class ConverterMasked(Converter): #override def __init__(self, predictor_func, predictor_input_size=0, output_size=0, face_type=FaceType.FULL, default_mode=4, base_erode_mask_modifier=0, base_blur_mask_modifier=0, default_erode_mask_modifier=0, default_blur_mask_modifier=0, clip_hborder_mask_per=0): super().__init__(predictor_func, Converter.TYPE_FACE) self.predictor_input_size = predictor_input_size self.output_size = output_size self.face_type = face_type self.clip_hborder_mask_per = clip_hborder_mask_per mode = io.input_int( "Choose mode: (1) overlay, (2) hist match, (3) hist match bw, (4) seamless, (5) raw. Default - %d : " % (default_mode), default_mode) mode_dict = { 1: 'overlay', 2: 'hist-match', 3: 'hist-match-bw', 4: 'seamless', 5: 'raw' } self.mode = mode_dict.get(mode, mode_dict[default_mode]) self.suppress_seamless_jitter = False if self.mode == 'raw': mode = io.input_int( "Choose raw mode: (1) rgb, (2) rgb+mask (default), (3) mask only, (4) predicted only : ", 2) self.raw_mode = { 1: 'rgb', 2: 'rgb-mask', 3: 'mask-only', 4: 'predicted-only' }.get(mode, 'rgb-mask') if self.mode != 'raw': if self.mode == 'seamless': io.input_bool( "Suppress seamless jitter? [ y/n ] (?:help skip:n ) : ", False, help_message= "Seamless clone produces face jitter. You can suppress it, but process can take a long time." ) if io.input_bool("Seamless hist match? (y/n skip:n) : ", False): self.mode = 'seamless-hist-match' if self.mode == 'hist-match' or self.mode == 'hist-match-bw': self.masked_hist_match = io.input_bool( "Masked hist match? (y/n skip:y) : ", True) if self.mode == 'hist-match' or self.mode == 'hist-match-bw' or self.mode == 'seamless-hist-match': self.hist_match_threshold = np.clip( io.input_int( "Hist match threshold [0..255] (skip:255) : ", 255), 0, 255) if face_type == FaceType.FULL: self.mask_mode = io.input_int( "Mask mode: (1) learned, (2) dst, (3) FAN-prd, (4) FAN-dst (?) help. Default - %d : " % (1), 1, help_message= "If you learned mask, then option 1 should be choosed. 'dst' mask is raw shaky mask from dst aligned images. 'FAN-prd' - using super smooth mask by pretrained FAN-model from predicted face. 'FAN-dst' - using super smooth mask by pretrained FAN-model from dst face." ) else: self.mask_mode = io.input_int( "Mask mode: (1) learned, (2) dst . Default - %d : " % (1), 1) if self.mask_mode == 3 or self.mask_mode == 4: self.fan_seg = None if self.mode != 'raw': self.erode_mask_modifier = base_erode_mask_modifier + np.clip( io.input_int( "Choose erode mask modifier [-200..200] (skip:%d) : " % (default_erode_mask_modifier), default_erode_mask_modifier), -200, 200) self.blur_mask_modifier = base_blur_mask_modifier + np.clip( io.input_int( "Choose blur mask modifier [-200..200] (skip:%d) : " % (default_blur_mask_modifier), default_blur_mask_modifier), -200, 200) self.seamless_erode_mask_modifier = 0 if 'seamless' in self.mode: self.seamless_erode_mask_modifier = np.clip( io.input_int( "Choose seamless erode mask modifier [-100..100] (skip:0) : ", 0), -100, 100) 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) self.color_transfer_mode = io.input_str( "Apply color transfer to predicted face? Choose mode ( rct/lct skip:None ) : ", None, ['rct', 'lct']) if self.mode != 'raw': self.final_image_color_degrade_power = np.clip( io.input_int( "Degrade color power of final image [0..100] (skip:0) : ", 0), 0, 100) self.alpha = io.input_bool( "Export png with alpha channel? (y/n skip:n) : ", False) io.log_info("") self.over_res = 4 if self.suppress_seamless_jitter else 1 #override def dummy_predict(self): self.predictor_func( np.zeros((self.predictor_input_size, self.predictor_input_size, 4), dtype=np.float32)) #override def convert_face(self, img_bgr, img_face_landmarks, debug): if (self.mask_mode == 3 or self.mask_mode == 4) and self.fan_seg == None: self.fan_seg = FANSegmentator(256, FaceType.toString(FaceType.FULL)) if self.over_res != 1: img_bgr = cv2.resize(img_bgr, (img_bgr.shape[1] * self.over_res, img_bgr.shape[0] * self.over_res)) img_face_landmarks = img_face_landmarks * self.over_res if debug: debugs = [img_bgr.copy()] img_size = img_bgr.shape[1], img_bgr.shape[0] img_face_mask_a = LandmarksProcessor.get_image_hull_mask( img_bgr.shape, img_face_landmarks) face_mat = LandmarksProcessor.get_transform_mat( img_face_landmarks, self.output_size, face_type=self.face_type) face_output_mat = LandmarksProcessor.get_transform_mat( img_face_landmarks, self.output_size, face_type=self.face_type, scale=self.output_face_scale) dst_face_bgr = cv2.warpAffine(img_bgr, face_mat, (self.output_size, self.output_size), flags=cv2.INTER_LANCZOS4) dst_face_mask_a_0 = cv2.warpAffine( img_face_mask_a, face_mat, (self.output_size, self.output_size), flags=cv2.INTER_LANCZOS4) predictor_input_bgr = cv2.resize( dst_face_bgr, (self.predictor_input_size, self.predictor_input_size)) predictor_input_mask_a_0 = cv2.resize( dst_face_mask_a_0, (self.predictor_input_size, self.predictor_input_size)) predictor_input_mask_a = np.expand_dims(predictor_input_mask_a_0, -1) predicted_bgra = self.predictor_func( np.concatenate((predictor_input_bgr, predictor_input_mask_a), -1)) prd_face_bgr = np.clip(predicted_bgra[:, :, 0:3], 0, 1.0) prd_face_mask_a_0 = np.clip(predicted_bgra[:, :, 3], 0.0, 1.0) if self.mask_mode == 2: #dst prd_face_mask_a_0 = predictor_input_mask_a_0 elif self.mask_mode == 3: #FAN-prd prd_face_bgr_256 = cv2.resize(prd_face_bgr, (256, 256)) prd_face_bgr_256_mask = self.fan_seg.extract_from_bgr( np.expand_dims(prd_face_bgr_256, 0))[0] prd_face_mask_a_0 = cv2.resize( prd_face_bgr_256_mask, (self.predictor_input_size, self.predictor_input_size)) elif self.mask_mode == 4: #FAN-dst face_256_mat = LandmarksProcessor.get_transform_mat( img_face_landmarks, 256, face_type=FaceType.FULL) dst_face_256_bgr = cv2.warpAffine(img_bgr, face_256_mat, (256, 256), flags=cv2.INTER_LANCZOS4) dst_face_256_mask = self.fan_seg.extract_from_bgr( np.expand_dims(dst_face_256_bgr, 0))[0] prd_face_mask_a_0 = cv2.resize( dst_face_256_mask, (self.predictor_input_size, self.predictor_input_size)) prd_face_mask_a_0[prd_face_mask_a_0 < 0.001] = 0.0 prd_face_mask_a = np.expand_dims(prd_face_mask_a_0, axis=-1) prd_face_mask_aaa = np.repeat(prd_face_mask_a, (3, ), axis=-1) img_face_mask_aaa = cv2.warpAffine(prd_face_mask_aaa, face_output_mat, img_size, np.zeros(img_bgr.shape, dtype=np.float32), flags=cv2.WARP_INVERSE_MAP | cv2.INTER_LANCZOS4) img_face_mask_aaa = np.clip(img_face_mask_aaa, 0.0, 1.0) img_face_mask_aaa[img_face_mask_aaa <= 0.1] = 0.0 #get rid of noise if debug: debugs += [img_face_mask_aaa.copy()] if 'seamless' in self.mode: #mask used for cv2.seamlessClone img_face_seamless_mask_aaa = None for i in range(9, 0, -1): a = img_face_mask_aaa > i / 10.0 if len(np.argwhere(a)) == 0: continue img_face_seamless_mask_aaa = img_face_mask_aaa.copy() img_face_seamless_mask_aaa[a] = 1.0 img_face_seamless_mask_aaa[img_face_seamless_mask_aaa <= i / 10.0] = 0.0 out_img = img_bgr.copy() if self.mode == 'raw': if self.raw_mode == 'rgb' or self.raw_mode == 'rgb-mask': out_img = cv2.warpAffine( prd_face_bgr, face_output_mat, img_size, out_img, cv2.WARP_INVERSE_MAP | cv2.INTER_LANCZOS4, cv2.BORDER_TRANSPARENT) if self.raw_mode == 'rgb-mask': out_img = np.concatenate( [out_img, np.expand_dims(img_face_mask_aaa[:, :, 0], -1)], -1) if self.raw_mode == 'mask-only': out_img = img_face_mask_aaa if self.raw_mode == 'predicted-only': out_img = cv2.warpAffine( prd_face_bgr, face_output_mat, img_size, np.zeros(out_img.shape, dtype=np.float32), cv2.WARP_INVERSE_MAP | cv2.INTER_LANCZOS4, cv2.BORDER_TRANSPARENT) elif ('seamless' not in self.mode) or (img_face_seamless_mask_aaa is not None): #averaging [lenx, leny, maskx, masky] by grayscale gradients of upscaled mask ar = [] for i in range(1, 10): maxregion = np.argwhere(img_face_mask_aaa > i / 10.0) if maxregion.size != 0: miny, minx = maxregion.min(axis=0)[:2] maxy, maxx = maxregion.max(axis=0)[:2] lenx = maxx - minx leny = maxy - miny maskx = (minx + (lenx / 2)) masky = (miny + (leny / 2)) if lenx >= 4 and leny >= 4: ar += [[lenx, leny, maskx, masky]] if len(ar) > 0: lenx, leny, maskx, masky = np.mean(ar, axis=0) if debug: io.log_info("lenx/leny:(%d/%d) maskx/masky:(%f/%f)" % (lenx, leny, maskx, masky)) maskx = int(maskx) masky = int(masky) lowest_len = min(lenx, leny) if debug: io.log_info("lowest_len = %f" % (lowest_len)) img_mask_blurry_aaa = img_face_mask_aaa if self.erode_mask_modifier != 0: ero = int(lowest_len * (0.126 - lowest_len * 0.00004551365) * 0.01 * self.erode_mask_modifier) if debug: io.log_info("erode_size = %d" % (ero)) if ero > 0: img_mask_blurry_aaa = cv2.erode( img_mask_blurry_aaa, cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (ero, ero)), iterations=1) elif ero < 0: img_mask_blurry_aaa = cv2.dilate( img_mask_blurry_aaa, cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (-ero, -ero)), iterations=1) if self.seamless_erode_mask_modifier != 0: ero = int(lowest_len * (0.126 - lowest_len * 0.00004551365) * 0.01 * self.seamless_erode_mask_modifier) if debug: io.log_info("seamless_erode_size = %d" % (ero)) if ero > 0: img_face_seamless_mask_aaa = cv2.erode( img_face_seamless_mask_aaa, cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (ero, ero)), iterations=1) elif