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Converter: added Apply super resolution? (y/n skip:n) : , Enhance details by applying DCSCN network.
refactorings
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iperov
parent
4683c362ac
commit
85c01e3b4a
12 changed files with 271 additions and 77 deletions
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@ -19,7 +19,11 @@ class Converter(object):
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pass
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#overridable
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def convert_face (self, img_bgr, img_face_landmarks, debug):
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def on_host_(self):
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pass
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#overridable
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def cli_convert_face (self, img_bgr, img_face_landmarks, debug):
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#return float32 image
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#if debug , return tuple ( images of any size and channels, ...)
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return image
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@ -7,6 +7,9 @@ import cv2
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import numpy as np
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import imagelib
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from interact import interact as io
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from joblib import SubprocessFunctionCaller
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from utils.pickle_utils import AntiPickler
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'''
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default_mode = {1:'overlay',
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2:'hist-match',
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@ -20,7 +23,7 @@ class ConverterMasked(Converter):
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#override
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def __init__(self, predictor_func,
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predictor_input_size=0,
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output_size=0,
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predictor_masked=True,
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face_type=FaceType.FULL,
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default_mode = 4,
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base_erode_mask_modifier = 0,
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@ -30,8 +33,12 @@ class ConverterMasked(Converter):
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clip_hborder_mask_per = 0):
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super().__init__(predictor_func, Converter.TYPE_FACE)
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predictor_func_host, predictor_func = SubprocessFunctionCaller.make_pair(predictor_func)
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self.predictor_func_host = AntiPickler(predictor_func_host)
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self.predictor_func = predictor_func
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self.predictor_masked = predictor_masked
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self.predictor_input_size = predictor_input_size
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self.output_size = output_size
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self.face_type = face_type
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self.clip_hborder_mask_per = clip_hborder_mask_per
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@ -85,6 +92,7 @@ class ConverterMasked(Converter):
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self.output_face_scale = np.clip ( 1.0 + io.input_int ("Choose output face scale modifier [-50..50] (skip:0) : ", 0)*0.01, 0.5, 1.5)
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self.color_transfer_mode = io.input_str ("Apply color transfer to predicted face? Choose mode ( rct/lct skip:None ) : ", None, ['rct','lct'])
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self.super_resolution = io.input_bool("Apply super resolution? (y/n skip:n) : ", False, help_message="Enhance details by applying DCSCN network.")
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if self.mode != 'raw':
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self.final_image_color_degrade_power = np.clip ( io.input_int ("Degrade color power of final image [0..100] (skip:0) : ", 0), 0, 100)
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@ -93,9 +101,19 @@ class ConverterMasked(Converter):
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io.log_info ("")
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self.over_res = 4 if self.suppress_seamless_jitter else 1
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#override
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def dummy_predict(self):
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self.predictor_func ( np.zeros ( (self.predictor_input_size,self.predictor_input_size,4), dtype=np.float32 ) )
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if self.super_resolution:
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host_proc, dc_upscale = SubprocessFunctionCaller.make_pair( imagelib.DCSCN().upscale )
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self.dc_host = AntiPickler(host_proc)
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self.dc_upscale = dc_upscale
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else:
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self.dc_host = None
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#overridable
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def on_host_tick(self):
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self.predictor_func_host.obj.process_messages()
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if self.dc_host is not None:
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self.dc_host.obj.process_messages()
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#overridable
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def on_cli_initialize(self):
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@ -103,7 +121,7 @@ class ConverterMasked(Converter):
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self.fan_seg = FANSegmentator(256, FaceType.toString(FaceType.FULL) )
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#override
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def convert_face (self, img_bgr, img_face_landmarks, debug):
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def cli_convert_face (self, img_bgr, img_face_landmarks, debug):
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if self.over_res != 1:
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img_bgr = cv2.resize ( img_bgr, ( img_bgr.shape[1]*self.over_res, img_bgr.shape[0]*self.over_res ) )
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img_face_landmarks = img_face_landmarks*self.over_res
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@ -115,32 +133,52 @@ class ConverterMasked(Converter):
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img_face_mask_a = LandmarksProcessor.get_image_hull_mask (img_bgr.shape, img_face_landmarks)
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face_mat = LandmarksProcessor.get_transform_mat (img_face_landmarks, self.output_size, face_type=self.face_type)
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face_output_mat = LandmarksProcessor.get_transform_mat (img_face_landmarks, self.output_size, face_type=self.face_type, scale=self.output_face_scale)
