fix: Universal Reader and RetinaNet Normalization

.github/workflows/conda-env.yml

@@ -10,18 +10,22 @@ jobs:
   conda:
     runs-on: ubuntu-latest
     steps:
-    - uses: actions/checkout@v3
-    - name: Set up Python 3.9
-      uses: actions/setup-python@v3
-      with:
-        python-version: 3.9
-    - name: Add conda to system path
-      run: |
-        # $CONDA is an environment variable pointing to the root of the miniconda directory
-        echo $CONDA/bin >> $GITHUB_PATH
-    - name: Install dependencies
-      run: |
-        conda env update --file environment.yml --name base
-    - name: Install facetorch from conda-forge
-      run: |
-        conda install -c conda-forge facetorch
+      - uses: actions/checkout@v3
+
+      - name: Create condarc file
+        run: |
+          echo "solver: classic" > condarc
+
+      - name: Set up Miniconda
+        uses: conda-incubator/setup-miniconda@v2
+        with:
+          python-version: 3.9
+          environment-file: environment.yml
+          channels: conda-forge, defaults
+          auto-activate-base: true
+          activate-environment: base
+          condarc-file: condarc
+
+      - name: Install facetorch from conda-forge
+        run: |
+          conda install -c conda-forge facetorch

.github/workflows/docker-push.yml

@@ -9,25 +9,37 @@ jobs:
     runs-on: ubuntu-latest
     steps:
       - uses: actions/checkout@v2
+      - name: Set VERSION variable
+        run: echo "VERSION=$(cat ./version)" >> $GITHUB_ENV
       - name: Docker compose build facetorch
         run: docker compose build facetorch
+      - name: Tag image with version
+        run: docker tag tomasgajarsky/facetorch:latest tomasgajarsky/facetorch:${{ env.VERSION }}
       - name: Login to Docker hub
         env:
           DOCKER_USERNAME: ${{ secrets.DOCKER_USERNAME }}
           DOCKER_PASSWORD: ${{ secrets.DOCKER_PASSWORD }}
         run: docker login -u $DOCKER_USERNAME -p $DOCKER_PASSWORD docker.io
-      - name: Docker compose push facetorch
-        run: docker compose push facetorch
+      - name: Push images
+        run: |
+          docker push tomasgajarsky/facetorch:latest
+          docker push tomasgajarsky/facetorch:${{ env.VERSION }}
   docker-push-gpu:
     runs-on: ubuntu-latest
     steps:
       - uses: actions/checkout@v2
+      - name: Set VERSION variable
+        run: echo "VERSION=$(cat ./version)" >> $GITHUB_ENV
       - name: Docker compose build facetorch-gpu
         run: docker compose build facetorch-gpu-no-device
+      - name: Tag image with version
+        run: docker tag tomasgajarsky/facetorch-gpu:latest tomasgajarsky/facetorch-gpu:${{ env.VERSION }}
       - name: Login to Docker hub
         env:
           DOCKER_USERNAME: ${{ secrets.DOCKER_USERNAME }}
           DOCKER_PASSWORD: ${{ secrets.DOCKER_PASSWORD }}
         run: docker login -u $DOCKER_USERNAME -p $DOCKER_PASSWORD docker.io
-      - name: Docker compose push facetorch-gpu
-        run: docker compose push facetorch-gpu-no-device
+      - name: Push images
+        run: |
+          docker push tomasgajarsky/facetorch-gpu:latest
+          docker push tomasgajarsky/facetorch-gpu:${{ env.VERSION }}

.github/workflows/python-package.yml

@@ -13,7 +13,7 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        python-version: ["3.8", "3.9", "3.10"]
+        python-version: ["3.8", "3.9", "3.10", "3.11", "3.12"]
 
     steps:
     - uses: actions/checkout@v3

CHANGELOG.md

@@ -1,5 +1,16 @@
 # Change Log
 
+## 0.5.1
+
+Released on November 17, 2024.
+
+### Changed
+
+* UnversalReader to read PIL images as RGB
+* UniversalReader to read numpy arrays to torch directly
+* RetinaFace pre-normalization color space to RGB
+* torch.cross torch.linalg.cross in 3D landmark drawer
+
 
 ## 0.5.0
 

README.md

@@ -6,7 +6,7 @@
 [![PyPI - License](https://img.shields.io/pypi/l/facetorch)](https://raw.githubusercontent.com/tomas-gajarsky/facetorch/main/LICENSE)
 <a href="https://github.com/psf/black"><img alt="Code style: black" src="https://img.shields.io/badge/code%20style-black-000000.svg"></a>
 
-[Hugging Face Space demo app 🤗 ](https://huggingface.co/spaces/tomas-gajarsky/facetorch-app)
+[Hugging Face Space demo app 🤗](https://huggingface.co/spaces/tomas-gajarsky/facetorch-app)
 
 [Google Colab notebook demo](https://colab.research.google.com/github/tomas-gajarsky/facetorch/blob/main/notebooks/facetorch_notebook_demo.ipynb)
 <a target="_blank" href="https://colab.research.google.com/github/tomas-gajarsky/facetorch/blob/main/notebooks/facetorch_notebook_demo.ipynb">
@@ -17,16 +17,21 @@
 