ero < 0: img_face_seamless_mask_aaa = cv2.dilate( img_face_seamless_mask_aaa, cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (-ero, -ero)), iterations=1) img_face_seamless_mask_aaa = np.clip( img_face_seamless_mask_aaa, 0, 1) if self.clip_hborder_mask_per > 0: #clip hborder before blur prd_hborder_rect_mask_a = np.ones(prd_face_mask_a.shape, dtype=np.float32) prd_border_size = int(prd_hborder_rect_mask_a.shape[1] * self.clip_hborder_mask_per) prd_hborder_rect_mask_a[:, 0:prd_border_size, :] = 0 prd_hborder_rect_mask_a[:, -prd_border_size:, :] = 0 prd_hborder_rect_mask_a = np.expand_dims( cv2.blur(prd_hborder_rect_mask_a, (prd_border_size, prd_border_size)), -1) img_prd_hborder_rect_mask_a = cv2.warpAffine( prd_hborder_rect_mask_a, face_output_mat, img_size, np.zeros(img_bgr.shape, dtype=np.float32), cv2.WARP_INVERSE_MAP | cv2.INTER_LANCZOS4) img_prd_hborder_rect_mask_a = np.expand_dims( img_prd_hborder_rect_mask_a, -1) img_mask_blurry_aaa *= img_prd_hborder_rect_mask_a img_mask_blurry_aaa = np.clip(img_mask_blurry_aaa, 0, 1.0) if debug: debugs += [img_mask_blurry_aaa.copy()] if self.blur_mask_modifier > 0: blur = int(lowest_len * 0.10 * 0.01 * self.blur_mask_modifier) if debug: io.log_info("blur_size = %d" % (blur)) if blur > 0: img_mask_blurry_aaa = cv2.blur(img_mask_blurry_aaa, (blur, blur)) img_mask_blurry_aaa = np.clip(img_mask_blurry_aaa, 0, 1.0) if debug: debugs += [img_mask_blurry_aaa.copy()] if self.color_transfer_mode is not None: if self.color_transfer_mode == 'rct': if debug: debugs += [ np.clip( cv2.warpAffine( prd_face_bgr, face_output_mat, img_size, np.zeros(img_bgr.shape, dtype=np.float32), cv2.WARP_INVERSE_MAP | cv2.INTER_LANCZOS4, cv2.BORDER_TRANSPARENT), 0, 1.0) ] prd_face_bgr = image_utils.reinhard_color_transfer( np.clip((prd_face_bgr * 255).astype(np.uint8), 0, 255), np.clip((dst_face_bgr * 255).astype(np.uint8), 0, 255), source_mask=prd_face_mask_a, target_mask=prd_face_mask_a) prd_face_bgr = np.clip( prd_face_bgr.astype(np.float32) / 255.0, 0.0, 1.0) if debug: debugs += [ np.clip( cv2.warpAffine( prd_face_bgr, face_output_mat, img_size, np.zeros(img_bgr.shape, dtype=np.float32), cv2.WARP_INVERSE_MAP | cv2.INTER_LANCZOS4, cv2.BORDER_TRANSPARENT), 0, 1.0) ] elif self.color_transfer_mode == 'lct': if debug: debugs += [ np.clip( cv2.warpAffine( prd_face_bgr, face_output_mat, img_size, np.zeros(img_bgr.shape, dtype=np.float32), cv2.WARP_INVERSE_MAP | cv2.INTER_LANCZOS4, cv2.BORDER_TRANSPARENT), 0, 1.0) ] prd_face_bgr = image_utils.linear_color_transfer( prd_face_bgr, dst_face_bgr) prd_face_bgr = np.clip(prd_face_bgr, 0.0, 1.0) if debug: debugs += [ np.clip( cv2.warpAffine( prd_face_bgr, face_output_mat, img_size, np.zeros(img_bgr.shape, dtype=np.float32), cv2.WARP_INVERSE_MAP | cv2.INTER_LANCZOS4, cv2.BORDER_TRANSPARENT), 0, 1.0) ] if self.mode == 'hist-match-bw': prd_face_bgr = cv2.cvtColor(prd_face_bgr, cv2.COLOR_BGR2GRAY) prd_face_bgr = np.repeat(np.expand_dims(prd_face_bgr, -1), (3, ), -1) if self.mode == 'hist-match' or self.mode == 'hist-match-bw': if debug: debugs += [ cv2.warpAffine( prd_face_bgr, face_output_mat, img_size, np.zeros(img_bgr.shape, dtype=np.float32), cv2.WARP_INVERSE_MAP | cv2.INTER_LANCZOS4, cv2.BORDER_TRANSPARENT) ] hist_mask_a = np.ones(prd_face_bgr.shape[:2] + (1, ), dtype=np.float32) if self.masked_hist_match: hist_mask_a *= prd_face_mask_a hist_match_1 = prd_face_bgr * hist_mask_a + ( 1.0 - hist_mask_a) * np.ones( prd_face_bgr.shape[:2] + (1, ), dtype=np.float32) hist_match_1[hist_match_1 > 1.0] = 1.0 hist_match_2 = dst_face_bgr * hist_mask_a + ( 1.0 - hist_mask_a) * np.ones( prd_face_bgr.shape[:2] + (1, ), dtype=np.float32) hist_match_2[hist_match_1 > 1.0] = 1.0 prd_face_bgr = image_utils.color_hist_match( hist_match_1, hist_match_2, self.hist_match_threshold) if self.mode == 'hist-match-bw': prd_face_bgr = prd_face_bgr.astype(dtype=np.float32) out_img = cv2.warpAffine( prd_face_bgr, face_output_mat, img_size, out_img, cv2.WARP_INVERSE_MAP | cv2.INTER_LANCZOS4, cv2.BORDER_TRANSPARENT) out_img = np.clip(out_img, 0.0, 1.0) if debug: debugs += [out_img.copy()] if self.mode == 'overlay': pass if 'seamless' in self.mode: try: out_img = cv2.seamlessClone( (out_img * 255).astype(np.uint8), (img_bgr * 255).astype(np.uint8), (img_face_seamless_mask_aaa * 255).astype( np.uint8), (maskx, masky), cv2.NORMAL_CLONE) out_img = out_img.astype(dtype=np.float32) / 255.0 except Exception as e: #seamlessClone may fail in some cases e_str = traceback.format_exc() if 'MemoryError' in e_str: raise Exception( "Seamless fail: " + e_str ) #reraise MemoryError