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added XSeg model.

Browse source 
with XSeg model you can train your own mask segmentator of dst(and src) faces
that will be used in merger for whole_face.

Instead of using a pretrained model (which does not exist),
you control which part of faces should be masked.

Workflow is not easy, but at the moment it is the best solution
for obtaining the best quality of whole_face's deepfakes using minimum effort
without rotoscoping in AfterEffects.

new scripts:
	XSeg) data_dst edit.bat
	XSeg) data_dst merge.bat
	XSeg) data_dst split.bat
	XSeg) data_src edit.bat
	XSeg) data_src merge.bat
	XSeg) data_src split.bat
	XSeg) train.bat

Usage:
	unpack dst faceset if packed

	run XSeg) data_dst split.bat
		this scripts extracts (previously saved) .json data from jpg faces to use in label tool.

	run XSeg) data_dst edit.bat
		new tool 'labelme' is used

		use polygon (CTRL-N) to mask the face
			name polygon "1" (one symbol) as include polygon
			name polygon "0" (one symbol) as exclude polygon

			'exclude polygons' will be applied after all 'include polygons'

		Hot keys:
		ctrl-N			create polygon
		ctrl-J			edit polygon
		A/D 			navigate between frames
		ctrl + mousewheel 	image zoom
		mousewheel		vertical scroll
		alt+mousewheel		horizontal scroll

		repeat for 10/50/100 faces,
			you don't need to mask every frame of dst,
			only frames where the face is different significantly,
			for example:
				closed eyes
				changed head direction
				changed light
			the more various faces you mask, the more quality you will get

			Start masking from the upper left area and follow the clockwise direction.
			Keep the same logic of masking for all frames, for example:
				the same approximated jaw line of the side faces, where the jaw is not visible
				the same hair line
			Mask the obstructions using polygon with name "0".

	run XSeg) data_dst merge.bat
		this script merges .json data of polygons into jpg faces,
		therefore faceset can be sorted or packed as usual.

	run XSeg) train.bat
		train the model

		Check the faces of 'XSeg dst faces' preview.

		if some faces have wrong or glitchy mask, then repeat steps:
			split
			run edit
			find these glitchy faces and mask them
			merge
			train further or restart training from scratch

Restart training of XSeg model is only possible by deleting all 'model\XSeg_*' files.

If you want to get the mask of the predicted face in merger,
you should repeat the same steps for src faceset.

New mask modes available in merger for whole_face:

XSeg-prd	  - XSeg mask of predicted face	 -> faces from src faceset should be labeled
XSeg-dst	  - XSeg mask of dst face        -> faces from dst faceset should be labeled
XSeg-prd*XSeg-dst - the smallest area of both

if workspace\model folder contains trained XSeg model, then merger will use it,
otherwise you will get transparent mask by using XSeg-* modes.

Some screenshots:
label tool: https://i.imgur.com/aY6QGw1.jpg
trainer   : https://i.imgur.com/NM1Kn3s.jpg
merger    : https://i.imgur.com/glUzFQ8.jpg

example of the fake using 13 segmented dst faces
          : https://i.imgur.com/wmvyizU.gifv
This commit is contained in:
Colombo 2020-03-15 15:12:44 +04:00
parent 2be940092b
commit 45582d129d
27 changed files with 577 additions and 711 deletions
Download patch file Download diff file Expand all files Collapse all files

