ...) -> UMat: ... + +@_typing.overload +def magnitude(x: cv2.typing.MatLike, y: cv2.typing.MatLike, magnitude: cv2.typing.MatLike | None = ...) -> cv2.typing.MatLike: ... +@_typing.overload +def magnitude(x: UMat, y: UMat, magnitude: UMat | None = ...) -> UMat: ... + +@_typing.overload +def matMulDeriv(A: cv2.typing.MatLike, B: cv2.typing.MatLike, dABdA: cv2.typing.MatLike | None = ..., dABdB: cv2.typing.MatLike | None = ...) -> tuple[cv2.typing.MatLike, cv2.typing.MatLike]: ... +@_typing.overload +def matMulDeriv(A: UMat, B: UMat, dABdA: UMat | None = ..., dABdB: UMat | None = ...) -> tuple[UMat, UMat]: ... + +@_typing.overload +def matchShapes(contour1: cv2.typing.MatLike, contour2: cv2.typing.MatLike, method: int, parameter: float) -> float: ... +@_typing.overload +def matchShapes(contour1: UMat, contour2: UMat, method: int, parameter: float) -> float: ... + +@_typing.overload +def matchTemplate(image: cv2.typing.MatLike, templ: cv2.typing.MatLike, method: int, result: cv2.typing.MatLike | None = ..., mask: cv2.typing.MatLike | None = ...) -> cv2.typing.MatLike: ... +@_typing.overload +def matchTemplate(image: UMat, templ: UMat, method: int, result: UMat | None = ..., mask: UMat | None = ...) -> UMat: ... + +@_typing.overload +def max(src1: cv2.typing.MatLike, src2: cv2.typing.MatLike, dst: cv2.typing.MatLike | None = ...) -> cv2.typing.MatLike: ... +@_typing.overload +def max(src1: UMat, src2: UMat, dst: UMat | None = ...) -> UMat: ... + +@_typing.overload +def mean(src: cv2.typing.MatLike, mask: cv2.typing.MatLike | None = ...) -> cv2.typing.Scalar: ... +@_typing.overload +def mean(src: UMat, mask: UMat | None = ...) -> cv2.typing.Scalar: ... + +@_typing.overload +def meanShift(probImage: cv2.typing.MatLike, window: cv2.typing.Rect, criteria: cv2.typing.TermCriteria) -> tuple[int, cv2.typing.Rect]: ... +@_typing.overload +def meanShift(probImage: UMat, window: cv2.typing.Rect, criteria: cv2.typing.TermCriteria) -> tuple[int, cv2.typing.Rect]: ... + +@_typing.overload +def meanStdDev(src: cv2.typing.MatLike, mean: cv2.typing.MatLike | None = ..., stddev: cv2.typing.MatLike | None = ..., mask: cv2.typing.MatLike | None = ...) -> tuple[cv2.typing.MatLike, cv2.typing.MatLike]: ... +@_typing.overload +def meanStdDev(src: UMat, mean: UMat | None = ..., stddev: UMat | None = ..., mask: UMat | None = ...) -> tuple[UMat, UMat]: ... + +@_typing.overload +def medianBlur(src: cv2.typing.MatLike, ksize: int, dst: cv2.typing.MatLike | None = ...) -> cv2.typing.MatLike: ... +@_typing.overload +def medianBlur(src: UMat, ksize: int, dst: UMat | None = ...) -> UMat: ... + +@_typing.overload +def merge(mv: _typing.Sequence[cv2.typing.MatLike], dst: cv2.typing.MatLike | None = ...) -> cv2.typing.MatLike: ... +@_typing.overload +def merge(mv: _typing.Sequence[UMat], dst: UMat | None = ...) -> UMat: ... + +@_typing.overload +def min(src1: cv2.typing.MatLike, src2: cv2.typing.MatLike, dst: cv2.typing.MatLike | None = ...) -> cv2.typing.MatLike: ... +@_typing.overload +def min(src1: UMat, src2: UMat, dst: UMat | None = ...) -> UMat: ... + +@_typing.overload +def minAreaRect(points: cv2.typing.MatLike) -> cv2.typing.RotatedRect: ... +@_typing.overload +def minAreaRect(points: UMat) -> cv2.typing.RotatedRect: ... + +@_typing.overload +def minEnclosingCircle(points: cv2.typing.MatLike) -> tuple[cv2.typing.Point2f, float]: ... +@_typing.overload +def minEnclosingCircle(points: UMat) -> tuple[cv2.typing.Point2f, float]: ... + +@_typing.overload +def minEnclosingTriangle(points: cv2.typing.MatLike, triangle: cv2.typing.MatLike | None = ...) -> tuple[float, cv2.typing.MatLike]: ... +@_typing.overload +def minEnclosingTriangle(points: UMat, triangle: UMat | None = ...) -> tuple[float, UMat]: ... + +@_typing.overload +def minMaxLoc(src: cv2.typing.MatLike, mask: cv2.typing.MatLike | None = ...) -> tuple[float, float, cv2.typing.Point, cv2.typing.Point]: ... +@_typing.overload +def minMaxLoc(src: UMat, mask: UMat | None = ...) -> tuple[float, float, cv2.typing.Point, cv2.typing.Point]: ... + +@_typing.overload +def mixChannels(src: _typing.Sequence[cv2.typing.MatLike], dst: _typing.Sequence[cv2.typing.MatLike], fromTo: _typing.Sequence[int]) -> _typing.Sequence[cv2.typing.MatLike]: ... +@_typing.overload +def mixChannels(src: _typing.Sequence[UMat], dst: _typing.Sequence[UMat], fromTo: _typing.Sequence[int]) -> _typing.Sequence[UMat]: ... + +@_typing.overload +def moments(array: cv2.typing.MatLike, binaryImage: bool = ...) -> cv2.typing.Moments: ... +@_typing.overload +def moments(array: UMat, binaryImage: bool = ...) -> cv2.typing.Moments: ... + +@_typing.overload +def morphologyEx(src: cv2.typing.MatLike, op: int, kernel: cv2.typing.MatLike, dst: cv2.typing.MatLike | None = ..., anchor: cv2.typing.Point = ..., iterations: int = ..., borderType: int = ..., borderValue: cv2.typing.Scalar = ...) -> cv2.typing.MatLike: ... +@_typing.overload +def morphologyEx(src: UMat, op: int, kernel: UMat, dst: UMat | None = ..., anchor: cv2.typing.Point = ..., iterations: int = ..., borderType: int = ..., borderValue: cv2.typing.Scalar = ...) -> UMat: ... + +def moveWindow(winname: str, x: int, y: int) -> None: ... + +@_typing.overload +def mulSpectrums(a: cv2.typing.MatLike, b: cv2.typing.MatLike, flags: int, c: cv2.typing.MatLike | None = ..., conjB: bool = ...) -> cv2.typing.MatLike: ... +@_typing.overload +def mulSpectrums(a: UMat, b: UMat, flags: int, c: UMat | None = ..., conjB: bool = ...) -> UMat: ... + +@_typing.overload +def mulTransposed(src: cv2.typing.MatLike, aTa: bool, dst: cv2.typing.MatLike | None = ..., delta: cv2.typing.MatLike | None = ..., scale: float = ..., dtype: int = ...) -> cv2.typing.MatLike: ... +@_typing.overload +def mulTransposed(src: UMat, aTa: bool, dst: UMat | None = ..., delta: UMat | None = ..., scale: float = ..., dtype: int = ...) -> UMat: ... + +@_typing.overload +def multiply(src1: cv2.typing.MatLike, src2: cv2.typing.MatLike, dst: cv2.typing.MatLike | None = ..., scale: float = ..., dtype: int = ...) -> cv2.typing.MatLike: ... +@_typing.overload +def multiply(src1: UMat, src2: UMat, dst: UMat | None = ..., scale: float = ..., dtype: int = ...) -> UMat: ... + +def namedWindow(winname: str, flags: int = ...) -> None: ... + +@_typing.overload +def norm(src1: cv2.typing.MatLike, normType: int = ..., mask: cv2.typing.MatLike | None = ...) -> float: ... +@_typing.overload +def norm(src1: UMat, normType: int = ..., mask: UMat | None = ...) -> float: ... +@_typing.overload +def norm(src1: cv2.typing.MatLike, src2: cv2.typing.MatLike, normType: int = ..., mask: cv2.typing.MatLike | None = ...) -> float: ... +@_typing.overload +def norm(src1: UMat, src2: UMat, normType: int = ..., mask: UMat | None = ...) -> float: ... + +@_typing.overload +def normalize(src: cv2.typing.MatLike, dst: cv2.typing.MatLike, alpha: float = ..., beta: float = ..., norm_type: int = ..., dtype: int = ..., mask: cv2.typing.MatLike | None = ...) -> cv2.typing.MatLike: ... +@_typing.overload +def normalize(src: UMat, dst: UMat, alpha: float = ..., beta: float = ..., norm_type: int = ..., dtype: int = ..., mask: UMat | None = ...) -> UMat: ... + +@_typing.overload +def patchNaNs(a: cv2.typing.MatLike, val: float = ...) -> cv2.typing.MatLike: ... +@_typing.overload +def patchNaNs(a: UMat, val: float = ...) -> UMat: ... + +@_typing.overload +def pencilSketch(src: cv2.typing.MatLike, dst1: cv2.typing.MatLike | None = ..., dst2: cv2.typing.MatLike | None = ..., sigma_s: float = ..., sigma_r: float = ..., shade_factor: float = ...) -> tuple[cv2.typing.MatLike, cv2.typing.MatLike]: ... +@_typing.overload +def pencilSketch(src: UMat, dst1: UMat | None = ..., dst2: UMat | None = ..., sigma_s: float = ..., sigma_r: float = ..., shade_factor: float = ...) -> tuple[UMat, UMat]: ... + +@_typing.overload +def perspectiveTransform(src: cv2.typing.MatLike, m: cv2.typing.MatLike, dst: cv2.typing.MatLike | None = ...) -> cv2.typing.MatLike: ... +@_typing.overload +def perspectiveTransform(src: UMat, m: UMat, dst: UMat | None = ...) -> UMat: ... + +@_typing.overload +def phase(x: cv2.typing.MatLike, y: cv2.typing.MatLike, angle: cv2.typing.MatLike | None = ..., angleInDegrees: bool = ...) -> cv2.typing.MatLike: ... +@_typing.overload +def phase(x: UMat, y: UMat, angle: UMat | None = ..., angleInDegrees: bool = ...) -> UMat: ... + +@_typing.overload +def phaseCorrelate(src1: cv2.typing.MatLike, src2: cv2.typing.MatLike, window: cv2.typing.MatLike | None = ...) -> tuple[cv2.typing.Point2d, float]: ... +@_typing.overload +def phaseCorrelate(src1: UMat, src2: UMat, window: UMat | None = ...) -> tuple[cv2.typing.Point2d, float]: ... + +@_typing.overload +def pointPolygonTest(contour: cv2.typing.MatLike, pt: cv2.typing.Point2f, measureDist: bool) -> float: ... +@_typing.overload +def pointPolygonTest(contour: UMat, pt: cv2.typing.Point2f, measureDist: bool) -> float: ... + +@_typing.overload +def polarToCart(magnitude: cv2.typing.MatLike, angle: cv2.typing.MatLike, x: cv2.typing.MatLike | None = ..., y: cv2.typing.MatLike | None = ..., angleInDegrees: bool = ...) -> tuple[cv2.typing.MatLike, cv2.typing.MatLike]: ... +@_typing.overload +def polarToCart(magnitude: UMat, angle: UMat, x: UMat | None = ..., y: UMat | None = ..., angleInDegrees: bool = ...) -> tuple[UMat, UMat]: ... + +def pollKey() -> int: ... + +@_typing.overload +def polylines(img: cv2.typing.MatLike, pts: _typing.Sequence[cv2.typing.MatLike], isClosed: bool, color: cv2.typing.Scalar, thickness: int = ..., lineType: int = ..., shift: int = ...) -> cv2.typing.MatLike: ... +@_typing.overload +def polylines(img: UMat, pts: _typing.Sequence[UMat], isClosed: bool, color: cv2.typing.Scalar, thickness: int = ..., lineType: int = ..., shift: int = ...) -> UMat: ... + +@_typing.overload +def pow(src: cv2.typing.MatLike, power: float, dst: cv2.typing.MatLike | None = ...) -> cv2.typing.MatLike: ... +@_typing.overload +def pow(src: UMat, power: float, dst: UMat | None = ...) -> UMat: ... + +@_typing.overload +def preCornerDetect(src: cv2.typing.MatLike, ksize: int, dst: cv2.typing.MatLike | None = ..., borderType: int = ...) -> cv2.typing.MatLike: ... +@_typing.overload +def preCornerDetect(src: UMat, ksize: int, dst: UMat | None = ..., borderType: int = ...) -> UMat: ... + +@_typing.overload +def projectPoints(objectPoints: cv2.typing.MatLike, rvec: cv2.typing.MatLike, tvec: cv2.typing.MatLike, cameraMatrix: cv2.typing.MatLike, distCoeffs: cv2.typing.MatLike, imagePoints: cv2.typing.MatLike | None = ..., jacobian: cv2.typing.MatLike | None = ..., aspectRatio: float = ...) -> tuple[cv2.typing.MatLike, cv2.typing.MatLike]: ... +@_typing.overload +def projectPoints(objectPoints: UMat, rvec: UMat, tvec: UMat, cameraMatrix: UMat, distCoeffs: UMat, imagePoints: UMat | None = ..., jacobian: UMat | None = ..., aspectRatio: float = ...) -> tuple[UMat, UMat]: ... + +@_typing.overload +def putText(img: cv2.typing.MatLike, text: str, org: cv2.typing.Point, fontFace: int, fontScale: float, color: cv2.typing.Scalar, thickness: int = ..., lineType: int = ..., bottomLeftOrigin: bool = ...) -> cv2.typing.MatLike: ... +@_typing.overload +def putText(img: UMat, text: str, org: cv2.typing.Point, fontFace: int, fontScale: float, color: cv2.typing.Scalar, thickness: int = ..., lineType: int = ..., bottomLeftOrigin: bool = ...) -> UMat: ... + +@_typing.overload +def pyrDown(src: cv2.typing.MatLike, dst: cv2.typing.MatLike | None = ..., dstsize: cv2.typing.Size = ..., borderType: int = ...) -> cv2.typing.MatLike: ... +@_typing.overload +def pyrDown(src: UMat, dst: UMat | None = ..., dstsize: cv2.typing.Size = ..., borderType: int = ...) -> UMat: ... + +@_typing.overload +def pyrMeanShiftFiltering(src: cv2.typing.MatLike, sp: float, sr: float, dst: cv2.typing.MatLike | None = ..., maxLevel: int = ..., termcrit: cv2.typing.TermCriteria = ...) -> cv2.typing.MatLike: ... +@_typing.overload +def pyrMeanShiftFiltering(src: UMat, sp: float, sr: float, dst: UMat | None = ..., maxLevel: int = ..., termcrit: cv2.typing.TermCriteria = ...) -> UMat: ... + +@_typing.overload +def pyrUp(src: cv2.typing.MatLike, dst: cv2.typing.MatLike | None = ..., dstsize: cv2.typing.Size = ..., borderType: int = ...) -> cv2.typing.MatLike: ... +@_typing.overload +def pyrUp(src: UMat, dst: UMat | None = ..., dstsize: cv2.typing.Size = ..., borderType: int = ...) -> UMat: ... + +@_typing.overload +def randShuffle(dst: cv2.typing.MatLike, iterFactor: float = ...) -> cv2.typing.MatLike: ... +@_typing.overload +def randShuffle(dst: UMat, iterFactor: float = ...) -> UMat: ... + +@_typing.overload +def randn(dst: cv2.typing.MatLike, mean: cv2.typing.MatLike, stddev: cv2.typing.MatLike) -> cv2.typing.MatLike: ... +@_typing.overload +def randn(dst: UMat, mean: UMat, stddev: UMat) -> UMat: ... + +@_typing.overload +def randu(dst: cv2.typing.MatLike, low: cv2.typing.MatLike, high: cv2.typing.MatLike) -> cv2.typing.MatLike: ... +@_typing.overload +def randu(dst: UMat, low: UMat, high: UMat) -> UMat: ... + +def readOpticalFlow(path: str) -> cv2.typing.MatLike: ... + +@_typing.overload +def recoverPose(points1: cv2.typing.MatLike, points2: cv2.typing.MatLike, cameraMatrix1: cv2.typing.MatLike, distCoeffs1: cv2.typing.MatLike, cameraMatrix2: cv2.typing.MatLike, distCoeffs2: cv2.typing.MatLike, E: cv2.typing.MatLike | None = ..., R: cv2.typing.MatLike | None = ..., t: cv2.typing.MatLike | None = ..., method: int = ..., prob: float = ..., threshold: float = ..., mask: cv2.typing.MatLike | None = ...) -> tuple[int, cv2.typing.MatLike, cv2.typing.MatLike, cv2.typing.MatLike, cv2.typing.MatLike]: ... +@_typing.overload +def recoverPose(points1: UMat, points2: UMat, cameraMatrix1: UMat, distCoeffs1: UMat, cameraMatrix2: UMat, distCoeffs2: UMat, E: UMat | None = ..., R: UMat | None = ..., t: UMat | None = ..., method: int = ..., prob: float = ..., threshold: float = ..., mask: UMat | None = ...) -> tuple[int, UMat, UMat, UMat, UMat]: ... +@_typing.overload +def recoverPose(E: cv2.typing.MatLike, points1: cv2.typing.MatLike, points2: cv2.typing.MatLike, cameraMatrix: cv2.typing.MatLike, R: cv2.typing.MatLike | None = ..., t: cv2.typing.MatLike | None = ..., mask: cv2.typing.MatLike | None = ...) -> tuple[int, cv2.typing.MatLike, cv2.typing.MatLike, cv2.typing.MatLike]: ... +@_typing.overload +def recoverPose(E: UMat, points1: UMat, points2: UMat, cameraMatrix: UMat, R: UMat | None = ..., t: UMat | None = ..., mask: UMat | None = ...) -> tuple[int, UMat, UMat, UMat]: ... +@_typing.overload +def recoverPose(E: cv2.typing.MatLike, points1: cv2.typing.MatLike, points2: cv2.typing.MatLike, R: cv2.typing.MatLike | None = ..., t: cv2.typing.MatLike | None = ..., focal: float = ..., pp: cv2.typing.Point2d = ..., mask: cv2.typing.MatLike | None = ...) -> tuple[int, cv2.typing.MatLike, cv2.typing.MatLike, cv2.typing.MatLike]: ... +@_typing.overload +def recoverPose(E: UMat, points1: UMat, points2: UMat, R: UMat | None = ..., t: UMat | None = ..., focal: float = ..., pp: cv2.typing.Point2d = ..., mask: UMat | None = ...) -> tuple[int, UMat, UMat, UMat]: ... +@_typing.overload +def recoverPose(E: cv2.typing.MatLike, points1: cv2.typing.MatLike, points2: cv2.typing.MatLike, cameraMatrix: cv2.typing.MatLike, distanceThresh: float, R: cv2.typing.MatLike | None = ..., t: cv2.typing.MatLike | None = ..., mask: cv2.typing.MatLike | None = ..., triangulatedPoints: cv2.typing.MatLike | None = ...) -> tuple[int, cv2.typing.MatLike, cv2.typing.MatLike, cv2.typing.MatLike, cv2.typing.MatLike]: ... +@_typing.overload +def recoverPose(E: UMat, points1: UMat, points2: UMat, cameraMatrix: UMat, distanceThresh: float, R: UMat | None = ..., t: UMat | None = ..., mask: UMat | None = ..., triangulatedPoints: UMat | None = ...) -> tuple[int, UMat, UMat, UMat, UMat]: ... + +@_typing.overload +def rectangle(img: cv2.typing.MatLike, pt1: cv2.typing.Point, pt2: cv2.typing.Point, color: cv2.typing.Scalar, thickness: int = ..., lineType: int = ..., shift: int = ...) -> cv2.typing.MatLike: ... +@_typing.overload +def rectangle(img: UMat, pt1: cv2.typing.Point, pt2: cv2.typing.Point, color: cv2.typing.Scalar, thickness: int = ..., lineType: int = ..., shift: int = ...) -> UMat: ... +@_typing.overload +def rectangle(img: cv2.typing.MatLike, rec: cv2.typing.Rect, color: cv2.typing.Scalar, thickness: int = ..., lineType: int = ..., shift: int = ...) -> cv2.typing.MatLike: ... +@_typing.overload +def rectangle(img: UMat, rec: cv2.typing.Rect, color: cv2.typing.Scalar, thickness: int = ..., lineType: int = ..., shift: int = ...) -> UMat: ... + +def rectangleIntersectionArea(a: cv2.typing.Rect2d, b: cv2.typing.Rect2d) -> float: ... + +@_typing.overload +def rectify3Collinear(cameraMatrix1: cv2.typing.MatLike, distCoeffs1: cv2.typing.MatLike, cameraMatrix2: cv2.typing.MatLike, distCoeffs2: cv2.typing.MatLike, cameraMatrix3: cv2.typing.MatLike, distCoeffs3: cv2.typing.MatLike, imgpt1: _typing.Sequence[cv2.typing.MatLike], imgpt3: _typing.Sequence[cv2.typing.MatLike], imageSize: cv2.typing.Size, R12: cv2.typing.MatLike, T12: cv2.typing.MatLike, R13: cv2.typing.MatLike, T13: cv2.typing.MatLike, alpha: float, newImgSize: cv2.typing.Size, flags: int, R1: cv2.typing.MatLike | None = ..., R2: cv2.typing.MatLike | None = ..., R3: cv2.typing.MatLike | None = ..., P1: cv2.typing.MatLike | None = ..., P2: cv2.typing.MatLike | None = ..., P3: cv2.typing.MatLike | None = ..., Q: cv2.typing.MatLike | None = ...) -> tuple[float, cv2.typing.MatLike, cv2.typing.MatLike, cv2.typing.MatLike, cv2.typing.MatLike, cv2.typing.MatLike, cv2.typing.MatLike, cv2.typing.MatLike, cv2.typing.Rect, cv2.typing.Rect]: ... +@_typing.overload +def rectify3Collinear(cameraMatrix1: UMat, distCoeffs1: UMat, cameraMatrix2: UMat, distCoeffs2: UMat, cameraMatrix3: UMat, distCoeffs3: UMat, imgpt1: _typing.Sequence[UMat], imgpt3: _typing.Sequence[UMat], imageSize: cv2.typing.Size, R12: UMat, T12: UMat, R13: UMat, T13: UMat, alpha: float, newImgSize: cv2.typing.Size, flags: int, R1: UMat | None = ..., R2: UMat | None = ..., R3: UMat | None = ..., P1: UMat | None = ..., P2: UMat | None = ..., P3: UMat | None = ..., Q: UMat | None = ...) -> tuple[float, UMat, UMat, UMat, UMat, UMat, UMat, UMat, cv2.typing.Rect, cv2.typing.Rect]: ... + +@_typing.overload +def reduce(src: cv2.typing.MatLike, dim: int, rtype: int, dst: cv2.typing.MatLike | None = ..., dtype: int = ...) -> cv2.typing.MatLike: ... +@_typing.overload +def reduce(src: UMat, dim: int, rtype: int, dst: UMat | None = ..., dtype: int = ...) -> UMat: ... + +@_typing.overload +def reduceArgMax(src: cv2.typing.MatLike, axis: int, dst: cv2.typing.MatLike | None = ..., lastIndex: bool = ...) -> cv2.typing.MatLike: ... +@_typing.overload +def reduceArgMax(src: UMat, axis: int, dst: UMat | None = ..., lastIndex: bool = ...) -> UMat: ... + +@_typing.overload +def reduceArgMin(src: cv2.typing.MatLike, axis: int, dst: cv2.typing.MatLike | None = ..., lastIndex: bool = ...) -> cv2.typing.MatLike: ... +@_typing.overload +def reduceArgMin(src: UMat, axis: int, dst: UMat | None = ..., lastIndex: bool = ...) -> UMat: ... + +@_typing.overload +def remap(src: cv2.typing.MatLike, map1: cv2.typing.MatLike, map2: cv2.typing.MatLike, interpolation: int, dst: cv2.typing.MatLike | None = ..., borderMode: int = ..., borderValue: cv2.typing.Scalar = ...) -> cv2.typing.MatLike: ... +@_typing.overload +def remap(src: UMat, map1: UMat, map2: UMat, interpolation: int, dst: UMat | None = ..., borderMode: int = ..., borderValue: cv2.typing.Scalar = ...) -> UMat: ... + +@_typing.overload +def repeat(src: cv2.typing.MatLike, ny: int, nx: int, dst: cv2.typing.MatLike | None = ...) -> cv2.typing.MatLike: ... +@_typing.overload +def repeat(src: UMat, ny: int, nx: int, dst: UMat | None = ...) -> UMat: ... + +@_typing.overload +def reprojectImageTo3D(disparity: cv2.typing.MatLike, Q: cv2.typing.MatLike, _3dImage: cv2.typing.MatLike | None = ..., handleMissingValues: bool = ..., ddepth: int = ...) -> cv2.typing.MatLike: ... +@_typing.overload +def reprojectImageTo3D(disparity: UMat, Q: UMat, _3dImage: UMat | None = ..., handleMissingValues: bool = ..., ddepth: int = ...) -> UMat: ... + +@_typing.overload +def resize(src: cv2.typing.MatLike, dsize: cv2.typing.Size | None, dst: cv2.typing.MatLike | None = ..., fx: float = ..., fy: float = ..., interpolation: int = ...) -> cv2.typing.MatLike: ... +@_typing.overload +def resize(src: UMat, dsize: cv2.typing.Size | None, dst: UMat | None = ..., fx: float = ..., fy: float = ..., interpolation: int = ...) -> UMat: ... + +@_typing.overload +def resizeWindow(winname: str, width: int, height: int) -> None: ... +@_typing.overload +def resizeWindow(winname: str, size: cv2.typing.Size) -> None: ... + +@_typing.overload +def rotate(src: cv2.typing.MatLike, rotateCode: int, dst: cv2.typing.MatLike | None = ...) -> cv2.typing.MatLike: ... +@_typing.overload +def rotate(src: UMat, rotateCode: int, dst: UMat | None = ...) -> UMat: ... + +@_typing.overload +def rotatedRectangleIntersection(rect1: cv2.typing.RotatedRect, rect2: cv2.typing.RotatedRect, intersectingRegion: cv2.typing.MatLike | None = ...) -> tuple[int, cv2.typing.MatLike]: ... +@_typing.overload +def rotatedRectangleIntersection(rect1: cv2.typing.RotatedRect, rect2: cv2.typing.RotatedRect, intersectingRegion: UMat | None = ...) -> tuple[int, UMat]: ... + +@_typing.overload +def sampsonDistance(pt1: cv2.typing.MatLike, pt2: cv2.typing.MatLike, F: cv2.typing.MatLike) -> float: ... +@_typing.overload +def