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output_size = self.predictor_input_size
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if self.super_resolution:
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output_size *= 2
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dst_face_bgr = cv2.warpAffine( img_bgr , face_mat, (self.output_size, self.output_size), flags=cv2.INTER_LANCZOS4 )
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dst_face_mask_a_0 = cv2.warpAffine( img_face_mask_a, face_mat, (self.output_size, self.output_size), flags=cv2.INTER_LANCZOS4 )
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face_mat = LandmarksProcessor.get_transform_mat (img_face_landmarks, output_size, face_type=self.face_type)
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face_output_mat = LandmarksProcessor.get_transform_mat (img_face_landmarks, output_size, face_type=self.face_type, scale=self.output_face_scale)
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dst_face_bgr = cv2.warpAffine( img_bgr , face_mat, (output_size, output_size), flags=cv2.INTER_LANCZOS4 )
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dst_face_mask_a_0 = cv2.warpAffine( img_face_mask_a, face_mat, (output_size, output_size), flags=cv2.INTER_LANCZOS4 )
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predictor_input_bgr = cv2.resize (dst_face_bgr, (self.predictor_input_size,self.predictor_input_size))
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predictor_input_mask_a_0 = cv2.resize (dst_face_mask_a_0, (self.predictor_input_size,self.predictor_input_size))
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predictor_input_mask_a = np.expand_dims (predictor_input_mask_a_0, -1)
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predicted_bgra = self.predictor_func ( np.concatenate( (predictor_input_bgr, predictor_input_mask_a), -1) )
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if self.predictor_masked:
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prd_face_bgr, prd_face_mask_a_0 = self.predictor_func (predictor_input_bgr)
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prd_face_bgr = np.clip (prd_face_bgr, 0, 1.0 )
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prd_face_mask_a_0 = np.clip (prd_face_mask_a_0, 0.0, 1.0)
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else:
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predicted = self.predictor_func (predictor_input_bgr)
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prd_face_bgr = np.clip (predicted, 0, 1.0 )
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prd_face_mask_a_0 = cv2.resize (dst_face_mask_a_0, (self.predictor_input_size,self.predictor_input_size))
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prd_face_bgr = np.clip (predicted_bgra[:,:,0:3], 0, 1.0 )
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prd_face_mask_a_0 = np.clip (predicted_bgra[:,:,3], 0.0, 1.0)
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if self.super_resolution:
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if debug:
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tmp = cv2.resize (prd_face_bgr, (output_size,output_size), cv2.INTER_CUBIC)
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debugs += [ np.clip( cv2.warpAffine( tmp, face_output_mat, img_size, img_bgr.copy(), cv2.WARP_INVERSE_MAP | cv2.INTER_LANCZOS4, cv2.BORDER_TRANSPARENT ), 0, 1.0) ]
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prd_face_bgr = self.dc_upscale(prd_face_bgr)
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if debug:
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debugs += [ np.clip( cv2.warpAffine( prd_face_bgr, face_output_mat, img_size, img_bgr.copy(), cv2.WARP_INVERSE_MAP | cv2.INTER_LANCZOS4, cv2.BORDER_TRANSPARENT ), 0, 1.0) ]
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if self.predictor_masked:
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prd_face_mask_a_0 = cv2.resize (prd_face_mask_a_0, (output_size, output_size), cv2.INTER_CUBIC)
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else:
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prd_face_mask_a_0 = cv2.resize (dst_face_mask_a_0, (output_size, output_size), cv2.INTER_CUBIC)
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if self.mask_mode == 2: #dst
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prd_face_mask_a_0 = predictor_input_mask_a_0
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prd_face_mask_a_0 = cv2.resize (dst_face_mask_a_0, (output_size,output_size), cv2.INTER_CUBIC)
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elif self.mask_mode == 3: #FAN-prd
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prd_face_bgr_256 = cv2.resize (prd_face_bgr, (256,256) )
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prd_face_bgr_256_mask = self.fan_seg.extract_from_bgr( np.expand_dims(prd_face_bgr_256,0) ) [0]
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prd_face_mask_a_0 = cv2.resize (prd_face_bgr_256_mask, (self.predictor_input_size, self.predictor_input_size))
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prd_face_bgr_256_mask = self.fan_seg.extract_from_bgr( prd_face_bgr_256[np.newaxis,...] ) [0]
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prd_face_mask_a_0 = cv2.resize (prd_face_bgr_256_mask, (output_size,output_size), cv2.INTER_CUBIC)
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elif self.mask_mode == 4: #FAN-dst
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face_256_mat = LandmarksProcessor.get_transform_mat (img_face_landmarks, 256, face_type=FaceType.FULL)
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dst_face_256_bgr = cv2.warpAffine(img_bgr, face_256_mat, (256, 256), flags=cv2.INTER_LANCZOS4 )
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dst_face_256_mask = self.fan_seg.extract_from_bgr( np.expand_dims(dst_face_256_bgr,0) ) [0]
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prd_face_mask_a_0 = cv2.resize (dst_face_256_mask, (self.predictor_input_size, self.predictor_input_size))
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dst_face_256_mask = self.fan_seg.extract_from_bgr( dst_face_256_bgr[np.newaxis,...] ) [0]
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prd_face_mask_a_0 = cv2.resize (dst_face_256_mask, (output_size,output_size), cv2.INTER_CUBIC)
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prd_face_mask_a_0[ prd_face_mask_a_0 < 0.001 ] = 0.0
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@ -333,7 +371,7 @@ class ConverterMasked(Converter):
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out_img = np.clip( img_bgr*(1-img_mask_blurry_aaa) + (out_img*img_mask_blurry_aaa) , 0, 1.0 )
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if self.mode == 'seamless-hist-match':
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out_face_bgr = cv2.warpAffine( out_img, face_mat, (self.output_size, self.output_size) )
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out_face_bgr = cv2.warpAffine( out_img, face_mat, (output_size, output_size) )
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new_out_face_bgr = imagelib.color_hist_match(out_face_bgr, dst_face_bgr, self.hist_match_threshold)
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new_out = cv2.warpAffine( new_out_face_bgr, face_mat, img_size, img_bgr.copy(), cv2.WARP_INVERSE_MAP | cv2.INTER_LANCZOS4, cv2.BORDER_TRANSPARENT )
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out_img = np.clip( img_bgr*(1-img_mask_blurry_aaa) + (new_out*img_mask_blurry_aaa) , 0, 1.0 )
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