mirror of
https://github.com/iperov/DeepFaceLab.git
synced 2025-08-20 21:43:21 -07:00
fixes and optimizations,
converters: added new option sharpen_mode and sharpen_amount
This commit is contained in:
iperov
parent
c39ed9d9c9
commit
63d30c49ae
12 changed files with 120 additions and 78 deletions
|
|
@ -29,6 +29,9 @@ def ConvertFaceAvatar (cfg, prev_temporal_frame_infos, frame_info, next_temporal
|
|||
if cfg.super_resolution_mode != 0:
|
||||
prd_f = cfg.superres_func(cfg.super_resolution_mode, prd_f)
|
||||
|
||||
if cfg.sharpen_mode != 0 and cfg.sharpen_amount != 0:
|
||||
prd_f = cfg.sharpen_func ( prd_f, cfg.sharpen_mode, 0.003, cfg.sharpen_amount)
|
||||
|
||||
out_img = np.clip(prd_f, 0.0, 1.0)
|
||||
|
||||
if cfg.add_source_image:
|
||||
|
|
|
|||
|
|
@ -293,43 +293,35 @@ def ConvertMaskedFace (cfg, frame_info, img_bgr_uint8, img_bgr, img_face_landmar
|
|||
|
||||
out_img = img_bgr*(1-img_face_mask_aaa) + (out_img*img_face_mask_aaa)
|
||||
|
||||
out_face_bgr = cv2.warpAffine( out_img, face_mat, (output_size, output_size) )
|
||||
|
||||
if 'seamless' in cfg.mode and cfg.color_transfer_mode != 0:
|
||||
out_face_bgr = cv2.warpAffine( out_img, face_mat, (output_size, output_size) )
|
||||
|
||||
if cfg.color_transfer_mode == 1:
|
||||
#if debug:
|
||||
# debugs += [ np.clip( cv2.warpAffine( out_face_bgr, face_output_mat, img_size, np.zeros(img_bgr.shape, dtype=np.float32), cv2.WARP_INVERSE_MAP | cv2.INTER_CUBIC, cv2.BORDER_TRANSPARENT ), 0, 1.0) ]
|
||||
face_mask_aaa = cv2.warpAffine( img_face_mask_aaa, face_mat, (output_size, output_size) )
|
||||
|
||||
new_out_face_bgr = imagelib.reinhard_color_transfer ( np.clip( (out_face_bgr*255).astype(np.uint8), 0, 255),
|
||||
out_face_bgr = imagelib.reinhard_color_transfer ( np.clip( (out_face_bgr*255).astype(np.uint8), 0, 255),
|
||||
np.clip( (dst_face_bgr*255).astype(np.uint8), 0, 255),
|
||||
source_mask=face_mask_aaa, target_mask=face_mask_aaa)
|
||||
new_out_face_bgr = np.clip( new_out_face_bgr.astype(np.float32) / 255.0, 0.0, 1.0)
|
||||
out_face_bgr = np.clip( out_face_bgr.astype(np.float32) / 255.0, 0.0, 1.0)
|
||||
|
||||
#if debug:
|
||||
# debugs += [ np.clip( cv2.warpAffine( new_out_face_bgr, face_output_mat, img_size, np.zeros(img_bgr.shape, dtype=np.float32), cv2.WARP_INVERSE_MAP | cv2.INTER_CUBIC, cv2.BORDER_TRANSPARENT ), 0, 1.0) ]
|
||||
|
||||
# debugs += [ np.clip( cv2.warpAffine( out_face_bgr, face_output_mat, img_size, np.zeros(img_bgr.shape, dtype=np.float32), cv2.WARP_INVERSE_MAP | cv2.INTER_CUBIC, cv2.BORDER_TRANSPARENT ), 0, 1.0) ]
|
||||
|
||||
elif cfg.color_transfer_mode == 2:
|
||||
#if debug:
|
||||
# debugs += [ np.clip( cv2.warpAffine( out_face_bgr, face_output_mat, img_size, np.zeros(img_bgr.shape, dtype=np.float32), cv2.WARP_INVERSE_MAP | cv2.INTER_CUBIC, cv2.BORDER_TRANSPARENT ), 0, 1.0) ]
|
||||
|
||||
new_out_face_bgr = imagelib.linear_color_transfer (out_face_bgr, dst_face_bgr)
|
||||
new_out_face_bgr = np.clip( new_out_face_bgr, 0.0, 1.0)
|
||||
out_face_bgr = imagelib.linear_color_transfer (out_face_bgr, dst_face_bgr)
|
||||
out_face_bgr = np.clip( out_face_bgr, 0.0, 1.0)
|
||||
|
||||
#if debug:
|
||||