sampsonDistance(pt1: UMat, pt2: UMat, F: UMat) -> float: ... + +@_typing.overload +def scaleAdd(src1: cv2.typing.MatLike, alpha: float, src2: cv2.typing.MatLike, dst: cv2.typing.MatLike | None = ...) -> cv2.typing.MatLike: ... +@_typing.overload +def scaleAdd(src1: UMat, alpha: float, src2: UMat, dst: UMat | None = ...) -> UMat: ... + +@_typing.overload +def seamlessClone(src: cv2.typing.MatLike, dst: cv2.typing.MatLike, mask: cv2.typing.MatLike, p: cv2.typing.Point, flags: int, blend: cv2.typing.MatLike | None = ...) -> cv2.typing.MatLike: ... +@_typing.overload +def seamlessClone(src: UMat, dst: UMat, mask: UMat, p: cv2.typing.Point, flags: int, blend: UMat | None = ...) -> UMat: ... + +@_typing.overload +def selectROI(windowName: str, img: cv2.typing.MatLike, showCrosshair: bool = ..., fromCenter: bool = ..., printNotice: bool = ...) -> cv2.typing.Rect: ... +@_typing.overload +def selectROI(windowName: str, img: UMat, showCrosshair: bool = ..., fromCenter: bool = ..., printNotice: bool = ...) -> cv2.typing.Rect: ... +@_typing.overload +def selectROI(img: cv2.typing.MatLike, showCrosshair: bool = ..., fromCenter: bool = ..., printNotice: bool = ...) -> cv2.typing.Rect: ... +@_typing.overload +def selectROI(img: UMat, showCrosshair: bool = ..., fromCenter: bool = ..., printNotice: bool = ...) -> cv2.typing.Rect: ... + +@_typing.overload +def selectROIs(windowName: str, img: cv2.typing.MatLike, showCrosshair: bool = ..., fromCenter: bool = ..., printNotice: bool = ...) -> _typing.Sequence[cv2.typing.Rect]: ... +@_typing.overload +def selectROIs(windowName: str, img: UMat, showCrosshair: bool = ..., fromCenter: bool = ..., printNotice: bool = ...) -> _typing.Sequence[cv2.typing.Rect]: ... + +@_typing.overload +def sepFilter2D(src: cv2.typing.MatLike, ddepth: int, kernelX: cv2.typing.MatLike, kernelY: cv2.typing.MatLike, dst: cv2.typing.MatLike | None = ..., anchor: cv2.typing.Point = ..., delta: float = ..., borderType: int = ...) -> cv2.typing.MatLike: ... +@_typing.overload +def sepFilter2D(src: UMat, ddepth: int, kernelX: UMat, kernelY: UMat, dst: UMat | None = ..., anchor: cv2.typing.Point = ..., delta: float = ..., borderType: int = ...) -> UMat: ... + +@_typing.overload +def setIdentity(mtx: cv2.typing.MatLike, s: cv2.typing.Scalar = ...) -> cv2.typing.MatLike: ... +@_typing.overload +def setIdentity(mtx: UMat, s: cv2.typing.Scalar = ...) -> UMat: ... + +def setLogLevel(level: int) -> int: ... + +def setNumThreads(nthreads: int) -> None: ... + +def setRNGSeed(seed: int) -> None: ... + +def setTrackbarMax(trackbarname: str, winname: str, maxval: int) -> None: ... + +def setTrackbarMin(trackbarname: str, winname: str, minval: int) -> None: ... + +def setTrackbarPos(trackbarname: str, winname: str, pos: int) -> None: ... + +def setUseOpenVX(flag: bool) -> None: ... + +def setUseOptimized(onoff: bool) -> None: ... + +def setWindowProperty(winname: str, prop_id: int, prop_value: float) -> None: ... + +def setWindowTitle(winname: str, title: str) -> None: ... + +@_typing.overload +def solve(src1: cv2.typing.MatLike, src2: cv2.typing.MatLike, dst: cv2.typing.MatLike | None = ..., flags: int = ...) -> tuple[bool, cv2.typing.MatLike]: ... +@_typing.overload +def solve(src1: UMat, src2: UMat, dst: UMat | None = ..., flags: int = ...) -> tuple[bool, UMat]: ... + +@_typing.overload +def solveCubic(coeffs: cv2.typing.MatLike, roots: cv2.typing.MatLike | None = ...) -> tuple[int, cv2.typing.MatLike]: ... +@_typing.overload +def solveCubic(coeffs: UMat, roots: UMat | None = ...) -> tuple[int, UMat]: ... + +@_typing.overload +def solveLP(Func: cv2.typing.MatLike, Constr: cv2.typing.MatLike, constr_eps: float, z: cv2.typing.MatLike | None = ...) -> tuple[int, cv2.typing.MatLike]: ... +@_typing.overload +def solveLP(Func: UMat, Constr: UMat, constr_eps: float, z: UMat | None = ...) -> tuple[int, UMat]: ... +@_typing.overload +def solveLP(Func: cv2.typing.MatLike, Constr: cv2.typing.MatLike, z: cv2.typing.MatLike | None = ...) -> tuple[int, cv2.typing.MatLike]: ... +@_typing.overload +def solveLP(Func: UMat, Constr: UMat, z: UMat | None = ...) -> tuple[int, UMat]: ... + +@_typing.overload +def solveP3P(objectPoints: cv2.typing.MatLike, imagePoints: cv2.typing.MatLike, cameraMatrix: cv2.typing.MatLike, distCoeffs: cv2.typing.MatLike, flags: int, rvecs: _typing.Sequence[cv2.typing.MatLike] | None = ..., tvecs: _typing.Sequence[cv2.typing.MatLike] | None = ...) -> tuple[int, _typing.Sequence[cv2.typing.MatLike], _typing.Sequence[cv2.typing.MatLike]]: ... +@_typing.overload +def solveP3P(objectPoints: UMat, imagePoints: UMat, cameraMatrix: UMat, distCoeffs: UMat, flags: int, rvecs: _typing.Sequence[UMat] | None = ..., tvecs: _typing.Sequence[UMat] | None = ...) -> tuple[int, _typing.Sequence[UMat], _typing.Sequence[UMat]]: ... + +@_typing.overload +def solvePnP(objectPoints: cv2.typing.MatLike, imagePoints: cv2.typing.MatLike, cameraMatrix: cv2.typing.MatLike, distCoeffs: cv2.typing.MatLike, rvec: cv2.typing.MatLike | None = ..., tvec: cv2.typing.MatLike | None = ..., useExtrinsicGuess: bool = ..., flags: int = ...) -> tuple[bool, cv2.typing.MatLike, cv2.typing.MatLike]: ... +@_typing.overload +def solvePnP(objectPoints: UMat, imagePoints: UMat, cameraMatrix: UMat, distCoeffs: UMat, rvec: UMat | None = ..., tvec: UMat | None = ..., useExtrinsicGuess: bool = ..., flags: int = ...) -> tuple[bool, UMat, UMat]: ... + +@_typing.overload +def solvePnPGeneric(objectPoints: cv2.typing.MatLike, imagePoints: cv2.typing.MatLike, cameraMatrix: cv2.typing.MatLike, distCoeffs: cv2.typing.MatLike, rvecs: _typing.Sequence[cv2.typing.MatLike] | None = ..., tvecs: _typing.Sequence[cv2.typing.MatLike] | None = ..., useExtrinsicGuess: bool = ..., flags: SolvePnPMethod = ..., rvec: cv2.typing.MatLike | None = ..., tvec: cv2.typing.MatLike | None = ..., reprojectionError: cv2.typing.MatLike | None = ...) -> tuple[int, _typing.Sequence[cv2.typing.MatLike], _typing.Sequence[cv2.typing.MatLike], cv2.typing.MatLike]: ... +@_typing.overload +def solvePnPGeneric(objectPoints: UMat, imagePoints: UMat, cameraMatrix: UMat, distCoeffs: UMat, rvecs: _typing.Sequence[UMat] | None = ..., tvecs: _typing.Sequence[UMat] | None = ..., useExtrinsicGuess: bool = ..., flags: SolvePnPMethod = ..., rvec: UMat | None = ..., tvec: UMat | None = ..., reprojectionError: UMat | None = ...) -> tuple[int, _typing.Sequence[UMat], _typing.Sequence[UMat], UMat]: ... + +@_typing.overload +def solvePnPRansac(objectPoints: cv2.typing.MatLike, imagePoints: cv2.typing.MatLike, cameraMatrix: cv2.typing.MatLike, distCoeffs: cv2.typing.MatLike, rvec: cv2.typing.MatLike | None = ..., tvec: cv2.typing.MatLike | None = ..., useExtrinsicGuess: bool = ..., iterationsCount: int = ..., reprojectionError: float = ..., confidence: float = ..., inliers: cv2.typing.MatLike | None = ..., flags: int = ...) -> tuple[bool, cv2.typing.MatLike, cv2.typing.MatLike, cv2.typing.MatLike]: ... +@_typing.overload +def solvePnPRansac(objectPoints: UMat, imagePoints: UMat, cameraMatrix: UMat, distCoeffs: UMat, rvec: UMat | None = ..., tvec: UMat | None = ..., useExtrinsicGuess: bool = ..., iterationsCount: int = ..., reprojectionError: float = ..., confidence: float = ..., inliers: UMat | None = ..., flags: int = ...) -> tuple[bool, UMat, UMat, UMat]: ... +@_typing.overload +def solvePnPRansac(objectPoints: cv2.typing.MatLike, imagePoints: cv2.typing.MatLike, cameraMatrix: cv2.typing.MatLike, distCoeffs: cv2.typing.MatLike, rvec: cv2.typing.MatLike | None = ..., tvec: cv2.typing.MatLike | None = ..., inliers: cv2.typing.MatLike | None = ..., params: UsacParams = ...) -> tuple[bool, cv2.typing.MatLike, cv2.typing.MatLike, cv2.typing.MatLike, cv2.typing.MatLike]: ... +@_typing.overload +def solvePnPRansac(objectPoints: UMat, imagePoints: UMat, cameraMatrix: UMat, distCoeffs: UMat, rvec: UMat | None = ..., tvec: UMat | None = ..., inliers: UMat | None = ..., params: UsacParams = ...) -> tuple[bool, UMat, UMat, UMat, UMat]: ... + +@_typing.overload +def solvePnPRefineLM(objectPoints: cv2.typing.MatLike, imagePoints: cv2.typing.MatLike, cameraMatrix: cv2.typing.MatLike, distCoeffs: cv2.typing.MatLike, rvec: cv2.typing.MatLike, tvec: cv2.typing.MatLike, criteria: cv2.typing.TermCriteria = ...) -> tuple[cv2.typing.MatLike, cv2.typing.MatLike]: ... +@_typing.overload +def solvePnPRefineLM(objectPoints: UMat, imagePoints: UMat, cameraMatrix: UMat, distCoeffs: UMat, rvec: UMat, tvec: UMat, criteria: cv2.typing.TermCriteria = ...) -> tuple[UMat, UMat]: ... + +@_typing.overload +def solvePnPRefineVVS(objectPoints: cv2.typing.MatLike, imagePoints: cv2.typing.MatLike, cameraMatrix: cv2.typing.MatLike, distCoeffs: cv2.typing.MatLike, rvec: cv2.typing.MatLike, tvec: cv2.typing.MatLike, criteria: cv2.typing.TermCriteria = ..., VVSlambda: float = ...) -> tuple[cv2.typing.MatLike, cv2.typing.MatLike]: ... +@_typing.overload +def solvePnPRefineVVS(objectPoints: UMat, imagePoints: UMat, cameraMatrix: UMat, distCoeffs: UMat, rvec: UMat, tvec: UMat, criteria: cv2.typing.TermCriteria = ..., VVSlambda: float = ...) -> tuple[UMat, UMat]: ... + +@_typing.overload +def solvePoly(coeffs: cv2.typing.MatLike, roots: cv2.typing.MatLike | None = ..., maxIters: int = ...) -> tuple[float, cv2.typing.MatLike]: ... +@_typing.overload +def solvePoly(coeffs: UMat, roots: UMat | None = ..., maxIters: int = ...) -> tuple[float, UMat]: ... + +@_typing.overload +def sort(src: cv2.typing.MatLike, flags: int, dst: cv2.typing.MatLike | None = ...) -> cv2.typing.MatLike: ... +@_typing.overload +def sort(src: UMat, flags: int, dst: UMat | None = ...) -> UMat: ... + +@_typing.overload +def sortIdx(src: cv2.typing.MatLike, flags: int, dst: cv2.typing.MatLike | None = ...) -> cv2.typing.MatLike: ... +@_typing.overload +def sortIdx(src: UMat, flags: int, dst: UMat | None = ...) -> UMat: ... + +@_typing.overload +def spatialGradient(src: cv2.typing.MatLike, dx: cv2.typing.MatLike | None = ..., dy: cv2.typing.MatLike | None = ..., ksize: int = ..., borderType: int = ...) -> tuple[cv2.typing.MatLike, cv2.typing.MatLike]: ... +@_typing.overload +def spatialGradient(src: UMat, dx: UMat | None = ..., dy: UMat | None = ..., ksize: int = ..., borderType: int = ...) -> tuple[UMat, UMat]: ... + +@_typing.overload +def split(m: cv2.typing.MatLike, mv: _typing.Sequence[cv2.typing.MatLike] | None = ...) -> _typing.Sequence[cv2.typing.MatLike]: ... +@_typing.overload +def split(m: UMat, mv: _typing.Sequence[UMat] | None = ...) -> _typing.Sequence[UMat]: ... + +@_typing.overload +def sqrBoxFilter(src: cv2.typing.MatLike, ddepth: int, ksize: cv2.typing.Size, dst: cv2.typing.MatLike | None = ..., anchor: cv2.typing.Point = ..., normalize: bool = ..., borderType: int = ...) -> cv2.typing.MatLike: ... +@_typing.overload +def sqrBoxFilter(src: UMat, ddepth: int, ksize: cv2.typing.Size, dst: UMat | None = ..., anchor: cv2.typing.Point = ..., normalize: bool = ..., borderType: int = ...) -> UMat: ... + +@_typing.overload +def sqrt(src: cv2.typing.MatLike, dst: cv2.typing.MatLike | None = ...) -> cv2.typing.MatLike: ... +@_typing.overload +def sqrt(src: UMat, dst: UMat | None = ...) -> UMat: ... + +@_typing.overload +def stackBlur(src: cv2.typing.MatLike, ksize: cv2.typing.Size, dst: cv2.typing.MatLike | None = ...) -> cv2.typing.MatLike: ... +@_typing.overload +def stackBlur(src: UMat, ksize: cv2.typing.Size, dst: UMat | None = ...) -> UMat: ... + +def startWindowThread() -> int: ... + +@_typing.overload +def stereoCalibrate(objectPoints: _typing.Sequence[cv2.typing.MatLike], imagePoints1: _typing.Sequence[cv2.typing.MatLike], imagePoints2: _typing.Sequence[cv2.typing.MatLike], cameraMatrix1: cv2.typing.MatLike, distCoeffs1: cv2.typing.MatLike, cameraMatrix2: cv2.typing.MatLike, distCoeffs2: cv2.typing.MatLike, imageSize: cv2.typing.Size, R: cv2.typing.MatLike | None = ..., T: cv2.typing.MatLike | None = ..., E: cv2.typing.MatLike | None = ..., F: cv2.typing.MatLike | None = ..., flags: int = ..., criteria: cv2.typing.TermCriteria = ...) -> tuple[float, cv2.typing.MatLike, cv2.typing.MatLike, cv2.typing.MatLike, cv2.typing.MatLike, cv2.typing.MatLike, cv2.typing.MatLike, cv2.typing.MatLike, cv2.typing.MatLike]: ... +@_typing.overload +def stereoCalibrate(objectPoints: _typing.Sequence[UMat], imagePoints1: _typing.Sequence[UMat], imagePoints2: _typing.Sequence[UMat], cameraMatrix1: UMat, distCoeffs1: UMat, cameraMatrix2: UMat, distCoeffs2: UMat, imageSize: cv2.typing.Size, R: UMat | None = ..., T: UMat | None = ..., E: UMat | None = ..., F: UMat | None = ..., flags: int = ..., criteria: cv2.typing.TermCriteria = ...) -> tuple[float, UMat, UMat, UMat, UMat, UMat, UMat, UMat, UMat]: ... +@_typing.overload +def stereoCalibrate(objectPoints: _typing.Sequence[cv2.typing.MatLike], imagePoints1: _typing.Sequence[cv2.typing.MatLike], imagePoints2: _typing.Sequence[cv2.typing.MatLike], cameraMatrix1: cv2.typing.MatLike, distCoeffs1: cv2.typing.MatLike, cameraMatrix2: cv2.typing.MatLike, distCoeffs2: cv2.typing.MatLike, imageSize: cv2.typing.Size, R: cv2.typing.MatLike, T: cv2.typing.MatLike, E: cv2.typing.MatLike | None = ..., F: cv2.typing.MatLike | None = ..., perViewErrors: cv2.typing.MatLike | None = ..., flags: int = ..., criteria: cv2.typing.TermCriteria = ...) -> tuple[float, cv2.typing.MatLike, cv2.typing.MatLike, cv2.typing.MatLike, cv2.typing.MatLike, cv2.typing.MatLike, cv2.typing.MatLike, cv2.typing.MatLike, cv2.typing.MatLike, cv2.typing.MatLike]: ... +@_typing.overload +def stereoCalibrate(objectPoints: _typing.Sequence[UMat], imagePoints1: _typing.Sequence[UMat], imagePoints2: _typing.Sequence[UMat], cameraMatrix1: UMat, distCoeffs1: UMat, cameraMatrix2: UMat, distCoeffs2: UMat, imageSize: cv2.typing.Size, R: UMat, T: UMat, E: UMat | None = ..., F: UMat | None = ..., perViewErrors: UMat | None = ..., flags: int = ..., criteria: cv2.typing.TermCriteria = ...) -> tuple[float, UMat, UMat, UMat, UMat, UMat, UMat, UMat, UMat, UMat]: ... + +@_typing.overload +def stereoCalibrateExtended(objectPoints: _typing.Sequence[cv2.typing.MatLike], imagePoints1: _typing.Sequence[cv2.typing.MatLike], imagePoints2: _typing.Sequence[cv2.typing.MatLike], cameraMatrix1: cv2.typing.MatLike, distCoeffs1: cv2.typing.MatLike, cameraMatrix2: cv2.typing.MatLike, distCoeffs2: cv2.typing.MatLike, imageSize: cv2.typing.Size, R: cv2.typing.MatLike, T: cv2.typing.MatLike, E: cv2.typing.MatLike | None = ..., F: cv2.typing.MatLike | None = ..., rvecs: _typing.Sequence[cv2.typing.MatLike] | None = ..., tvecs: _typing.Sequence[cv2.typing.MatLike] | None = ..., perViewErrors: cv2.typing.MatLike | None = ..., flags: int = ..., criteria: cv2.typing.TermCriteria = ...) -> tuple[float, cv2.typing.MatLike, cv2.typing.MatLike, cv2.typing.MatLike, cv2.typing.MatLike, cv2.typing.MatLike, cv2.typing.MatLike, cv2.typing.MatLike, cv2.typing.MatLike, _typing.Sequence[cv2.typing.MatLike], _typing.Sequence[cv2.typing.MatLike], cv2.typing.MatLike]: ... +@_typing.overload +def stereoCalibrateExtended(objectPoints: _typing.Sequence[UMat], imagePoints1: _typing.Sequence[UMat], imagePoints2: _typing.Sequence[UMat], cameraMatrix1: UMat, distCoeffs1: UMat, cameraMatrix2: UMat, distCoeffs2: UMat, imageSize: cv2.typing.Size, R: UMat, T: UMat, E: UMat | None = ..., F: UMat | None = ..., rvecs: _typing.Sequence[UMat] | None = ..., tvecs: _typing.Sequence[UMat] | None = ..., perViewErrors: UMat | None = ..., flags: int = ..., criteria: cv2.typing.TermCriteria = ...) -> tuple[float, UMat, UMat, UMat, UMat, UMat, UMat, UMat, UMat, _typing.Sequence[UMat], _typing.Sequence[UMat], UMat]: ... + +@_typing.overload +def stereoRectify(cameraMatrix1: cv2.typing.MatLike, distCoeffs1: cv2.typing.MatLike, cameraMatrix2: cv2.typing.MatLike, distCoeffs2: cv2.typing.MatLike, imageSize: cv2.typing.Size, R: cv2.typing.MatLike, T: cv2.typing.MatLike, R1: cv2.typing.MatLike | None = ..., R2: cv2.typing.MatLike | None = ..., P1: cv2.typing.MatLike | None = ..., P2: cv2.typing.MatLike | None = ..., Q: cv2.typing.MatLike | None = ..., flags: int = ..., alpha: float = ..., newImageSize: cv2.typing.Size = ...) -> tuple[cv2.typing.MatLike, cv2.typing.MatLike, cv2.typing.MatLike, cv2.typing.MatLike, cv2.typing.MatLike, cv2.typing.Rect, cv2.typing.Rect]: ... +@_typing.overload +def stereoRectify(cameraMatrix1: UMat, distCoeffs1: UMat, cameraMatrix2: UMat, distCoeffs2: UMat, imageSize: cv2.typing.Size, R: UMat, T: UMat, R1: UMat | None = ..., R2: UMat | None = ..., P1: UMat | None = ..., P2: UMat | None = ..., Q: UMat | None = ..., flags: int = ..., alpha: float = ..., newImageSize: cv2.typing.Size = ...) -> tuple[UMat, UMat, UMat, UMat, UMat, cv2.typing.Rect, cv2.typing.Rect]: ... + +@_typing.overload +def stereoRectifyUncalibrated(points1: cv2.typing.MatLike, points2: cv2.typing.MatLike, F: cv2.typing.MatLike, imgSize: cv2.typing.Size, H1: cv2.typing.MatLike | None = ..., H2: cv2.typing.MatLike | None = ..., threshold: float = ...) -> tuple[bool, cv2.typing.MatLike, cv2.typing.MatLike]: ... +@_typing.overload +def stereoRectifyUncalibrated(points1: UMat, points2: UMat, F: UMat, imgSize: cv2.typing.Size, H1: UMat | None = ..., H2: UMat | None = ..., threshold: float = ...) -> tuple[bool, UMat, UMat]: ... + +@_typing.overload +def stylization(src: cv2.typing.MatLike, dst: cv2.typing.MatLike | None = ..., sigma_s: float = ..., sigma_r: float = ...) -> cv2.typing.MatLike: ... +@_typing.overload +def stylization(src: UMat, dst: UMat | None = ..., sigma_s: float = ..., sigma_r: float = ...) -> UMat: ... + +@_typing.overload +def subtract(src1: cv2.typing.MatLike, src2: cv2.typing.MatLike, dst: cv2.typing.MatLike | None = ..., mask: cv2.typing.MatLike | None = ..., dtype: int = ...) -> cv2.typing.MatLike: ... +@_typing.overload +def subtract(src1: UMat, src2: UMat, dst: UMat | None = ..., mask: UMat | None = ..., dtype: int = ...) -> UMat: ... + +@_typing.overload +def sumElems(src: cv2.typing.MatLike) -> cv2.typing.Scalar: ... +@_typing.overload +def sumElems(src: UMat) -> cv2.typing.Scalar: ... + +@_typing.overload +def textureFlattening(src: cv2.typing.MatLike, mask: cv2.typing.MatLike, dst: cv2.typing.MatLike | None = ..., low_threshold: float = ..., high_threshold: float = ..., kernel_size: int = ...) -> cv2.typing.MatLike: ... +@_typing.overload +def textureFlattening(src: UMat, mask: UMat, dst: UMat | None = ..., low_threshold: float = ..., high_threshold: float = ..., kernel_size: int = ...) -> UMat: ... + +@_typing.overload +def threshold(src: cv2.typing.MatLike, thresh: float, maxval: float, type: int, dst: cv2.typing.MatLike | None = ...) -> tuple[float, cv2.typing.MatLike]: ... +@_typing.overload +def threshold(src: UMat, thresh: float, maxval: float, type: int, dst: UMat | None = ...) -> tuple[float, UMat]: ... + +@_typing.overload +def trace(mtx: cv2.typing.MatLike) -> cv2.typing.Scalar: ... +@_typing.overload +def trace(mtx: UMat) -> cv2.typing.Scalar: ... + +@_typing.overload +def transform(src: cv2.typing.MatLike, m: cv2.typing.MatLike, dst: cv2.typing.MatLike | None = ...) -> cv2.typing.MatLike: ... +@_typing.overload +def transform(src: UMat, m: UMat, dst: UMat | None = ...) -> UMat: ... + +@_typing.overload +def transpose(src: cv2.typing.MatLike, dst: cv2.typing.MatLike | None = ...) -> cv2.typing.MatLike: ... +@_typing.overload +def transpose(src: UMat, dst: UMat | None = ...) -> UMat: ... + +@_typing.overload +def transposeND(src: cv2.typing.MatLike, order: _typing.Sequence[int], dst: cv2.typing.MatLike | None = ...) -> cv2.typing.MatLike: ... +@_typing.overload +def transposeND(src: UMat, order: _typing.Sequence[int], dst: UMat | None = ...) -> UMat: ... + +@_typing.overload +def triangulatePoints(projMatr1: cv2.typing.MatLike, projMatr2: cv2.typing.MatLike, projPoints1: cv2.typing.MatLike, projPoints2: cv2.typing.MatLike, points4D: cv2.typing.MatLike | None = ...) -> cv2.typing.MatLike: ... +@_typing.overload +def triangulatePoints(projMatr1: UMat, projMatr2: UMat, projPoints1: UMat, projPoints2: UMat, points4D: UMat | None = ...) -> UMat: ... + +@_typing.overload +def undistort(src: cv2.typing.MatLike, cameraMatrix: cv2.typing.MatLike, distCoeffs: cv2.typing.MatLike, dst: cv2.typing.MatLike | None = ..., newCameraMatrix: cv2.typing.MatLike | None = ...) -> cv2.typing.MatLike: ... +@_typing.overload +def undistort(src: UMat, cameraMatrix: UMat, distCoeffs: UMat, dst: UMat | None = ..., newCameraMatrix: UMat | None = ...) -> UMat: ... + +@_typing.overload +def undistortImagePoints(src: cv2.typing.MatLike, cameraMatrix: cv2.typing.MatLike, distCoeffs: cv2.typing.MatLike, dst: cv2.typing.MatLike | None = ..., arg1: cv2.typing.TermCriteria = ...) -> cv2.typing.MatLike: ... +@_typing.overload +def undistortImagePoints(src: UMat, cameraMatrix: UMat, distCoeffs: UMat, dst: UMat | None = ..., arg1: cv2.typing.TermCriteria = ...) -> UMat: ... + +@_typing.overload +def undistortPoints(src: cv2.typing.MatLike, cameraMatrix: cv2.typing.MatLike, distCoeffs: cv2.typing.MatLike, dst: cv2.typing.MatLike | None = ..., R: cv2.typing.MatLike | None = ..., P: cv2.typing.MatLike | None = ...) -> cv2.typing.MatLike: ... +@_typing.overload +def undistortPoints(src: UMat, cameraMatrix: UMat, distCoeffs: UMat, dst: UMat | None = ..., R: UMat | None = ..., P: UMat | None = ...) -> UMat: ... + +@_typing.overload +def undistortPointsIter(src: cv2.typing.MatLike, cameraMatrix: cv2.typing.MatLike, distCoeffs: cv2.typing.MatLike, R: cv2.typing.MatLike, P: cv2.typing.MatLike, criteria: cv2.typing.TermCriteria, dst: cv2.typing.MatLike | None = ...) -> cv2.typing.MatLike: ... +@_typing.overload +def undistortPointsIter(src: UMat, cameraMatrix: UMat, distCoeffs: UMat, R: UMat, P: UMat, criteria: cv2.typing.TermCriteria, dst: UMat | None = ...) -> UMat: ... + +def useOpenVX() -> bool: ... + +def useOptimized() -> bool: ... + +@_typing.overload +def validateDisparity(disparity: cv2.typing.MatLike, cost: cv2.typing.MatLike, minDisparity: int, numberOfDisparities: int, disp12MaxDisp: int = ...) -> cv2.typing.MatLike: ... +@_typing.overload +def validateDisparity(disparity: UMat, cost: UMat, minDisparity: int, numberOfDisparities: int, disp12MaxDisp: int = ...) -> UMat: ... + +@_typing.overload +def vconcat(src: _typing.Sequence[cv2.typing.MatLike], dst: cv2.typing.MatLike | None = ...) -> cv2.typing.MatLike: ... +@_typing.overload +def vconcat(src: _typing.Sequence[UMat], dst: UMat | None = ...) -> UMat: ... + +def waitKey(delay: int = ...) -> int: ... + +def waitKeyEx(delay: int = ...) -> int: ... + +@_typing.overload +def warpAffine(src: cv2.typing.MatLike, M: cv2.typing.MatLike, dsize: cv2.typing.Size, dst: cv2.typing.MatLike | None = ..., flags: int = ..., borderMode: int = ..., borderValue: cv2.typing.Scalar = ...) -> cv2.typing.MatLike: ... +@_typing.overload +def warpAffine(src: UMat, M: UMat, dsize: cv2.typing.Size, dst: UMat | None = ..., flags: int = ..., borderMode: int = ..., borderValue: cv2.typing.Scalar = ...) -> UMat: ... + +@_typing.overload +def warpPerspective(src: cv2.typing.MatLike, M: cv2.typing.MatLike, dsize: cv2.typing.Size, dst: cv2.typing.MatLike | None = ..., flags: int = ..., borderMode: int = ..., borderValue: cv2.typing.Scalar = ...) -> cv2.typing.MatLike: ... +@_typing.overload +def warpPerspective(src: UMat, M: UMat, dsize: cv2.typing.Size, dst: UMat | None = ..., flags: int = ..., borderMode: int = ..., borderValue: cv2.typing.Scalar = ...) -> UMat: ... + +@_typing.overload +def warpPolar(src: cv2.typing.MatLike, dsize: cv2.typing.Size, center: cv2.typing.Point2f, maxRadius: float, flags: int, dst: cv2.typing.MatLike | None = ...) -> cv2.typing.MatLike: ... +@_typing.overload +def warpPolar(src: UMat, dsize: cv2.typing.Size, center: cv2.typing.Point2f, maxRadius: float, flags: int, dst: UMat | None = ...) -> UMat: ... + +@_typing.overload +def watershed(image: cv2.typing.MatLike, markers: cv2.typing.MatLike) -> cv2.typing.MatLike: ... +@_typing.overload +def watershed(image: UMat, markers: UMat) -> UMat: ... + +@_typing.overload +def writeOpticalFlow(path: str, flow: cv2.typing.MatLike) -> bool: ... +@_typing.overload +def writeOpticalFlow(path: str, flow: UMat) -> bool: ... + +def createTrackbar(trackbarName: str, windowName: str, value: int, count: int, onChange: _typing.Callable[[int], None]) -> None: ... + +def createButton(buttonName: str, onChange: _typing.Callable[[tuple[int] | tuple[int, _typing.Any]], None], userData: _typing.Any | None = ..., buttonType: int = ..., initialButtonState: int = ...) -> None: ... + +def setMouseCallback(windowName: str, onMouse: _typing.Callable[[int, int, int, int, _typing.Any | None], None], param: _typing.Any | None = ...) -> None: ... + +def CV_8UC(channels: int) -> int: ... + +def CV_8SC(channels: int) -> int: ... + +def CV_16UC(channels: int) -> int: ... + +def CV_16SC(channels: int) -> int: ... + +def CV_32SC(channels: int) -> int: ... + +def CV_32FC(channels: int) -> int: ... + +def CV_64FC(channels: int) -> int: ... + +def CV_16FC(channels: int) -> int: ... + +def CV_MAKETYPE(depth: int, channels: int) -> int: ... + +def dnn_registerLayer(layerTypeName: str, layerClass: _typing.Type[cv2.dnn.LayerProtocol]) -> None: ... + +def dnn_unregisterLayer(layerTypeName: str) -> None: ... + +def redirectError(onError: _typing.Callable[[int, str, str, str, int], None] | None) -> None: ... + + diff --git a/.venv/lib/python3.11/site-packages/cv2/config-3.py b/.venv/lib/python3.11/site-packages/cv2/config-3.py new file mode 100644 index 0000000000000000000000000000000000000000..587a42bfacd8401281a51fe5dbc8ea44f4e156d5 --- /dev/null +++ b/.venv/lib/python3.11/site-packages/cv2/config-3.py @@ -0,0 +1,24 @@ +PYTHON_EXTENSIONS_PATHS = [ + LOADER_DIR +] + PYTHON_EXTENSIONS_PATHS + +ci_and_not_headless = False + +try: + from .version import ci_build, headless + + ci_and_not_headless = ci_build and not headless +except: + pass + +# the Qt plugin is included currently only in the pre-built wheels +if sys.platform.startswith("linux") and ci_and_not_headless: + os.environ["QT_QPA_PLATFORM_PLUGIN_PATH"] = os.path.join( + os.path.dirname(os.path.abspath(__file__)), "qt", "plugins" + ) + +# Qt will throw warning on Linux if fonts are not found +if sys.platform.startswith("linux") and ci_and_not_headless: + os.environ["QT_QPA_FONTDIR"] = os.path.join( + os.path.dirname(os.path.abspath(__file__)), "qt", "fonts" + ) diff --git a/.venv/lib/python3.11/site-packages/cv2/config.py b/.venv/lib/python3.11/site-packages/cv2/config.py new file mode 100644 index 0000000000000000000000000000000000000000..a95fbcf0db643541979c557588cb264233bc6891 --- /dev/null +++ b/.venv/lib/python3.11/site-packages/cv2/config.py @@ -0,0 +1,5 @@ +import os + +BINARIES_PATHS = [ + os.path.join(os.path.join(LOADER_DIR, '../../'), 'lib64') +] + BINARIES_PATHS diff --git a/.venv/lib/python3.11/site-packages/cv2/data/haarcascade_frontalcatface.xml b/.venv/lib/python3.11/site-packages/cv2/data/haarcascade_frontalcatface.xml new file mode 100644 index 0000000000000000000000000000000000000000..1c38a8bab222c50f2962713930b7e971c6cc3abc --- /dev/null +++ b/.venv/lib/python3.11/site-packages/cv2/data/haarcascade_frontalcatface.xml @@ -0,0 +1,14382 @@ + + + + + BOOST + HAAR + 24 + 24 + + GAB + 9.9500000476837158e-01 + 5.0000000000000000e-01 + 9.4999999999999996e-01 + 1 + 100 + + 0 + 1 + BASIC + 20 + + + <_> + 16 + -1.4806525707244873e+00 + + <_> + + 0 -1 472 -1.5126220881938934e-02 + + 7.5887596607208252e-01 -3.4230688214302063e-01 + <_> + + 0 -1 839 3.9337221533060074e-03 + + -3.3288389444351196e-01 5.2361363172531128e-01 + <_> + + 0 -1 858 -1.5044892206788063e-02 + + 5.5565774440765381e-01 -2.2505992650985718e-01 + <_> + + 0 -1 387 -1.2927042320370674e-02 + + 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sys.version_info[:2] < (3, 0): + def exec_file_wrapper(fpath, g_vars, l_vars): + execfile(fpath, g_vars, l_vars) diff --git a/.venv/lib/python3.11/site-packages/cv2/load_config_py3.py b/.venv/lib/python3.11/site-packages/cv2/load_config_py3.py new file mode 100644 index 0000000000000000000000000000000000000000..6f3b21ab862d42ed4572bcd52c2c41a72dbc0521 --- /dev/null +++ b/.venv/lib/python3.11/site-packages/cv2/load_config_py3.py @@ -0,0 +1,9 @@ +# flake8: noqa +import os +import sys + +if sys.version_info[:2] >= (3, 0): + def exec_file_wrapper(fpath, g_vars, l_vars): + with open(fpath) as f: + code = compile(f.read(), os.path.basename(fpath), 'exec') + exec(code, g_vars, l_vars) diff --git a/.venv/lib/python3.11/site-packages/cv2/mat_wrapper/__pycache__/__init__.cpython-311.pyc b/.venv/lib/python3.11/site-packages/cv2/mat_wrapper/__pycache__/__init__.cpython-311.pyc new file mode 100644 index 0000000000000000000000000000000000000000..611886636033bcd6010018c381504367db8a13b7 Binary files /dev/null and b/.venv/lib/python3.11/site-packages/cv2/mat_wrapper/__pycache__/__init__.cpython-311.pyc differ diff --git a/.venv/lib/python3.11/site-packages/cv2/py.typed b/.venv/lib/python3.11/site-packages/cv2/py.typed new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/.venv/lib/python3.11/site-packages/cv2/version.py b/.venv/lib/python3.11/site-packages/cv2/version.py new file mode 100644 index 0000000000000000000000000000000000000000..39dd0c8a6cfaac3207b31648d5812f122dcb20aa --- /dev/null +++ b/.venv/lib/python3.11/site-packages/cv2/version.py @@ -0,0 +1,5 @@ +opencv_version = "4.11.0.86" +contrib = False +headless = True +rolling = False +ci_build = True \ No newline at end of file diff --git a/.venv/lib/python3.11/site-packages/cv2/videoio_registry/__init__.pyi b/.venv/lib/python3.11/site-packages/cv2/videoio_registry/__init__.pyi new file mode 100644 index 0000000000000000000000000000000000000000..5dd99d09bf5994b43e81728b25b84465d197cb76 --- /dev/null +++ b/.venv/lib/python3.11/site-packages/cv2/videoio_registry/__init__.pyi @@ -0,0 +1,31 @@ +__all__: list[str] = [] + +import cv2 + + +# Functions +def getBackendName(api: cv2.VideoCaptureAPIs) -> str: ... + +def getBackends() -> _typing.Sequence[cv2.VideoCaptureAPIs]: ... + +def getCameraBackendPluginVersion(api: cv2.VideoCaptureAPIs) -> tuple[str, int, int]: ... + +def getCameraBackends() -> _typing.Sequence[cv2.VideoCaptureAPIs]: ... + +def getStreamBackendPluginVersion(api: cv2.VideoCaptureAPIs) -> tuple[str, int, int]: ... + +def getStreamBackends() -> _typing.Sequence[cv2.VideoCaptureAPIs]: ... + +def getStreamBufferedBackendPluginVersion(api: cv2.VideoCaptureAPIs) -> tuple[str, int, int]: ... + +def getStreamBufferedBackends() -> _typing.Sequence[cv2.VideoCaptureAPIs]: ... + +def getWriterBackendPluginVersion(api: cv2.VideoCaptureAPIs) -> tuple[str, int, int]: ... + +def getWriterBackends() -> _typing.Sequence[cv2.VideoCaptureAPIs]: ... + +def hasBackend(api: cv2.VideoCaptureAPIs) -> bool: ... + +def isBackendBuiltIn(api: cv2.VideoCaptureAPIs) -> bool: ... + + diff --git a/.venv/lib/python3.11/site-packages/dill-0.3.9.dist-info/INSTALLER b/.venv/lib/python3.11/site-packages/dill-0.3.9.dist-info/INSTALLER new file mode 100644 index 0000000000000000000000000000000000000000..a1b589e38a32041e49332e5e81c2d363dc418d68 --- /dev/null +++ b/.venv/lib/python3.11/site-packages/dill-0.3.9.dist-info/INSTALLER @@ -0,0 +1 @@ +pip diff --git a/.venv/lib/python3.11/site-packages/dill-0.3.9.dist-info/LICENSE b/.venv/lib/python3.11/site-packages/dill-0.3.9.dist-info/LICENSE new file mode 100644 index 0000000000000000000000000000000000000000..1eb999e0babe28897c4544d034b36f5f0fe77ca6 --- /dev/null +++ b/.venv/lib/python3.11/site-packages/dill-0.3.9.dist-info/LICENSE @@ -0,0 +1,35 @@ +Copyright (c) 2004-2016 California Institute of Technology. +Copyright (c) 2016-2024 The Uncertainty Quantification Foundation. +All rights reserved. + +This software is available subject to the conditions and terms laid +out below. By downloading and using this software you are agreeing +to the following conditions. + +Redistribution and use in source and binary forms, with or without +modification, are permitted provided that the following conditions +are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + - Neither the names of the copyright holders nor the names of any of + the contributors may be used to endorse or promote products derived + from this software without specific prior written permission. + +THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS +"AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED +TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR +PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR +CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, +EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, +PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; +OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, +WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR +OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF +ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + diff --git a/.venv/lib/python3.11/site-packages/dill-0.3.9.dist-info/METADATA b/.venv/lib/python3.11/site-packages/dill-0.3.9.dist-info/METADATA new file mode 100644 index 0000000000000000000000000000000000000000..3397cd0a0120e3e1a45094165d28b2e904e815a8 --- /dev/null +++ b/.venv/lib/python3.11/site-packages/dill-0.3.9.dist-info/METADATA @@ -0,0 +1,281 @@ +Metadata-Version: 2.1 +Name: dill +Version: 0.3.9 +Summary: serialize all of Python +Home-page: https://github.com/uqfoundation/dill +Download-URL: https://pypi.org/project/dill/#files +Author: Mike McKerns +Author-email: mmckerns@uqfoundation.org +Maintainer: Mike McKerns +Maintainer-email: mmckerns@uqfoundation.org +License: BSD-3-Clause +Project-URL: Documentation, http://dill.rtfd.io +Project-URL: Source Code, https://github.com/uqfoundation/dill +Project-URL: Bug Tracker, https://github.com/uqfoundation/dill/issues +Platform: Linux +Platform: Windows +Platform: Mac +Classifier: Development Status :: 5 - Production/Stable +Classifier: Intended Audience :: Developers +Classifier: Intended Audience :: Science/Research +Classifier: License :: OSI Approved :: BSD License +Classifier: Programming Language :: Python :: 3 +Classifier: Programming Language :: Python :: 3.8 +Classifier: Programming Language :: Python :: 3.9 +Classifier: Programming Language :: Python :: 3.10 +Classifier: Programming Language :: Python :: 3.11 +Classifier: Programming Language :: Python :: 3.12 +Classifier: Programming Language :: Python :: 3.13 +Classifier: Programming Language :: Python :: Implementation :: CPython +Classifier: Programming Language :: Python :: Implementation :: PyPy +Classifier: Topic :: Scientific/Engineering +Classifier: Topic :: Software Development +Requires-Python: >=3.8 +License-File: LICENSE +Provides-Extra: graph +Requires-Dist: objgraph >=1.7.2 ; extra == 'graph' +Provides-Extra: profile +Requires-Dist: gprof2dot >=2022.7.29 ; extra == 'profile' +Provides-Extra: readline + +----------------------------- +dill: serialize all of Python +----------------------------- + +About Dill +========== + +``dill`` extends Python's ``pickle`` module for serializing and de-serializing +Python objects to the majority of the built-in Python types. Serialization +is the process of converting an object to a byte stream, and the inverse +of which is converting a byte stream back to a Python object hierarchy. + +``dill`` provides the user the same interface as the ``pickle`` module, and +also includes some additional features. In addition to pickling Python +objects, ``dill`` provides the ability to save the state of an interpreter +session in a single command. Hence, it would be feasible to save an +interpreter session, close the interpreter, ship the pickled file to +another computer, open a new interpreter, unpickle the session and +thus continue from the 'saved' state of the original interpreter +session. + +``dill`` can be used to store Python objects to a file, but the primary +usage is to send Python objects across the network as a byte stream. +``dill`` is quite flexible, and allows arbitrary user defined classes +and functions to be serialized. Thus ``dill`` is not intended to be +secure against erroneously or maliciously constructed data. It is +left to the user to decide whether the data they unpickle is from +a trustworthy source. + +``dill`` is part of ``pathos``, a Python framework for heterogeneous computing. +``dill`` is in active development, so any user feedback, bug reports, comments, +or suggestions are highly appreciated. A list of issues is located at +https://github.com/uqfoundation/dill/issues, with a legacy list maintained at +https://uqfoundation.github.io/project/pathos/query. + + +Major Features +============== + +``dill`` can pickle the following standard types: + + - none, type, bool, int, float, complex, bytes, str, + - tuple, list, dict, file, buffer, builtin, + - Python classes, namedtuples, dataclasses, metaclasses, + - instances of classes, + - set, frozenset, array, functions, exceptions + +``dill`` can also pickle more 'exotic' standard types: + + - functions with yields, nested functions, lambdas, + - cell, method, unboundmethod, module, code, methodwrapper, + - methoddescriptor, getsetdescriptor, memberdescriptor, wrapperdescriptor, + - dictproxy, slice, notimplemented, ellipsis, quit + +``dill`` cannot yet pickle these standard types: + + - frame, generator, traceback + +``dill`` also provides the capability to: + + - save and load Python interpreter sessions + - save and extract the source code from functions and classes + - interactively diagnose pickling errors + + +Current Release +=============== + +The latest released version of ``dill`` is available from: + + https://pypi.org/project/dill + +``dill`` is distributed under a 3-clause BSD license. + + +Development Version +=================== + +You can get the latest development version with all the shiny new features at: + + https://github.com/uqfoundation + +If you have a new contribution, please submit a pull request. + + +Installation +============ + +``dill`` can be installed with ``pip``:: + + $ pip install dill + +To optionally include the ``objgraph`` diagnostic tool in the install:: + + $ pip install dill[graph] + +To optionally include the ``gprof2dot`` diagnostic tool in the install:: + + $ pip install dill[profile] + +For windows users, to optionally install session history tools:: + + $ pip install dill[readline] + + +Requirements +============ + +``dill`` requires: + + - ``python`` (or ``pypy``), **>=3.8** + - ``setuptools``, **>=42** + +Optional requirements: + + - ``objgraph``, **>=1.7.2** + - ``gprof2dot``, **>=2022.7.29** + - ``pyreadline``, **>=1.7.1** (on windows) + + +Basic Usage +=========== + +``dill`` is a drop-in replacement for ``pickle``. Existing code can be +updated to allow complete pickling using:: + + >>> import dill as pickle + +or:: + + >>> from dill import dumps, loads + +``dumps`` converts the object to a unique byte string, and ``loads`` performs +the inverse operation:: + + >>> squared = lambda x: x**2 + >>> loads(dumps(squared))(3) + 9 + +There are a number of options to control serialization which are provided +as keyword arguments to several ``dill`` functions: + +* with *protocol*, the pickle protocol level can be set. This uses the + same value as the ``pickle`` module, *DEFAULT_PROTOCOL*. +* with *byref=True*, ``dill`` to behave a lot more like pickle with + certain objects (like modules) pickled by reference as opposed to + attempting to pickle the object itself. +* with *recurse=True*, objects referred to in the global dictionary are + recursively traced and pickled, instead of the default behavior of + attempting to store the entire global dictionary. +* with *fmode*, the contents of the file can be pickled along with the file + handle, which is useful if the object is being sent over the wire to a + remote system which does not have the original file on disk. Options are + *HANDLE_FMODE* for just the handle, *CONTENTS_FMODE* for the file content + and *FILE_FMODE* for content and handle. +* with *ignore=False*, objects reconstructed with types defined in the + top-level script environment use the existing type in the environment + rather than a possibly different reconstructed type. + +The default serialization can also be set globally in *dill.settings*. +Thus, we can modify how ``dill`` handles references to the global dictionary +locally or globally:: + + >>> import dill.settings + >>> dumps(absolute) == dumps(absolute, recurse=True) + False + >>> dill.settings['recurse'] = True + >>> dumps(absolute) == dumps(absolute, recurse=True) + True + +``dill`` also includes source code inspection, as an alternate to pickling:: + + >>> import dill.source + >>> print(dill.source.getsource(squared)) + squared = lambda x:x**2 + +To aid in debugging pickling issues, use *dill.detect* which provides +tools like pickle tracing:: + + >>> import dill.detect + >>> with dill.detect.trace(): + >>> dumps(squared) + ┬ F1: at 0x7fe074f8c280> + ├┬ F2: + │└ # F2 [34 B] + ├┬ Co: at 0x7fe07501eb30, file "", line 1> + │├┬ F2: + ││└ # F2 [19 B] + │└ # Co [87 B] + ├┬ D1: + │└ # D1 [22 B] + ├┬ D2: + │└ # D2 [2 B] + ├┬ D2: + │├┬ D2: + ││└ # D2 [2 B] + │└ # D2 [23 B] + └ # F1 [180 B] + +With trace, we see how ``dill`` stored the lambda (``F1``) by first storing +``_create_function``, the underlying code object (``Co``) and ``_create_code`` +(which is used to handle code objects), then we handle the reference to +the global dict (``D2``) plus other dictionaries (``D1`` and ``D2``) that +save the lambda object's state. A ``#`` marks when the object is actually stored. + + +More Information +================ + +Probably the best way to get started is to look at the documentation at +http://dill.rtfd.io. Also see ``dill.tests`` for a set of scripts that +demonstrate how ``dill`` can serialize different Python objects. You can +run the test suite with ``python -m dill.tests``. The contents of any +pickle file can be examined with ``undill``. As ``dill`` conforms to +the ``pickle`` interface, the examples and documentation found at +http://docs.python.org/library/pickle.html also apply to ``dill`` +if one will ``import dill as pickle``. The source code is also generally +well documented, so further questions may be resolved by inspecting the +code itself. Please feel free to submit a ticket on github, or ask a +question on stackoverflow (**@Mike McKerns**). +If you would like to share how you use ``dill`` in your work, please send +an email (to **mmckerns at uqfoundation dot org**). + + +Citation +======== + +If you use ``dill`` to do research that leads to publication, we ask that you +acknowledge use of ``dill`` by citing the following in your publication:: + + M.M. McKerns, L. Strand, T. Sullivan, A. Fang, M.A.G. Aivazis, + "Building a framework for predictive science", Proceedings of + the 10th Python in Science Conference, 2011; + http://arxiv.org/pdf/1202.1056 + + Michael McKerns and Michael Aivazis, + "pathos: a framework for heterogeneous computing", 2010- ; + https://uqfoundation.github.io/project/pathos + +Please see https://uqfoundation.github.io/project/pathos or +http://arxiv.org/pdf/1202.1056 for further information. diff --git a/.venv/lib/python3.11/site-packages/dill-0.3.9.dist-info/RECORD b/.venv/lib/python3.11/site-packages/dill-0.3.9.dist-info/RECORD new file mode 100644 index 0000000000000000000000000000000000000000..cb5a4bda8177198b0d321fe5784699bf92dbb1af --- /dev/null +++ b/.venv/lib/python3.11/site-packages/dill-0.3.9.dist-info/RECORD @@ -0,0 +1,101 @@ +../../../bin/get_gprof,sha256=RgBLTDxh91z853BWSUCGy-Qd7fY8DkEJid6ngHVUK4c,2475 +../../../bin/get_objgraph,sha256=jw_MPxK2KzpuMBPHKQtiA62seFSGET8ZFg9xHHh-dwk,1669 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100644 index 0000000000000000000000000000000000000000..bab98d675883cc7567a79df485cd7b4f015e376f --- /dev/null +++ b/.venv/lib/python3.11/site-packages/dill-0.3.9.dist-info/WHEEL @@ -0,0 +1,5 @@ +Wheel-Version: 1.0 +Generator: bdist_wheel (0.43.0) +Root-Is-Purelib: true +Tag: py3-none-any + diff --git a/.venv/lib/python3.11/site-packages/dill-0.3.9.dist-info/top_level.txt b/.venv/lib/python3.11/site-packages/dill-0.3.9.dist-info/top_level.txt new file mode 100644 index 0000000000000000000000000000000000000000..85eea7018a40c657c08ef73fcf3a39024b2df2cb --- /dev/null +++ b/.venv/lib/python3.11/site-packages/dill-0.3.9.dist-info/top_level.txt @@ -0,0 +1 @@ +dill diff --git a/.venv/lib/python3.11/site-packages/google_ai_generativelanguage-0.6.15.dist-info/INSTALLER b/.venv/lib/python3.11/site-packages/google_ai_generativelanguage-0.6.15.dist-info/INSTALLER new file mode 100644 index 0000000000000000000000000000000000000000..a1b589e38a32041e49332e5e81c2d363dc418d68 --- /dev/null +++ b/.venv/lib/python3.11/site-packages/google_ai_generativelanguage-0.6.15.dist-info/INSTALLER @@ -0,0 +1 @@ +pip diff --git a/.venv/lib/python3.11/site-packages/google_ai_generativelanguage-0.6.15.dist-info/LICENSE b/.venv/lib/python3.11/site-packages/google_ai_generativelanguage-0.6.15.dist-info/LICENSE new file mode 100644 index 0000000000000000000000000000000000000000..d645695673349e3947e8e5ae42332d0ac3164cd7 --- /dev/null +++ b/.venv/lib/python3.11/site-packages/google_ai_generativelanguage-0.6.15.dist-info/LICENSE @@ -0,0 +1,202 @@ + + Apache License + Version 2.0, January 2004 + http://www.apache.org/licenses/ + + TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION + + 1. Definitions. + + "License" shall mean the terms and conditions for use, reproduction, + and distribution as defined by Sections 1 through 9 of this document. + + "Licensor" shall mean the copyright owner or entity authorized by + the copyright owner that is granting the License. + + "Legal Entity" shall mean the union of the acting entity and all + other entities that control, are controlled by, or are under common + control with that entity. 