in order to reprocess this data by other processes else: print("Seamless fail: " + e_str) if debug: debugs += [out_img.copy()] out_img = np.clip( img_bgr * (1 - img_mask_blurry_aaa) + (out_img * img_mask_blurry_aaa), 0, 1.0) if self.mode == 'seamless-hist-match': out_face_bgr = cv2.warpAffine( out_img, face_mat, (self.output_size, self.output_size)) new_out_face_bgr = image_utils.color_hist_match( out_face_bgr, dst_face_bgr, self.hist_match_threshold) 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) out_img = np.clip( img_bgr * (1 - img_mask_blurry_aaa) + (new_out * img_mask_blurry_aaa), 0, 1.0) if self.final_image_color_degrade_power != 0: if debug: debugs += [out_img.copy()] out_img_reduced = image_utils.reduce_colors(out_img, 256) if self.final_image_color_degrade_power == 100: out_img = out_img_reduced else: alpha = self.final_image_color_degrade_power / 100.0 out_img = (out_img * (1.0 - alpha) + out_img_reduced * alpha) if self.alpha: out_img = np.concatenate([ out_img, np.expand_dims(img_mask_blurry_aaa[:, :, 0], -1) ], -1) if self.over_res != 1: out_img = cv2.resize(out_img, (img_bgr.shape[1] // self.over_res, img_bgr.shape[0] // self.over_res)) out_img = np.clip(out_img, 0.0, 1.0) if debug: debugs += [out_img.copy()] return debugs if debug else out_img
class Model(ModelBase): def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs, ask_write_preview_history=False, ask_target_iter=False, ask_sort_by_yaw=False, ask_random_flip=False, ask_src_scale_mod=False) #override def onInitialize(self): exec(nnlib.import_all(), locals(), globals()) self.set_vram_batch_requirements({1.5: 4}) self.resolution = 256 self.face_type = FaceType.FULL self.fan_seg = FANSegmentator( self.resolution, FaceType.toString(self.face_type), load_weights=not self.is_first_run(), weights_file_root=self.get_model_root_path(), training=True) if self.is_training_mode: f = SampleProcessor.TypeFlags f_type = f.FACE_ALIGN_FULL self.set_training_data_generators([ SampleGeneratorFace( self.training_data_src_path, debug=self.is_debug(), batch_size=self.batch_size, sample_process_options=SampleProcessor.Options( random_flip=True, normalize_tanh=True), output_sample_types=[[ f.TRANSFORMED | f_type | f.MODE_BGR_SHUFFLE, self.resolution ], [ f.TRANSFORMED | f_type | f.MODE_M | f.FACE_MASK_FULL, self.resolution ]]), SampleGeneratorFace( self.training_data_dst_path, debug=self.is_debug(), batch_size=self.batch_size, sample_process_options=SampleProcessor.Options( random_flip=True, normalize_tanh=True), output_sample_types=[[ f.TRANSFORMED | f_type | f.MODE_BGR_SHUFFLE, self.resolution ]]) ]) #override def onSave(self): self.fan_seg.save_weights() #override def onTrainOneIter(self, generators_samples, generators_list): target_src, target_src_mask = generators_samples[0] loss = self.fan_seg.train_on_batch([target_src], [target_src_mask]) return (('loss', loss), ) #override def onGetPreview(self, sample): test_A = sample[0][0][0:4] #first 4 samples test_B = sample[1][0][0:4] #first 4 samples mAA = self.fan_seg.extract_from_bgr([test_A]) mBB = self.fan_seg.extract_from_bgr([test_B]) test_A, test_B, = [ np.clip((x + 1.0) / 2.0, 0.0, 1.0) for x in [test_A, test_B] ] mAA = np.repeat(mAA, (3, ), -1) mBB = np.repeat(mBB, (3, ), -1) st = [] for i in range(0, len(test_A)): st.append( np.concatenate(( test_A[i, :, :, 0:3], mAA[i], test_A[i, :, :, 0:3] * mAA[i], ), axis=1)) st2 = [] for i in range(0, len(test_B)): st2.append( np.concatenate(( test_B[i, :, :, 0:3], mBB[i], test_B[i, :, :, 0:3] * mBB[i], ), axis=1)) return [ ('FANSegmentator', np.concatenate(st, axis=0)), ('never seen', np.concatenate(st2, axis=0)), ]
class ConverterMasked(Converter): #override def __init__(self, predictor_func, predictor_input_size=0, predictor_masked=True, face_type=FaceType.FULL, default_mode=4, base_erode_mask_modifier=0, base_blur_mask_modifier=0, default_erode_mask_modifier=0, default_blur_mask_modifier=0, clip_hborder_mask_per=0): super().