# debugs += [ np.clip( cv2.warpAffine( new_out_face_bgr, face_output_mat, img_size, np.zeros(img_bgr.shape, dtype=np.float32), cv2.WARP_INVERSE_MAP | cv2.INTER_CUBIC, cv2.BORDER_TRANSPARENT ), 0, 1.0) ]
|
||||
|
||||
new_out = cv2.warpAffine( new_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 )
|
||||
|
||||
# debugs += [ np.clip( cv2.warpAffine( out_face_bgr, face_output_mat, img_size, np.zeros(img_bgr.shape, dtype=np.float32), cv2.WARP_INVERSE_MAP | cv2.INTER_CUBIC, cv2.BORDER_TRANSPARENT ), 0, 1.0) ]
|
||||
|
||||
if cfg.mode == 'seamless-hist-match':
|
||||
out_face_bgr = cv2.warpAffine( out_img, face_mat, (output_size, output_size) )
|
||||
new_out_face_bgr = imagelib.color_hist_match(out_face_bgr, dst_face_bgr, cfg.hist_match_threshold)
|
||||
new_out = cv2.warpAffine( new_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 )
|
||||
|
||||
|
||||
out_face_bgr = imagelib.color_hist_match(out_face_bgr, dst_face_bgr, cfg.hist_match_threshold)
|
||||
|
||||
cfg_mp = cfg.motion_blur_power / 100.0
|
||||
if cfg_mp != 0:
|
||||
k_size = int(frame_info.motion_power*cfg_mp)
|
||||
|
|
@ -337,10 +329,14 @@ def ConvertMaskedFace (cfg, frame_info, img_bgr_uint8, img_bgr, img_face_landmar
|
|||
k_size = np.clip (k_size+1, 2, 50)
|
||||
if cfg.super_resolution_mode:
|
||||
k_size *= 2
|
||||
out_face_bgr = cv2.warpAffine( out_img, face_mat, (output_size, output_size) )
|
||||
new_out_face_bgr = imagelib.LinearMotionBlur (out_face_bgr, k_size , frame_info.motion_deg)
|
||||
new_out = cv2.warpAffine( new_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 )
|
||||
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, 0.003, cfg.sharpen_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 )
|
||||
|
||||
|
||||
if cfg.color_degrade_power != 0:
|
||||
#if debug:
|
||||
|
|
|
|||
|
|
@ -20,29 +20,69 @@ class ConverterConfig(object):
|
|||
self.predictor_func = predictor_func
|
||||
self.predictor_input_shape = predictor_input_shape
|
||||
|
||||
self.dcscn_upscale_func = None
|
||||
self.superres_func = None
|
||||
self.sharpen_func = None
|
||||
self.fanseg_input_size = None
|
||||
self.fanseg_extract_func = None
|
||||
|
||||
self.super_res_dict = {0:"None", 1:'RankSRGAN'}
|
||||
self.sharpen_dict = {0:"None", 1:'box', 2:'gaussian'}
|
||||
|
||||
#default changeable params
|
||||
self.super_resolution_mode = 0
|
||||
self.sharpen_mode = 0
|
||||
self.sharpen_amount = 0
|
||||
|
||||
def copy(self):
|
||||
return copy.copy(self)
|
||||
|
||||
#overridable
|
||||
def ask_settings(self):
|
||||
pass
|
||||
s = """Choose sharpen mode: \n"""
|
||||
for key in self.sharpen_dict.keys():
|
||||
s += f"""({key}) {self.sharpen_dict[key]}\n"""
|
||||
s += f"""?:help Default: {list(self.sharpen_dict.keys())[0]} : """
|
||||
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 )
|
||||
|
||||
s = """Choose super resolution mode: \n"""
|
||||
for key in self.super_res_dict.keys():
|
||||
s += f"""({key}) {self.super_res_dict[key]}\n"""
|
||||
s += f"""?:help Default: {list(self.super_res_dict.keys())[0]} : """
|
||||
self.super_resolution_mode = io.input_int (s, 0, valid_list=self.super_res_dict.keys(), help_message="Enhance details by applying superresolution network.")
