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| Perspective Fields for Single Image Camera Calibration | |
| ================================================================ | |
| [](https://huggingface.co/spaces/jinlinyi/PerspectiveFields) | |
| ### [Project Page](https://jinlinyi.github.io/PerspectiveFields/) | [Paper](https://arxiv.org/abs/2212.03239) | [Live Demo 🤗](https://huggingface.co/spaces/jinlinyi/PerspectiveFields) | |
| CVPR 2023 (✨Highlight) | |
| <h4> | |
| [Linyi Jin](https://jinlinyi.github.io/)<sup>1</sup>, [Jianming Zhang](https://jimmie33.github.io/)<sup>2</sup>, [Yannick Hold-Geoffroy](https://yannickhold.com/)<sup>2</sup>, [Oliver Wang](http://www.oliverwang.info/)<sup>2</sup>, [Kevin Matzen](http://kmatzen.com/)<sup>2</sup>, [Matthew Sticha](https://www.linkedin.com/in/matthew-sticha-746325202/)<sup>1</sup>, [David Fouhey](https://web.eecs.umich.edu/~fouhey/)<sup>1</sup> | |
| <span style="font-size: 14pt; color: #555555"> | |
| <sup>1</sup>University of Michigan, <sup>2</sup>Adobe Research | |
| </span> | |
| </h4> | |
| <hr> | |
| <p align="center"> | |
|  | |
| </p> | |
| We propose Perspective Fields as a representation that models the local perspective properties of an image. Perspective Fields contain per-pixel information about the camera view, parameterized as an up vector and a latitude value. | |
| <p align="center"> | |
| <img height="100" alt="swiping-1" src="assets/swiping-1.gif"> <img height="100" alt="swiping-2" src="assets/swiping-2.gif"> <img height="100" alt="swiping-3" src="assets/swiping-3.gif"> <img height="100" alt="swiping-4" src="assets/swiping-4.gif"> | |
| </p> | |
| 📷 From Perspective Fields, you can also get camera parameters if you assume certain camera models. We provide models to recover camera roll, pitch, fov and principal point location. | |
| <p align="center"> | |
| <img src="assets/vancouver/IMG_2481.jpg" alt="Image 1" height="200px" style="margin-right:10px;"> | |
| <img src="assets/vancouver/pred_pers.png" alt="Image 2" height="200px" style="margin-center:10px;"> | |
| <img src="assets/vancouver/pred_param.png" alt="Image 2" height="200px" style="margin-left:10px;"> | |
| </p> | |
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| Updates | |
| ------------------ | |
| - [April 2024]: 🚀 We've launched an inference version (`main` branch) with minimal dependencies. For training and evaluation, please checkout [`train_eval` branch](https://github.com/jinlinyi/PerspectiveFields/tree/train_eval). | |
| - [July 2023]: We released a new model trained on [360cities](https://www.360cities.net/) and [EDINA](https://github.com/tien-d/EgoDepthNormal/blob/main/README_dataset.md) dataset, consisting of indoor🏠, outdoor🏙️, natural🌳, and egocentric👋 data! | |
| - [May 2023]: Live demo released 🤗. https://huggingface.co/spaces/jinlinyi/PerspectiveFields. Thanks Huggingface for funding this demo! | |
| <!-- omit in toc --> | |
| Table of Contents | |
| ------------------ | |
| - [Environment Setup](#environment-setup) | |
| - [Inference](#inference) | |
| - [Train / Eval](#train--eval) | |
| - [Demo](#demo) | |
| - [Model Zoo](#model-zoo) | |
| - [Coordinate Frame](#coordinate-frame) | |
| - [Camera Parameters to Perspective Fields](#camera-parameters-to-perspective-fields) | |
| - [Visualize Perspective Fields](#visualize-perspective-fields) | |
| - [Citation](#citation) | |
| - [Acknowledgment](#acknowledgment) | |
| [1]: ./docs/environment.md | |
| [2]: ./jupyter-notebooks/camera2perspective.ipynb | |
| [3]: ./jupyter-notebooks/predict_perspective_fields.ipynb | |
| [4]: ./jupyter-notebooks/perspective_paramnet.ipynb | |
| [5]: ./docs/train.md | |
| [6]: ./docs/test.md | |
| [7]: ./docs/models.md | |
| ## Environment Setup | |
| ### Inference | |
| PerspectiveFields requires python >= 3.8 and [PyTorch](https://pytorch.org/). | |
