87 lines
No EOL
4.2 KiB
Markdown
87 lines
No EOL
4.2 KiB
Markdown
# Immich Selfie Timelapse Tool
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This tool helps create selfie timelapses from your Immich instance.
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It uses the powerful machine learning features of Immich to gather all the photographs where a particular individual appears, retrieves the bounding box metadata, and automatically crops and aligns the photos.
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<p align="center">
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<img src="resources/example.jpg" alt="Example Image">
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</p>
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<p align="center">
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<img src="resources/example_gif.gif" alt="Example GIF">
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</p>
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## Features
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- Automatically fetch images featuring a specified individual from your Immich instance.
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- Extract bounding box metadata and crop/align photos using machine learning.
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- Discard photos with low resolution (set by threshold).
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- Discard photos where the subject is viewed from the side.
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- Adds timestamp in the filename for easy chronological ordering.
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## Setup
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1. **Generate an API Key in Immich:**
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- Log in to your Immich web UI.
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- Navigate to the API settings (or your profile settings) and generate an API key.
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- Copy the API key for use with the script.
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2. **Find the Person ID:**
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- In the Immich web UI, view photos sorted by person.
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- When you click on a specific person, check the URL in your browser.
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- The person ID (usually a UUID) is part of the URL. Copy this ID for use in the script.
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3. **Install Dlib python library**
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- Download the wheel for your python version here: https://github.com/z-mahmud22/Dlib_Windows_Python3.x
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- Install it with `python -m pip install dlib-19.24.99-cp312-cp312-win_amd64.whl`
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5. **Download the face detection CNN model**
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- Download mmod_human_face_detector.dat from:
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https://github.com/justadudewhohacks/face-recognition.js-models/blob/master/models/mmod_human_face_detector.dat
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- Place the file in the same folder as the script, or update the predictor path in the script accordingly.
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4. **Download the Face Landmark Data:**
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- Download the 68-point face landmark model from:
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https://github.com/italojs/facial-landmarks-recognition/blob/master/shape_predictor_68_face_landmarks.dat
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- Place the file in the same folder as the script, or update the predictor path in the script accordingly.
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5. **Install the required python modules from requirements.txt**
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- Note that an old version of Numpy is required for compatibility with dlib.
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## Usage
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Run the script from the command line with the required arguments. For example:
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python process_faces.py \
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--api-key YOUR_API_KEY \
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--base-url http://your.immich.server:2283/api \
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--person-id YOUR_PERSON_ID \
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--output-folder output
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### Command-line Arguments
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- **--api-key**: API key generated from Immich.
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- **--base-url**: Base URL of your Immich API (e.g., http://192.168.1.123:2283/api).
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- **--person-id**: The ID of the person (obtained from the Immich web UI).
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- **--output-folder**: Directory where the aligned face images will be saved (default: output).
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- **--padding-percent**: Padding added around the face as a percentage (default: 0.3).
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- **--resize-width** and **--resize-height**: Dimensions for the output image (default: 512 x 512).
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- **--min-face-width** and **--min-face-height**: Minimum acceptable face dimensions (default: 128 x 128).
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- **--pose-threshold**: Threshold for acceptable head pose.
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- **--desired-left-eye**: Desired left eye position as a fraction (x y) in the output image (default: 0.35 0.45).
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- **--max-workers**: Number of parallel processes to use (default: 4).
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- **--face-detect-model-paths**: Path to the CNN face detector model file (default: mmod_human_face_detector.dat).
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- **--landmark-model-path**: Path to the face landmark predictor model file (default: shape_predictor_68_face_landmarks.dat).
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## Additional Notes
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- Ensure that the `shape_predictor_68_face_landmarks.dat` file is accessible by the script. Update the path if necessary.
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- 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.
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- Execution speed: on my i5 11400, it can process about 2 image per second.
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- I find that a video framerate of 15 fps gives good results.
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- Contributions and improvements are welcome.
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## License
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This project is open source and available under the MIT License. |