__init__(predictor_func, Converter.TYPE_FACE) #dummy predict and sleep, tensorflow caching kernels. If remove it, conversion speed will be x2 slower predictor_func( np.zeros((predictor_input_size, predictor_input_size, 3), dtype=np.float32)) time.sleep(2) predictor_func_host, predictor_func = SubprocessFunctionCaller.make_pair( predictor_func) self.predictor_func_host = AntiPickler(predictor_func_host) self.predictor_func = predictor_func self.predictor_masked = predictor_masked self.predictor_input_size = predictor_input_size self.face_type = face_type self.clip_hborder_mask_per = clip_hborder_mask_per mode = io.input_int( "Choose mode: (1) overlay, (2) hist match, (3) hist match bw, (4) seamless, (5) raw. Default - %d : " % (default_mode), default_mode) mode_dict = { 1: 'overlay', 2: 'hist-match', 3: 'hist-match-bw', 4: 'seamless', 5: 'raw' } self.mode = mode_dict.get(mode, mode_dict[default_mode]) if self.mode == 'raw': mode = io.input_int( "Choose raw mode: (1) rgb, (2) rgb+mask (default), (3) mask only, (4) predicted only : ", 2) self.raw_mode = { 1: 'rgb', 2: 'rgb-mask', 3: 'mask-only', 4: 'predicted-only' }.get(mode, 'rgb-mask') if self.mode != 'raw': if self.mode == 'seamless': if io.input_bool("Seamless hist match? (y/n skip:n) : ", False): self.mode = 'seamless-hist-match' if self.mode == 'hist-match' or self.mode == 'hist-match-bw': self.masked_hist_match = io.input_bool( "Masked hist match? (y/n skip:y) : ", True) if self.mode == 'hist-match' or self.mode == 'hist-match-bw' or self.mode == 'seamless-hist-match': self.hist_match_threshold = np.clip( io.input_int( "Hist match threshold [0..255] (skip:255) : ", 255), 0, 255) if face_type == FaceType.FULL: self.mask_mode = np.clip( io.input_int( "Mask mode: (1) learned, (2) dst, (3) FAN-prd, (4) FAN-dst , (5) FAN-prd*FAN-dst (6) learned*FAN-prd*FAN-dst (?) help. Default - %d : " % (1), 1, help_message= "If you learned mask, then option 1 should be choosed. 'dst' mask is raw shaky mask from dst aligned images. 'FAN-prd' - using super smooth mask by pretrained FAN-model from predicted face. 'FAN-dst' - using super smooth mask by pretrained FAN-model from dst face. 'FAN-prd*FAN-dst' or 'learned*FAN-prd*FAN-dst' - using multiplied masks." ), 1, 6) else: self.mask_mode = np.clip( io.input_int( "Mask mode: (1) learned, (2) dst . Default - %d : " % (1), 1), 1, 2) if self.mask_mode >= 3 and self.mask_mode <= 6: self.fan_seg = None if self.mode != 'raw': self.erode_mask_modifier = base_erode_mask_modifier + np.clip( io.input_int( "Choose erode mask modifier [-200..200] (skip:%d) : " % (default_erode_mask_modifier), default_erode_mask_modifier), -200, 200) self.blur_mask_modifier = base_blur_mask_modifier + np.clip( io.input_int( "Choose blur mask modifier [-200..200] (skip:%d) : " % (default_blur_mask_modifier), default_blur_mask_modifier), -200, 200) 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) if self.mode != 'raw': self.color_transfer_mode = io.input_str( "Apply color transfer to predicted face? Choose mode ( rct/lct skip:None ) : ", None, ['rct', 'lct']) self.super_resolution = io.input_bool( "Apply super resolution? (y/n ?:help skip:n) : ", False, help_message="Enhance details by applying DCSCN network.") if self.mode != 'raw': self.final_image_color_degrade_power = np.clip( io.input_int( "Degrade color power of final image [0..100] (skip:0) : ", 0), 0, 100) self.alpha = io.input_bool( "Export png with alpha channel? (y/n skip:n) : ", False) io.log_info("") if self.super_resolution: host_proc, dc_upscale = SubprocessFunctionCaller.make_pair( imagelib.DCSCN().upscale) self.dc_host = AntiPickler(host_proc) self.dc_upscale = dc_upscale else: self.dc_host = None #overridable def on_host_tick(self): self.predictor_func_host.obj.process_messages() if self.dc_host is not None: self.dc_host.obj.process_messages() #overridable def on_cli_initialize(self): if (self.mask_mode >= 3 and self.mask_mode <= 6) and self.fan_seg == None: self.fan_seg = FANSegmentator(256, FaceType.toString(FaceType.FULL)) #override def cli_convert_face(self, img_bgr, img_face_landmarks, debug): if debug: debugs = [img_bgr.copy()] img_size = img_bgr.shape[1], img_bgr.shape[0] img_face_mask_a = LandmarksProcessor.get_image_hull_mask( img_bgr.shape, img_face_landmarks) output_size = self.predictor_input_size if self.super_resolution: output_size *= 2 face_mat = LandmarksProcessor.get_transform_mat( img_face_landmarks, output_size, face_type=self.face_type) face_output_mat = LandmarksProcessor.get_transform_mat( img_face_landmarks, output_size, face_type=self.face_type, scale=self.output_face_scale) dst_face_bgr = cv2.warpAffine(img_bgr, face_mat, (output_size, output_size), flags=cv2.INTER_LANCZOS4) dst_face_mask_a_0 = cv2.warpAffine(img_face_mask_a, face_mat, (output_size, output_size), flags=cv2.INTER_LANCZOS4) predictor_input_bgr = cv2.resize( dst_face_bgr, (self.predictor_input_size, self.predictor_input_size)) if self.predictor_masked: prd_face_bgr, prd_face_mask_a_0 = self.predictor_func( predictor_input_bgr) prd_face_bgr = np.clip(prd_face_bgr, 0, 1.0) prd_face_mask_a_0 = np.clip(prd_face_mask_a_0, 0.0, 1.0) else: predicted = self.predictor_func(predictor_input_bgr) prd_face_bgr = np.clip(predicted, 0, 1.0) prd_face_mask_a_0 = cv2.resize( dst_face_mask_a_0, (self.predictor_input_size, self.predictor_input_size)) if self.super_resolution: if debug: tmp = cv2.resize(prd_face_bgr, (output_size, output_size), cv2.INTER_CUBIC) 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) ] prd_face_bgr = self.dc_upscale(prd_face_bgr) if debug: 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) ] if self.predictor_masked: prd_face_mask_a_0 = cv2.resize(prd_face_mask_a_0, (output_size, output_size), cv2.INTER_CUBIC) else: prd_face_mask_a_0 = cv2.resize(dst_face_mask_a_0, (output_size, output_size), cv2.INTER_CUBIC) if self.mask_mode == 2: #dst prd_face_mask_a_0 = cv2.resize(dst_face_mask_a_0, (output_size, output_size), cv2.INTER_CUBIC) elif self.mask_mode >= 3 and self.mask_mode <= 6: if self.mask_mode == 3 or self.mask_mode == 5 or self.mask_mode == 6: prd_face_bgr_256 = cv2.resize(prd_face_bgr, (256, 256)) prd_face_bgr_256_mask = self.fan_seg.extract_from_bgr( prd_face_bgr_256[np.newaxis, ...])[0] FAN_prd_face_mask_a_0 = cv2.resize(prd_face_bgr_256_mask, (output_size, output_size), cv2.INTER_CUBIC) if self.mask_mode == 4 or self.mask_mode == 5 or self.mask_mode == 6: face_256_mat = LandmarksProcessor.get_transform_mat( img_face_landmarks, 256, face_type=FaceType.FULL) dst_face_256_bgr = cv2.warpAffine(img_bgr, face_256_mat, (256, 256), flags=cv2.INTER_LANCZOS4) dst_face_256_mask = self.fan_seg.extract_from_bgr( dst_face_256_bgr[np.newaxis, ...])[0] FAN_dst_face_mask_a_0 = cv2.resize(dst_face_256_mask, (output_size, output_size), cv2.INTER_CUBIC) if self.mask_mode == 3: #FAN-prd prd_face_mask_a_0 = FAN_prd_face_mask_a_0 elif self.mask_mode == 4: #FAN-dst prd_face_mask_a_0 = FAN_dst_face_mask_a_0 elif self.mask_mode == 5: prd_face_mask_a_0 = FAN_prd_face_mask_a_0 * FAN_dst_face_mask_a_0 elif self.mask_mode == 6: prd_face_mask_a_0 = prd_face_mask_a_0 * FAN_prd_face_mask_a_0 * FAN_dst_face_mask_a_0 prd_face_mask_a_0[prd_face_mask_a_0 < 0.001] = 0.0 prd_face_mask_a = prd_face_mask_a_0[..., np.newaxis] prd_face_mask_aaa = np.repeat(prd_face_mask_a, (3, ), axis=-1) img_face_mask_aaa = cv2.warpAffine(prd_face_mask_aaa, face_output_mat, img_size, np.zeros(img_bgr.shape, dtype=np.float32), flags=cv2.WARP_INVERSE_MAP | cv2.INTER_LANCZOS4) img_face_mask_aaa = np.clip(img_face_mask_aaa, 0.0, 1.0) img_face_mask_aaa[img_face_mask_aaa <= 0.1] = 0.0 #get rid of noise if debug: debugs += [img_face_mask_aaa.copy()] out_img = img_bgr.copy() if self.mode == 'raw': if self.raw_mode == 'rgb' or self.raw_mode == 'rgb-mask': out_img = cv2.warpAffine( prd_face_bgr, face_output_mat, img_size, out_img, cv2.WARP_INVERSE_MAP | cv2.INTER_LANCZOS4, cv2.BORDER_TRANSPARENT) if self.raw_mode == 'rgb-mask': out_img = np.concatenate( [out_img, np.expand_dims(img_face_mask_aaa[:, :, 0], -1)], -1) if self.raw_mode == 'mask-only': out_img = img_face_mask_aaa if self.raw_mode == 'predicted-only': out_img = cv2.warpAffine( prd_face_bgr, face_output_mat, img_size, np.zeros(out_img.shape, dtype=np.float32), cv2.WARP_INVERSE_MAP | cv2.INTER_LANCZOS4, cv2.BORDER_TRANSPARENT) else: #averaging [lenx, leny, maskx, masky] by grayscale gradients of upscaled mask ar = [] for i in range(1, 10): maxregion = np.argwhere(img_face_mask_aaa > i / 10.0) if maxregion.size != 0: miny, minx = maxregion.min(axis=0)[:2] maxy, maxx = maxregion.max(axis=0)[:2] lenx = maxx - minx leny = maxy - miny if min(lenx, leny) >= 4: ar += [[lenx, leny]] if len(ar) > 0: lenx, leny = np.mean(ar, axis=0) lowest_len = min(lenx, leny) if debug: io.log_info("lenx/leny:(%d/%d) " % (lenx, leny)) io.log_info("lowest_len = %f" % (lowest_len)) if self.erode_mask_modifier != 0: ero = int(lowest_len * (0.126 - lowest_len * 0.00004551365) * 0.01 * self.erode_mask_modifier) if debug: io.log_info("erode_size = %d" % (ero)) if ero > 0: img_face_mask_aaa = cv2.erode( img_face_mask_aaa, cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (ero, ero)), iterations=1) elif ero < 0: img_face_mask_aaa = cv2.dilate( img_face_mask_aaa, cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (-ero, -ero)), iterations=1) img_mask_blurry_aaa = img_face_mask_aaa if self.clip_hborder_mask_per > 0: #clip hborder before blur prd_hborder_rect_mask_a = np.ones(prd_face_mask_a.shape, dtype=np.float32) prd_border_size = int(prd_hborder_rect_mask_a.shape[1] * self.clip_hborder_mask_per) prd_hborder_rect_mask_a[:, 0:prd_border_size, :] = 0 prd_hborder_rect_mask_a[:, -prd_border_size:, :] = 0 prd_hborder_rect_mask_a = np.expand_dims( cv2.blur(prd_hborder_rect_mask_a, (prd_border_size, prd_border_size)), -1) img_prd_hborder_rect_mask_a = cv2.warpAffine( prd_hborder_rect_mask_a, face_output_mat, img_size, np.zeros(img_bgr.shape, dtype=np.float32), cv2.WARP_INVERSE_MAP | cv2.INTER_LANCZOS4) img_prd_hborder_rect_mask_a = np.expand_dims( img_prd_hborder_rect_mask_a, -1) img_mask_blurry_aaa *= img_prd_hborder_rect_mask_a img_mask_blurry_aaa = np.clip(img_mask_blurry_aaa, 0, 1.0) if debug: debugs += [img_mask_blurry_aaa.copy()] if self.blur_mask_modifier > 0: blur = int(lowest_len * 0.10 * 0.01 * self.blur_mask_modifier) if debug: io.log_info("blur_size = %d" % (blur)) if blur > 0: img_mask_blurry_aaa = cv2.blur(img_mask_blurry_aaa, (blur, blur)) img_mask_blurry_aaa = np.clip(img_mask_blurry_aaa, 0, 1.0) face_mask_blurry_aaa = cv2.warpAffine( img_mask_blurry_aaa, face_mat, (output_size, output_size)) if debug: debugs += [img_mask_blurry_aaa.copy()] if 'seamless' not in self.mode and self.color_transfer_mode is not None: if self.color_transfer_mode == 'rct': if debug: debugs += [ np.clip( cv2.warpAffine( prd_face_bgr, face_output_mat, img_size, np.zeros(img_bgr.shape, dtype=np.float32), cv2.WARP_INVERSE_MAP | cv2.INTER_LANCZOS4, cv2.BORDER_TRANSPARENT), 0, 1.0) ] prd_face_bgr = imagelib.reinhard_color_transfer( np.clip((prd_face_bgr * 255).astype(np.uint8), 0, 255), np.clip((dst_face_bgr * 255).astype(np.uint8), 0, 255), source_mask=prd_face_mask_a, target_mask=prd_face_mask_a) prd_face_bgr = np.clip( prd_face_bgr.astype(np.float32) / 255.0, 0.0, 1.0) if debug: debugs += [ np.clip( cv2.warpAffine( prd_face_bgr, face_output_mat, img_size, np.zeros(img_bgr.shape, dtype=np.float32), cv2.WARP_INVERSE_MAP | cv2.INTER_LANCZOS4, cv2.BORDER_TRANSPARENT), 0, 1.0) ] elif self.color_transfer_mode == 'lct': if debug: debugs += [ np.clip( cv2.warpAffine( prd_face_bgr, face_output_mat, img_size, np.zeros(img_bgr.shape, dtype=np.float32), cv2.WARP_INVERSE_MAP | cv2.INTER_LANCZOS4, cv2.BORDER_TRANSPARENT), 0, 1.0) ] prd_face_bgr = imagelib.linear_color_transfer( prd_face_bgr, dst_face_bgr) prd_face_bgr = np.clip(prd_face_bgr, 0.0, 1.0) if debug: debugs += [ np.clip( cv2.warpAffine( prd_face_bgr, face_output_mat, img_size, np.zeros(img_bgr.shape, dtype=np.float32), cv2.WARP_INVERSE_MAP | cv2.INTER_LANCZOS4, cv2.BORDER_TRANSPARENT), 0, 1.0) ] if self.mode == 'hist-match-bw': prd_face_bgr = cv2.cvtColor(prd_face_bgr, cv2.COLOR_BGR2GRAY) prd_face_bgr = np.repeat(np.expand_dims(prd_face_bgr, -1), (3, ), -1) if self.mode == 'hist-match' or self.mode == 'hist-match-bw': if debug: debugs += [ cv2.warpAffine( prd_face_bgr, face_output_mat, img_size, np.zeros(img_bgr.shape, dtype=np.float32), cv2.WARP_INVERSE_MAP | cv2.INTER_LANCZOS4, cv2.BORDER_TRANSPARENT) ] hist_mask_a = np.ones(prd_face_bgr.shape[:2] + (1, ), dtype=np.float32) if self.masked_hist_match: hist_mask_a *= prd_face_mask_a white = (1.0 - hist_mask_a) * np.ones( prd_face_bgr.shape[:2] + (1, ), dtype=np.float32) hist_match_1 = prd_face_bgr * hist_mask_a + white hist_match_1[hist_match_1 > 1.0] = 1.0 hist_match_2 = dst_face_bgr * hist_mask_a + white hist_match_2[hist_match_1 > 1.0] = 1.0 prd_face_bgr = imagelib.color_hist_match( hist_match_1, hist_match_2, self.hist_match_threshold) #if self.masked_hist_match: # prd_face_bgr -= white if self.mode == 'hist-match-bw': prd_face_bgr = prd_face_bgr.astype(dtype=np.float32) out_img = cv2.warpAffine( prd_face_bgr, face_output_mat, img_size, out_img, cv2.WARP_INVERSE_MAP | cv2.INTER_LANCZOS4, cv2.BORDER_TRANSPARENT) out_img = np.clip(out_img, 0.0, 1.0) if debug: debugs += [out_img.copy()] if self.mode == 'overlay': pass if 'seamless' in self.mode: #mask used for cv2.seamlessClone img_face_seamless_mask_a = None img_face_mask_a = img_mask_blurry_aaa[..., 0:1] for i in range(1, 10): a = img_face_mask_a > i / 10.0 if len(np.argwhere(a)) == 0: continue img_face_seamless_mask_a = img_mask_blurry_aaa[ ..., 0:1].copy() img_face_seamless_mask_a[a] = 1.0 img_face_seamless_mask_a[ img_face_seamless_mask_a <= i / 10.0] = 0.0 break try: #calc same bounding rect and center point as in cv2.seamlessClone to prevent jittering l, t, w, h = cv2.boundingRect( (img_face_seamless_mask_a * 255).astype(np.uint8)) s_maskx, s_masky = int(l + w / 2), int(t + h / 2) out_img = cv2.seamlessClone( (out_img * 255).astype(np.uint8), (img_bgr * 255).astype(np.uint8), (img_face_seamless_mask_a * 255).astype(np.uint8), (s_maskx, s_masky), cv2.NORMAL_CLONE) out_img = out_img.astype(dtype=np.float32) / 255.0 except Exception as e: #seamlessClone may fail in some cases e_str = traceback.format_exc() if 'MemoryError' in e_str: raise Exception( "Seamless fail: " + e_str ) #reraise MemoryError in order to reprocess this data by other processes else: