diff --git a/merger/MergeMasked.py b/merger/MergeMasked.py index edc3de3..c5ca2d8 100644 --- a/merger/MergeMasked.py +++ b/merger/MergeMasked.py @@ -16,22 +16,30 @@ def MergeMaskedFace (predictor_func, predictor_input_shape, cfg, frame_info, img return img_bgr, img_face_mask_a out_img = img_bgr.copy() - out_merging_mask = None + out_merging_mask_a = None - output_size = predictor_input_shape[0] + mask_subres = 4 + input_size = predictor_input_shape[0] + mask_subres_size = input_size*4 + output_size = input_size if cfg.super_resolution_mode != 0: output_size *= 4 - face_mat = LandmarksProcessor.get_transform_mat (img_face_landmarks, output_size, face_type=cfg.face_type) + face_mat = LandmarksProcessor.get_transform_mat (img_face_landmarks, output_size, face_type=cfg.face_type) face_output_mat = LandmarksProcessor.get_transform_mat (img_face_landmarks, output_size, face_type=cfg.face_type, scale= 1.0 + 0.01*cfg.output_face_scale ) + if mask_subres_size == output_size: + face_mask_output_mat = face_output_mat + else: + face_mask_output_mat = LandmarksProcessor.get_transform_mat (img_face_landmarks, mask_subres_size, face_type=cfg.face_type, scale= 1.0 + 0.01*cfg.output_face_scale ) + dst_face_bgr = cv2.warpAffine( img_bgr , face_mat, (output_size, output_size), flags=cv2.INTER_CUBIC ) dst_face_bgr = np.clip(dst_face_bgr, 0, 1) dst_face_mask_a_0 = cv2.warpAffine( img_face_mask_a, face_mat, (output_size, output_size), flags=cv2.INTER_CUBIC ) dst_face_mask_a_0 = np.clip(dst_face_mask_a_0, 0, 1) - predictor_input_bgr = cv2.resize (dst_face_bgr, predictor_input_shape[0:2] ) + predictor_input_bgr = cv2.resize (dst_face_bgr, (input_size,input_size) ) predicted = predictor_func (predictor_input_bgr) if isinstance(predicted, tuple): @@ -42,7 +50,7 @@ def MergeMaskedFace (predictor_func, predictor_input_shape, cfg, frame_info, img else: #merger return bgr only, using dst mask prd_face_bgr = np.clip (predicted, 0, 1.0 ) - prd_face_mask_a_0 = cv2.resize (dst_face_mask_a_0, predictor_input_shape[0:2] ) + prd_face_mask_a_0 = cv2.resize (dst_face_mask_a_0, (input_size,input_size) ) predictor_masked = False if cfg.super_resolution_mode != 0: @@ -91,29 +99,65 @@ def MergeMaskedFace (predictor_func, predictor_input_shape, cfg, frame_info, img prd_face_mask_a_0 = prd_face_mask_a_0 * FAN_prd_face_mask_a_0 * FAN_dst_face_mask_a_0 elif cfg.mask_mode == 7: prd_face_mask_a_0 = prd_face_mask_a_0 * FAN_dst_face_mask_a_0 - #elif cfg.mask_mode == 8: #FANCHQ-dst - # prd_face_mask_a_0 = FANCHQ_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_CUBIC ) - 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 + # process mask in local predicted space + if 'raw' not in cfg.mode: + # resize to mask_subres_size + if prd_face_mask_a_0.shape[0] != mask_subres_size: + prd_face_mask_a_0 = cv2.resize (prd_face_mask_a_0, (mask_subres_size, mask_subres_size), cv2.INTER_CUBIC) + + # add zero pad + prd_face_mask_a_0 = np.pad (prd_face_mask_a_0, input_size) + + ero = cfg.erode_mask_modifier + blur = cfg.blur_mask_modifier + + if ero > 0: + prd_face_mask_a_0 = cv2.erode(prd_face_mask_a_0, cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(ero,ero)), iterations = 1 ) + elif ero < 0: + prd_face_mask_a_0 = cv2.dilate(prd_face_mask_a_0, cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(-ero,-ero)), iterations = 1 ) + + # clip eroded/dilated mask in actual predict area + # pad with half blur size in order to accuratelly fade to zero at the boundary + clip_size = input_size + blur // 2 + + prd_face_mask_a_0[:clip_size,:] = 0 + prd_face_mask_a_0[-clip_size:,:] = 0 + prd_face_mask_a_0[:,:clip_size] = 0 + prd_face_mask_a_0[:,-clip_size:] = 0 + + if blur > 0: + blur = blur + (1-blur % 2) + prd_face_mask_a_0 = cv2.GaussianBlur(prd_face_mask_a_0, (blur, blur) , 0) + + prd_face_mask_a_0 = prd_face_mask_a_0[input_size:-input_size,input_size:-input_size] + prd_face_mask_a_0 = np.clip(prd_face_mask_a_0, 0, 1) + + img_face_mask_a = cv2.warpAffine( prd_face_mask_a_0, face_mask_output_mat, img_size, np.zeros(img_bgr.shape[0:2], dtype=np.float32), flags=cv2.WARP_INVERSE_MAP | cv2.INTER_CUBIC )[...,None] + img_face_mask_a = np.clip (img_face_mask_a, 0.0, 1.0) + img_face_mask_a [ img_face_mask_a <= 0.1 ] = 0.0 #get rid of noise + + if prd_face_mask_a_0.shape[0] != output_size: + prd_face_mask_a_0 = cv2.resize (prd_face_mask_a_0, (output_size,output_size), cv2.INTER_CUBIC) + + prd_face_mask_a = prd_face_mask_a_0[...,None] + prd_face_mask_area_a = prd_face_mask_a.copy() + prd_face_mask_area_a[prd_face_mask_area_a>0] = 1.0 if 'raw' in cfg.mode: if cfg.mode == 'raw-rgb': out_img = cv2.warpAffine( prd_face_bgr, face_output_mat, img_size, out_img, cv2.WARP_INVERSE_MAP | cv2.INTER_CUBIC, cv2.BORDER_TRANSPARENT ) - out_merging_mask = img_face_mask_aaa - + out_merging_mask_a = img_face_mask_a + out_img = np.clip (out_img, 0.0, 1.0 ) 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 ) + maxregion = np.argwhere( img_face_mask_a > i / 10.0 ) if maxregion.size != 0: miny,minx = maxregion.min(axis=0)[:2] maxy,maxx = maxregion.max(axis=0)[:2] @@ -123,67 +167,34 @@ def MergeMaskedFace (predictor_func, predictor_input_shape, cfg, frame_info, img ar += [ [ lenx, leny] ] if len(ar) > 0: - lenx, leny = np.mean ( ar, axis=0 ) - lowest_len = min (lenx, leny) - - if cfg.erode_mask_modifier != 0: - ero = int( lowest_len * ( 0.126 - lowest_len * 0.00004551365 ) * 0.01*cfg.erode_mask_modifier ) - 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 ) - - if cfg.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] * cfg.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[-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_CUBIC ) - img_prd_hborder_rect_mask_a = np.expand_dims (img_prd_hborder_rect_mask_a, -1) - img_face_mask_aaa *= img_prd_hborder_rect_mask_a - img_face_mask_aaa = np.clip( img_face_mask_aaa, 0, 1.0 ) - - if cfg.blur_mask_modifier > 0: - blur = int( lowest_len * 0.10 * 0.01*cfg.blur_mask_modifier ) - if blur > 0: - img_face_mask_aaa = cv2.blur(img_face_mask_aaa, (blur, blur) ) - - img_face_mask_aaa = np.clip( img_face_mask_aaa, 0, 1.0 ) if 'seamless' not in cfg.mode and cfg.color_transfer_mode != 0: if cfg.color_transfer_mode == 1: #rct prd_face_bgr = imagelib.reinhard_color_transfer ( np.clip( prd_face_bgr*255, 0, 255).astype(np.uint8), np.clip( dst_face_bgr*255, 0, 255).astype(np.uint8), - source_mask=prd_face_mask_a, target_mask=prd_face_mask_a) + source_mask=prd_face_mask_area_a, target_mask=prd_face_mask_area_a) prd_face_bgr = np.clip( prd_face_bgr.astype(np.float32) / 255.0, 0.0, 1.0) elif cfg.color_transfer_mode == 2: #lct prd_face_bgr = imagelib.linear_color_transfer (prd_face_bgr, dst_face_bgr) elif cfg.color_transfer_mode == 3: #mkl prd_face_bgr = imagelib.color_transfer_mkl (prd_face_bgr, dst_face_bgr) elif cfg.color_transfer_mode == 4: #mkl-m - prd_face_bgr = imagelib.color_transfer_mkl (prd_face_bgr*prd_face_mask_a, dst_face_bgr*prd_face_mask_a) + prd_face_bgr = imagelib.color_transfer_mkl (prd_face_bgr*prd_face_mask_area_a, dst_face_bgr*prd_face_mask_area_a) elif cfg.color_transfer_mode == 5: #idt prd_face_bgr = imagelib.color_transfer_idt (prd_face_bgr, dst_face_bgr) elif cfg.color_transfer_mode == 6: #idt-m - prd_face_bgr = imagelib.color_transfer_idt (prd_face_bgr*prd_face_mask_a, dst_face_bgr*prd_face_mask_a) + prd_face_bgr = imagelib.color_transfer_idt (prd_face_bgr*prd_face_mask_area_a, dst_face_bgr*prd_face_mask_area_a) elif cfg.color_transfer_mode == 7: #sot-m - prd_face_bgr = imagelib.color_transfer_sot (prd_face_bgr*prd_face_mask_a, dst_face_bgr*prd_face_mask_a) + prd_face_bgr = imagelib.color_transfer_sot (prd_face_bgr*prd_face_mask_area_a, dst_face_bgr*prd_face_mask_area_a) prd_face_bgr = np.clip (prd_face_bgr, 0.0, 1.0) elif cfg.color_transfer_mode == 8: #mix-m - prd_face_bgr = imagelib.color_transfer_mix (prd_face_bgr*prd_face_mask_a, dst_face_bgr*prd_face_mask_a) + prd_face_bgr = imagelib.color_transfer_mix (prd_face_bgr*prd_face_mask_area_a, dst_face_bgr*prd_face_mask_area_a) - if cfg.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 cfg.mode == 'hist-match' or cfg.mode == 'hist-match-bw': + if cfg.mode == 'hist-match': hist_mask_a = np.ones ( prd_face_bgr.shape[:2] + (1,) , dtype=np.float32) if cfg.masked_hist_match: - hist_mask_a *= prd_face_mask_a + hist_mask_a *= prd_face_mask_area_a white = (1.0-hist_mask_a)* np.ones ( prd_face_bgr.shape[:2] + (1,) , dtype=np.float32) @@ -195,13 +206,8 @@ def MergeMaskedFace (predictor_func, predictor_input_shape, cfg, frame_info, img prd_face_bgr = imagelib.color_hist_match(hist_match_1, hist_match_2, cfg.hist_match_threshold ).astype(dtype=np.float32) - if cfg.mode == 'hist-match-bw': - prd_face_bgr = prd_face_bgr.astype(dtype=np.float32) - if 'seamless' in cfg.mode: #mask used for cv2.seamlessClone - img_face_mask_a = img_face_mask_aaa[...,0:1] - img_face_seamless_mask_a = None for i in range(1,10): a = img_face_mask_a > i / 10.0 @@ -233,33 +239,33 @@ def MergeMaskedFace (predictor_func, predictor_input_shape, cfg, frame_info, img print ("Seamless fail: " + e_str) - out_img = img_bgr*(1-img_face_mask_aaa) + (out_img*img_face_mask_aaa) + out_img = img_bgr*(1-img_face_mask_a) + (out_img*img_face_mask_a) out_face_bgr = cv2.warpAffine( out_img, face_mat, (output_size, output_size) ) if 'seamless' in cfg.mode and cfg.color_transfer_mode != 0: if cfg.color_transfer_mode == 1: - face_mask_aaa = cv2.warpAffine( img_face_mask_aaa, face_mat, (output_size, output_size) ) + face_mask_a = cv2.warpAffine( img_face_mask_a, face_mat, (output_size, output_size) )[...,None] out_face_bgr = imagelib.reinhard_color_transfer ( (out_face_bgr*255).astype(np.uint8), (dst_face_bgr*255).astype(np.uint8), - source_mask=face_mask_aaa, target_mask=face_mask_aaa) + source_mask=face_mask_a, target_mask=face_mask_a) out_face_bgr = np.clip( out_face_bgr.astype(np.float32) / 255.0, 0.0, 1.0) elif cfg.color_transfer_mode == 2: #lct out_face_bgr = imagelib.linear_color_transfer (out_face_bgr, dst_face_bgr) elif cfg.color_transfer_mode == 3: #mkl out_face_bgr = imagelib.color_transfer_mkl (out_face_bgr, dst_face_bgr) elif cfg.color_transfer_mode == 4: #mkl-m - out_face_bgr = imagelib.color_transfer_mkl (out_face_bgr*prd_face_mask_a, dst_face_bgr*prd_face_mask_a) + out_face_bgr = imagelib.color_transfer_mkl (out_face_bgr*prd_face_mask_area_a, dst_face_bgr*prd_face_mask_area_a) elif cfg.color_transfer_mode == 5: #idt out_face_bgr = imagelib.color_transfer_idt (out_face_bgr, dst_face_bgr) elif cfg.color_transfer_mode == 6: #idt-m - out_face_bgr = imagelib.color_transfer_idt (out_face_bgr*prd_face_mask_a, dst_face_bgr*prd_face_mask_a) + out_face_bgr = imagelib.color_transfer_idt (out_face_bgr*prd_face_mask_area_a, dst_face_bgr*prd_face_mask_area_a) elif cfg.color_transfer_mode == 7: #sot-m - out_face_bgr = imagelib.color_transfer_sot (out_face_bgr*prd_face_mask_a, dst_face_bgr*prd_face_mask_a) + out_face_bgr = imagelib.color_transfer_sot (out_face_bgr*prd_face_mask_area_a, dst_face_bgr*prd_face_mask_area_a) out_face_bgr = np.clip (out_face_bgr, 0.0, 1.0) elif cfg.color_transfer_mode == 8: #mix-m - out_face_bgr = imagelib.color_transfer_mix (out_face_bgr*prd_face_mask_a, dst_face_bgr*prd_face_mask_a) + out_face_bgr = imagelib.color_transfer_mix (out_face_bgr*prd_face_mask_area_a, dst_face_bgr*prd_face_mask_area_a) if cfg.mode == 'seamless-hist-match': out_face_bgr = imagelib.color_hist_match(out_face_bgr, dst_face_bgr, cfg.hist_match_threshold) @@ -294,7 +300,7 @@ def MergeMaskedFace (predictor_func, predictor_input_shape, cfg, frame_info, img img_bgr = cv2.resize (img_bgr_downscaled, img_size, cv2.INTER_CUBIC) 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 ) + out_img = np.clip( img_bgr*(1-img_face_mask_a) + (new_out*img_face_mask_a) , 0, 1.0 ) if cfg.color_degrade_power != 0: out_img_reduced = imagelib.reduce_colors(out_img, 256) @@ -304,9 +310,9 @@ def MergeMaskedFace (predictor_func, predictor_input_shape, cfg, frame_info, img alpha = cfg.color_degrade_power / 100.0 out_img = (out_img*(1.0-alpha) + out_img_reduced*alpha) - out_merging_mask = img_face_mask_aaa + out_merging_mask_a = img_face_mask_a - return out_img, out_merging_mask[...,0:1] + return out_img, out_merging_mask_a def MergeMasked (predictor_func, predictor_input_shape, cfg, frame_info): diff --git a/merger/MergerConfig.py b/merger/MergerConfig.py index cff0439..13b9a77 100644 --- a/merger/MergerConfig.py +++ b/merger/MergerConfig.py @@ -133,8 +133,8 @@ class MergerConfigMasked(MergerConfig): masked_hist_match=True, hist_match_threshold = 238, mask_mode = 1, - erode_mask_modifier = 50, - blur_mask_modifier = 50, + erode_mask_modifier = 100, + blur_mask_modifier = 200, motion_blur_power = 0, output_face_scale = 0, color_transfer_mode = ctm_str_dict['rct'], @@ -177,11 +177,11 @@ class MergerConfigMasked(MergerConfig): self.mode = mode_dict.get (mode, self.default_mode) def toggle_masked_hist_match(self): - if self.mode == 'hist-match' or self.mode == 'hist-match-bw': + if self.mode == 'hist-match': self.masked_hist_match = not self.masked_hist_match def add_hist_match_threshold(self, diff): - if self.mode == 'hist-match' or self.mode == 'hist-match-bw' or self.mode == 'seamless-hist-match': + if self.mode == 'hist-match' or self.mode == 'seamless-hist-match': self.hist_match_threshold = np.clip ( self.hist_match_threshold+diff , 0, 255) def toggle_mask_mode(self): @@ -195,7 +195,7 @@ class MergerConfigMasked(MergerConfig): self.erode_mask_modifier = np.clip ( self.erode_mask_modifier+diff , -400, 400) def add_blur_mask_modifier(self, diff): - self.blur_mask_modifier = np.clip ( self.blur_mask_modifier+diff , -400, 400) + self.blur_mask_modifier = np.clip ( self.blur_mask_modifier+diff , 0, 400) def add_motion_blur_power(self, diff): self.motion_blur_power = np.clip ( self.motion_blur_power+diff, 0, 100) @@ -225,10 +225,10 @@ class MergerConfigMasked(MergerConfig): self.mode = mode_dict.get (mode, self.default_mode ) if 'raw' not in self.mode: - if self.mode == 'hist-match' or self.mode == 'hist-match-bw': + if self.mode == 'hist-match': self.masked_hist_match = io.input_bool("Masked hist match?", True) - if self.mode == 'hist-match' or self.mode == 'hist-match-bw' or self.mode == 'seamless-hist-match': + if self.mode == 'hist-match' or self.mode == 'seamless-hist-match': self.hist_match_threshold = np.clip ( io.input_int("Hist match threshold", 255, add_info="0..255"), 0, 255) if self.face_type == FaceType.FULL: @@ -247,7 +247,7 @@ class MergerConfigMasked(MergerConfig): if 'raw' not in self.mode: self.erode_mask_modifier = np.clip ( io.input_int ("Choose erode mask modifier", 0, add_info="-400..400"), -400, 400) - self.blur_mask_modifier = np.clip ( io.input_int ("Choose blur mask modifier", 0, add_info="-400..400"), -400, 400) + self.blur_mask_modifier = np.clip ( io.input_int ("Choose blur mask modifier", 0, add_info="0..400"), 0, 400) self.motion_blur_power = np.clip ( io.input_int ("Choose motion blur power", 0, add_info="0..100"), 0, 100) self.output_face_scale = np.clip (io.input_int ("Choose output face scale modifier", 0, add_info="-50..50" ), -50, 50) @@ -291,10 +291,10 @@ class MergerConfigMasked(MergerConfig): f"""Mode: {self.mode}\n""" ) - if self.mode == 'hist-match' or self.mode == 'hist-match-bw': + if self.mode == 'hist-match': r += f"""masked_hist_match: {self.masked_hist_match}\n""" - if self.mode == 'hist-match' or self.mode == 'hist-match-bw' or self.mode == 'seamless-hist-match': + if self.mode == 'hist-match' or self.mode == 'seamless-hist-match': r += f"""hist_match_threshold: {self.hist_match_threshold}\n""" if self.face_type == FaceType.FULL: