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README.md

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----
-license: mit
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+---
+license: mit
+title: AI Vision Application - Cross-platform, Offline
+description: An open-source cross-platform AI Vision application built on onnxruntime, numpy & opencv. It can perform tasks like object detection, image classification on your image. It also uses a model like Google SpeciesNet (cameratrapai) to identify wildlife species.
+meta:
+  - name: keywords
+    content: ai vision, object detection, speciesnet google, google cameratrapai, speciesnet onnxruntime model, onnxruntime, opencv, numpy, offline ai, cross-platform ai app, onnxruntime android, python on android
+  - name: author
+    content: Somnath Das
+  - name: robots
+    content: index, follow
+  - property: og:image
+    content: https://blog.daslearning.in/llm_ai/ml/images/ogImg.png
+  - property: og:title
+    content: AI Vision Application - Cross-platform, Offline
+  - property: og:description
+    content: An open-source cross-platform AI Vision application built on onnxruntime, numpy & opencv. It can perform tasks like object detection, image classification on your image. It also uses a model like Google SpeciesNet (cameratrapai) to identify wildlife species.
+  - property: og:url
+    content: https://blog.daslearning.in/llm_ai/ml/ai-vision.html
+  - name: canonical
+    content: https://blog.daslearning.in/llm_ai/ml/ai-vision.html
+  - property: og:type
+    content: article
+  - property: article:author
+    content: https://daslearning.in/contact/
+  - property: og:site_name
+    content: DasLearning
+  - name: theme-color
+    content: "#0f172a"  # dark blue-grey for Chrome mobile UI
+  - name: viewport
+    content: width=device-width, initial-scale=1.0
+---
+
+# 👁️ AI Vision
+Using small machine learning or AI models we will be performing various `Computer Vision` related operations, for example `Object Detection` on images. This project purely focused on cross-platform applications & we can run small AI/ML models on our mobile phones in offline mode.
+
+> This project is buid on `kivy`, `kivymd` and uses `onnxruntime`, `numpy`, `opencv` etc. to perform the tasks. This is still at very early phase before this project matures at some level.
+
+<a href="https://github.com/daslearning-org/vision-ai/releases" target="_blank" rel="noopener noreferrer"><img alt="GitHub Downloads (all assets, all releases)" src="https://img.shields.io/github/downloads/daslearning-org/vision-ai/total"></a>
+<a href="https://www.youtube.com/watch?v=wUABgn4JYc4" target="_blank" rel="noopener noreferrer"><img alt="GitHub Downloads (all assets, all releases)" src="https://img.shields.io/youtube/views/wUABgn4JYc4"></a>
+
+### Features
+1. Objects detection from `Image` in local storage. It uses [SSD-MobilenetV1](https://github.com/onnx/models/tree/main/validated/vision/object_detection_segmentation/ssd-mobilenetv1)
+2. Directly capture from `Camera` & detect objects on the image. It also uses the same model as above.
+3. `Image Prediction`: if you have an image of a single object & want to get top-5 predictions about the image, just upload & click `Classify`. It uses [ResNet18](https://github.com/onnx/models/tree/main/validated/vision/classification/resnet) which has around `1000` categories.
+4. `Identify Species` from a photo. It uses [Google SpeciesNet](https://github.com/google/cameratrapai) & it has around `2000` odd species.
+5. `No Ads`, completely `open-source` (free).
+6. No `trackers`, no `data collection` etc. Actually you own your data & your app.
+
+## 📽️ Demo
+You can play below Video or click this [Youtube Link](https://www.youtube.com/watch?v=wUABgn4JYc4) to see the demo. Please let me know in the comments, how do you feel about this App. <br>
+
+<iframe width="560" height="315" src="https://www.youtube.com/embed/wUABgn4JYc4" title="YouTube video player" frameborder="0" allow="accelerometer; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" referrerpolicy="strict-origin-when-cross-origin" allowfullscreen></iframe>
+
+## 🖧 Our Scematic Architecture
+To be added...
+
+## 🧑‍💻 Quickstart Guide
+
+### 📱 Download & Run the Android App
+You can check the [Releases](https://github.com/daslearning-org/vision-ai/releases) and downlaod the latest version of the android app on your phone.
+
+1. If you want to select an image from phone memory, do it either from `DCIM` or `Downloads` or `Pictures` or any subfolders of this three folders. Same goes while saving the output image too.
+
+2. If you use the camera detection, please note that it uses back camera in `Landscape` mode only, so you may need to rotate your phone to capture it properly.
+
+#### Requirements
+- Minimum `Android 9`
+
+### 💻 Download & Run the Windows or Linux App
+To be built later. For now you may use with python directly as mentioned below.
+
+### 🐍 Run with Python
+
+1. Clone the repo
+```bash
+git clone https://github.com/daslearning-org/vision-ai.git
+```
+
+2. Run the application
+```bash
+cd vision-ai/onnx/
+pip install -r requirements.txt # virtual environment is recommended
+python main.py
+```
+
+### 📥 More open-source apps from us
+You may check our other apps from our [app list](https://daslearning.in/apps/)
+
+## 🦾 Build your own App
+The Kivy project has a great tool named [Buildozer](https://buildozer.readthedocs.io/en/latest/) which can make mobile apps for `Android` & `iOS`
+
+### 📱 Build Android App
+A Linux environment is recommended for the app development. If you are on Windows, you may use `WSL` or any `Virtual Machine`. As of now the `buildozer` tool works on Python version `3.11` at maximum. I am going to use Python `3.11`
+
+```bash
+# add the python repository
+sudo add-apt-repository ppa:deadsnakes/ppa
+sudo apt update
+
+# install all dependencies.
+sudo apt install -y ant autoconf automake ccache cmake g++ gcc libbz2-dev libffi-dev libltdl-dev libtool libssl-dev lbzip2 make ninja-build openjdk-17-jdk patch patchelf pkg-config protobuf-compiler python3.11 python3.11-venv python3.11-dev
+
+# optionally we can default to python 3.11
+sudo ln -sf /usr/bin/python3.11 /usr/bin/python3
+sudo ln -sf /usr/bin/python3.11 /usr/bin/python
+sudo ln -sf /usr/bin/python3.11-config /usr/bin/python3-config
+
+# optionally you may check the java installation with below commands
+java -version
+javac -version
+
+# install python modules
+git clone https://github.com/daslearning-org/vision-ai.git
+cd vision-ai/onnx/
+python3.11 -m venv .env # create python virtual environment
+source .env/bin/activate
+pip install -r req_android.txt
+
+# build the android apk
+buildozer android debug # this may take a good amount of time for the first time & will generate the apk in the bin directory
+```