From 42f0e438f37baaf08ecf8457f254fde8fb3f443f Mon Sep 17 00:00:00 2001 From: iperov Date: Wed, 9 Jan 2019 13:35:10 +0400 Subject: [PATCH] added 'raw' mode to convertermasked --- models/ConverterMasked.py | 301 ++++++++++++++++++++------------------ 1 file changed, 158 insertions(+), 143 deletions(-) diff --git a/models/ConverterMasked.py b/models/ConverterMasked.py index 440fa02..a241048 100644 --- a/models/ConverterMasked.py +++ b/models/ConverterMasked.py @@ -24,31 +24,44 @@ class ConverterMasked(ConverterBase): self.face_type = face_type self.TFLabConverter = None - mode = input_int ("Choose mode: (1) overlay, (2) hist match, (3) hist match bw, (4) seamless (default), (5) seamless hist match : ", 4) + mode = input_int ("Choose mode: (1) overlay, (2) hist match, (3) hist match bw, (4) seamless (default), (5) seamless hist match, (6) raw : ", 4) self.mode = {1:'overlay', 2:'hist-match', 3:'hist-match-bw', 4:'seamless', - 5:'seamless-hist-match'}.get (mode, 'seamless') + 5:'seamless-hist-match', + 6:'raw'}.get (mode, 'seamless') - if self.mode == 'hist-match' or self.mode == 'hist-match-bw': - self.masked_hist_match = 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 ( input_int("Hist match threshold [0..255] (skip:255) : ", 255), 0, 255) + if self.mode == 'raw': + mode = input_int ("Choose raw mode: (1) rgb, (2) rgb+mask (default), (3) mask only : ", 2) + self.raw_mode = {1:'rgb', + 2:'rgb-mask', + 3:'mask-only'}.get (mode, 'rgb-mask') + if self.mode != 'raw': + if self.mode == 'hist-match' or self.mode == 'hist-match-bw': + self.masked_hist_match = 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 ( input_int("Hist match threshold [0..255] (skip:255) : ", 255), 0, 255) + self.use_predicted_mask = input_bool("Use predicted mask? (y/n skip:y) : ", True) - self.erode_mask_modifier = base_erode_mask_modifier + np.clip ( input_int ("Choose erode mask modifier [-200..200] (skip:0) : ", 0), -200, 200) - self.blur_mask_modifier = base_blur_mask_modifier + np.clip ( input_int ("Choose blur mask modifier [-200..200] (skip:0) : ", 0), -200, 200) - - self.seamless_erode_mask_modifier = 0 - if self.mode == 'seamless' or self.mode == 'seamless-hist-match': - self.seamless_erode_mask_modifier = np.clip ( input_int ("Choose seamless erode mask modifier [-100..100] (skip:0) : ", 0), -100, 100) - + + if self.mode != 'raw': + self.erode_mask_modifier = base_erode_mask_modifier + np.clip ( input_int ("Choose erode mask modifier [-200..200] (skip:0) : ", 0), -200, 200) + self.blur_mask_modifier = base_blur_mask_modifier + np.clip ( input_int ("Choose blur mask modifier [-200..200] (skip:0) : ", 0), -200, 200) + + self.seamless_erode_mask_modifier = 0 + if self.mode == 'seamless' or self.mode == 'seamless-hist-match': + self.seamless_erode_mask_modifier = np.clip ( input_int ("Choose seamless erode mask modifier [-100..100] (skip:0) : ", 0), -100, 100) + self.output_face_scale = np.clip ( 1.0 + input_int ("Choose output face scale modifier [-50..50] (skip:0) : ", 0)*0.01, 0.5, 1.5) - self.transfercolor = input_bool("Transfer color from dst face to converted final face? (y/n skip:n) : ", False) - self.final_image_color_degrade_power = np.clip ( input_int ("Degrade color power of final image [0..100] (skip:0) : ", 0), 0, 100) - self.alpha = input_bool("Export png with alpha channel? (y/n skip:n) : ", False) + + if self.mode != 'raw': + self.transfercolor = input_bool("Transfer color from dst face to converted final face? (y/n skip:n) : ", False) + self.final_image_color_degrade_power = np.clip ( input_int ("Degrade color power of final image [0..100] (skip:0) : ", 0), 0, 100) + self.alpha = input_bool("Export png with alpha channel? (y/n skip:n) : ", False) + print ("") #override @@ -107,135 +120,137 @@ class ConverterMasked(ConverterBase): maxregion = np.argwhere(img_face_mask_flatten_aaa==1.0) out_img = img_bgr.copy() - if maxregion.size != 0: - miny,minx = maxregion.min(axis=0)[:2] - maxy,maxx = maxregion.max(axis=0)[:2] - - if debug: - print ("maxregion.size: %d, minx:%d, maxx:%d miny:%d, maxy:%d" % (maxregion.size, minx, maxx, miny, maxy ) ) - - lenx = maxx - minx - leny = maxy - miny - if lenx >= 4 and leny >= 4: - masky = int(minx+(lenx//2)) - maskx = int(miny+(leny//2)) - lowest_len = min (lenx, leny) - - if debug: - print ("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: - print ("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: - print ("seamless_erode_size = %d" % (ero) ) - if ero > 0: - img_face_mask_flatten_aaa = cv2.erode(img_face_mask_flatten_aaa, cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(ero,ero)), iterations = 1 ) - elif ero < 0: - img_face_mask_flatten_aaa = cv2.dilate(img_face_mask_flatten_aaa, cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(-ero,-ero)), iterations = 1 ) - - - if self.blur_mask_modifier > 0: - blur = int( lowest_len * 0.10 * 0.01*self.blur_mask_modifier ) - if debug: - print ("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 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=prd_face_bgr.dtype) - - 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=prd_face_bgr.dtype) - 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=prd_face_bgr.dtype) - 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(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 ) - if debug: - debugs += [out_img.copy()] - debugs += [img_mask_blurry_aaa.copy()] - - if self.mode == 'overlay': - pass - - if self.mode == 'seamless' or self.mode == 'seamless-hist-match': - out_img = np.clip( img_bgr*(1-img_face_mask_aaa) + (out_img*img_face_mask_aaa) , 0, 1.0 ) - if debug: - debugs += [out_img.copy()] - - out_img = cv2.seamlessClone( (out_img*255).astype(np.uint8), (img_bgr*255).astype(np.uint8), (img_face_mask_flatten_aaa*255).astype(np.uint8), (masky,maskx) , cv2.NORMAL_CLONE ) - out_img = out_img.astype(np.float32) / 255.0 - - 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.transfercolor: - if self.TFLabConverter is None: - self.TFLabConverter = image_utils.TFLabConverter() - - img_lab_l, img_lab_a, img_lab_b = np.split ( self.TFLabConverter.bgr2lab (img_bgr), 3, axis=-1 ) - out_img_lab_l, out_img_lab_a, out_img_lab_b = np.split ( self.TFLabConverter.bgr2lab (out_img), 3, axis=-1 ) - - out_img = self.TFLabConverter.lab2bgr ( np.concatenate([out_img_lab_l, img_lab_a, img_lab_b], axis=-1) ) - - 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: - new_image = out_img.copy() - new_image = (new_image*255).astype(np.uint8) #convert image to int - b_channel, g_channel, r_channel = cv2.split(new_image) #splitting RGB - alpha_channel = img_mask_blurry_aaa.copy() #making copy of alpha channel - alpha_channel = (alpha_channel*255).astype(np.uint8) - alpha_channel, tmp2, tmp3 = cv2.split(alpha_channel) #splitting alpha to three channels, they all same in original alpha channel, we need just one - out_img = cv2.merge((b_channel,g_channel, r_channel, alpha_channel)) #mergin RGB with alpha - out_img = out_img.astype(np.float32) / 255.0 - + if self.mode == 'raw': + if self.raw_mode == 'rgb': + 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 + else: + if maxregion.size != 0: + miny,minx = maxregion.min(axis=0)[:2] + maxy,maxx = maxregion.max(axis=0)[:2] + + if debug: + print ("maxregion.size: %d, minx:%d, maxx:%d miny:%d, maxy:%d" % (maxregion.size, minx, maxx, miny, maxy ) ) + + lenx = maxx - minx + leny = maxy - miny + if lenx >= 4 and leny >= 4: + masky = int(minx+(lenx//2)) + maskx = int(miny+(leny//2)) + lowest_len = min (lenx, leny) + if debug: + print ("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: + print ("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: + print ("seamless_erode_size = %d" % (ero) ) + if ero > 0: + img_face_mask_flatten_aaa = cv2.erode(img_face_mask_flatten_aaa, cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(ero,ero)), iterations = 1 ) + elif ero < 0: + img_face_mask_flatten_aaa = cv2.dilate(img_face_mask_flatten_aaa, cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(-ero,-ero)), iterations = 1 ) + + + if self.blur_mask_modifier > 0: + blur = int( lowest_len * 0.10 * 0.01*self.blur_mask_modifier ) + if debug: + print ("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 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=prd_face_bgr.dtype) + + 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=prd_face_bgr.dtype) + 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=prd_face_bgr.dtype) + 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(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 ) + + if debug: + debugs += [out_img.copy()] + debugs += [img_mask_blurry_aaa.copy()] + + if self.mode == 'overlay': + pass + + if self.mode == 'seamless' or self.mode == 'seamless-hist-match': + out_img = np.clip( img_bgr*(1-img_face_mask_aaa) + (out_img*img_face_mask_aaa) , 0, 1.0 ) + if debug: + debugs += [out_img.copy()] + + out_img = cv2.seamlessClone( (out_img*255).astype(np.uint8), (img_bgr*255).astype(np.uint8), (img_face_mask_flatten_aaa*255).astype(np.uint8), (masky,maskx) , cv2.NORMAL_CLONE ) + out_img = out_img.astype(np.float32) / 255.0 + + 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.transfercolor: + if self.TFLabConverter is None: + self.TFLabConverter = image_utils.TFLabConverter() + + img_lab_l, img_lab_a, img_lab_b = np.split ( self.TFLabConverter.bgr2lab (img_bgr), 3, axis=-1 ) + out_img_lab_l, out_img_lab_a, out_img_lab_b = np.split ( self.TFLabConverter.bgr2lab (out_img), 3, axis=-1 ) + + out_img = self.TFLabConverter.lab2bgr ( np.concatenate([out_img_lab_l, img_lab_a, img_lab_b], axis=-1) ) + + 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 debug: debugs += [out_img.copy()]