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Gemini is our most capable model, built from the ground up to be multimodal. It can generalize and seamlessly understand, operate across, and combine different types of information including language, images, audio, video, and code. You can use the Gemini API for use cases like reasoning across text and images, content generation, dialogue agents, summarization and classification systems, and more. + +- `Client Library Documentation`_ +- `Product Documentation`_ + +.. |preview| image:: https://img.shields.io/badge/support-preview-orange.svg + :target: https://github.com/googleapis/google-cloud-python/blob/main/README.rst#stability-levels +.. |pypi| image:: https://img.shields.io/pypi/v/google-ai-generativelanguage.svg + :target: https://pypi.org/project/google-ai-generativelanguage/ +.. |versions| image:: https://img.shields.io/pypi/pyversions/google-ai-generativelanguage.svg + :target: https://pypi.org/project/google-ai-generativelanguage/ +.. _Generative Language API: https://ai.google.dev/docs +.. _Client Library Documentation: https://googleapis.dev/python/generativelanguage/latest +.. _Product Documentation: https://ai.google.dev/docs + +Quick Start +----------- + +In order to use this library, you first need to go through the following steps: + +1. `Select or create a Cloud Platform project.`_ +2. `Enable billing for your project.`_ +3. `Enable the Generative Language API.`_ +4. `Setup Authentication.`_ + +.. _Select or create a Cloud Platform project.: https://console.cloud.google.com/project +.. _Enable billing for your project.: https://cloud.google.com/billing/docs/how-to/modify-project#enable_billing_for_a_project +.. _Enable the Generative Language API.: https://ai.google.dev/docs +.. _Setup Authentication.: https://googleapis.dev/python/google-api-core/latest/auth.html + +Installation +~~~~~~~~~~~~ + +Install this library in a virtual environment using `venv`_. `venv`_ is a tool that +creates isolated Python environments. These isolated environments can have separate +versions of Python packages, which allows you to isolate one project's dependencies +from the dependencies of other projects. + +With `venv`_, it's possible to install this library without needing system +install permissions, and without clashing with the installed system +dependencies. + +.. _`venv`: https://docs.python.org/3/library/venv.html + + +Code samples and snippets +~~~~~~~~~~~~~~~~~~~~~~~~~ + +Code samples and snippets live in the `samples/`_ folder. + +.. _samples/: https://github.com/googleapis/google-cloud-python/tree/main/packages/google-ai-generativelanguage/samples + + +Supported Python Versions +^^^^^^^^^^^^^^^^^^^^^^^^^ +Our client libraries are compatible with all current `active`_ and `maintenance`_ versions of +Python. + +Python >= 3.7 + +.. _active: https://devguide.python.org/devcycle/#in-development-main-branch +.. _maintenance: https://devguide.python.org/devcycle/#maintenance-branches + +Unsupported Python Versions +^^^^^^^^^^^^^^^^^^^^^^^^^^^ +Python <= 3.6 + +If you are using an `end-of-life`_ +version of Python, we recommend that you update as soon as possible to an actively supported version. + +.. _end-of-life: https://devguide.python.org/devcycle/#end-of-life-branches + +Mac/Linux +^^^^^^^^^ + +.. code-block:: console + + python3 -m venv + source /bin/activate + pip install google-ai-generativelanguage + + +Windows +^^^^^^^ + +.. code-block:: console + + py -m venv + .\\Scripts\activate + pip install google-ai-generativelanguage + +Next Steps +~~~~~~~~~~ + +- Read the `Client Library Documentation`_ for Generative Language API + to see other available methods on the client. +- Read the `Generative Language API Product documentation`_ to learn + more about the product and see How-to Guides. +- View this `README`_ to see the full list of Cloud + APIs that we cover. + +.. _Generative Language API Product documentation: https://ai.google.dev/docs +.. _README: 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+Tag: py3-none-any + diff --git a/.venv/lib/python3.11/site-packages/google_ai_generativelanguage-0.6.15.dist-info/top_level.txt b/.venv/lib/python3.11/site-packages/google_ai_generativelanguage-0.6.15.dist-info/top_level.txt new file mode 100644 index 0000000000000000000000000000000000000000..cb429113e0f9a73019fd799e8052093fea7f0c8b --- /dev/null +++ b/.venv/lib/python3.11/site-packages/google_ai_generativelanguage-0.6.15.dist-info/top_level.txt @@ -0,0 +1 @@ +google diff --git a/.venv/lib/python3.11/site-packages/iniconfig/__init__.py b/.venv/lib/python3.11/site-packages/iniconfig/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..c18a8e4b8f07ac2efc18aa662cb5f6fc9b44f13e --- /dev/null +++ b/.venv/lib/python3.11/site-packages/iniconfig/__init__.py @@ -0,0 +1,216 @@ +""" brain-dead simple parser for ini-style files. +(C) Ronny Pfannschmidt, Holger Krekel -- MIT licensed +""" +from __future__ import annotations +from typing import ( + Callable, + Iterator, + Mapping, + Optional, + Tuple, + TypeVar, + Union, + TYPE_CHECKING, + NoReturn, + NamedTuple, + overload, + cast, +) + +import os + +if TYPE_CHECKING: + from typing_extensions import Final + +__all__ = ["IniConfig", "ParseError", "COMMENTCHARS", "iscommentline"] + +from .exceptions import ParseError +from . import _parse +from ._parse import COMMENTCHARS, iscommentline + +_D = TypeVar("_D") +_T = TypeVar("_T") + + +class SectionWrapper: + config: Final[IniConfig] + name: Final[str] + + def __init__(self, config: IniConfig, name: str) -> None: + self.config = config + self.name = name + + def lineof(self, name: str) -> int | None: + return self.config.lineof(self.name, name) + + @overload + def get(self, key: str) -> str | None: + ... + + @overload + def get( + self, + key: str, + convert: Callable[[str], _T], + ) -> _T | None: + ... + + @overload + def get( + self, + key: str, + default: None, + convert: Callable[[str], _T], + ) -> _T | None: + ... + + @overload + def get(self, key: str, default: _D, convert: None = None) -> str | _D: + ... + + @overload + def get( + self, + key: str, + default: _D, + convert: Callable[[str], _T], + ) -> _T | _D: + ... + + # TODO: investigate possible mypy bug wrt matching the passed over data + def get( # type: ignore [misc] + self, + key: str, + default: _D | None = None, + convert: Callable[[str], _T] | None = None, + ) -> _D | _T | str | None: + return self.config.get(self.name, key, convert=convert, default=default) + + def __getitem__(self, key: str) -> str: + return self.config.sections[self.name][key] + + def __iter__(self) -> Iterator[str]: + section: Mapping[str, str] = self.config.sections.get(self.name, {}) + + def lineof(key: str) -> int: + return self.config.lineof(self.name, key) # type: ignore[return-value] + + yield from sorted(section, key=lineof) + + def items(self) -> Iterator[tuple[str, str]]: + for name in self: + yield name, self[name] + + +class IniConfig: + path: Final[str] + sections: Final[Mapping[str, Mapping[str, str]]] + + def __init__( + self, + path: str | os.PathLike[str], + data: str | None = None, + encoding: str = "utf-8", + ) -> None: + self.path = os.fspath(path) + if data is None: + with open(self.path, encoding=encoding) as fp: + data = fp.read() + + tokens = _parse.parse_lines(self.path, data.splitlines(True)) + + self._sources = {} + sections_data: dict[str, dict[str, str]] + self.sections = sections_data = {} + + for lineno, section, name, value in tokens: + if section is None: + raise ParseError(self.path, lineno, "no section header defined") + self._sources[section, name] = lineno + if name is None: + if section in self.sections: + raise ParseError( + self.path, lineno, f"duplicate section {section!r}" + ) + sections_data[section] = {} + else: + if name in self.sections[section]: + raise ParseError(self.path, lineno, f"duplicate name {name!r}") + assert value is not None + sections_data[section][name] = value + + def lineof(self, section: str, name: str | None = None) -> int | None: + lineno = self._sources.get((section, name)) + return None if lineno is None else lineno + 1 + + @overload + def get( + self, + section: str, + name: str, + ) -> str | None: + ... + + @overload + def get( + self, + section: str, + name: str, + convert: Callable[[str], _T], + ) -> _T | None: + ... + + @overload + def get( + self, + section: str, + name: str, + default: None, + convert: Callable[[str], _T], + ) -> _T | None: + ... + + @overload + def get( + self, section: str, name: str, default: _D, convert: None = None + ) -> str | _D: + ... + + @overload + def get( + self, + section: str, + name: str, + default: _D, + convert: Callable[[str], _T], + ) -> _T | _D: + ... + + def get( # type: ignore + self, + section: str, + name: str, + default: _D | None = None, + convert: Callable[[str], _T] | None = None, + ) -> _D | _T | str | None: + try: + value: str = self.sections[section][name] + except KeyError: + return default + else: + if convert is not None: + return convert(value) + else: + return value + + def __getitem__(self, name: str) -> SectionWrapper: + if name not in self.sections: + raise KeyError(name) + return SectionWrapper(self, name) + + def __iter__(self) -> Iterator[SectionWrapper]: + for name in sorted(self.sections, key=self.lineof): # type: ignore + yield SectionWrapper(self, name) + + def __contains__(self, arg: str) -> bool: + return arg in self.sections diff --git a/.venv/lib/python3.11/site-packages/iniconfig/__pycache__/__init__.cpython-311.pyc b/.venv/lib/python3.11/site-packages/iniconfig/__pycache__/__init__.cpython-311.pyc new file mode 100644 index 0000000000000000000000000000000000000000..48632d128c345e14b338e047971fe67f488cb9af Binary files /dev/null and b/.venv/lib/python3.11/site-packages/iniconfig/__pycache__/__init__.cpython-311.pyc differ diff --git a/.venv/lib/python3.11/site-packages/iniconfig/__pycache__/_parse.cpython-311.pyc b/.venv/lib/python3.11/site-packages/iniconfig/__pycache__/_parse.cpython-311.pyc new file mode 100644 index 0000000000000000000000000000000000000000..5c1a38eeb95c4675f54cdd7389dfefbbdb88f5a2 Binary files /dev/null and b/.venv/lib/python3.11/site-packages/iniconfig/__pycache__/_parse.cpython-311.pyc differ diff --git a/.venv/lib/python3.11/site-packages/iniconfig/__pycache__/_version.cpython-311.pyc b/.venv/lib/python3.11/site-packages/iniconfig/__pycache__/_version.cpython-311.pyc new file mode 100644 index 0000000000000000000000000000000000000000..66fa1fae8bbd799d59db399e5b14996ab24c0981 Binary files /dev/null and b/.venv/lib/python3.11/site-packages/iniconfig/__pycache__/_version.cpython-311.pyc differ diff --git a/.venv/lib/python3.11/site-packages/iniconfig/__pycache__/exceptions.cpython-311.pyc b/.venv/lib/python3.11/site-packages/iniconfig/__pycache__/exceptions.cpython-311.pyc new file mode 100644 index 0000000000000000000000000000000000000000..4d9c2f3e8696cd089c30bed7083e3d510d5c4794 Binary files /dev/null and b/.venv/lib/python3.11/site-packages/iniconfig/__pycache__/exceptions.cpython-311.pyc differ diff --git a/.venv/lib/python3.11/site-packages/iniconfig/_parse.py b/.venv/lib/python3.11/site-packages/iniconfig/_parse.py new file mode 100644 index 0000000000000000000000000000000000000000..2d03437bb1c41915f0faf610b8a1fc54637e74fd --- /dev/null +++ b/.venv/lib/python3.11/site-packages/iniconfig/_parse.py @@ -0,0 +1,82 @@ +from __future__ import annotations +from .exceptions import ParseError + +from typing import NamedTuple + + +COMMENTCHARS = "#;" + + +class _ParsedLine(NamedTuple): + lineno: int + section: str | None + name: str | None + value: str | None + + +def parse_lines(path: str, line_iter: list[str]) -> list[_ParsedLine]: + result: list[_ParsedLine] = [] + section = None + for lineno, line in enumerate(line_iter): + name, data = _parseline(path, line, lineno) + # new value + if name is not None and data is not None: + result.append(_ParsedLine(lineno, section, name, data)) + # new section + elif name is not None and data is None: + if not name: + raise ParseError(path, lineno, "empty section name") + section = name + result.append(_ParsedLine(lineno, section, None, None)) + # continuation + elif name is None and data is not None: + if not result: + raise ParseError(path, lineno, "unexpected value continuation") + last = result.pop() + if last.name is None: + raise ParseError(path, lineno, "unexpected value continuation") + + if last.value: + last = last._replace(value=f"{last.value}\n{data}") + else: + last = last._replace(value=data) + result.append(last) + return result + + +def _parseline(path: str, line: str, lineno: int) -> tuple[str | None, str | None]: + # blank lines + if iscommentline(line): + line = "" + else: + line = line.rstrip() + if not line: + return None, None + # section + if line[0] == "[": + realline = line + for c in COMMENTCHARS: + line = line.split(c)[0].rstrip() + if line[-1] == "]": + return line[1:-1], None + return None, realline.strip() + # value + elif not line[0].isspace(): + try: + name, value = line.split("=", 1) + if ":" in name: + raise ValueError() + except ValueError: + try: + name, value = line.split(":", 1) + except ValueError: + raise ParseError(path, lineno, "unexpected line: %r" % line) + return name.strip(), value.strip() + # continuation + else: + return None, line.strip() + + +def iscommentline(line: str) -> bool: + c = line.lstrip()[:1] + return c in COMMENTCHARS diff --git a/.venv/lib/python3.11/site-packages/iniconfig/_version.py b/.venv/lib/python3.11/site-packages/iniconfig/_version.py new file mode 100644 index 0000000000000000000000000000000000000000..dd1883d734afc15316f97e78f04f7553d85ae088 --- /dev/null +++ b/.venv/lib/python3.11/site-packages/iniconfig/_version.py @@ -0,0 +1,4 @@ +# file generated by setuptools_scm +# don't change, don't track in version control +__version__ = version = '2.0.0' +__version_tuple__ = version_tuple = (2, 0, 0) diff --git a/.venv/lib/python3.11/site-packages/iniconfig/exceptions.py b/.venv/lib/python3.11/site-packages/iniconfig/exceptions.py new file mode 100644 index 0000000000000000000000000000000000000000..bc898e68ee6709bed0f70499f755e6be3f5b5961 --- /dev/null +++ b/.venv/lib/python3.11/site-packages/iniconfig/exceptions.py @@ -0,0 +1,20 @@ +from __future__ import annotations +from typing import TYPE_CHECKING + +if TYPE_CHECKING: + from typing_extensions import Final + + +class ParseError(Exception): + path: Final[str] + lineno: Final[int] + msg: Final[str] + + def __init__(self, path: str, lineno: int, msg: str) -> None: + super().__init__(path, lineno, msg) + self.path = path + self.lineno = lineno + self.msg = msg + + def __str__(self) -> str: + return f"{self.path}:{self.lineno + 1}: {self.msg}" diff --git a/.venv/lib/python3.11/site-packages/iniconfig/py.typed b/.venv/lib/python3.11/site-packages/iniconfig/py.typed new file mode 100644 index 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b/.venv/lib/python3.11/site-packages/outlines/models/__init__.py @@ -0,0 +1,19 @@ +"""Module that contains all the models integrated in outlines. + +We group the models in submodules by provider instead of theme (completion, chat +completion, diffusers, etc.) and use routing functions everywhere else in the +codebase. + +""" + +from typing import Union + +from .exllamav2 import ExLlamaV2Model, exl2 +from .llamacpp import LlamaCpp, llamacpp +from .mlxlm import MLXLM, mlxlm +from .openai import OpenAI, azure_openai, openai +from .transformers import Transformers, TransformerTokenizer, mamba, transformers +from .transformers_vision import TransformersVision, transformers_vision +from .vllm import VLLM, vllm + +LogitsGenerator = Union[Transformers, LlamaCpp, OpenAI, ExLlamaV2Model, MLXLM, VLLM] diff --git a/.venv/lib/python3.11/site-packages/outlines/models/__pycache__/__init__.cpython-311.pyc b/.venv/lib/python3.11/site-packages/outlines/models/__pycache__/__init__.cpython-311.pyc new 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0000000000000000000000000000000000000000..d2aa84b0dbbcdcc3c337dfad6fafc092f98b64c2 --- /dev/null +++ b/.venv/lib/python3.11/site-packages/outlines/models/exllamav2.py @@ -0,0 +1,357 @@ +import dataclasses +from typing import TYPE_CHECKING, Iterator, List, Optional, Tuple, TypedDict, Union + +import torch +from typing_extensions import Unpack + +from outlines.generate.api import GenerationParameters, SamplingParameters + +if TYPE_CHECKING: + import torch.LongTensor + from exllamav2.generator import ExLlamaV2DynamicGenerator, ExLlamaV2Sampler + + +class ExllamaV2Params(TypedDict, total=False): + max_tokens: int + stop_conditions: Optional[List[Union[int, str]]] + seed: Optional[int] + gen_settings: "ExLlamaV2Sampler.Settings" + max_new_tokens: List[int] + + +class OutlinesExLlamaV2Tokenizer: + def __init__(self, tokenizer): + self.exl2_tokenizer = tokenizer + self.vocabulary = self.exl2_tokenizer.get_piece_to_id_dict() + self.special_tokens = set(self.exl2_tokenizer.extended_piece_to_id) + self.eos_token_id = self.exl2_tokenizer.eos_token_id + + def convert_token_to_string(self, token): + return token + + def decode(self, token_ids: "torch.LongTensor") -> List[str]: + decoded = self.exl2_tokenizer.decode( + torch.tensor(token_ids), + decode_special_tokens=False, + ) + if isinstance(decoded, str): + return [decoded] + return decoded + + +class ExLlamaV2Model: + """Represents a `exl2` model.""" + + def __init__( + self, + generator: "ExLlamaV2DynamicGenerator", + tokenizer: "OutlinesExLlamaV2Tokenizer", + max_seq_len: int, + ): + self.generator = generator + self.tokenizer = tokenizer + self.max_seq_len = max_seq_len + + def prepare_generation_parameters( + self, + prompts: Union[str, List[str]], + generation_parameters: GenerationParameters, + sampling_parameters: SamplingParameters, + structure_logits_processor, + **exllamav2_params: Unpack[ExllamaV2Params], + ) -> Tuple[ExllamaV2Params, Union[str, List[str]]]: + """Prepare the generation parameters. + + `exllamav2` uses different default values + + """ + from exllamav2.generator import ExLlamaV2Sampler + + if isinstance(prompts, str): + prompts = [prompts] + max_tokens, stop_at, seed = dataclasses.astuple(generation_parameters) + + if max_tokens is None: + max_tokens = [] + for prompt in prompts: + ids = self.generator.tokenizer.encode( + prompt, encode_special_tokens=True + ) + prompt_tokens = ids.shape[-1] + max_tokens.append(self.max_seq_len - prompt_tokens) + exllamav2_params["max_new_tokens"] = max_tokens + else: + exllamav2_params["max_new_tokens"] = [ + max_tokens for _ in range(len(prompts)) + ] + + stop_conditions = [self.generator.tokenizer.eos_token_id] + if isinstance(generation_parameters.stop_at, str): + stop_conditions.append(generation_parameters.stop_at) + elif isinstance(generation_parameters.stop_at, list): + for stop_at in generation_parameters.stop_at: + stop_conditions.append(stop_at) + exllamav2_params["stop_conditions"] = stop_conditions + exllamav2_params["seed"] = seed + + gen_settings = ExLlamaV2Sampler.Settings() + if sampling_parameters.temperature is not None: + gen_settings.temperature = sampling_parameters.temperature + if sampling_parameters.top_p is not None: + gen_settings.top_p = sampling_parameters.top_p + if sampling_parameters.top_k is not None: + gen_settings.top_k = sampling_parameters.top_k + gen_settings.logits_processor = structure_logits_processor + exllamav2_params["gen_settings"] = gen_settings + if sampling_parameters.num_samples > 1: + prompts = prompts * sampling_parameters.num_samples + exllamav2_params["max_new_tokens"] = ( + exllamav2_params["max_new_tokens"] * sampling_parameters.num_samples + ) + + if len(prompts) == 1: + prompts = prompts[0] + + return exllamav2_params, prompts + + def reformat_output( + self, output: Union[str, List[str]], sampling_parameters: SamplingParameters + ): + """ + The purpose of this function is to reformat the output from exllamav2's output format to outline's output format + For exllamav2, it mainly accepts only a list or a string(they also do cfg sampling with tuples but we will ignore this for now) + The exllamav2's logic is + 1. If the prompt is a string, return a string. This is the same as outlines + 2. If a prompt is a list, return a list. This is not the same as outlines output in that if the list is only one element, the string is expected to be outputted. + 3. There is no such thing as num_samples, so the prompts had to be duplicated by num_samples times. Then, we had the function output a list of lists + """ + if isinstance(output, str): + return output + if len(output) == 1: + return output[0] + if sampling_parameters.num_samples > 1: + if len(output) == sampling_parameters.num_samples: + return output + assert len(output) % sampling_parameters.num_samples == 0 + num_items_per_sample = len(output) // sampling_parameters.num_samples + new_output = [] + for i in range(sampling_parameters.num_samples): + curr_sample = [] + for j in range(num_items_per_sample): + curr_sample.append(output[i * num_items_per_sample + j]) + new_output.append(curr_sample) + return new_output + return output + + def generate( + self, + prompts: Union[str, List[str]], + generation_parameters: GenerationParameters, + structure_logits_processor, + sampling_parameters: SamplingParameters, + **exllamav2_params: Unpack[ExllamaV2Params], + ) -> Union[str, List[str]]: + exllamav2_params, prompts = self.prepare_generation_parameters( + prompts, + generation_parameters, + sampling_parameters, + structure_logits_processor, + ) + """ + In exllamav2, it needs the max amount of new tokens generated. + The reason exllamav2_params["max_new_tokens"] is a list is because in prepare_generation_parameters + the max amount of tokens that can be generated by the model for each prompt(by encoding with tokenizer) is calculated. + The minimum is picked because otherwise it might be possible for one of the + prompts to exceed the max sequence length. + """ + output = self.generator.generate( + prompt=prompts, + gen_settings=exllamav2_params["gen_settings"], + max_new_tokens=min(exllamav2_params["max_new_tokens"]), + completion_only=True, + encode_special_tokens=True, + stop_conditions=exllamav2_params["stop_conditions"], + add_bos=False, + seed=exllamav2_params["seed"], + ) + + return self.reformat_output(output, sampling_parameters) + + def stream( + self, + prompts: Union[str, List[str]], + generation_parameters: GenerationParameters, + structure_logits_processor, + sampling_parameters: SamplingParameters, + **exllamav2_params: Unpack[ExllamaV2Params], + ) -> Iterator[Union[str, List[str]]]: + from exllamav2.generator import ExLlamaV2DynamicJob + + exllamav2_params, prompts = self.prepare_generation_parameters( + prompts, + generation_parameters, + sampling_parameters, + structure_logits_processor, + ) + + order = {} + if isinstance(prompts, str): + prompts = [prompts] + batch_size = len(prompts) + seed = exllamav2_params["seed"] + for idx, p in enumerate(prompts): + input_ids = self.generator.tokenizer.encode( + p, encode_special_tokens=True, add_bos=False + ) + + job = ExLlamaV2DynamicJob( + input_ids=input_ids, + max_new_tokens=exllamav2_params["max_new_tokens"][idx], + min_new_tokens=0, + seed=seed, + stop_conditions=exllamav2_params["stop_conditions"], + gen_settings=exllamav2_params["gen_settings"], + token_healing=False, + decode_special_tokens=False, + ) + + if seed is not None: + seed += 1 + + serial = self.generator.enqueue(job) + order[serial] = idx + + # Collect outputs until all jobs finish + + next_text = [""] * batch_size + + def token_generator() -> Iterator[str]: + while self.generator.num_remaining_jobs(): + results = self.generator.iterate() + for r in results: + idx = order[r["serial"]] + if r["stage"] == "streaming": + text = r.get("text", "") + next_text[idx] = text + if r["eos"]: + next_text[idx] = "" + yield self.reformat_output(next_text, sampling_parameters) + return + + return token_generator() + + +def exl2( + model_path: str, + draft_model_path: Optional[str] = None, + max_seq_len: Optional[int] = None, + cache_q4: bool = False, + paged: bool = True, + max_chunk_size: Optional[int] = None, +) -> ExLlamaV2Model: + """ + Load an ExLlamaV2 model. + + Parameters + ---------- + model_path (str) + Path to the model directory. + device (str) + Device to load the model on. Pass in 'cuda' for GPU or 'cpu' for CPU + max_seq_len (Optional[int], optional) + Maximum sequence length. Defaults to None. + scale_pos_emb (Optional[float], optional) + Scale factor for positional embeddings. Defaults to None. + scale_alpha_value (Optional[float], optional) + Scale alpha value. Defaults to None. + no_flash_attn (Optional[bool], optional) + Disable flash attention. Defaults to None. + num_experts_per_token (Optional[int], optional) + Number of experts per token. Defaults to None. + cache_q4 (bool, optional) + Use Q4 cache. Defaults to False. + tokenizer_kwargs (dict, optional) + Additional keyword arguments for the tokenizer. Defaults to {}. + gpu_split (str) + \"auto\", or VRAM allocation per GPU in GB. Auto will use exllama's autosplit feature + low_mem (bool, optional) + Enable VRAM optimizations, potentially trading off speed + verbose (bool, optional) + Enable if you want debugging statements + + Returns + ------- + An `ExLlamaV2Model` instance. + + Raises + ------ + `ImportError` if the `exllamav2` library is not installed. + + """ + try: + from exllamav2 import ( + ExLlamaV2, + ExLlamaV2Cache, + ExLlamaV2Cache_Q4, + ExLlamaV2Config, + ExLlamaV2Tokenizer, + ) + from exllamav2.generator import ExLlamaV2DynamicGenerator + + except ImportError: + raise ImportError( + "The `exllamav2`, `transformers` and `torch` libraries needs to be installed in order to use `exllamav2` models. " + "Please run `pip install transformers torch git+https://github.com/lapp0/exllamav2@sampler-logits-processor` " + "Documentation: https://dottxt-ai.github.io/outlines/latest/reference/models/exllamav2/" + ) + config = ExLlamaV2Config(model_path) + if max_chunk_size is not None: + config.max_input_len = max_chunk_size + config.max_attention_size = max_chunk_size**2 + + config.arch_compat_overrides() + model = ExLlamaV2(config) + if max_seq_len is None: + max_seq_len = -1 + if cache_q4: + cache = ExLlamaV2Cache_Q4(model, max_seq_len=max_seq_len, lazy=True) + else: + cache = ExLlamaV2Cache(model, max_seq_len=max_seq_len, lazy=True) + model.load_autosplit(cache, progress=True) + + print("Loading tokenizer...") + tokenizer = ExLlamaV2Tokenizer(config) + max_batch_size = 4 if paged else 1 + + draft_model = None + draft_cache = None + if draft_model_path is not None: + draft_config = ExLlamaV2Config(draft_model_path) + draft_model = ExLlamaV2(draft_config) + + if cache_q4: + draft_cache = ExLlamaV2Cache_Q4( + draft_model, max_seq_len=max_seq_len, lazy=True + ) + else: + draft_cache = ExLlamaV2Cache( + draft_model, max_seq_len=max_seq_len, lazy=True + ) + + # Initialize the generator with all default parameters + generator = ExLlamaV2DynamicGenerator( + model=model, + cache=cache, + draft_model=draft_model, + draft_cache=draft_cache, + tokenizer=tokenizer, + max_batch_size=max_batch_size, + use_ngram_draft=False, + max_chunk_size=max_chunk_size, + paged=paged, + ) + max_seq_len = cache.max_seq_len + + outlines_tokenizer = OutlinesExLlamaV2Tokenizer(tokenizer) + outlines_exl2_model = ExLlamaV2Model(generator, outlines_tokenizer, max_seq_len) + return outlines_exl2_model diff --git a/.venv/lib/python3.11/site-packages/outlines/models/llamacpp.py b/.venv/lib/python3.11/site-packages/outlines/models/llamacpp.py new file mode 100644 index 0000000000000000000000000000000000000000..904b193c457d9d965aeda023f6b02d2860940eba --- /dev/null +++ b/.venv/lib/python3.11/site-packages/outlines/models/llamacpp.py @@ -0,0 +1,407 @@ +import dataclasses +import pickle +import warnings +from typing import ( + TYPE_CHECKING, + Dict, + Iterator, + List, + Optional, + Set, + Tuple, + TypedDict, + Union, +) + +from typing_extensions import Unpack + +from outlines.generate.api import GenerationParameters, SamplingParameters +from outlines.models.tokenizer import Tokenizer + +if TYPE_CHECKING: + from llama_cpp import Llama, LogitsProcessorList + + +class LlamaCppTokenizer(Tokenizer): + def __init__(self, model: "Llama"): + self.eos_token_id = model.token_eos() + self.eos_token = model.tokenizer().decode([self.eos_token_id]) + self.pad_token_id = self.eos_token_id + self.special_tokens: Set[str] = set() + + self.vocabulary: Dict[str, int] = dict() + + self.tokenizer = model.tokenizer() + + # TODO: Remove when https://github.com/ggerganov/llama.cpp/pull/5613 is resolved + self._hf_tokenizer = None + try: + self.vocabulary = model.tokenizer_.hf_tokenizer.get_vocab() + self._hf_tokenizer = model.tokenizer_.hf_tokenizer + except AttributeError: + # ### + for t in range(model.n_vocab()): + token_piece = model.tokenizer().decode([t]) + self.vocabulary[token_piece] = t + + # ensure stable ordering of vocabulary + self.vocabulary = { + tok: tok_id + for tok, tok_id in sorted(self.vocabulary.items(), key=lambda x: x[1]) + } + + self._hash = None + + def decode(self, token_ids: List[int]) -> List[str]: + decoded_bytes = self.tokenizer.detokenize(token_ids) + return [decoded_bytes.decode("utf-8", errors="ignore")] + + def encode( + self, prompt: Union[str, List[str]], add_bos: bool = True, special: bool = True + ) -> Tuple[List[int], List[int]]: + if isinstance(prompt, list): + raise NotImplementedError( + "llama-cpp-python tokenizer doesn't support batch tokenization" + ) + token_ids = self.tokenizer.tokenize( + prompt.encode("utf-8", errors="ignore"), add_bos=add_bos, special=special + ) + # generate attention mask, missing from llama-cpp-python + attention_mask = [ + 1 if token_id != self.pad_token_id else 0 for token_id in token_ids + ] + return token_ids, attention_mask + + def convert_token_to_string(self, token: str) -> str: + if self._hf_tokenizer is not None: + from transformers.file_utils import SPIECE_UNDERLINE + + token_str = self._hf_tokenizer.convert_tokens_to_string([token]) + if token.startswith(SPIECE_UNDERLINE) or token == "<0x20>": + token_str = " " + token_str + return token_str + else: + return token + + def __eq__(self, other): + if not isinstance(other, LlamaCppTokenizer): + return False + return self.__getstate__() == other.__getstate__() + + def __hash__(self): + if self._hash is None: + self._hash = hash(pickle.dumps(self)) + return self._hash + + def __getstate__(self): + """Create a stable representation for outlines.caching""" + return ( + self.vocabulary, + self.eos_token_id, + self.eos_token, + self.pad_token_id, + sorted(self.special_tokens), + ) + + def __setstate__(self, state): + raise NotImplementedError("Cannot load a pickled llamacpp tokenizer") + + +class LlamaCppParams(TypedDict, total=False): + suffix: Optional[str] + temperature: float + top_p: float + min_p: float + typical_p: float + seed: int + max_tokens: int + logits_processor: "LogitsProcessorList" + stop: Optional[Union[str, List[str]]] + frequence_penalty: float + presence_penalty: float + repeat_penalty: float + top_k: int + tfs_z: float + mirostat_mode: int + mirostat_tau: float + mirostat_eta: float + stream: bool + + +class LlamaCpp: + """Represents a model provided by the `llama-cpp-python` library. + + We wrap models from model providing libraries in order to give all of + them the same interface in Outlines and allow users to easily switch + between providers. This class wraps the `llama_cpp.Llama` class from the + `llama-cpp-python` library. + + """ + + def __init__(self, model: "Llama"): + self.model = model + + @property + def tokenizer(self): + return LlamaCppTokenizer(self.model) + + def prepare_generation_parameters( + self, + generation_parameters: GenerationParameters, + sampling_parameters: SamplingParameters, + structure_logits_processor, + **llama_cpp_params: Unpack[LlamaCppParams], + ): + """Prepare the generation parameters. + + `llama-cpp-python` uses different default values + + """ + from llama_cpp import LogitsProcessorList + + max_tokens, stop_at, seed = dataclasses.astuple(generation_parameters) + + # We update `llama_cpp_params` with the values the user passed to the + # generator. + if "stop" not in llama_cpp_params: + llama_cpp_params["stop"] = stop_at + if "seed" not in llama_cpp_params: + llama_cpp_params["seed"] = seed + + # Somehow `llama-cpp-python` generates `max_tokens + 1` tokens + if "max_tokens" not in llama_cpp_params: + if max_tokens is None: + llama_cpp_params["max_tokens"] = -1 # indicates unlimited tokens + else: + llama_cpp_params["max_tokens"] = max_tokens - 1 + else: + llama_cpp_params["max_tokens"] = llama_cpp_params["max_tokens"] - 1 + + sampler, num_samples, top_p, top_k, temperature = dataclasses.astuple( + sampling_parameters + ) + + # We update the `llama_cpp_params` with the sampling values that + # were specified by the user via the `Sampler` class, unless they + # are also specified in `llama_cpp_params`. We also disable other + # sampling methods that are enabled by default and reset the temperature + # value. + # + # See https://github.com/ggerganov/llama.cpp/blob/e11a8999b5690f810c2c99c14347f0834e68c524/common/sampling.h#L22 + # for the default values in `llama.cpp` and indications to disable the sampling modes. + # Mirostat sampling, tail-free sampling and all penalties are disabled by default. + # + # See https://llama-cpp-python.readthedocs.io/en/latest/api-reference/#llama_cpp.Llama.__call__ + # for default values in `llama-cpp-python` + if sampler == "beam_search": + raise NotImplementedError( + "The `llama_cpp_python` library does not support Beam Search." + ) + if num_samples != 1: + raise NotImplementedError( + "The `llama_cpp_python` library does not allow to take several samples." + ) + if "top_p" not in llama_cpp_params: + if top_p is not None: + llama_cpp_params["top_p"] = top_p + else: + llama_cpp_params["top_p"] = 1.0 + + if "min_p" not in llama_cpp_params: + llama_cpp_params["min_p"] = 0.0 + + if "top_k" not in llama_cpp_params: + if top_k is not None: + llama_cpp_params["top_k"] = top_k + else: + llama_cpp_params["top_k"] = -1 + + if "temperature" not in llama_cpp_params: + if temperature is not None: + llama_cpp_params["temperature"] = temperature + else: + llama_cpp_params["temperature"] = 1.0 + + if "repeat_penalty" not in llama_cpp_params: + llama_cpp_params["repeat_penalty"] = 1.0 + + # The choice to stream or not should happen via the high-level API + llama_cpp_params["stream"] = False + + if structure_logits_processor is not None: + if "logits_processor" in llama_cpp_params: + llama_cpp_params["logits_processor"].append(structure_logits_processor) + else: + llama_cpp_params["logits_processor"] = LogitsProcessorList( + [structure_logits_processor] + ) + + return llama_cpp_params + + def generate( + self, + prompts: Union[str, List[str]], + generation_parameters: GenerationParameters, + structure_logits_processor, + sampling_parameters: SamplingParameters, + **llama_cpp_params: Unpack[LlamaCppParams], + ) -> str: + """Generate text using `llama-cpp-python`. + + Arguments + --------- + prompts + A prompt or list of prompts. + generation_parameters + An instance of `GenerationParameters` that contains the prompt, + the maximum number of tokens, stop sequences and seed. All the + arguments to `SequenceGeneratorAdapter`'s `__cal__` method. + logits_processor + The logits processor to use when generating text. + sampling_parameters + An instance of `SamplingParameters`, a dataclass that contains + the name of the sampler to use and related parameters as available + in Outlines. + llama_cpp_params + Keyword arguments that can be passed to + `llama_cpp_python.Llama.__call__`. The values in `llama_cpp_params` + supersede the values of the parameters in `generation_parameters` and + `sampling_parameters`. See the `llama_cpp_python` documentation for + a list of possible values: https://llama-cpp-python.readthedocs.io/en/latest/api-reference/#llama_cpp.Llama.__call__ + + Returns + ------- + The generated text. + + """ + if not isinstance(prompts, str): + raise NotImplementedError( + "The `llama-cpp-python` library does not support batch inference." + ) + + llama_cpp_params = self.prepare_generation_parameters( + generation_parameters, + sampling_parameters, + structure_logits_processor, + **llama_cpp_params, + ) + completion = self.model(prompts, **llama_cpp_params) + result = completion["choices"][0]["text"] + + self.model.reset() + + return result + + def stream( + self, + prompts: Union[str, List[str]], + generation_parameters: GenerationParameters, + structure_logits_processor, + sampling_parameters: SamplingParameters, + **llama_cpp_params: Unpack[LlamaCppParams], + ) -> Iterator[str]: + """Stream text using `llama-cpp-python`. + + Arguments + --------- + prompts + A prompt or list of prompts. + generation_parameters + An instance of `GenerationParameters` that contains the prompt, + the maximum number of tokens, stop sequences and seed. All the + arguments to `SequenceGeneratorAdapter`'s `__cal__` method. + logits_processor + The logits processor to use when generating text. + sampling_parameters + An instance of `SamplingParameters`, a dataclass that contains + the name of the sampler to use and related parameters as available + in Outlines. + llama_cpp_params + Keyword arguments that can be passed to + `llama_cpp_python.Llama.__call__`. The values in `llama_cpp_params` + supersede the values of the parameters in `generation_parameters` and + `sampling_parameters`. See the `llama_cpp_python` documentation for + a list of possible values: https://llama-cpp-python.readthedocs.io/en/latest/api-reference/#llama_cpp.Llama.__call__ + + Returns + ------- + A generator that return strings. + + """ + + if not isinstance(prompts, str): + raise NotImplementedError( + "The `llama-cpp-python` library does not support batch inference." + ) + + llama_cpp_params = self.prepare_generation_parameters( + generation_parameters, + sampling_parameters, + structure_logits_processor, + **llama_cpp_params, + ) + llama_cpp_params["stream"] = True + generator = self.model(prompts, **llama_cpp_params) + + def token_generator() -> Iterator[str]: + while True: + try: + result = next(generator) + yield result["choices"][0]["text"] + except StopIteration: + self.model.reset() + return + + return token_generator() + + def load_lora(self, adapter_path: str): + if self.model._model.apply_lora_from_file( + adapter_path, + 1.0, + ): + raise RuntimeError(f"Failed to apply LoRA from lora path: {adapter_path}") + + +def llamacpp( + repo_id: str, filename: Optional[str] = None, **llamacpp_model_params +) -> LlamaCpp: + """Load a model from the `llama-cpp-python` library. + + We use the `Llama.from_pretrained` classmethod that downloads models + directly from the HuggingFace hub, instead of asking users to specify + a path to the downloaded model. One can still load a local model + by initializing `llama_cpp.Llama` directly. + + Arguments + --------- + repo_id + The name of the model repository. + filename: + A filename of glob pattern to match the model file in the repo. + llama_cpp_model_params + Llama-specific model parameters. See the `llama-cpp-python` documentation + for the full list: https://llama-cpp-python.readthedocs.io/en/latest/api-reference/#llama_cpp.Llama.__init__ + + """ + from llama_cpp import Llama + + # Default to using the model's full context length + if "n_ctx" not in llamacpp_model_params: + llamacpp_model_params["n_ctx"] = 0 + + if "verbose" not in llamacpp_model_params: + llamacpp_model_params["verbose"] = False + + # TODO: Remove when https://github.com/ggerganov/llama.cpp/pull/5613 is resolved + if "tokenizer" not in llamacpp_model_params: + warnings.warn( + "The pre-tokenizer in `llama.cpp` handles unicode improperly " + + "(https://github.com/ggerganov/llama.cpp/pull/5613)\n" + + "Outlines may raise a `RuntimeError` when building the regex index.