cosanlab/py-feat: v0.5.0

This is a large overhaul and refactor of some of the core testing and API functionality to make future development, maintenance, and testing easier. Notable highlights include:

• tighter integration with torch data loaders
• dropping opencv as a dependency
• experimental support for macOS m1 GPUs
• passing keyword arguments to underlying torch models for more control

• you can now pass keyword arguments directly to the underlying pytorch/sklearn models on Detector initialization using dictionaries. For example you can do: detector = Detector(facepose_model_kwargs={'keep_top_k': 500}) to initialize img2pose to only use 500 instead of 750 features
• all .detect_* methods can also pass keyword arguments to the underlying pytorch/sklearn models, albeit these will be passed to their underlying __call__ methods
• SVM AU model has been retrained with new HOG feature PCA pipeline
• new XGBoost AU model with new HOG feature PCA pipeline
• .detect_image and .detect_video now display a tqdm progressbar
• new skip_failed_detections keyword argument to still generate a Fex object when processing multiple images and one or more detections fail

• the new default model for landmark detection was changed from mobilenet to mobilefacenet.
• the new default model for AU detection was changed to our new xgb model which gives continuous valued predictions between 0-1
• remove support for fer emotion model
• remove support for jaanet AU model
• remove support for pnp facepose detector
• drop support for reading and manipulating Affectiva and FACET data
• .detect_image will no longer resize images on load as the new default for output_size=None. If you want to process images with batch_size > 1 and images differ in size, then you will be required to manually set output_size otherwise py-feat will raise a helpful error message

• new .update_sessions() method that returns a copy of a Fex frame with the .sessions attribute updated, making it easy to chain operations
• .predict() and .regress() now support passing attributes to X and or Y using string names that match the attribute names:
  • 'emotions' use all emotion columns (i.e. fex.emotions)
  • 'aus' use all AU columns (i.e. fex.aus)
  • 'poses' use all pose columns (i.e. fex.poses)
  • 'landmarks' use all landmark columns (i.e. fex.landmarks)
  • 'faceboxes' use all facebox columns (i.e. fex.faceboxes)
  • You can also combine feature groups using a comma-separated string e.g. fex.regress(X='emotions,poses', y='landmarks')
• .extract_* methods now include std and sem. These are also included in .extract_summary()

• All Fex attributes have been pluralized as indicated below. For the time-being old attribute access will continue to work but will show a warning. We plan to formally drop support in a few versions
  • .landmark -> .landmarks
  • .facepose -> .poses
  • .input -> .inputs
  • .landmark_x -> .landmarks_x
  • .landmark_y -> .landmarks_y
  • .facebox -> .faceboxes

• test_pretrained_models.py is now more organized using pytest classes
• added tests for img2pose models
• added more robust testing for the interaction between batch_size and output_size

• data loading with multiple images of potentially different sizes should be faster and more reliable
• fix bug in resmasknet that would give poor predictions when multiple faces were present and particularly small
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