print("Seamless fail: " + e_str) if debug: debugs += [out_img.copy()] out_img = np.clip( img_bgr * (1 - img_mask_blurry_aaa) + (out_img * img_mask_blurry_aaa), 0, 1.0) if 'seamless' in self.mode and self.color_transfer_mode is not None: out_face_bgr = cv2.warpAffine(out_img, face_mat, (output_size, output_size)) if self.color_transfer_mode == 'rct': 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_LANCZOS4, cv2.BORDER_TRANSPARENT), 0, 1.0) ] new_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_blurry_aaa, target_mask=face_mask_blurry_aaa) new_out_face_bgr = np.clip( new_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_LANCZOS4, cv2.BORDER_TRANSPARENT), 0, 1.0) ] elif self.color_transfer_mode == 'lct': 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_LANCZOS4, 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) 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_LANCZOS4, 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_LANCZOS4, cv2.BORDER_TRANSPARENT) out_img = np.clip( img_bgr * (1 - img_mask_blurry_aaa) + (new_out * img_mask_blurry_aaa), 0, 1.0) if self.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, self.hist_match_threshold) 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) out_img = np.clip( img_bgr * (1 - img_mask_blurry_aaa) + (new_out * img_mask_blurry_aaa), 0, 1.0) if self.final_image_color_degrade_power != 0: if debug: debugs += [out_img.copy()] out_img_reduced = imagelib.reduce_colors(out_img, 256) if self.final_image_color_degrade_power == 100: out_img = out_img_reduced else: alpha = self.final_image_color_degrade_power / 100.0 out_img = (out_img * (1.0 - alpha) + out_img_reduced * alpha) if self.alpha: out_img = np.concatenate([ out_img, np.expand_dims(img_mask_blurry_aaa[:, :, 0], -1) ], -1) out_img = np.clip(out_img, 0.0, 1.0) if debug: debugs += [out_img.copy()] return debugs if debug else out_img
class Model(ModelBase): #override def onInitialize(self): exec(nnlib.import_all(), locals(), globals()) self.set_vram_batch_requirements({1.5: 4}) self.resolution = 256 self.face_type = FaceType.FULL self.fan_seg = FANSegmentator( self.resolution, FaceType.toString(self.face_type), load_weights=not self.is_first_run(), weights_file_root=self.get_model_root_path()) if self.is_training_mode: f = SampleProcessor.TypeFlags f_type = f.FACE_ALIGN_FULL #if self.face_type == FaceType.FULL else f.FACE_ALIGN_HALF self.set_training_data_generators([ SampleGeneratorFace( self.training_data_src_path, debug=self.is_debug(), batch_size=self.batch_size, sample_process_options=SampleProcessor.Options( random_flip=self.random_flip, normalize_tanh=True, scale_range=np.array([-0.05, 0.05]) + self.src_scale_mod / 100.0), output_sample_types=[ [f.TRANSFORMED | f_type | f.MODE_BGR, self.resolution], [ f.TRANSFORMED | f_type | f.MODE_M | f.FACE_MASK_FULL, self.resolution ] ]), SampleGeneratorFace( self.training_data_dst_path, debug=self.is_debug(), batch_size=self.batch_size, sample_process_options=SampleProcessor.Options( random_flip=self.random_flip, normalize_tanh=True, scale_range=np.array([-0.05, 0.05]) + self.src_scale_mod / 100.0), output_sample_types=[[ f.TRANSFORMED | f_type | f.MODE_BGR, self.resolution ]]) ]) #override def onSave(self): self.fan_seg.save_weights() #override def onTrainOneIter(self, generators_samples, generators_list): target_src, target_src_mask = generators_samples[0] loss = self.fan_seg.train_on_batch([target_src], [target_src_mask]) return (('loss', loss), ) #override def onGetPreview(self, sample): test_A = sample[0][0][0:4] #first 4 samples test_B = sample[1][0][0:4] #first 4 samples mAA = self.fan_seg.extract_from_bgr([test_A]) mBB = self.fan_seg.extract_from_bgr([test_B]) mAA = np.repeat(mAA, (3, ), -1) mBB = np.repeat(mBB, (3, ), -1) st = [] for i in range(0, len(test_A)): st.append( np.concatenate(( test_A[i, :, :, 0:3], mAA[i], test_A[i, :, :, 0:3] * mAA[i], ), axis=1)) st2 = [] for i in range(0, len(test_B)): st2.append( np.concatenate(( test_B[i, :, :, 0:3], mBB[i], test_B[i, :, :, 0:3] * mBB[i], ), axis=1)) return [ ('FANSegmentator', np.concatenate(st, axis=0)), ('never seen', np.concatenate(st2, axis=0)), ] def predictor_func(self, face): face_64_bgr = face[..., 0:3] face_64_mask = np.expand_dims(face[..., 3], -1) x, mx = self.src_view([np.expand_dims(face_64_bgr, 0)]) x, mx = x[0], mx[0] return np.concatenate((x, mx), -1) #override def get_converter(self): from converters import ConverterMasked return ConverterMasked(self.predictor_func, predictor_input_size=64, output_size=64, face_type=FaceType.HALF, base_erode_mask_modifier=100, base_blur_mask_modifier=100)