diff --git a/.github/workflows/docker-image.yml b/.github/workflows/docker-image.yml index d509568..7792646 100644 --- a/.github/workflows/docker-image.yml +++ b/.github/workflows/docker-image.yml @@ -4,6 +4,8 @@ on: branches: - docker - main + tags: + - 'v*' jobs: publish_images: @@ -11,9 +13,32 @@ jobs: steps: - name: checkout uses: actions/checkout@v4 - - name: build image - run: docker build . -t arnaudcayrol/immich-selfie-timelapse:latest - - name: push image to docker hub - run: | - docker login -u arnaudcayrol -p ${{ secrets.DOCKERHUB_TOKEN }} - docker push arnaudcayrol/immich-selfie-timelapse:latest + + - name: Extract metadata (tags, labels) for Docker + id: meta + uses: docker/metadata-action@v5 + with: + images: arnaudcayrol/immich-selfie-timelapse + tags: | + type=ref,event=branch + type=ref,event=tag + type=semver,pattern={{version}} + type=semver,pattern={{major}}.{{minor}} + type=raw,value=latest,enable={{is_default_branch}} + + - name: Set up Docker Buildx + uses: docker/setup-buildx-action@v3 + + - name: Login to Docker Hub + uses: docker/login-action@v3 + with: + username: arnaudcayrol + password: ${{ secrets.DOCKERHUB_TOKEN }} + + - name: Build and push + uses: docker/build-push-action@v5 + with: + context: . + push: true + tags: ${{ steps.meta.outputs.tags }} + labels: ${{ steps.meta.outputs.labels }} diff --git a/README.md b/README.md index 628990d..3534f05 100644 --- a/README.md +++ b/README.md @@ -20,66 +20,31 @@ It uses the powerful machine learning features of Immich to gather all the photo - Discard photos where the subject is viewed from the side. - Adds timestamp in the filename for easy chronological ordering. -## Setup +## Docker compose setup -1. **Generate an API Key in Immich:** - - Log in to your Immich web UI. - - Navigate to the API settings (or your profile settings) and generate an API key. - - Copy the API key for use with the script. +```yml +services: + immich-selfie-timelapse: + image: arnaudcayrol/immich-selfie-timelapse + container_name: immich-selfie-timelapse + ports: + - "5000:5000" + volumes: + - ./immich_selfie_timelapse:/app/output + environment: + - IMMICH_API_KEY=abcdefghijklmnopqrstuvwxyz + - IMMICH_BASE_URL=http://192.168.1.94:2283/api +``` -2. **Find the Person ID:** - - In the Immich web UI, view photos sorted by person. - - When you click on a specific person, check the URL in your browser. - - The person ID (usually a UUID) is part of the URL. Copy this ID for use in the script. +Once the service is started, access the webpage to configure the tool: http://127.0.0.1:5000. -3. **Install Dlib python library** - - Download the wheel for your python version here: https://github.com/z-mahmud22/Dlib_Windows_Python3.x - - Install it with `python -m pip install dlib-19.24.99-cp312-cp312-win_amd64.whl` - -5. **Download the face detection CNN model** - - Download mmod_human_face_detector.dat from: - https://github.com/justadudewhohacks/face-recognition.js-models/blob/master/models/mmod_human_face_detector.dat - - Place the file in the same folder as the script, or update the predictor path in the script accordingly. - -4. **Download the Face Landmark Data:** - - Download the 68-point face landmark model from: - https://github.com/italojs/facial-landmarks-recognition/blob/master/shape_predictor_68_face_landmarks.dat - - Place the file in the same folder as the script, or update the predictor path in the script accordingly. - -5. **Install the required python modules from requirements.txt** - - Note that an old version of Numpy is required for compatibility with dlib. - -## Usage - -Run the script from the command line with the required arguments. For example: - - python process_faces.py \ - --api-key YOUR_API_KEY \ - --base-url http://your.immich.server:2283/api \ - --person-id YOUR_PERSON_ID \ - --output-folder output - -### Command-line Arguments - -- **--api-key**: API key generated from Immich. -- **--base-url**: Base URL of your Immich API (e.g., http://192.168.1.123:2283/api). -- **--person-id**: The ID of the person (obtained from the Immich web UI). -- **--output-folder**: Directory where the aligned face images will be saved (default: output). -- **--padding-percent**: Padding added around the face as a percentage (default: 0.3). -- **--resize-width** and **--resize-height**: Dimensions for the output image (default: 512 x 512). -- **--min-face-width** and **--min-face-height**: Minimum acceptable face dimensions (default: 128 x 128). -- **--pose-threshold**: Threshold for acceptable head pose. -- **--desired-left-eye**: Desired left eye position as a fraction (x y) in the output image (default: 0.35 0.45). -- **--max-workers**: Number of parallel processes to use (default: 4). -- **--face-detect-model-paths**: Path to the CNN face detector model file (default: mmod_human_face_detector.dat). -- **--landmark-model-path**: Path to the face landmark predictor model file (default: shape_predictor_68_face_landmarks.dat). +

+ Example GIF +

## Additional Notes -- Ensure that the `shape_predictor_68_face_landmarks.dat` file is accessible by the script. Update the path if necessary. -- The tool may require some manual sorting of the output images to achieve the best video effect. In particular, the face landmark detection is not super robust. -- Execution speed: on my i5 11400, it can process about 2 image per second. -- I find that a video framerate of 15 fps gives good results. +- For best looking results, I still recommand a manual pass over the final image folder to remove some outliers. The face orientation detection is not perfect at all. Hopefully someday I can get a better results. - Contributions and improvements are welcome. ## License diff --git a/resources/webpage.jpg b/resources/webpage.jpg new file mode 100644 index 0000000..cb0334d Binary files /dev/null and b/resources/webpage.jpg differ