| | ***Pro tip:*** *use [mamba](https://github.com/conda-forge/miniforge) in place of conda for much faster installs.* | |
| ```bash | |
| # install pytorch compatible to your system https://pytorch.org/get-started/previous-versions/ | |
| conda install pytorch=1.10.0 torchvision cudatoolkit=11.3 -c pytorch | |
| pip install git+https://github.com/jinlinyi/PerspectiveFields.git | |
| ``` | |
| Alternatively, install the package locally, | |
| ```bash | |
| git clone git@github.com:jinlinyi/PerspectiveFields.git | |
| # create virtual env | |
| conda create -n perspective python=3.9 | |
| conda activate perspective | |
| # install pytorch compatible to your system https://pytorch.org/get-started/previous-versions/ | |
| # conda install pytorch torchvision cudatoolkit -c pytorch | |
| conda install pytorch=1.10.0 torchvision cudatoolkit=11.3 -c pytorch | |
| # install Perspective Fields. | |
| cd PerspectiveFields | |
| pip install -e . | |
| ``` | |
| ### Train / Eval | |
| For training and evaluation, please checkout the [`train_eval` branch](https://github.com/jinlinyi/PerspectiveFields/tree/train_eval). | |
| ## Demo | |
| Here is a minimal script to run on a single image, see [`demo/demo.py`](demo/demo.py): | |
| ```python | |
| import cv2 | |
| from perspective2d import PerspectiveFields | |
| # specify model version | |
| version = 'Paramnet-360Cities-edina-centered' | |
| # load model | |
| pf_model = PerspectiveFields(version).eval().cuda() | |
| # load image | |
| img_bgr = cv2.imread('assets/imgs/cityscape.jpg') | |
| # inference | |
| predictions = pf_model.inference(img_bgr=img_bgr) | |
| # alternatively, inference a batch of images | |
| predictions = pf_model.inference_batch(img_bgr_list=[img_bgr_0, img_bgr_1, img_bgr_2]) | |
| ``` | |
| - Or checkout [Live Demo 🤗](https://huggingface.co/spaces/jinlinyi/PerspectiveFields). | |
| - Notebook to [Predict Perspective Fields](./notebooks/predict_perspective_fields.ipynb). | |
| ## Model Zoo | |
| | Model Name and Weights | Training Dataset | Config File | Outputs | Expected input | | |
| | ------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------- | ----------------------------------------------------------------- | -------------------------------------------------------------------------------------------- | | |
| | [NEW][Paramnet-360Cities-edina-centered](https://www.dropbox.com/s/z2dja70bgy007su/paramnet_360cities_edina_rpf.pth) | [360cities](https://www.360cities.net/) and [EDINA](https://github.com/tien-d/EgoDepthNormal/blob/main/README_dataset.md) | [paramnet_360cities_edina_rpf.yaml](models/paramnet_360cities_edina_rpf.yaml) | Perspective Field + camera parameters (roll, pitch, vfov) | Uncropped, indoor🏠, outdoor🏙️, natural🌳, and egocentric👋 data | | |
| | [NEW][Paramnet-360Cities-edina-uncentered](https://www.dropbox.com/s/nt29e1pi83mm1va/paramnet_360cities_edina_rpfpp.pth) | [360cities](https://www.360cities.net/) and [EDINA](https://github.com/tien-d/EgoDepthNormal/blob/main/README_dataset.md) | [paramnet_360cities_edina_rpfpp.yaml](models/paramnet_360cities_edina_rpfpp.yaml) | Perspective Field + camera parameters (roll, pitch, vfov, cx, cy) | Cropped, indoor🏠, outdoor🏙️, natural🌳, and egocentric👋 data | | |
| | [PersNet-360Cities](https://www.dropbox.com/s/czqrepqe7x70b7y/cvpr2023.pth) | [360cities](https://www.360cities.net) | [cvpr2023.yaml](models/cvpr2023.yaml) | Perspective Field | Indoor🏠, outdoor🏙️, and natural🌳 data. | | |
| | [PersNet_paramnet-GSV-centered](https://www.dropbox.com/s/g6xwbgnkggapyeu/paramnet_gsv_rpf.pth) | [GSV](https://research.google/pubs/pub36899/) | [paramnet_gsv_rpf.yaml](models/paramnet_gsv_rpf.yaml) | Perspective Field + camera parameters (roll, pitch, vfov) | Uncropped, street view🏙️ data. | | |
