* port 'face_alignment' from PyTorch to Keras. It works x2 faster, but initialization takes 20secs.
2DFAN-4.h5 and mmod_human_face_detector.dat included in lib\FaceLandmarksExtractor
fixed dlib vs tensorflow conflict: dlib must do op first, then load keras model, otherwise CUDA OOM error
if face location not found by CNN, its try to find by HOG.
removed this:
- if face.landmarks == None:
- print("Warning! landmarks not found. Switching to crop!")
- return cv2.resize(face.image, (size, size))
because DetectedFace always has landmarks
* removing DetectedFace.landmarks
* Preparing GAN plugin
* Adding multithreading for extract
* Adding support for mmod human face detector
* Adding face filter argument
* Added process number argument to multiprocessing extractor.
Fixed progressbar for multiprocessing.
* Added tiff as image type.
compression artefacts hurt my feelings.
* Cleanup
* Making Models as plugins
* Do not reload model on each image #39 + Adding FaceFilter #53
* Adding @lukaville PR for #43 and #44 (possibly)
* Training done in a separate thread
* Better log for plugin load
* Adding a prefetch to train.py #49
(Note that we prefetch 2 batches of images, due to the queue behavior)
+ More compact logging with verbose info included
* correction of DirectoryProcessor signature
* adding missing import
* Convert with parallel preprocessing of files
* Added coverage var for trainer
Added a var with comment. Feel free to add it as argument
* corrections
* Modifying preview and normalization of image + correction
* Cleanup
Added face_recongition from
https://github.com/ageitgey/face_recognition/
It is based on dlib, but does not have the glitches I had previously!
Also reworked Dockerfile (sticking to Python2 for now)