\n" + + "To circumvent this error when using `models.llamacpp()` you may pass the argument" + + "`tokenizer=llama_cpp.llama_tokenizer.LlamaHFTokenizer.from_pretrained()`\n" + ) + + model = Llama.from_pretrained(repo_id, filename, **llamacpp_model_params) + + return LlamaCpp(model) diff --git a/.venv/lib/python3.11/site-packages/outlines/models/mlxlm.py b/.venv/lib/python3.11/site-packages/outlines/models/mlxlm.py new file mode 100644 index 0000000000000000000000000000000000000000..d8b7e032c49a505d3e2483cf81b6e1020752e915 --- /dev/null +++ b/.venv/lib/python3.11/site-packages/outlines/models/mlxlm.py @@ -0,0 +1,242 @@ +import dataclasses +from typing import TYPE_CHECKING, Generator, Iterator, List, Optional, Tuple, Union + +from .transformers import TransformerTokenizer + +if TYPE_CHECKING: + import mlx.core as mx + import mlx.nn as nn + from transformers import PreTrainedTokenizer + + from outlines.generate.api import GenerationParameters, SamplingParameters + from outlines.processors import OutlinesLogitsProcessor + + +class MLXLM: + """ + Represents an `mlx_lm` model + """ + + def __init__( + self, + model: "nn.Module", + tokenizer: "PreTrainedTokenizer", + ): + self.model = model + self.mlx_tokenizer = tokenizer # returns mlx tensors, used for encode() + self.tokenizer = TransformerTokenizer( + tokenizer._tokenizer + ) # _tokenizer is HF Tokenizer + + def generate( + self, + prompts: Union[str, List[str]], + generation_parameters: "GenerationParameters", + logits_processor, + sampling_parameters: "SamplingParameters", + ) -> str: + streamer = self.stream( + prompts, generation_parameters, logits_processor, sampling_parameters + ) + return "".join(list(streamer)) + + def stream( + self, + prompts: Union[str, List[str]], + generation_parameters: "GenerationParameters", + logits_processor, + sampling_parameters: "SamplingParameters", + ) -> Iterator[str]: + """Generate text using `mlx_lm`. + + Arguments + --------- + prompts + A prompt or list of prompts. + generation_parameters + An instance of `GenerationParameters` that contains the prompt, + the maximum number of tokens, stop sequences and seed. All the + arguments to `SequenceGeneratorAdapter`'s `__cal__` method. + logits_processor + The logits processor to use when generating text. + sampling_parameters + An instance of `SamplingParameters`, a dataclass that contains + the name of the sampler to use and related parameters as available + in Outlines. + Returns + ------- + The generated text. + """ + import mlx.core as mx + + max_tokens, stop_at, seed = dataclasses.astuple(generation_parameters) + sampler, num_samples, top_p, top_k, temperature = dataclasses.astuple( + sampling_parameters + ) + if max_tokens is None: + max_tokens = int(1e9) + + if not isinstance(prompts, str): + raise NotImplementedError( + "The `mlx-lm` library does not support batch inference." + ) + if sampler == "beam_search": + raise NotImplementedError( + "The `mlx-lm` library does not support Beam Search." + ) + if num_samples != 1: + raise NotImplementedError( + "The `mlx-lm` library does not allow to take several samples." + ) + if top_k is not None: + raise NotImplementedError("The `mlx-lm` library does not support top_k.") + if seed is not None: + raise NotImplementedError("The `mlx-lm` library does not support seed.") + if stop_at is not None: + raise NotImplementedError("The `mlx-lm` library does not support stop_at.") + + generate_kwargs = { + "temp": temperature, + "top_p": top_p, + "sampler": sampler, + "logits_processor": logits_processor, + } + + # Adapted from + # https://github.com/ml-explore/mlx-examples/blob/4872727/llms/mlx_lm/utils.py#L267 + prompt_tokens = mx.array(self.mlx_tokenizer.encode(prompts)) + + detokenizer = self.mlx_tokenizer.detokenizer + detokenizer.reset() + + for (token, prob), n in zip( + self.generate_step(prompt_tokens, **generate_kwargs), + range(max_tokens), + ): + if token == self.tokenizer.eos_token_id: + break + detokenizer.add_token(token) + yield detokenizer.last_segment + + detokenizer.finalize() + yield detokenizer.last_segment + + def generate_step( + self, + prompt: "mx.array", + temp: Optional[float], + top_p: Optional[float], + sampler: str, + logits_processor: "OutlinesLogitsProcessor", + ) -> Generator[Tuple[int, float], None, None]: + """ + Adapted from + https://github.com/ml-explore/mlx-examples/blob/4872727/llms/mlx_lm/utils.py#L129 + + A generator producing token ids based on the given prompt from the model. + + Args: + prompt (mx.array): The input prompt. + temp (float): The temperature for sampling, if 0 the argmax is used. + Default: ``0``. + top_p (float, optional): Nulceus sampling, higher means model considers + more less likely words. + sampler (str): The sampler string defined by SequenceGeneratorAdapter + logits_processor (OutlinesLogitsProcessor): Augment logits before sampling. + """ + import mlx.core as mx + import mlx_lm + + temperature: float = temp or 1.0 + + def sample(logits: "mx.array") -> Tuple["mx.array", float]: + softmax_logits = mx.softmax(logits) + + if temperature == 0.0 or sampler == "greedy": + token = mx.argmax(logits, axis=-1) + elif sampler == "multinomial": + if top_p is not None and top_p > 0 and top_p < 1.0: + token = mlx_lm.sample_utils.top_p_sampling( + logits, top_p, temperature + ) + else: + token = mx.random.categorical(logits * (1 / temperature)) + else: + raise ValueError(f"Invalid mlx-lm sampler: `{sampler}`") + + prob = softmax_logits[0, token] + return token, prob + + cache = mlx_lm.models.cache.make_prompt_cache(self.model) + + # kv cache contains processed input IDs, we pass the unprocessed inputs and cache to model() + unprocessed_input_ids = prompt + generated_ids: List[int] = [] + + while True: + logits = self.model(unprocessed_input_ids[None], cache=cache) + logits = logits[:, -1, :] + + if logits_processor is not None: + # convert to logits_processor 1d expectation, apply, then convert back + logits_1d = logits.reshape(-1) + logits_1d = logits_processor(generated_ids, logits_1d) + logits = logits_1d.reshape(1, -1) + + new_token_single, prob = sample(logits) + new_token = new_token_single.item() + yield new_token, prob + + generated_ids.append(new_token) + unprocessed_input_ids = new_token_single + + +def mlxlm( + model_name: str, + tokenizer_config: dict = {}, + model_config: dict = {}, + adapter_path: Optional[str] = None, + lazy: bool = False, +): + """Instantiate a model from the `mlx_lm` library and its tokenizer. + + Signature adapted from + https://github.com/ml-explore/mlx-examples/blob/4872727/llms/mlx_lm/utils.py#L422 + + Parameters + ---------- + Args: + path_or_hf_repo (Path): The path or the huggingface repository to load the model from. + tokenizer_config (dict, optional): Configuration parameters specifically for the tokenizer. + Defaults to an empty dictionary. + model_config(dict, optional): Configuration parameters specifically for the model. + Defaults to an empty dictionary. + adapter_path (str, optional): Path to the LoRA adapters. If provided, applies LoRA layers + to the model. Default: ``None``. + lazy (bool): If False eval the model parameters to make sure they are + loaded in memory before returning, otherwise they will be loaded + when needed. Default: ``False`` + + Returns + ------- + A `MLXLM` model instance. + + """ + try: + import mlx.core as mx + import mlx_lm + except ImportError: + raise ImportError( + "The `mlx_lm` library needs to be installed in order to use `mlx_lm` models." + ) + if not mx.metal.is_available(): + raise RuntimeError("You cannot use `mlx_lm` without Apple Silicon (Metal)") + + model, tokenizer = mlx_lm.load( + model_name, + tokenizer_config=tokenizer_config, + model_config=model_config, + adapter_path=adapter_path, + lazy=lazy, + ) + return MLXLM(model, tokenizer) diff --git a/.venv/lib/python3.11/site-packages/outlines/models/openai.py b/.venv/lib/python3.11/site-packages/outlines/models/openai.py new file mode 100644 index 0000000000000000000000000000000000000000..89c26f217938f5cff6883da719dfa5e0b19e3f88 --- /dev/null +++ b/.venv/lib/python3.11/site-packages/outlines/models/openai.py @@ -0,0 +1,297 @@ +"""Integration with OpenAI's API.""" +import copy +import functools +from dataclasses import asdict, dataclass, field, replace +from typing import Callable, Dict, List, Optional, Tuple, Union + +import numpy as np + +from outlines.base import vectorize +from outlines.caching import cache + +__all__ = ["OpenAI", "openai", "azure_openai"] + + +@dataclass(frozen=True) +class OpenAIConfig: + """Represents the parameters of the OpenAI API. + + The information was last fetched on 2023/11/20. We document below the + properties that are specific to the OpenAI API. Not all these properties are + supported by Outlines. + + Properties + ---------- + model + The name of the model. Available models can be found on OpenAI's website. + frequence_penalty + Number between 2.0 and -2.0. Positive values penalize new tokens based on + their existing frequency in the text, + logit_bias + Modifies the likelihood of specified tokens to appear in the completion. + Number between -100 (forbid) and +100 (only allows). + n + The number of completions to return for each prompt. + presence_penalty + Similar to frequency penalty. + response_format + Specifies the format the model must output. `{"type": "json_object"}` + enables JSON mode. + seed + Two completions with the same `seed` value should return the same + completion. This is however not guaranteed. + stop + Up to 4 words where the API will stop the completion. + temperature + Number between 0 and 2. Higher values make the output more random, while + lower values make it more deterministic. + top_p + Number between 0 and 1. Parameter for nucleus sampling. + user + A unique identifier for the end-user. + + """ + + model: str = "" + frequency_penalty: float = 0 + logit_bias: Dict[int, int] = field(default_factory=dict) + max_tokens: Optional[int] = None + n: int = 1 + presence_penalty: float = 0 + response_format: Optional[Dict[str, str]] = None + seed: Optional[int] = None + stop: Optional[Union[str, List[str]]] = None + temperature: float = 1.0 + top_p: int = 1 + user: str = field(default_factory=str) + + +class OpenAI: + """An object that represents the OpenAI API.""" + + def __init__( + self, + client, + config, + system_prompt: Optional[str] = None, + ): + """Create an `OpenAI` instance. + + This class supports the standard OpenAI API, the Azure OpeanAI API as + well as compatible APIs that rely on the OpenAI client. + + Parameters + ---------- + client + An instance of the API's async client. + config + An instance of `OpenAIConfig`. Can be useful to specify some + parameters that cannot be set by calling this class' methods. + """ + + self.client = client + self.config = config + + # We count the total number of prompt and generated tokens as returned + # by the OpenAI API, summed over all the requests performed with this + # model instance. + self.prompt_tokens = 0 + self.completion_tokens = 0 + + self.format_sequence = lambda seq: seq + + def __call__( + self, + prompt: Union[str, List[str]], + max_tokens: Optional[int] = None, + stop_at: Optional[Union[List[str], str]] = None, + *, + system_prompt: Optional[str] = None, + temperature: Optional[float] = None, + samples: Optional[int] = None, + ) -> np.ndarray: + """Call the OpenAI API to generate text. + + Parameters + ---------- + prompt + A string or list of strings that will be used to prompt the model + max_tokens + The maximum number of tokens to generate + stop_at + A string or array of strings which, such that the generation stops + when they are generated. + system_prompt + The content of the system message that precedes the user's prompt. + temperature + The value of the temperature used to sample tokens + samples + The number of completions to generate for each prompt + stop_at + Up to 4 words where the API will stop the completion. + + """ + if max_tokens is None: + max_tokens = self.config.max_tokens + if stop_at is None: + stop_at = self.config.stop + if temperature is None: + temperature = self.config.temperature + if samples is None: + samples = self.config.n + + config = replace(self.config, max_tokens=max_tokens, temperature=temperature, n=samples, stop=stop_at) # type: ignore + + response, prompt_tokens, completion_tokens = generate_chat( + prompt, system_prompt, self.client, config + ) + self.prompt_tokens += prompt_tokens + self.completion_tokens += completion_tokens + + return self.format_sequence(response) + + def stream(self, *args, **kwargs): + raise NotImplementedError( + "Streaming is currently not supported for the OpenAI API" + ) + + def new_with_replacements(self, **kwargs): + new_instance = copy.copy(self) + new_instance.config = replace(new_instance.config, **kwargs) + return new_instance + + def __str__(self): + return self.__class__.__name__ + " API" + + def __repr__(self): + return str(self.config) + + +@functools.partial(vectorize, signature="(),(),(),()->(s),(),()") +async def generate_chat( + prompt: str, + system_prompt: Union[str, None], + client, + config: OpenAIConfig, +) -> Tuple[np.ndarray, int, int]: + """Call OpenAI's Chat Completion API. + + Parameters + ---------- + prompt + The prompt we use to start the generation. Passed to the model + with the "user" role. + system_prompt + The system prompt, passed to the model with the "system" role + before the prompt. + client + The API client + config + An `OpenAIConfig` instance. + + Returns + ------- + A tuple that contains the model's response(s) and usage statistics. + + """ + + @error_handler + @cache() + async def call_api(prompt, system_prompt, config): + responses = await client.chat.completions.create( + messages=system_message + user_message, + **asdict(config), # type: ignore + ) + return responses.model_dump() + + system_message = ( + [{"role": "system", "content": system_prompt}] if system_prompt else [] + ) + user_message = [{"role": "user", "content": prompt}] + + responses = await call_api(prompt, system_prompt, config) + + results = np.array( + [responses["choices"][i]["message"]["content"] for i in range(config.n)] + ) + usage = responses["usage"] + + return results, usage["prompt_tokens"], usage["completion_tokens"] + + +def error_handler(api_call_fn: Callable) -> Callable: + """Handle OpenAI API errors and missing API key.""" + + def call(*args, **kwargs): + import openai + + try: + return api_call_fn(*args, **kwargs) + except ( + openai.APITimeoutError, + openai.InternalServerError, + openai.RateLimitError, + ) as e: + raise OSError(f"Could not connect to the OpenAI API: {e}") + except ( + openai.AuthenticationError, + openai.BadRequestError, + openai.ConflictError, + openai.PermissionDeniedError, + openai.NotFoundError, + openai.UnprocessableEntityError, + ) as e: + raise e + + return call + + +@functools.singledispatch +def openai(model_or_client, *args, **kwargs): + return OpenAI(model_or_client, *args, **kwargs) + + +@openai.register(str) +def openai_model( + model_name: str, + config: Optional[OpenAIConfig] = None, + **openai_client_params, +): + try: + from openai import AsyncOpenAI + except ImportError: + raise ImportError( + "The `openai` library needs to be installed in order to use Outlines' OpenAI integration." + ) + + if config is not None: + config = replace(config, model=model_name) # type: ignore + else: + config = OpenAIConfig(model=model_name) + + client = AsyncOpenAI(**openai_client_params) + + return OpenAI(client, config) + + +def azure_openai( + deployment_name: str, + model_name: Optional[str] = None, + config: Optional[OpenAIConfig] = None, + **azure_openai_client_params, +): + try: + from openai import AsyncAzureOpenAI + except ImportError: + raise ImportError( + "The `openai` library needs to be installed in order to use Outlines' Azure OpenAI integration." + ) + + if config is not None: + config = replace(config, model=deployment_name) # type: ignore + if config is None: + config = OpenAIConfig(model=deployment_name) + + client = AsyncAzureOpenAI(**azure_openai_client_params) + + return OpenAI(client, config) diff --git a/.venv/lib/python3.11/site-packages/outlines/models/tokenizer.py b/.venv/lib/python3.11/site-packages/outlines/models/tokenizer.py new file mode 100644 index 0000000000000000000000000000000000000000..1a5708d85fdb6a6ddcb92f924464e1077b035f2d --- /dev/null +++ b/.venv/lib/python3.11/site-packages/outlines/models/tokenizer.py @@ -0,0 +1,31 @@ +from typing import Dict, Hashable, List, Protocol, Set, Tuple, Union + +import numpy as np +from numpy.typing import NDArray + + +class Tokenizer(Hashable, Protocol): + eos_token: str + eos_token_id: int + pad_token_id: int + vocabulary: Dict[str, int] + special_tokens: Set[str] + + def encode( + self, prompt: Union[str, List[str]] + ) -> Tuple[NDArray[np.int64], NDArray[np.int64]]: + """Translate the input prompts into arrays of token ids and attention mask.""" + ... + + def decode(self, token_ids: NDArray[np.int64]) -> List[str]: + """Translate an array of token ids to a string or list of strings.""" + ... + + def convert_token_to_string(self, token: str) -> str: + """Convert a token to its equivalent string. + + This is for instance useful for BPE tokenizers where whitespaces are + represented by the special characted `Ġ`. This prevents matching a raw + token that includes `Ġ` with a string. + """ + ... diff --git a/.venv/lib/python3.11/site-packages/outlines/models/transformers.py b/.venv/lib/python3.11/site-packages/outlines/models/transformers.py new file mode 100644 index 0000000000000000000000000000000000000000..444492500e7508963953174dbd3b9d0f36853ffe --- /dev/null +++ b/.venv/lib/python3.11/site-packages/outlines/models/transformers.py @@ -0,0 +1,458 @@ +import dataclasses +import inspect +from typing import TYPE_CHECKING, Iterator, List, Optional, Tuple, Union + +from outlines.generate.api import GenerationParameters, SamplingParameters +from outlines.models.tokenizer import Tokenizer + +if TYPE_CHECKING: + import torch + from transformers import PreTrainedModel, PreTrainedTokenizer + + from outlines.processors import OutlinesLogitsProcessor + +__all__ = ["transformers"] + + +KVCacheType = Tuple[Tuple["torch.DoubleTensor", "torch.DoubleTensor"], ...] + + +def get_llama_tokenizer_types(): + """Get all the Llama tokenizer types/classes that need work-arounds. + + When they can't be imported, a dummy class is created. + + """ + try: + from transformers.models.llama import LlamaTokenizer + except ImportError: + + class LlamaTokenizer: # type: ignore + pass + + try: + from transformers.models.llama import LlamaTokenizerFast + except ImportError: + + class LlamaTokenizerFast: # type: ignore + pass + + try: + from transformers.models.code_llama import CodeLlamaTokenizer + except ImportError: + + class CodeLlamaTokenizer: # type: ignore + pass + + try: + from transformers.models.code_llama import CodeLlamaTokenizerFast + except ImportError: + + class CodeLlamaTokenizerFast: # type: ignore + pass + + return ( + LlamaTokenizer, + LlamaTokenizerFast, + CodeLlamaTokenizer, + CodeLlamaTokenizerFast, + ) + + +class TransformerTokenizer(Tokenizer): + """Represents a tokenizer for models in the `transformers` library.""" + + def __init__(self, tokenizer: "PreTrainedTokenizer", **kwargs): + self.tokenizer = tokenizer + self.eos_token_id = self.tokenizer.eos_token_id + self.eos_token = self.tokenizer.eos_token + + if self.tokenizer.pad_token_id is None: + self.tokenizer.pad_token_id = self.tokenizer.eos_token_id + self.pad_token_id = self.eos_token_id + else: + self.pad_token_id = self.tokenizer.pad_token_id + self.pad_token = self.tokenizer.pad_token + + self.special_tokens = set(self.tokenizer.all_special_tokens) + + self.vocabulary = self.tokenizer.get_vocab() + self.is_llama = isinstance(self.tokenizer, get_llama_tokenizer_types()) + + def encode( + self, prompt: Union[str, List[str]], **kwargs + ) -> Tuple["torch.LongTensor", "torch.LongTensor"]: + kwargs["padding"] = True + kwargs["return_tensors"] = "pt" + output = self.tokenizer(prompt, **kwargs) + return output["input_ids"], output["attention_mask"] + + def decode(self, token_ids: "torch.LongTensor") -> List[str]: + text = self.tokenizer.batch_decode(token_ids, skip_special_tokens=True) + return text + + def convert_token_to_string(self, token: str) -> str: + from transformers.file_utils import SPIECE_UNDERLINE + + string = self.tokenizer.convert_tokens_to_string([token]) + + if self.is_llama: + # A hack to handle missing spaces to HF's Llama tokenizers + if token.startswith(SPIECE_UNDERLINE) or token == "<0x20>": + return " " + string + + return string + + def __eq__(self, other): + if isinstance(other, type(self)): + if hasattr(self, "model_name") and hasattr(self, "kwargs"): + return ( + other.model_name == self.model_name and other.kwargs == self.kwargs + ) + else: + return other.tokenizer == self.tokenizer + return NotImplemented + + def __hash__(self): + from datasets.fingerprint import Hasher + + return hash(Hasher.hash(self.tokenizer)) + + def __getstate__(self): + state = {"tokenizer": self.tokenizer} + return state + + def __setstate__(self, state): + self.