 [Docker Hub](https://hub.docker.com/repository/docker/tomasgajarsky/facetorch) [(GPU)](https://hub.docker.com/repository/docker/tomasgajarsky/facetorch-gpu)
 
-Facetorch is a Python library that can detect faces and analyze facial features using deep neural networks. The goal is to gather open sourced face analysis models from the community, optimize them for performance using TorchScript and combine them to create a face analysis tool that one can:
 
-1. configure using [Hydra](https://hydra.cc/docs/intro/) (OmegaConf)
-2. reproduce with [conda-lock](https://github.com/conda-incubator/conda-lock) and [Docker](https://docs.docker.com/get-docker/)
-3. accelerate on CPU and GPU with [TorchScript](https://pytorch.org/docs/stable/jit.html)
-4. extend by uploading a model file to Google Drive and adding a config yaml file to the repository
+**Facetorch** is a Python library designed for facial detection and analysis, leveraging the power of deep neural networks. Its primary aim is to curate open-source face analysis models from the community, optimize them for high performance using TorchScript, and integrate them into a versatile face analysis toolkit. The library offers the following key features:
+
+1. **Customizable Configuration:** Easily configure your setup using [Hydra](https://hydra.cc/docs/intro/) and its powerful [OmegaConf](https://omegaconf.readthedocs.io/) capabilities.
+
+2. **Reproducible Environments:** Ensure reproducibility with tools like [conda-lock](https://github.com/conda-incubator/conda-lock) for dependency management and [Docker](https://docs.docker.com/get-docker/) for containerization.
+
+3. **Accelerated Performance:** Enjoy enhanced performance on both CPU and GPU with [TorchScript](https://pytorch.org/docs/stable/jit.html) optimization.
+
+4. **Simple Extensibility:** Extend the library by uploading your model file to Google Drive and adding a corresponding configuration YAML file to the repository.
+
+Facetorch provides an efficient, scalable, and user-friendly solution for facial analysis tasks, catering to developers and researchers looking for flexibility and performance.
+
+Please use this library responsibly and with caution. Adhere to the [European Commission's Ethics Guidelines for Trustworthy AI](https://ec.europa.eu/futurium/en/ai-alliance-consultation.1.html) to ensure ethical and fair usage. Keep in mind that the models may have limitations and potential biases, so it is crucial to evaluate their outputs critically and consider their impact.
 
-Please, use the library responsibly with caution and follow the 
-[ethics guidelines for Trustworthy AI from European Commission](https://ec.europa.eu/futurium/en/ai-alliance-consultation.1.html). 
-The models are not perfect and may be biased.
 
 ## Install
 [PyPI](https://pypi.org/project/facetorch/)
@@ -301,10 +306,36 @@ GPU:
 1. Run profiling of the example script: ```python -m cProfile -o profiling/example.prof scripts/example.py```
 2. Open profiling file in the browser: ```snakeviz profiling/example.prof```
 
+## Research Highlights Leveraging facetorch
+
+### [Sharma et al. (2024)](https://aclanthology.org/2024.signlang-1.39.pdf)
+
+Sharma, Paritosh, Camille Challant, and Michael Filhol. "Facial Expressions for Sign Language Synthesis using FACSHuman and AZee." *Proceedings of the LREC-COLING 2024 11th Workshop on the Representation and Processing of Sign Languages*, pp. 354–360, 2024.
+
+### [Liang et al. (2023)](https://dl.acm.org/doi/abs/10.1145/3581783.3612854)
+
+Liang, Cong, Jiahe Wang, Haofan Zhang, Bing Tang, Junshan Huang, Shangfei Wang, and Xiaoping Chen. "Unifarn: Unified transformer for facial reaction generation." *Proceedings of the 31st ACM International Conference on Multimedia*, pp. 9506–9510, 2023.
+
+### [Gue et al. (2023)](https://research.monash.edu/en/publications/facial-expression-recognition-as-markers-of-depression)
+
+Gue, Jia Xuan, Chun Yong Chong, and Mei Kuan Lim. "Facial Expression Recognition as markers of Depression." *2023 Asia Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC)*, pp. 674–680, 2023.
+
+
 ## Acknowledgements
-I want to thank the open source code community and the researchers who have published the models. This project would not be possible without their work.
+I would like to thank the open-source community and the researchers who have shared their work and published models. This project would not have been possible without their contributions.
+
 
-![](https://raw.githubusercontent.com/tomas-gajarsky/facetorch/main/data/facetorch-logo-64.png "facetorch logo")
+## Citing
 
+If you use facetorch in your work, please make sure to appropriately credit the original authors of the models it employs. Additionally, you may consider citing the facetorch library itself. Below is an example citation for facetorch:
 
-Logo was generated using [DeepAI Text To Image API](https://deepai.org/machine-learning-model/text2img)
+```
+@misc{facetorch,
+    author = {Gajarsky, Tomas},
+    title = {Facetorch: A Python Library for Analyzing Faces Using PyTorch},
+    year = {2024},
+    publisher = {GitHub},
+    journal = {GitHub Repository},
+    howpublished = {\url{https://github.com/tomas-gajarsky/facetorch}}
+}
+```