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SAE : WARNING, RETRAIN IS REQUIRED !

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fixed model sizes from previous update.
avoided bug in ML framework(keras) that forces to train the model on random noise.

Converter: added blur on the same keys as sharpness

Added new model 'TrueFace'. This is a GAN model ported from https://github.com/NVlabs/FUNIT
Model produces near zero morphing and high detail face.
Model has higher failure rate than other models.
Keep src and dst faceset in same lighting conditions.
This commit is contained in:
Colombo 2019-09-19 11:13:56 +04:00
parent 201b762541
commit dc11ec32be
26 changed files with 1308 additions and 250 deletions
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54
mainscripts/Converter.py
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@ -87,22 +87,26 @@ class ConvertSubprocessor(Subprocessor):
#therefore forcing active_DeviceConfig to CPU only
nnlib.active_DeviceConfig = nnlib.DeviceConfig (cpu_only=True)
def sharpen_func (img, sharpen_mode=0, kernel_size=3, amount=150):
def blursharpen_func (img, sharpen_mode=0, kernel_size=3, amount=100):
if kernel_size % 2 == 0:
kernel_size += 1
if sharpen_mode == 1: #box
kernel = np.zeros( (kernel_size, kernel_size), dtype=np.float32)
kernel[ kernel_size//2, kernel_size//2] = 1.0
box_filter = np.ones( (kernel_size, kernel_size), dtype=np.float32) / (kernel_size**2)
kernel = kernel + (kernel - box_filter) * amount
return cv2.filter2D(img, -1, kernel)
elif sharpen_mode == 2: #gaussian
if amount > 0:
if sharpen_mode == 1: #box
kernel = np.zeros( (kernel_size, kernel_size), dtype=np.float32)
kernel[ kernel_size//2, kernel_size//2] = 1.0
box_filter = np.ones( (kernel_size, kernel_size), dtype=np.float32) / (kernel_size**2)
kernel = kernel + (kernel - box_filter) * amount
return cv2.filter2D(img, -1, kernel)
elif sharpen_mode == 2: #gaussian
blur = cv2.GaussianBlur(img, (kernel_size, kernel_size) , 0)
img = cv2.addWeighted(img, 1.0 + (0.5 * amount), blur, -(0.5 * amount), 0)
return img
elif amount < 0:
blur = cv2.GaussianBlur(img, (kernel_size, kernel_size) , 0)
img = cv2.addWeighted(img, 1.0 + (0.5 * amount), blur, -(0.5 * amount), 0)
return img
img = cv2.addWeighted(img, 1.0 - a / 50.0, blur, a /50.0, 0)
return img
return img
self.sharpen_func = sharpen_func
self.blursharpen_func = blursharpen_func
self.fanseg_by_face_type = {}
self.fanseg_input_size = 256
@ -128,7 +132,7 @@ class ConvertSubprocessor(Subprocessor):
#override
def process_data(self, pf): #pf=ProcessingFrame
cfg = pf.cfg.copy()
cfg.sharpen_func = self.sharpen_func
cfg.blursharpen_func = self.blursharpen_func
cfg.superres_func = self.superres_func
cfg.ebs_ct_func = self.ebs_ct_func
@ -221,11 +225,13 @@ class ConvertSubprocessor(Subprocessor):
session_data = None
if self.is_interactive and self.converter_session_filepath.exists():
try:
with open( str(self.converter_session_filepath), "rb") as f:
session_data = pickle.loads(f.read())
except Exception as e:
pass
if io.input_bool ("Use saved session? (y/n skip:y) : ", True):
try:
with open( str(self.converter_session_filepath), "rb") as f:
session_data = pickle.loads(f.read())
except Exception as e:
pass
self.frames = frames
self.frames_idxs = [ *range(len(self.frames)) ]
@ -430,9 +436,9 @@ class ConvertSubprocessor(Subprocessor):
elif chr_key == 'g':
cfg.add_color_degrade_power(-1 if not shift_pressed else -5)
elif chr_key == 'y':
cfg.add_sharpen_amount(1 if not shift_pressed else 5)
cfg.add_blursharpen_amount(1 if not shift_pressed else 5)
elif chr_key == 'h':
cfg.add_sharpen_amount(-1 if not shift_pressed else -5)
cfg.add_blursharpen_amount(-1 if not shift_pressed else -5)
elif chr_key == 'u':
cfg.add_output_face_scale(1 if not shift_pressed else 5)
elif chr_key == 'j':
@ -453,9 +459,9 @@ class ConvertSubprocessor(Subprocessor):
else:
if chr_key == 'y':
cfg.add_sharpen_amount(1 if not shift_pressed else 5)
cfg.add_blursharpen_amount(1 if not shift_pressed else 5)
elif chr_key == 'h':
cfg.add_sharpen_amount(-1 if not shift_pressed else -5)
cfg.add_blursharpen_amount(-1 if not shift_pressed else -5)
elif chr_key == 's':
cfg.toggle_add_source_image()
elif chr_key == 'v':
@ -576,6 +582,8 @@ class ConvertSubprocessor(Subprocessor):
def main (args, device_args):
io.log_info ("Running converter.\r\n")
training_data_src_dir = args.get('training_data_src_dir', None)
training_data_src_path = Path(training_data_src_dir) if training_data_src_dir is not None else None
aligned_dir = args.get('aligned_dir', None)
avaperator_aligned_dir = args.get('avaperator_aligned_dir', None)
@ -598,7 +606,7 @@ def main (args, device_args):
is_interactive = io.input_bool ("Use interactive converter? (y/n skip:y) : ", True) if not io.is_colab() else False
import models
model = models.import_model( args['model_name'] )(model_path, device_args=device_args)
model = models.import_model( args['model_name'])(model_path, device_args=device_args, training_data_src_path=training_data_src_path)
converter_session_filepath = model.get_strpath_storage_for_file('converter_session.dat')
predictor_func, predictor_input_shape, cfg = model.get_ConverterConfig()