diff --git a/facelib/LandmarksProcessor.py b/facelib/LandmarksProcessor.py index 4ffa972..1c813c0 100644 --- a/facelib/LandmarksProcessor.py +++ b/facelib/LandmarksProcessor.py @@ -35,8 +35,8 @@ landmarks_68_pt = { "mouth": (48,68), "left_eye": (42, 48), "nose": (27, 36), # missed one point "jaw": (0, 17) } - -def get_transform_mat (image_landmarks, output_size, face_type): + +def get_transform_mat (image_landmarks, output_size, face_type, scale=1.0): if not isinstance(image_landmarks, np.ndarray): image_landmarks = np.array (image_landmarks) @@ -63,13 +63,15 @@ def get_transform_mat (image_landmarks, output_size, face_type): padding = (output_size / 64) * 24 else: raise ValueError ('wrong face_type') - + mat = umeyama(image_landmarks[17:], landmarks_2D, True)[0:2] mat = mat * (output_size - 2 * padding) - mat[:,2] += padding - + mat[:,2] += padding + mat *= (1 / scale) + mat[:,2] += -output_size*( ( (1 / scale) - 1.0 ) / 2 ) + return mat - + def transform_points(points, mat, invert=False): if invert: mat = cv2.invertAffineTransform (mat) diff --git a/main.py b/main.py index 7ca8fd7..115b865 100644 --- a/main.py +++ b/main.py @@ -137,6 +137,11 @@ if __name__ == "__main__": except: arguments.blur_mask_modifier = 0 + try: + arguments.output_face_scale_modifier = int ( input ("Choose output face scale modifier [-50..50] (default 0) : ") ) + except: + arguments.output_face_scale_modifier = 0 + try: arguments.alpha = bool ( {"1":True,"0":False}[input("Export png with alpha channel? [0..1] (default 0) : ").lower()] ) except: @@ -149,6 +154,7 @@ if __name__ == "__main__": arguments.erode_mask_modifier = np.clip ( int(arguments.erode_mask_modifier), -100, 100) arguments.blur_mask_modifier = np.clip ( int(arguments.blur_mask_modifier), -100, 200) + arguments.output_face_scale_modifier = np.clip ( int(arguments.output_face_scale_modifier), -50, 50) from mainscripts import Converter Converter.main ( @@ -162,6 +168,7 @@ if __name__ == "__main__": masked_hist_match = arguments.masked_hist_match, erode_mask_modifier = arguments.erode_mask_modifier, blur_mask_modifier = arguments.blur_mask_modifier, + output_face_scale_modifier = arguments.output_face_scale_modifier, force_best_gpu_idx = arguments.force_best_gpu_idx, alpha = arguments.alpha, transfercolor = arguments.transfercolor, @@ -178,6 +185,7 @@ if __name__ == "__main__": convert_parser.add_argument('--masked-hist-match', type=str2bool, nargs='?', const=True, default=None, help="True or False. Excludes background for hist match. Default - model decide.") convert_parser.add_argument('--erode-mask-modifier', type=int, dest="erode_mask_modifier", default=0, help="Automatic erode mask modifier. Valid range [-100..100].") convert_parser.add_argument('--blur-mask-modifier', type=int, dest="blur_mask_modifier", default=0, help="Automatic blur mask modifier. Valid range [-100..200].") + convert_parser.add_argument('--output-face-scale-modifier', type=int, dest="output_face_scale_modifier", default=0, help="Output face scale modifier. Valid range [-50..50].") convert_parser.add_argument('--debug', action="store_true", dest="debug", default=False, help="Debug converter.") convert_parser.add_argument('--alpha', action="store_true", dest="alpha", default=False, help="alpha channel.") convert_parser.add_argument('--transfercolor', action="store_true", dest="transfercolor", default=False, help="transfer color from dst to