| | [PersNet_Paramnet-GSV-uncentered](https://www.dropbox.com/s/ufdadxigewakzlz/paramnet_gsv_rpfpp.pth) | [GSV](https://research.google/pubs/pub36899/) | [paramnet_gsv_rpfpp.yaml](models/paramnet_gsv_rpfpp.yaml) | Perspective Field + camera parameters (roll, pitch, vfov, cx, cy) | Cropped, street view🏙️ data. | | |
| ## Coordinate Frame | |
| <p align="center"> | |
|  | |
| `yaw / azimuth`: camera rotation about the y-axis | |
| `pitch / elevation`: camera rotation about the x-axis | |
| `roll`: camera rotation about the z-axis | |
| Extrinsics: `rotz(roll).dot(rotx(elevation)).dot(roty(azimuth))` | |
| </p> | |
| ## Camera Parameters to Perspective Fields | |
| Checkout [Jupyter Notebook](./notebooks/camera2perspective.ipynb). | |
| Perspective Fields can be calculated from camera parameters. If you prefer, you can also manually calculate the corresponding Up-vector and Latitude map by following Equations 1 and 2 in our paper. | |
| Our code currently supports: | |
| 1) [Pinhole model](https://hedivision.github.io/Pinhole.html) [Hartley and Zisserman 2004] (Perspective Projection) | |
| ```python | |
| from perspective2d.utils.panocam import PanoCam | |
| # define parameters | |
| roll = 0 | |
| pitch = 20 | |
| vfov = 70 | |
| width = 640 | |
| height = 480 | |
| # get Up-vectors. | |
| up = PanoCam.get_up(np.radians(vfov), width, height, np.radians(pitch), np.radians(roll)) | |
| # get Latitude. | |
| lati = PanoCam.get_lat(np.radians(vfov), width, height, np.radians(pitch), np.radians(roll)) | |
| ``` | |
| 2) [Unified Spherical Model](https://drive.google.com/file/d/1pZgR3wNS6Mvb87W0ixOHmEVV6tcI8d50/view) [Barreto 2006; Mei and Rives 2007] (Distortion). | |
| ```python | |
| xi = 0.5 # distortion parameter from Unified Spherical Model | |
| x = -np.sin(np.radians(vfov/2)) | |
| z = np.sqrt(1 - x**2) | |
| f_px_effective = -0.5*(width/2)*(xi+z)/x | |
| crop, _, _, _, up, lat, xy_map = PanoCam.crop_distortion(equi_img, | |
| f=f_px_effective, | |
| xi=xi, | |
| H=height, | |
| W=width, | |
| az=yaw, # degrees | |
| el=-pitch, | |
| roll=-roll) | |
| ``` | |
| ## Visualize Perspective Fields | |
| We provide a one-line code to blend Perspective Fields onto input image. | |
| ```python | |
| import matplotlib.pyplot as plt | |
| from perspective2d.utils import draw_perspective_fields | |
| # Draw up and lati on img. lati is in radians. | |
| blend = draw_perspective_fields(img, up, lati) | |
| # visualize with matplotlib | |
| plt.imshow(blend) | |
| plt.show() | |
| ``` | |
| Perspective Fields can serve as an easy visual check for correctness of the camera parameters. | |
| - For example, we can visualize the Perspective Fields based on calibration results from this awesome [repo](https://github.com/dompm/spherical-distortion-dataset). | |
| <p align="center"> | |
|  | |
| - Left: We plot the perspective fields based on the numbers printed on the image, they look accurate😊; | |
| - Mid: If we try a number that is 10% off (0.72*0.9=0.648), we see mismatch in Up directions at the top right corner; | |
| - Right: If distortion is 20% off (0.72*0.8=0.576), the mismatch becomes more obvious. | |
| </p> | |
| Citation | |
| -------- | |
| If you find this code useful, please consider citing: | |
| ```text | |
| @inproceedings{jin2023perspective, | |
| title={Perspective Fields for Single Image Camera Calibration}, | |
| author={Linyi Jin and Jianming Zhang and Yannick Hold-Geoffroy and Oliver Wang and Kevin Matzen and Matthew Sticha and David F. Fouhey}, | |
| booktitle = {CVPR}, | |
| year={2023} | |
| } | |
| ``` | |
| Acknowledgment | |
| -------------- | |
| This work was partially funded by the DARPA Machine Common Sense Program. | |
| We thank authors from [A Deep Perceptual Measure for Lens and Camera Calibration](https://github.com/dompm/spherical-distortion-dataset) for releasing their code on Unified Spherical Model. | |