Update readme and setup docker image tagging

.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 }}

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).
+<p align="center">
+  <img src="resources/webpage.jpg" alt="Example GIF">
+</p>
 
 ## 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