|
||||
|
||||
def toggle_sharpen_mode(self):
|
||||
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 toggle_super_resolution_mode(self):
|
||||
a = list( self.super_res_dict.keys() )
|
||||
self.super_resolution_mode = a[ (a.index(self.super_resolution_mode)+1) % len(a) ]
|
||||
|
||||
#overridable
|
||||
def __eq__(self, other):
|
||||
#check equality of changeable params
|
||||
|
||||
if isinstance(other, ConverterConfig):
|
||||
return True
|
||||
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.super_resolution_mode == other.super_resolution_mode
|
||||
|
||||
return False
|
||||
|
||||
#overridable
|
||||
def __str__(self):
|
||||
return "ConverterConfig: ."
|
||||
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"super_resolution_mode : {self.super_res_dict[self.super_resolution_mode]}\n"
|
||||
return r
|
||||
|
||||
class ConverterConfigMasked(ConverterConfig):
|
||||
|
||||
|
|
@ -81,8 +121,6 @@ class ConverterConfigMasked(ConverterConfig):
|
|||
self.clip_hborder_mask_per = clip_hborder_mask_per
|
||||
|
||||
#default changeable params
|
||||
|
||||
|
||||
self.mode = 'overlay'
|
||||
self.masked_hist_match = True
|
||||
self.hist_match_threshold = 238
|
||||
|
|
@ -92,7 +130,6 @@ class ConverterConfigMasked(ConverterConfig):
|
|||
self.motion_blur_power = 0
|
||||
self.output_face_scale = 0
|
||||
self.color_transfer_mode = 0
|
||||
self.super_resolution_mode = 0
|
||||
self.color_degrade_power = 0
|
||||
self.export_mask_alpha = False
|
||||
|
||||
|
|
@ -118,11 +155,9 @@ class ConverterConfigMasked(ConverterConfig):
|
|||
2:'dst',
|
||||
4:'FAN-dst',
|
||||
7:'learned*FAN-dst'}
|
||||
|
||||
|
||||
self.ctm_dict = { 0: "None", 1:"rct", 2:"lct" }
|
||||
self.ctm_str_dict = {None:0, "rct":1, "lct": 2 }
|
||||
|
||||
self.super_res_dict = {0:"None", 1:'RankSRGAN'}
|
||||
|
||||
def copy(self):
|
||||
return copy.copy(self)
|
||||
|
|
@ -160,10 +195,6 @@ class ConverterConfigMasked(ConverterConfig):
|
|||
def toggle_color_transfer_mode(self):
|
||||
self.color_transfer_mode = (self.color_transfer_mode+1) % 3
|
||||
|
||||
def toggle_super_resolution_mode(self):
|
||||
a = list( self.super_res_dict.keys() )
|
||||
self.super_resolution_mode = a[ (a.index(self.super_resolution_mode)+1) % len(a) ]
|
||||
|
||||
def add_color_degrade_power(self, diff):
|
||||
self.color_degrade_power = np.clip ( self.color_degrade_power+diff , 0, 100)
|
||||
|
||||
|
|
@ -213,13 +244,8 @@ class ConverterConfigMasked(ConverterConfig):
|
|||
self.color_transfer_mode = io.input_str ("Apply color transfer to predicted face? Choose mode ( rct/lct skip:None ) : ", None, ['rct','lct'])
|
||||
self.color_transfer_mode = self.ctm_str_dict[self.color_transfer_mode]
|
||||
|
||||
s = """Choose super resolution mode: \n"""
|
||||
for key in self.super_res_dict.keys():
|
||||
s += f"""({key}) {self.super_res_dict[key]}\n"""
|
||||
s += f"""?:help Default: {list(self.super_res_dict.keys())[0]} : """
|
||||
self.super_resolution_mode = io.input_int (s, 0, valid_list=self.super_res_dict.keys(), help_message="Enhance details by applying superresolution network.")