__init__(state["tokenizer"]) + + +class Transformers: + """Represents a `transformers` model.""" + + def __init__( + self, + model: "PreTrainedModel", + tokenizer: "PreTrainedTokenizer", + ): + self.model = model + self.tokenizer = TransformerTokenizer(tokenizer) + + def forward( + self, + input_ids: "torch.LongTensor", + attention_mask: "torch.LongTensor", + past_key_values: Optional[Tuple] = None, + ) -> Tuple["torch.FloatTensor", Optional[KVCacheType]]: + """Compute a forward pass through the transformer model. + + Parameters + ---------- + input_ids + The input token ids. Must be one or two dimensional. + attention_mask + The attention mask. Must be one or two dimensional. + past_key_values + A tuple of tuples containing the cached key and value tensors for each + attention head. + + Returns + ------- + The computed logits and the new cached key and value tensors. + + """ + try: + import torch + except ImportError: + ImportError( + "The `torch` library needs to be installed to use `transformers` models." + ) + assert 0 < input_ids.ndim < 3 + + if past_key_values: + input_ids = input_ids[..., -1].unsqueeze(-1) + + with torch.inference_mode(): + output = self.model( + input_ids, + attention_mask=attention_mask, + return_dict=True, + output_attentions=False, + output_hidden_states=False, + past_key_values=past_key_values, + ) + + return output.logits, output.past_key_values + + def __call__( + self, + input_ids: "torch.LongTensor", + attention_mask: "torch.LongTensor", + past_key_values: Optional[Tuple] = None, + ) -> "torch.FloatTensor": + logits, kv_cache = self.forward(input_ids, attention_mask, past_key_values) + next_token_logits = logits[..., -1, :] + + return next_token_logits, kv_cache + + def generate( + self, + prompts: Union[str, List[str]], + generation_parameters: GenerationParameters, + logits_processor: Optional["OutlinesLogitsProcessor"], + sampling_parameters: SamplingParameters, + ) -> Union[str, List[str], List[List[str]]]: + """Generate text using `transformers`. + + Arguments + --------- + prompts + A prompt or list of prompts. + generation_parameters + An instance of `GenerationParameters` that contains the prompt, + the maximum number of tokens, stop sequences and seed. All the + arguments to `SequenceGeneratorAdapter`'s `__cal__` method. + logits_processor + The logits processor to use when generating text. + sampling_parameters + An instance of `SamplingParameters`, a dataclass that contains + the name of the sampler to use and related parameters as available + in Outlines. + + Returns + ------- + The generated text + """ + if isinstance(prompts, str): + # convert to 2d + input_ids, attention_mask = self.tokenizer.encode([prompts]) + else: + input_ids, attention_mask = self.tokenizer.encode(prompts) + + inputs = { + "input_ids": input_ids.to(self.model.device), + "attention_mask": attention_mask.to(self.model.device), + } + if ( + "attention_mask" + not in inspect.signature(self.model.forward).parameters.keys() + ): + del inputs["attention_mask"] + + generation_kwargs = self._get_generation_kwargs( + prompts, + generation_parameters, + logits_processor, + sampling_parameters, + ) + generated_ids = self._generate_output_seq(prompts, inputs, **generation_kwargs) + + # if single str input and single sample per input, convert to a 1D output + if isinstance(prompts, str): + generated_ids = generated_ids.squeeze(0) + + return self._decode_generation(generated_ids) + + def stream( + self, + prompts: Union[str, List[str]], + generation_parameters: GenerationParameters, + logits_processor: Optional["OutlinesLogitsProcessor"], + sampling_parameters: SamplingParameters, + ) -> Iterator[Union[str, List[str]]]: + """ + Temporary stream stand-in which implements stream() signature + and equivalent behaviour but isn't yielded until generation completes. + + TODO: implement following completion of https://github.com/huggingface/transformers/issues/30810 + """ + if isinstance(prompts, str): + # convert to 2d + input_ids, attention_mask = self.tokenizer.encode([prompts]) + else: + input_ids, attention_mask = self.tokenizer.encode(prompts) + inputs = { + "input_ids": input_ids.to(self.model.device), + "attention_mask": attention_mask.to(self.model.device), + } + if ( + "attention_mask" + not in inspect.signature(self.model.forward).parameters.keys() + ): + del inputs["attention_mask"] + + generation_kwargs = self._get_generation_kwargs( + prompts, + generation_parameters, + logits_processor, + sampling_parameters, + ) + generated_ids = self._generate_output_seq(prompts, inputs, **generation_kwargs) + + # if single str input and single sample per input, convert to a 1D output + if isinstance(prompts, str): + generated_ids = generated_ids.squeeze(0) + + for i in range(generated_ids.size(-1)): + output_group_ids = generated_ids.select(-1, i).unsqueeze(-1) + yield self._decode_generation(output_group_ids) + + def _get_generation_kwargs( + self, + prompts: Union[str, List[str]], + generation_parameters: GenerationParameters, + logits_processor: Optional["OutlinesLogitsProcessor"], + sampling_parameters: SamplingParameters, + ) -> dict: + """ + Conert outlines generation parameters into model.generate kwargs + """ + from transformers import GenerationConfig, LogitsProcessorList, set_seed + + max_new_tokens, stop_at, seed = dataclasses.astuple(generation_parameters) + sampler, num_samples, top_p, top_k, temperature = dataclasses.astuple( + sampling_parameters + ) + if max_new_tokens is None: + max_new_tokens = int(2**30) + + # global seed, not desirable + if seed is not None: + set_seed(seed) + + if logits_processor is not None: + logits_processor_list = LogitsProcessorList([logits_processor]) + else: + logits_processor_list = None + + generation_config = GenerationConfig( + max_new_tokens=max_new_tokens, + stop_strings=stop_at, + num_return_sequences=(num_samples or 1), + top_p=top_p, + top_k=top_k, + temperature=temperature, + do_sample=(sampler == "multinomial"), + num_beams=(num_samples if sampler == "beam_search" else 1), + eos_token_id=self.tokenizer.eos_token_id, + pad_token_id=self.tokenizer.pad_token_id, + ) + + return dict( + logits_processor=logits_processor_list, + generation_config=generation_config, + tokenizer=self.tokenizer.tokenizer, + ) + + def _generate_output_seq( + self, prompts, inputs, generation_config, **generation_kwargs + ): + input_ids = inputs["input_ids"] + output_ids = self.model.generate( + **inputs, generation_config=generation_config, **generation_kwargs + ) + + # encoder-decoder returns output_ids only, decoder-only returns full seq ids + if self.model.config.is_encoder_decoder: + generated_ids = output_ids + else: + generated_ids = output_ids[:, input_ids.shape[1] :] + + # if batch list inputs AND multiple samples per input, convert generated_id to 3D view + num_samples = generation_config.num_return_sequences or 1 + + if num_samples > 1 and isinstance(prompts, list): + batch_size = input_ids.size(0) + num_return_sequences = generation_config.num_return_sequences or 1 + generated_ids = generated_ids.view(batch_size, num_return_sequences, -1) + + return generated_ids + + def _decode_generation(self, generated_ids: "torch.Tensor"): + if len(generated_ids.shape) == 1: + return self.tokenizer.decode([generated_ids])[0] + elif len(generated_ids.shape) == 2: + return self.tokenizer.decode(generated_ids) + elif len(generated_ids.shape) == 3: + return [ + self.tokenizer.decode(generated_ids[i]) + for i in range(len(generated_ids)) + ] + else: + raise TypeError( + f"Generated outputs aren't 1D, 2D or 3D, but instead are {generated_ids.shape}" + ) + + +def transformers( + model_name: str, + device: Optional[str] = None, + model_kwargs: dict = {}, + tokenizer_kwargs: dict = {}, + model_class=None, + tokenizer_class=None, +): + """Instantiate a model from the `transformers` library and its tokenizer. + + Parameters + ---------- + model_name + The name of the model as listed on Hugging Face's model page. + device + The device(s) on which the model should be loaded. This overrides + the `device_map` entry in `model_kwargs` when provided. + model_kwargs + A dictionary that contains the keyword arguments to pass to the + `from_pretrained` method when loading the model. + tokenizer_kwargs + A dictionary that contains the keyword arguments to pass to the + `from_pretrained` method when loading the tokenizer. + + Returns + ------- + A `TransformersModel` model instance. + + """ + if model_class is None or tokenizer_class is None: + try: + from transformers import AutoModelForCausalLM, AutoTokenizer + except ImportError: + raise ImportError( + "The `transformers` library needs to be installed in order to use `transformers` models." + ) + if model_class is None: + model_class = AutoModelForCausalLM + if tokenizer_class is None: + tokenizer_class = AutoTokenizer + + if device is not None: + model_kwargs["device_map"] = device + + model = model_class.from_pretrained(model_name, **model_kwargs) + + tokenizer_kwargs.setdefault("padding_side", "left") + tokenizer = tokenizer_class.from_pretrained(model_name, **tokenizer_kwargs) + + return Transformers(model, tokenizer) + + +def mamba( + model_name: str, + device: Optional[str] = None, + model_kwargs: dict = {}, + tokenizer_kwargs: dict = {}, +): + try: + from transformers import MambaForCausalLM + + except ImportError: + raise ImportError( + "The `mamba_ssm`, `torch` and `transformer` libraries needs to be installed in order to use Mamba." + ) + + return transformers( + model_name=model_name, + device=device, + model_kwargs=model_kwargs, + tokenizer_kwargs=tokenizer_kwargs, + model_class=MambaForCausalLM, + ) diff --git a/.venv/lib/python3.11/site-packages/outlines/models/transformers_vision.py b/.venv/lib/python3.11/site-packages/outlines/models/transformers_vision.py new file mode 100644 index 0000000000000000000000000000000000000000..772645b80d36ee240b2a91078f10fffef0738128 --- /dev/null +++ b/.venv/lib/python3.11/site-packages/outlines/models/transformers_vision.py @@ -0,0 +1,138 @@ +from typing import TYPE_CHECKING, Any, Iterator, List, Optional, Union + +from outlines.generate.api import GenerationParameters, SamplingParameters +from outlines.models import Transformers + +if TYPE_CHECKING: + from outlines.processors import OutlinesLogitsProcessor + + +class TransformersVision(Transformers): + def __init__(self, model, tokenizer, processor): + super().__init__(model, tokenizer) + self.processor = processor + + def generate( # type: ignore + self, + prompts: Union[str, List[str]], + media: Union[List[Any], List[List[Any]]], + generation_parameters: GenerationParameters, + logits_processor: Optional["OutlinesLogitsProcessor"], + sampling_parameters: SamplingParameters, + ) -> Union[str, List[str], List[List[str]]]: + """Generate text using `transformers`. + + Arguments + --------- + prompts + A prompt or list of prompts. + media + A List[PIL.Image] or List[List[PIL.Image]] + generation_parameters + An instance of `GenerationParameters` that contains the prompt, + the maximum number of tokens, stop sequences and seed. All the + arguments to `SequenceGeneratorAdapter`'s `__cal__` method. + logits_processor + The logits processor to use when generating text. + sampling_parameters + An instance of `SamplingParameters`, a dataclass that contains + the name of the sampler to use and related parameters as available + in Outlines. + + Returns + ------- + The generated text + """ + inputs = self.processor( + text=prompts, images=media, padding=True, return_tensors="pt" + ).to(self.model.device) + + generation_kwargs = self._get_generation_kwargs( + prompts, + generation_parameters, + logits_processor, + sampling_parameters, + ) + generated_ids = self._generate_output_seq(prompts, inputs, **generation_kwargs) + + # if single str input and single sample per input, convert to a 1D output + if isinstance(prompts, str): + # Should always be true until NotImplementedError above is fixed + generated_ids = generated_ids.squeeze(0) + + return self._decode_generation(generated_ids) + + def stream( # type: ignore + self, + prompts: Union[str, List[str]], + media: Union[Any, List[Any]], # TODO: docstring + generation_parameters: GenerationParameters, + logits_processor: Optional["OutlinesLogitsProcessor"], + sampling_parameters: SamplingParameters, + ) -> Iterator[Union[str, List[str]]]: + raise NotImplementedError + + +def transformers_vision( + model_name: str, + model_class, + device: Optional[str] = None, + model_kwargs: dict = {}, + processor_kwargs: dict = {}, + tokenizer_class=None, + processor_class=None, +): + """Instantiate a model from the `transformers` library and its tokenizer. + + Parameters + ---------- + model_name + The name of the model as listed on Hugging Face's model page. + model_class + The `PreTrainedModel` class from transformers to use in initializing the vision model from `model_name`. + https://huggingface.co/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel + device + The device(s) on which the model should be loaded. This overrides + the `device_map` entry in `model_kwargs` when provided. + model_kwargs + A dictionary that contains the keyword arguments to pass to the + `from_pretrained` method when loading the model. + processor_kwargs + A dictionary that contains the keyword arguments to pass to the + `from_pretrained` method when loading the processor. + + Returns + ------- + A `TransformersModel` model instance. + + """ + if processor_class is None or tokenizer_class is None: + try: + from transformers import AutoProcessor, AutoTokenizer + except ImportError: + raise ImportError( + "The `transformers` library needs to be installed in order to use `transformers` models." + ) + if processor_class is None: + processor_class = AutoProcessor + if tokenizer_class is None: + tokenizer_class = AutoTokenizer + + if device is not None: + model_kwargs["device_map"] = device + + model = model_class.from_pretrained(model_name, **model_kwargs) + + processor_kwargs.setdefault("padding_side", "left") + processor_kwargs.setdefault("pad_token", "[PAD]") + processor = processor_class.from_pretrained(model_name, **processor_kwargs) + + if tokenizer_class is None: + if getattr(processor, "tokenizer", None): + tokenizer = processor.tokenizer + else: + tokenizer = AutoTokenizer.from_pretrained(model_name, **processor_kwargs) + else: + tokenizer = tokenizer_class.from_pretrained(model_name, **processor_kwargs) + + return TransformersVision(model, tokenizer, processor) diff --git a/.venv/lib/python3.11/site-packages/outlines/models/vllm.py b/.venv/lib/python3.11/site-packages/outlines/models/vllm.py new file mode 100644 index 0000000000000000000000000000000000000000..778c27c6fcec35fd14832459b4daf22e413de85b --- /dev/null +++ b/.venv/lib/python3.11/site-packages/outlines/models/vllm.py @@ -0,0 +1,227 @@ +import dataclasses +from typing import TYPE_CHECKING, List, Optional, Union + +from outlines.generate.api import GenerationParameters, SamplingParameters + +if TYPE_CHECKING: + from transformers import PreTrainedTokenizerBase + from vllm import LLM + from vllm.sampling_params import SamplingParams + + +class VLLM: + """Represents a vLLM model. + + We wrap models from model providing libraries in order to give all of + them the same interface in Outlines and allow users to easily switch + between providers. This class wraps the `vllm.LLM` class from the + `vllm` library. + + """ + + def __init__(self, model: "LLM"): + self.model = model + self.lora_request = None + + self.tokenizer = self._get_tokenizer() + + def _get_tokenizer(self): + if hasattr(self.model, "get_tokenizer"): + tokenizer = self.model.get_tokenizer() + elif hasattr(self.model, "tokenizer"): + if hasattr(self.model.tokenizer, "tokenizer"): + tokenizer = self.model.tokenizer.tokenizer + else: + tokenizer = self.model.tokenizer + else: + raise ValueError( + "The provided LLM instance neither has a " + "`tokenizer` attribute or a `get_tokenizer` method." + ) + return adapt_tokenizer(tokenizer=tokenizer) + + def generate( + self, + prompts: Union[str, List[str]], + generation_parameters: GenerationParameters, + logits_processor, + sampling_parameters: SamplingParameters, + *, + sampling_params: Optional["SamplingParams"] = None, + use_tqdm: bool = True, + ): + """Generate text using vLLM. + + Arguments + --------- + prompts + A prompt or list of prompts. + generation_parameters + An instance of `GenerationParameters` that contains the prompt, + the maximum number of tokens, stop sequences and seed. All the + arguments to `SequenceGeneratorAdapter`'s `__cal__` method. + logits_processor + The logits processor to use when generating text. + sampling_parameters + An instance of `SamplingParameters`, a dataclass that contains + the name of the sampler to use and related parameters as available + in Outlines. + sampling_params + An instance of `vllm.sampling_params.SamplingParams`. The values + passed via this dataclass supersede the values of the parameters + in `generation_parameters` and `sampling_parameters`. See the + vLLM documentation for more details: https://docs.vllm.ai/en/latest/dev/sampling_params.html. + use_tqdm + A boolean in order to display progress bar while inferencing + + Returns + ------- + The generated text, of shape `(n_batch, n_samples)`. If there are only + one batch and several samples, the list is of shape `(n_samples)`. If + this is a batch with several sequences but only one sample the list is + of shape `(n_batch)`. If there is only one sequence and one sample, a + string is returned. + + """ + from vllm.sampling_params import SamplingParams + + if sampling_params is None: + sampling_params = SamplingParams() + + max_tokens, stop_at, seed = dataclasses.astuple(generation_parameters) + + # We only update the values in `sampling_params` if they + # are specified by the user when calling the generator. + if max_tokens is not None: + sampling_params.max_tokens = max_tokens + if stop_at is not None: + if isinstance(stop_at, str): + stop_at = [stop_at] + sampling_params.stop = stop_at + if seed is not None: + sampling_params.seed = seed + + sampling_params.logits_processors = ( + [logits_processor] if logits_processor is not None else [] + ) + + sampler, num_samples, top_p, top_k, temperature = dataclasses.astuple( + sampling_parameters + ) + + # We only update the values in `sampling_params` that + # were not specified by the user. + if sampling_params.n == 1: + sampling_params.n = num_samples + sampling_params.best_of = num_samples + if top_p is not None and sampling_params.top_p == 1.0: + sampling_params.top_p = top_p + if top_k is not None and sampling_params.top_k == -1: + sampling_params.top_k = top_k + # TODO: remove this if statement once fixed + # https://github.com/vllm-project/vllm/issues/5404#issuecomment-2175972897 + if top_k == 1: + sampling_params.repetition_penalty = 0 + if temperature is not None and sampling_params.temperature == 1.0: + sampling_params.temperature = temperature + if sampler == "beam_search": + sampling_params.use_beam_search = True + + results = self.model.generate( + prompts, + sampling_params=sampling_params, + lora_request=self.lora_request, + use_tqdm=use_tqdm, + ) + results = [[sample.text for sample in batch.outputs] for batch in results] + + batch_size = len(results) + sample_size = len(results[0]) + + if batch_size == 1 and sample_size == 1: + return results[0][0] + elif batch_size == 1: + return results[0] + elif sample_size == 1: + return [batch[0] for batch in results] + + return results + + def stream(self, *args, **kwargs): + """Return a text generator. + + Streaming is not yet available for `vllm.LLM`. + + TODO: Implement the streaming functionality ourselves. + + """ + raise NotImplementedError( + "Streaming is not available for the vLLM integration." + ) + + def load_lora(self, adapter_path: Optional[str]): + from vllm.lora.request import LoRARequest + + if adapter_path is None: + self.lora_request = None + else: + self.lora_request = LoRARequest(adapter_path, 1, adapter_path) + + +def vllm(model_name: str, **vllm_model_params): + """Load a vLLM model. + + Arguments + --------- + model_name + The name of the model to load from the HuggingFace hub. + vllm_model_params + vLLM-specific model parameters. See the vLLM code for the full list: + https://github.com/vllm-project/vllm/blob/main/vllm/entrypoints/llm.py + + """ + from vllm import LLM + + model = LLM(model_name, **vllm_model_params) + + return VLLM(model) + + +def adapt_tokenizer(tokenizer: "PreTrainedTokenizerBase") -> "PreTrainedTokenizerBase": + """Adapt a tokenizer to use to compile the FSM. + + The API of Outlines tokenizers is slightly different to that of `transformers`. In + addition we need to handle the missing spaces to Llama's tokenizer to be able to + compile FSMs for this model. + + Parameters + ---------- + tokenizer + The tokenizer of the model. + + Returns + ------- + PreTrainedTokenizerBase + The adapted tokenizer. + """ + from transformers import SPIECE_UNDERLINE + + tokenizer.vocabulary = tokenizer.get_vocab() + tokenizer.special_tokens = set(tokenizer.all_special_tokens) + + def convert_token_to_string(token: Union[str, bytes]) -> str: + string = tokenizer.convert_tokens_to_string([token]) + + # A hack to handle missing spaces to HF's Llama tokenizers + if ( + type(token) is str + and token.startswith(SPIECE_UNDERLINE) + or token == "<0x20>" + ): + return " " + string + + return string + + tokenizer.convert_token_to_string = convert_token_to_string + + return tokenizer diff --git a/.venv/lib/python3.11/site-packages/uritemplate/__init__.py b/.venv/lib/python3.11/site-packages/uritemplate/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..65015c1a619acbe8461c06b35fa5e07babed6ac8 --- /dev/null +++ b/.venv/lib/python3.11/site-packages/uritemplate/__init__.py @@ -0,0 +1,35 @@ +""" + +uritemplate +=========== + +URI templates implemented as close to :rfc:`6570` as possible + +See http://uritemplate.rtfd.org/ for documentation + +:copyright: + (c) 2013 Ian Stapleton Cordasco +:license: + Modified BSD Apache License (Version 2.0), see LICENSE for more details + and either LICENSE.BSD or LICENSE.APACHE for the details of those specific + licenses + +""" + +__title__ = "uritemplate" +__author__ = "Ian Stapleton Cordasco" +__license__ = "Modified BSD or Apache License, Version 2.0" +__copyright__ = "Copyright 2013 Ian Stapleton Cordasco" +__version__ = "4.1.1" +__version_info__ = tuple( + int(i) for i in __version__.split(".") if i.isdigit() +) + +from uritemplate.api import ( + URITemplate, + expand, + partial, + variables, +) + +__all__ = ("URITemplate", "expand", "partial", 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str: + """Expand the template with the given parameters. + + :param str uri: The templated URI to expand + :param dict var_dict: Optional dictionary with variables and values + :param kwargs: Alternative way to pass arguments + :returns: str + + Example:: + + expand('https://api.github.com{/end}', {'end': 'users'}) + expand('https://api.github.com{/end}', end='gists') + + .. note:: Passing values by both parts, may override values in + ``var_dict``. For example:: + + expand('https://{var}', {'var': 'val1'}, var='val2') + + ``val2`` will be used instead of ``val1``. + + """ + return URITemplate(uri).expand(var_dict, **kwargs) + + +def partial( + uri: str, + var_dict: t.Optional[variable.VariableValueDict] = None, + **kwargs: variable.VariableValue, +) -> URITemplate: + """Partially expand the template with the given parameters. + + If all of the parameters for the template are not given, return a + partially expanded template. + + :param dict var_dict: Optional dictionary with variables and values + :param kwargs: Alternative way to pass arguments + :returns: :class:`URITemplate` + + Example:: + + t = URITemplate('https://api.github.com{/end}') + t.partial() # => URITemplate('https://api.github.com{/end}') + + """ + return URITemplate(uri).partial(var_dict, **kwargs) + + +def variables(uri: str) -> OrderedSet: + """Parse the variables of the template. + + This returns all of the variable names in the URI Template. + + :returns: Set of variable names + :rtype: set + + Example:: + + variables('https://api.github.com{/end}) + # => {'end'} + variables('https://api.github.com/repos{/username}{/repository}') + # => {'username', 'repository'} + + """ + return OrderedSet(URITemplate(uri).variable_names) diff --git a/.venv/lib/python3.11/site-packages/uritemplate/orderedset.py b/.venv/lib/python3.11/site-packages/uritemplate/orderedset.py new file mode 100644 index 0000000000000000000000000000000000000000..dd7cd1a5867d472fdae1cd065b7586c2f17a22c2 --- /dev/null +++ b/.venv/lib/python3.11/site-packages/uritemplate/orderedset.py @@ -0,0 +1,92 @@ +# From: https://github.com/ActiveState/code/blob/master/recipes/Python/576696_OrderedSet_with_Weakrefs/ # noqa +import typing as t +import weakref + + +class Link: + """Representation of one item in a doubly-linked list.""" + + __slots__ = ("prev", "next", "key", "__weakref__") + prev: "Link" + next: "Link" + key: str + + +class OrderedSet(t.MutableSet[str]): + """A set that remembers the order in which items were added.""" + + # Big-O running times for all methods are the same as for regular sets. + # The internal self.__map dictionary maps keys to links in a doubly linked + # list. The circular doubly linked list starts and ends with a sentinel + # element. The sentinel element never gets deleted (this simplifies the + # algorithm). The prev/next links are weakref proxies (to prevent circular + # references). Individual links are kept alive by the hard reference in + # self.__map. Those hard references disappear when a key is deleted from + # an OrderedSet. + + def __init__(self, iterable: t.Optional[t.Iterable[str]] = None): + self.__root = root = Link() # sentinel node for doubly linked list + root.prev = root.next = root + self.__map: t.MutableMapping[str, Link] = {} # key --> link + if iterable is not None: + self |= iterable # type: ignore + + def __len__(self) -> int: + return len(self.__map) + + def __contains__(self, key: object) -> bool: + return key in self.__map + + def add(self, key: str) -> None: + # Store new key in a new link at the end of the linked list + if key not in self.__map: + self.__map[key] = link = Link() + root = self.__root + last = root.prev + link.prev, link.next, link.key = last, root, key + last.next = root.prev = weakref.proxy(link) + + def discard(self, key: str) -> None: + # Remove an existing item using self.__map to find the link which is + # then removed by updating the links in the predecessor and successors. + if key in self.__map: + link = self.__map.pop(key) + link.prev.next = link.next + link.next.prev = link.prev + + def __iter__(self) -> t.Generator[str, None, None]: + # Traverse the linked list in order. + root = self.__root + curr = root.next + while curr is not root: + yield curr.key + curr = curr.next + + def __reversed__(self) -> t.Generator[str, None, None]: + # Traverse the linked list in reverse order. + root = self.__root + curr = root.prev + while curr is not root: + yield curr.key + curr = curr.prev + + def pop(self, last: bool = True) -> str: + if not self: + raise KeyError("set is empty") + key = next(reversed(self)) if last else next(iter(self)) + self.discard(key) + return key + + def __repr__(self) -> str: + if not self: + return f"{self.__class__.__name__}()" + return f"{self.__class__.__name__}({list(self)!r})" + + def __str__(self) -> str: + return self.__repr__() + + def __eq__(self, other: object) -> bool: + if isinstance(other, OrderedSet): + return len(self) == len(other) and list(self) == list(other) + other = t.cast(t.Iterable[str], other) + return not self.isdisjoint(other) diff --git a/.venv/lib/python3.11/site-packages/uritemplate/py.typed b/.venv/lib/python3.11/site-packages/uritemplate/py.typed new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/.venv/lib/python3.11/site-packages/uritemplate/template.py b/.venv/lib/python3.11/site-packages/uritemplate/template.py new file mode 100644 index 0000000000000000000000000000000000000000..73ef89c93ca169c60fa6b4029ea5e5b4c6db05bb --- /dev/null +++ b/.venv/lib/python3.11/site-packages/uritemplate/template.py @@ -0,0 +1,169 @@ +""" + +uritemplate.template +==================== + +This module contains the essential inner workings of uritemplate. + +What treasures await you: + +- URITemplate class + +You see a treasure chest of knowledge in front of you. +What do you do? +> + +""" +import re +import typing as t + +from uritemplate import orderedset +from uritemplate import variable + +template_re = re.compile("{([^}]+)}") + + +def _merge( + var_dict: t.Optional[variable.VariableValueDict], + overrides: variable.VariableValueDict, +) -> variable.VariableValueDict: + if var_dict: + opts = var_dict.copy() + opts.update(overrides) + return opts + return overrides + + +class URITemplate: + + """This parses the template and will be used to expand it. + + This is the most important object as the center of the API. + + Example:: + + from uritemplate import URITemplate + import requests + + + t = URITemplate( + 'https://api.github.com/users/sigmavirus24/gists{/gist_id}' + ) + uri = t.expand(gist_id=123456) + resp = requests.get(uri) + for gist in resp.json(): + print(gist['html_url']) + + Please note:: + + str(t) + # 'https://api.github.com/users/sigmavirus24/gists{/gistid}' + repr(t) # is equivalent to + # URITemplate(str(t)) + # Where str(t) is interpreted as the URI string. + + Also, ``URITemplates`` are hashable so they can be used as keys in + dictionaries. + + """ + + def __init__(self, uri: str): + #: The original URI to be parsed. + self.uri: str = uri + #: A list of the variables in the URI. They are stored as + #: :class:`~uritemplate.variable.URIVariable`\ s + self.variables: t.List[variable.URIVariable] = [ + variable.URIVariable(m.groups()[0]) + for m in template_re.finditer(self.uri) + ] + #: A set of variable names in the URI. + self.variable_names = orderedset.OrderedSet() + for var in self.variables: + for name in var.variable_names: + self.variable_names.add(name) + + def __repr__(self) -> str: + return 'URITemplate("%s")' % self + + def __str__(self) -> str: + return self.uri + + def __eq__(self, other: object) -> bool: + if not isinstance(other, URITemplate): + return NotImplemented + return self.uri == other.uri + + def __hash__(self) -> int: + return hash(self.uri) + + def _expand( + self, var_dict: variable.VariableValueDict, replace: bool + ) -> str: + if not self.variables: + return self.uri + + expansion = var_dict + expanded: t.Dict[str, str] = {} + for v in self.variables: + expanded.update(v.expand(expansion)) + + def replace_all(match: "re.Match[str]") -> str: + return expanded.get(match.groups()[0], "") + + def replace_partial(match: "re.Match[str]") -> str: + match_group = match.groups()[0] + var = "{%s}" % match_group + return expanded.get(match_group) or var + + replace_func = replace_partial if replace else replace_all + + return template_re.sub(replace_func, self.uri) + + def expand( + self, + var_dict: t.Optional[variable.VariableValueDict] = None, + **kwargs: variable.VariableValue, + ) -> str: + """Expand the template with the given parameters. + + :param dict var_dict: Optional dictionary with variables and values + :param kwargs: Alternative way to pass arguments + :returns: str + + Example:: + + t = URITemplate('https://api.github.com{/end}') + t.expand({'end': 'users'}) + t.expand(end='gists') + + .. note:: Passing values by both parts, may override values in + ``var_dict``. For example:: + + expand('https://{var}', {'var': 'val1'}, var='val2') + + ``val2`` will be used instead of ``val1``. + + """ + return self._expand(_merge(var_dict, kwargs), False) + + def partial( + self, + var_dict: t.Optional[variable.VariableValueDict] = None, + **kwargs: variable.VariableValue, + ) -> "URITemplate": + """Partially expand the template with the given parameters. + + If all of the parameters for the template are not given, return a + partially expanded template. + + :param dict var_dict: Optional dictionary with variables and values + :param kwargs: Alternative way to pass arguments + :returns: :class:`URITemplate` + + Example:: + + t = URITemplate('https://api.github.com{/end}') + t.partial() # => URITemplate('https://api.github.com{/end}') + + """ + return URITemplate(self._expand(_merge(var_dict, kwargs), True)) diff --git a/.venv/lib/python3.11/site-packages/uritemplate/variable.py b/.venv/lib/python3.11/site-packages/uritemplate/variable.py new file mode 100644 index 0000000000000000000000000000000000000000..da406cab262cf6065dd32e1ca2ebd2dde231823d --- /dev/null +++ b/.venv/lib/python3.11/site-packages/uritemplate/variable.py @@ -0,0 +1,419 @@ +""" + +uritemplate.variable +==================== + +This module contains the URIVariable class which powers the URITemplate class. + +What treasures await you: + +- URIVariable class + +You see a hammer in front of you. +What do you do? +> + +""" +import collections.abc +import typing as t +import urllib.parse + +ScalarVariableValue = t.Union[int, float, complex, str] +VariableValue = t.Union[ + t.Sequence[ScalarVariableValue], + t.Mapping[str, ScalarVariableValue], + t.Tuple[str, ScalarVariableValue], + ScalarVariableValue, +] +VariableValueDict = t.Dict[str, VariableValue] + + +class URIVariable: + + """This object validates everything inside the URITemplate object. + + It validates template expansions and will truncate length as decided by + the template. + + Please note that just like the :class:`URITemplate `, this + object's ``__str__`` and ``__repr__`` methods do not return the same + information. Calling ``str(var)`` will return the original variable. + + This object does the majority of the heavy lifting. The ``URITemplate`` + object finds the variables in the URI and then creates ``URIVariable`` + objects. Expansions of the URI are handled by each ``URIVariable`` + object. ``URIVariable.expand()`` returns a dictionary of the original + variable and the expanded value. Check that method's documentation for + more information. + + """ + + operators = ("+", "#", ".", "/", ";", "?", "&", "|", "!", "@") + reserved = ":/?#[]@!$&'()*+,;=" + + def __init__(self, var: str): + #: The original string that comes through with the variable + self.original: str = var + #: The operator for the variable + self.operator: str = "" + #: List of safe characters when quoting the string + self.safe: str = "" + #: List of variables in this variable + self.variables: t.List[ + t.Tuple[str, t.MutableMapping[str, t.Any]] + ] = [] + #: List of variable names + self.variable_names: t.List[str] = [] + #: List of defaults passed in + self.defaults: t.MutableMapping[str, ScalarVariableValue] = {} + # Parse the variable itself. + self.parse() + self.post_parse() + + def __repr__(self) -> str: + return "URIVariable(%s)" % self + + def __str__(self) -> str: + return self.original + + def parse(self) -> None: + """Parse the variable. + + This finds the: + - operator, + - set of safe characters, + - variables, and + - defaults. + + """ + var_list_str = self.original + if self.original[0] in URIVariable.operators: + self.operator = self.original[0] + var_list_str = self.original[1:] + + if self.operator in URIVariable.operators[:2]: + self.safe = URIVariable.reserved + + var_list = var_list_str.split(",") + + for var in var_list: + default_val = None + name = var + if "=" in var: + name, default_val = tuple(var.split("=", 1)) + + explode = False + if name.endswith("*"): + explode = True + name = name[:-1] + + prefix: t.Optional[int] = None + if ":" in name: + name, prefix_str = tuple(name.split(":", 1)) + prefix = int(prefix_str) + + if default_val: + self.defaults[name] = default_val + + self.variables.append( + (name, {"explode": explode, "prefix": prefix}) + ) + + self.variable_names = [varname for (varname, _) in self.variables] + + def post_parse(self) -> None: + """Set ``start``, ``join_str`` and ``safe`` attributes. + + After parsing the variable, we need to set up these attributes and it + only makes sense to do it in a more easily testable way. + """ + self.safe = "" + self.start = self.join_str = self.operator + if self.operator == "+": + self.start = "" + if self.operator in ("+", "#", ""): + self.join_str = "," + if self.operator == "#": + self.start = "#" + if self.operator == "?": + self.start = "?" + self.join_str = "&" + + if self.operator in ("+", "#"): + self.safe = URIVariable.reserved + + def _query_expansion( + self, + name: str, + value: VariableValue, + explode: bool, + prefix: t.Optional[int], + ) -> t.Optional[str]: + """Expansion method for the '?' and '&' operators.""" + if value is None: + return None + + tuples, items = is_list_of_tuples(value) + + safe = self.safe + if list_test(value) and not tuples: + if not value: + return None + value = t.cast(t.Sequence[ScalarVariableValue], value) + if explode: + return self.join_str.join( + f"{name}={quote(v, safe)}" for v in value + ) + else: + value = ",".join(quote(v, safe) for v in value) + return f"{name}={value}" + + if dict_test(value) or tuples: + if not value: + return None + value = t.cast(t.Mapping[str, ScalarVariableValue], value) + items = items or sorted(value.items()) + if explode: + return self.join_str.join( + f"{quote(k, safe)}={quote(v, safe)}" for k, v in items + ) + else: + value = ",".join( + f"{quote(k, safe)},{quote(v, safe)}" for k, v in items + ) + return f"{name}={value}" + + if value: + value = t.cast(t.Text, value) + value = value[:prefix] if prefix else value + return f"{name}={quote(value, safe)}" + return name + "=" + + def _label_path_expansion( + self, + name: str, + value: VariableValue, + explode: bool, + prefix: t.Optional[int], + ) -> t.Optional[str]: + """Label and path expansion method. + + Expands for operators: '/', '.' + + """ + join_str = self.join_str + safe = self.safe + + if value is None or ( + not isinstance(value, (str, int, float, complex)) + and len(value) == 0 + ): + return None + + tuples, items = is_list_of_tuples(value) + + if list_test(value) and not tuples: + if not explode: + join_str = "," + + value = t.cast(t.Sequence[ScalarVariableValue], value) + fragments = [quote(v, safe) for v in value if v is not None] + return join_str.join(fragments) if fragments else None + + if dict_test(value) or tuples: + value = t.cast(t.Mapping[str, ScalarVariableValue], value) + items = items or sorted(value.items()) + format_str = "%s=%s" + if not explode: + format_str = "%s,%s" + join_str = "," + + expanded = join_str.join( + format_str % (quote(k, safe), quote(v, safe)) + for k, v in items + if v is not None + ) + return expanded if expanded else None + + value = t.cast(t.Text, value) + value = value[:prefix] if prefix else value + return quote(value, safe) + + def _semi_path_expansion( + self, + name: str, + value: VariableValue, + explode: bool, + prefix: t.Optional[int], + ) -> t.Optional[str]: + """Expansion method for ';' operator.""" + join_str = self.join_str + safe = self.safe + + if value is None: + return None + + if self.operator == "?": + join_str = "&" + + tuples, items = is_list_of_tuples(value) + + if list_test(value) and not tuples: + value = t.cast(t.Sequence[ScalarVariableValue], value) + if explode: + expanded = join_str.join( + f"{name}={quote(v, safe)}" for v in value if v is not None + ) + return expanded if expanded else None + else: + value = ",".join(quote(v, safe) for v in value) + return f"{name}={value}" + + if dict_test(value) or tuples: + value = t.cast(t.Mapping[str, ScalarVariableValue], value) + items = items or sorted(value.items()) + + if explode: + return join_str.join( + f"{quote(k, safe)}={quote(v, safe)}" + for k, v in items + if v is not None + ) + else: + expanded = ",".join( + f"{quote(k, safe)},{quote(v, safe)}" + for k, v in items + if v is not None + ) + return f"{name}={expanded}" + + value = t.cast(t.Text, value) + value = value[:prefix] if prefix else value + if value: + return f"{name}={quote(value, safe)}" + + return name + + def _string_expansion( + self, + name: str, + value: VariableValue, + explode: bool, + prefix: t.Optional[int], + ) -> t.Optional[str]: + if value is None: + return None + + tuples, items = is_list_of_tuples(value) + + if list_test(value) and not tuples: + value = t.cast(t.Sequence[ScalarVariableValue], value) + return ",".join(quote(v, self.safe) for v in value) + + if dict_test(value) or tuples: + value = t.cast(t.Mapping[str, ScalarVariableValue], value) + items = items or sorted(value.items()) + format_str = "%s=%s" if explode else "%s,%s" + + return ",".join( + format_str % (quote(k, self.safe), quote(v, self.safe)) + for k, v in items + ) + + value = t.cast(t.Text, value) + value = value[:prefix] if prefix else value + return quote(value, self.safe) + + def expand( + self, var_dict: t.Optional[VariableValueDict] = None + ) -> t.Mapping[str, str]: + """Expand the variable in question. + + Using ``var_dict`` and the previously parsed defaults, expand this + variable and subvariables. + + :param dict var_dict: dictionary of key-value pairs to be used during + expansion + :returns: dict(variable=value) + + Examples:: + + # (1) + v = URIVariable('/var') + expansion = v.expand({'var': 'value'}) + print(expansion) + # => {'/var': '/value'} + + # (2) + v = URIVariable('?var,hello,x,y') + expansion = v.expand({'var': 'value', 'hello': 'Hello World!', + 'x': '1024', 'y': '768'}) + print(expansion) + # => {'?var,hello,x,y': + # '?var=value&hello=Hello%20World%21&x=1024&y=768'} + + """ + return_values = [] + if var_dict is None: + return {self.original: self.original} + + for name, opts in self.variables: + value = var_dict.get(name, None) + if not value and value != "" and name in self.defaults: + value = self.defaults[name] + + if value is None: + continue + + expanded = None + if self.operator in ("/", "."): + expansion = self._label_path_expansion + elif self.operator in ("?", "&"): + expansion = self._query_expansion + elif self.operator == ";": + expansion = self._semi_path_expansion + else: + expansion = self._string_expansion + + expanded = expansion(name, value, opts["explode"], opts["prefix"]) + + if expanded is not None: + return_values.append(expanded) + + value = "" + if return_values: + value = self.start + self.join_str.join(return_values) + return {self.original: value} + + +def is_list_of_tuples( + value: t.Any, +) -> t.Tuple[bool, t.Optional[t.Sequence[t.Tuple[str, ScalarVariableValue]]]]: + if ( + not value + or not isinstance(value, (list, tuple)) + or not all(isinstance(t, tuple) and len(t) == 2 for t in value) + ): + return False, None + + return True, value + + +def list_test(value: t.Any) -> bool: + return isinstance(value, (list, tuple)) + + +def dict_test(value: t.Any) -> bool: + return isinstance(value, (dict, collections.abc.MutableMapping)) + + +def _encode(value: t.AnyStr, encoding: str = "utf-8") -> bytes: + if isinstance(value, str): + return value.encode(encoding) + return value + + +def quote(value: t.Any, safe: str) -> str: + if not isinstance(value, (str, bytes)): + value = str(value) + return urllib.parse.quote(_encode(value), safe)