127
models/ModelBase.py
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@ -32,6 +32,7 @@ class ModelBase(object):
force_gpu_idxs=None,
cpu_only=False,
debug=False,
force_model_class_name=None,
**kwargs):
self.is_training = is_training
self.saved_models_path = saved_models_path
@ -44,80 +45,84 @@ class ModelBase(object):
self.model_class_name = model_class_name = Path(inspect.getmodule(self).__file__).parent.name.rsplit("_", 1)[1]
if force_model_name is not None:
self.model_name = force_model_name
else:
while True:
# gather all model dat files
saved_models_names = []
for filepath in pathex.get_file_paths(saved_models_path):
filepath_name = filepath.name
if filepath_name.endswith(f'{model_class_name}_data.dat'):
saved_models_names += [ (filepath_name.split('_')[0], os.path.getmtime(filepath)) ]
if force_model_class_name is None:
if force_model_name is not None:
self.model_name = force_model_name
else:
while True:
# gather all model dat files
saved_models_names = []
for filepath in pathex.get_file_paths(saved_models_path):
filepath_name = filepath.name
if filepath_name.endswith(f'{model_class_name}_data.dat'):
saved_models_names += [ (filepath_name.split('_')[0], os.path.getmtime(filepath)) ]
# sort by modified datetime
saved_models_names = sorted(saved_models_names, key=operator.itemgetter(1), reverse=True )
saved_models_names = [ x[0] for x in saved_models_names ]
# sort by modified datetime
saved_models_names = sorted(saved_models_names, key=operator.itemgetter(1), reverse=True )
saved_models_names = [ x[0] for x in saved_models_names ]
if len(saved_models_names) != 0:
io.log_info ("Choose one of saved models, or enter a name to create a new model.")
io.log_info ("[r] : rename")
io.log_info ("[d] : delete")
io.log_info ("")
for i, model_name in enumerate(saved_models_names):
s = f"[{i}] : {model_name} "
if i == 0:
s += "- latest"
io.log_info (s)
if len(saved_models_names) != 0:
io.log_info ("Choose one of saved models, or enter a name to create a new model.")
io.log_info ("[r] : rename")
io.log_info ("[d] : delete")
io.log_info ("")
for i, model_name in enumerate(saved_models_names):
s = f"[{i}] : {model_name} "
if i == 0:
s += "- latest"
io.log_info (s)
inp = io.input_str(f"", "0", show_default_value=False )
model_idx = -1
try:
model_idx = np.clip ( int(inp), 0, len(saved_models_names)-1 )
except:
pass
inp = io.input_str(f"", "0", show_default_value=False )
model_idx = -1
try:
model_idx = np.clip ( int(inp), 0, len(saved_models_names)-1 )
except:
pass
if model_idx == -1:
if len(inp) == 1:
is_rename = inp[0] == 'r'
is_delete = inp[0] == 'd'
if model_idx == -1:
if len(inp) == 1:
is_rename = inp[0] == 'r'
is_delete = inp[0] == 'd'
if is_rename or is_delete:
if len(saved_models_names) != 0:
if is_rename:
name = io.input_str(f"Enter the name of the model you want to rename")
elif is_delete:
name = io.input_str(f"Enter the name of the model you want to delete")
if name in saved_models_names:
if is_rename or is_delete:
if len(saved_models_names) != 0:
if is_rename:
new_model_name = io.input_str(f"Enter new name of the model")
name = io.input_str(f"Enter the name of the model you want to rename")
elif is_delete:
name = io.input_str(f"Enter the name of the model you want to delete")
for filepath in pathex.get_paths(saved_models_path):
filepath_name = filepath.name
if name in saved_models_names:
model_filename, remain_filename = filepath_name.split('_', 1)
if model_filename == name:
if is_rename:
new_model_name = io.input_str(f"Enter new name of the model")
if is_rename:
new_filepath = filepath.parent / ( new_model_name + '_' + remain_filename )
filepath.rename (new_filepath)
elif is_delete:
filepath.unlink()
continue
for filepath in pathex.get_paths(saved_models_path):
filepath_name = filepath.name
model_filename, remain_filename = filepath_name.split('_', 1)
if model_filename == name:
if is_rename:
new_filepath = filepath.parent / ( new_model_name + '_' + remain_filename )
filepath.rename (new_filepath)
elif is_delete:
filepath.unlink()
continue
self.model_name = inp
else:
self.model_name = saved_models_names[model_idx]
self.model_name = inp
else:
self.model_name = saved_models_names[model_idx]
self.model_name = io.input_str(f"No saved models found. Enter a name of a new model", "new")
self.model_name = self.model_name.replace('_', ' ')
break
else:
self.model_name = io.input_str(f"No saved models found. Enter a name of a new model", "new")
self.model_name = self.model_name.replace('_', ' ')
break
self.model_name = self.model_name + '_' + self.model_class_name
self.model_name = self.model_name + '_' + self.model_class_name
else:
self.model_name = force_model_class_name
self.iter = 0
self.options = {}