CalvinBackup/iperov-DeepFaceLab
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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
Download Patch File Download Diff File Expand all files Collapse all files
converters/ConvertMasked.py
+2 -2
View File
@@ -302,8 +302,8 @@ def ConvertMaskedFace (predictor_func, predictor_input_shape, cfg, frame_info, i
k_size *= 2
out_face_bgr = imagelib.LinearMotionBlur (out_face_bgr, k_size , frame_info.motion_deg)
if cfg.sharpen_mode != 0 and cfg.sharpen_amount != 0:
out_face_bgr = cfg.sharpen_func ( out_face_bgr, cfg.sharpen_mode, 3, cfg.sharpen_amount)
if cfg.blursharpen_amount != 0:
out_face_bgr = cfg.blursharpen_func ( out_face_bgr, cfg.sharpen_mode, 3, cfg.blursharpen_amount)
new_out = cv2.warpAffine( out_face_bgr, face_mat, img_size, img_bgr.copy(), cv2.WARP_INVERSE_MAP | cv2.INTER_CUBIC, cv2.BORDER_TRANSPARENT )
out_img = np.clip( img_bgr*(1-img_face_mask_aaa) + (new_out*img_face_mask_aaa) , 0, 1.0 )
converters/ConverterConfig.py
+7 -8
View File
@@ -18,7 +18,7 @@ class ConverterConfig(object):
self.type = type
self.superres_func = None
self.sharpen_func = None
self.blursharpen_func = None
self.fanseg_input_size = None
self.fanseg_extract_func = None
self.ebs_ct_func = None
@@ -29,7 +29,7 @@ class ConverterConfig(object):
#default changeable params
self.super_resolution_mode = 0
self.sharpen_mode = 0
self.sharpen_amount = 0
self.blursharpen_amount = 0
def copy(self):
return copy.copy(self)
@@ -43,7 +43,7 @@ class ConverterConfig(object):
self.sharpen_mode = io.input_int (s, 0, valid_list=self.sharpen_dict.keys(), help_message="Enhance details by applying sharpen filter.")
if self.sharpen_mode != 0:
self.sharpen_amount = np.clip ( io.input_int ("Choose sharpen amount [0..100] (skip:%d) : " % 10, 10), 0, 100 )
self.blursharpen_amount = np.clip ( io.input_int ("Choose blur/sharpen amount [-100..100] (skip:0) : ", 0), -100, 100 )
s = """Choose super resolution mode: \n"""
for key in self.super_res_dict.keys():
@@ -55,8 +55,8 @@ class ConverterConfig(object):
a = list( self.sharpen_dict.keys() )
self.sharpen_mode = a[ (a.index(self.sharpen_mode)+1) % len(a) ]
def add_sharpen_amount(self, diff):
self.sharpen_amount = np.clip ( self.sharpen_amount+diff, 0, 100)
def add_blursharpen_amount(self, diff):
self.blursharpen_amount = np.clip ( self.blursharpen_amount+diff, -100, 100)
def toggle_super_resolution_mode(self):
a = list( self.super_res_dict.keys() )
@@ -68,7 +68,7 @@ class ConverterConfig(object):
if isinstance(other, ConverterConfig):
return self.sharpen_mode == other.sharpen_mode and \
(self.sharpen_mode == 0 or ((self.sharpen_mode == other.sharpen_mode) and (self.sharpen_amount == other.sharpen_amount) )) and \
self.blursharpen_amount == other.blursharpen_amount and \
self.super_resolution_mode == other.super_resolution_mode
return False
@@ -77,8 +77,7 @@ class ConverterConfig(object):
def to_string(self, filename):
r = ""
r += f"sharpen_mode : {self.sharpen_dict[self.sharpen_mode]}\n"
if self.sharpen_mode != 0:
r += f"sharpen_amount : {self.sharpen_amount}\n"
r += f"blursharpen_amount : {self.blursharpen_amount}\n"
r += f"super_resolution_mode : {self.super_res_dict[self.super_resolution_mode]}\n"
return r