|
||||
super().ask_settings()
|
||||
|
||||
|
||||
if 'raw' not in self.mode:
|
||||
self.color_degrade_power = np.clip ( io.input_int ("Degrade color power of final image [0..100] (skip:0) : ", 0), 0, 100)
|
||||
self.export_mask_alpha = io.input_bool("Export png with alpha channel of the mask? (y/n skip:n) : ", False)
|
||||
|
|
@ -230,7 +256,8 @@ class ConverterConfigMasked(ConverterConfig):
|
|||
#check equality of changeable params
|
||||
|
||||
if isinstance(other, ConverterConfigMasked):
|
||||
return self.mode == other.mode and \
|
||||
return super().__eq__(other) and \
|
||||
self.mode == other.mode and \
|
||||
self.masked_hist_match == other.masked_hist_match and \
|
||||
self.hist_match_threshold == other.hist_match_threshold and \
|
||||
self.mask_mode == other.mask_mode and \
|
||||
|
|
@ -239,7 +266,6 @@ class ConverterConfigMasked(ConverterConfig):
|
|||
self.motion_blur_power == other.motion_blur_power and \
|
||||
self.output_face_scale == other.output_face_scale and \
|
||||
self.color_transfer_mode == other.color_transfer_mode and \
|
||||
self.super_resolution_mode == other.super_resolution_mode and \
|
||||
self.color_degrade_power == other.color_degrade_power and \
|
||||
self.export_mask_alpha == other.export_mask_alpha
|
||||
|
||||
|
|
@ -272,7 +298,7 @@ class ConverterConfigMasked(ConverterConfig):
|
|||
if 'raw' not in self.mode:
|
||||
r += f"""color_transfer_mode: { self.ctm_dict[self.color_transfer_mode]}\n"""
|
||||
|
||||
r += f"""super_resolution_mode: {self.super_res_dict[self.super_resolution_mode]}\n"""
|
||||
r += super().__str__()
|
||||
|
||||
if 'raw' not in self.mode:
|
||||
r += (f"""color_degrade_power: {self.color_degrade_power}\n"""
|
||||
|
|
@ -297,8 +323,6 @@ class ConverterConfigFaceAvatar(ConverterConfig):
|
|||
|
||||
#changeable params
|
||||
self.add_source_image = False
|
||||
self.super_resolution_mode = 0
|
||||
self.super_res_dict = {0:"None", 1:'RankSRGAN'}
|
||||
|
||||
def copy(self):
|
||||
return copy.copy(self)
|
||||
|
|
@ -306,33 +330,24 @@ class ConverterConfigFaceAvatar(ConverterConfig):
|
|||
#override
|
||||
def ask_settings(self):
|
||||
self.add_source_image = io.input_bool("Add source image? (y/n ?:help skip:n) : ", False, help_message="Add source image for comparison.")
|
||||
|
||||
s = """Choose super resolution mode: \n"""
|
||||
for key in self.super_res_dict.keys():
|
||||
s += f"""({key}) {self.super_res_dict[key]}\n"""
|
||||
s += f"""?:help Default: {list(self.super_res_dict.keys())[0]} : """
|
||||
self.super_resolution_mode = io.input_int (s, 0, valid_list=self.super_res_dict.keys(), help_message="Enhance details by applying superresolution network.")
|
||||
super().ask_settings()
|
||||
|
||||
def toggle_add_source_image(self):
|
||||
self.add_source_image = not self.add_source_image
|
||||
|
||||
def toggle_super_resolution_mode(self):
|
||||
a = list( self.super_res_dict.keys() )
|
||||
self.super_resolution_mode = a[ (a.index(self.super_resolution_mode)+1) % len(a) ]
|
||||
|
||||
#override
|
||||
def __eq__(self, other):
|
||||
#check equality of changeable params
|
||||
|
||||
if isinstance(other, ConverterConfigFaceAvatar):
|
||||
return self.add_source_image == other.add_source_image and \
|
||||
self.super_resolution_mode == other.super_resolution_mode
|
||||
return super().__eq__(other) and \
|
||||
self.add_source_image == other.add_source_image
|
||||
|
||||
return False
|
||||
|
||||
#override
|
||||
def __str__(self):
|
||||
return ("ConverterConfig: \n"
|
||||
f"add_source_image : {self.add_source_image}\n"
|
||||
f"super_resolution_mode : {self.super_res_dict[self.super_resolution_mode]}\n"
|
||||
"================"
|
||||
)
|
||||
f"add_source_image : {self.add_source_image}\n") + \
|
||||
super().__str__() + "================"
|
||||
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue