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added experimental face type 'whole_face'

Browse source 
Basic usage instruction: https://i.imgur.com/w7LkId2.jpg

	'whole_face' requires skill in Adobe After Effects.

	For using whole_face you have to extract whole_face's by using
	4) data_src extract whole_face
	and
	5) data_dst extract whole_face
	Images will be extracted in 512 resolution, so they can be used for regular full_face's and half_face's.

	'whole_face' covers whole area of face include forehead in training square,
	but training mask is still 'full_face'
	therefore it requires manual final masking and composing in Adobe After Effects.

added option 'masked_training'
	This option is available only for 'whole_face' type.
	Default is ON.
	Masked training clips training area to full_face mask,
	thus network will train the faces properly.
	When the face is trained enough, disable this option to train all area of the frame.
	Merge with 'raw-rgb' mode, then use Adobe After Effects to manually mask, tune color, and compose whole face include forehead.
This commit is contained in:
Colombo 2020-02-21 16:21:04 +04:00
parent 778fb94246
commit f1d115b63b
10 changed files with 74 additions and 58 deletions
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3
mainscripts/dev_misc.py
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@ -394,7 +394,6 @@ def extract_fanseg(input_dir, device_args={} ):
#unused in end user workflow
def extract_umd_csv(input_file_csv,
image_size=256,
face_type='full_face',
device_args={} ):
@ -456,7 +455,7 @@ def extract_umd_csv(input_file_csv,
data = ExtractSubprocessor (data, 'landmarks', multi_gpu=multi_gpu, cpu_only=cpu_only).run()
io.log_info ('Performing 3rd pass...')
data = ExtractSubprocessor (data, 'final', image_size, face_type, None, multi_gpu=multi_gpu, cpu_only=cpu_only, manual=False, final_output_path=output_path).run()
data = ExtractSubprocessor (data, 'final', face_type, None, multi_gpu=multi_gpu, cpu_only=cpu_only, manual=False, final_output_path=output_path).run()
faces_detected += sum([d.faces_detected for d in data])