merged.") diff --git a/models/ConverterMasked.py b/models/ConverterMasked.py index 92e5296..f975359 100644 --- a/models/ConverterMasked.py +++ b/models/ConverterMasked.py @@ -5,12 +5,7 @@ import cv2 import numpy as np from utils import image_utils -''' -predictor: - input: [predictor_input_size, predictor_input_size, BGRA] - output: [predictor_input_size, predictor_input_size, BGRA] -''' - + class ConverterMasked(ConverterBase): #override @@ -24,9 +19,10 @@ class ConverterMasked(ConverterBase): masked_hist_match = False, mode='seamless', erode_mask_modifier=0, - blur_mask_modifier=0, + blur_mask_modifier=0, + output_face_scale_modifier=0.0, alpha=False, - transfercolor=False, + transfercolor=False, **in_options): super().__init__(predictor) @@ -41,6 +37,7 @@ class ConverterMasked(ConverterBase): self.mode = mode self.erode_mask_modifier = erode_mask_modifier self.blur_mask_modifier = blur_mask_modifier + self.output_face_scale = np.clip(1.0 + output_face_scale_modifier*0.01, 0.5, 1.0) self.alpha = alpha self.transfercolor = transfercolor @@ -68,6 +65,8 @@ class ConverterMasked(ConverterBase): img_face_mask_a = LandmarksProcessor.get_image_hull_mask (img_bgr, 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 ) @@ -84,7 +83,7 @@ class ConverterMasked(ConverterBase): 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_prd_face_mask_aaa = cv2.warpAffine( prd_face_mask_aaa, face_mat, img_size, np.zeros(img_bgr.shape, dtype=float), flags=cv2.WARP_INVERSE_MAP | cv2.INTER_LANCZOS4 ) + img_prd_face_mask_aaa = cv2.warpAffine( prd_face_mask_aaa, face_output_mat, img_size, np.zeros(img_bgr.shape, dtype=float), flags=cv2.WARP_INVERSE_MAP | cv2.INTER_LANCZOS4 ) img_prd_face_mask_aaa = np.clip (img_prd_face_mask_aaa, 0.0, 1.0) img_face_mask_aaa = img_prd_face_mask_aaa @@ -146,7 +145,7 @@ class ConverterMasked(ConverterBase): if self.mode == 'hist-match' or self.mode == 'hist-match-bw': if debug: - debugs += [ cv2.warpAffine( prd_face_bgr, face_mat, img_size, np.zeros(img_bgr.shape, dtype=np.float32), cv2.WARP_INVERSE_MAP | cv2.INTER_LANCZOS4, cv2.BORDER_TRANSPARENT ) ] + 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) @@ -159,8 +158,9 @@ class ConverterMasked(ConverterBase): if self.mode == 'hist-match-bw': prd_face_bgr = prd_face_bgr.astype(np.float32) + - out_img = cv2.warpAffine( prd_face_bgr, face_mat, img_size, out_img, cv2.WARP_INVERSE_MAP | cv2.INTER_LANCZOS4, cv2.BORDER_TRANSPARENT ) + 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()] @@ -177,7 +177,7 @@ class ConverterMasked(ConverterBase): debugs += [out_img.copy()] if self.clip_border_mask_per > 0: - img_prd_border_rect_mask_a = cv2.warpAffine( prd_border_rect_mask_a, face_mat, img_size, np.zeros(img_bgr.shape, dtype=np.float32), cv2.WARP_INVERSE_MAP | cv2.INTER_LANCZOS4, cv2.BORDER_TRANSPARENT ) + img_prd_border_rect_mask_a = cv2.warpAffine( prd_border_rect_mask_a, face_output_mat, img_size, np.zeros(img_bgr.shape, dtype=np.float32), cv2.WARP_INVERSE_MAP | cv2.INTER_LANCZOS4, cv2.BORDER_TRANSPARENT ) img_prd_border_rect_mask_a = np.expand_dims (img_prd_border_rect_mask_a, -1) out_img = out_img * img_prd_border_rect_mask_a + img_bgr * (1.0 - img_prd_border_rect_mask_a) @@ -186,7 +186,7 @@ class ConverterMasked(ConverterBase): 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) ) + 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 ) 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 )