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## Face Video-Analyzer Framework
This demo application provides a general framework for tracking, detecting and extracting features of human faces in images or videos.
Currently, the following tools are integrated in a video-demo available at ```./spiga/demo/app.py```:
* Tracking:
* FaceTracker : RetinaFace + SORT.
* Detectors:
* RetinaFace (bbox + 5 landmarks).
* Backbone Mobilenet 0.25.
* Backbone Resnet50.
* Associators:
* SORT: Frame by frame
* Extractors:
* SPIGA architecture
* Features: Landmarks and headpose.
* Datasets: 300W Public, 300W Private, WFLW, MERLRAV.
* Viewers:
* Landmarks, headpose and bbox (score + face_id).
### Demo Application
```
python ./spiga/demo/app.py \
[--input] \ # Webcam ID or Video Path. Dft: Webcam '0'.
[--dataset] \ # SPIGA pretrained weights per dataset. Dft: 'wflw'.
[--tracker] \ # Tracker name. Dft: 'RetinaSort'.
[--show] \ # Select the attributes of the face to be displayed. Dft: ['fps', 'face_id', 'landmarks', 'headpose']
[--save] \ # Save record.
[--noview] \ # Do not visualize window.
[--outpath] \ # Recorded output directory. Dft: './spiga/demo/outputs'
[--fps] \ # Frames per second.
[--shape] \ # Visualizer shape (W,H).
```
### Code Structure
The demo framework has been organised according to the following structure:
```
./spiga/demo/
| app.py # Video-demo
|
└───analyze
│ | analyzer.py # Generic video/image analyzer compositor
│ │
│ └───features # Object/Faces classes
│ │
│ └───track # Task heads
│ | | tracker.py # Tracker class
│ | | get_tracker.py # Get model tracker from zoo by name
│ | └─── retinasort # RetinaFace + SORT tracker (tracker + zoo files)
| |
| └───extract
| | processor.py # Processor classes
| └ spiga_processor.py # SPIGA wrapper
|
└───visualize
| | viewer.py # Viewer manager
| | plotter.py # Englobe available features drawers
| └─── layouts # Landmarks, bbox, headpose drawers
|
└───utils # Video converters
``` |