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fixed error "Failed to get convolution algorithm" on some systems

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fixed error "dll load failed" on some systems
Expanded eyebrows line of face masks. It does not affect mask of FAN-x converter mode.
This commit is contained in:
iperov
2019-08-11 11:17:22 +04:00
parent 582c974851
commit b72d5a3f9a
15 changed files with 367 additions and 222 deletions
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converters/ConverterAvatar.py
+18 -27
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@@ -20,8 +20,9 @@ class ConverterAvatar(Converter):
self.predictor_input_size = predictor_input_size
#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 ),
np.zeros ( (predictor_input_size,predictor_input_size,1), dtype=np.float32 ) )
predictor_func ( np.zeros ( (predictor_input_size,predictor_input_size,3), dtype=np.float32 ),
np.zeros ( (predictor_input_size,predictor_input_size,3), dtype=np.float32 ),
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)
@@ -33,38 +34,28 @@ class ConverterAvatar(Converter):
self.predictor_func_host.obj.process_messages()
#override
def cli_convert_face (self, img_bgr, img_face_landmarks, debug, avaperator_face_bgr=None, **kwargs):
def cli_convert_face (self, f0, f0_lmrk, f1, f1_lmrk, f2, f2_lmrk, debug, **kwargs):
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)
img_face_mask_aaa = np.repeat(img_face_mask_a, 3, -1)
debugs = []
inp_size = self.predictor_input_size
f0_mat = LandmarksProcessor.get_transform_mat (f0_lmrk, inp_size, face_type=FaceType.FULL_NO_ALIGN)
f1_mat = LandmarksProcessor.get_transform_mat (f1_lmrk, inp_size, face_type=FaceType.FULL_NO_ALIGN)
f2_mat = LandmarksProcessor.get_transform_mat (f2_lmrk, inp_size, face_type=FaceType.FULL_NO_ALIGN)
output_size = self.predictor_input_size
face_mat = LandmarksProcessor.get_transform_mat (img_face_landmarks, output_size, face_type=FaceType.FULL)
inp_f0 = cv2.warpAffine( f0, f0_mat, (inp_size, inp_size), flags=cv2.INTER_CUBIC )
inp_f1 = cv2.warpAffine( f1, f1_mat, (inp_size, inp_size), flags=cv2.INTER_CUBIC )
inp_f2 = cv2.warpAffine( f2, f2_mat, (inp_size, inp_size), flags=cv2.INTER_CUBIC )
dst_face_mask_a_0 = cv2.warpAffine( img_face_mask_a, face_mat, (output_size, output_size), flags=cv2.INTER_CUBIC )
prd_f = self.predictor_func ( inp_f0, inp_f1, inp_f2 )
predictor_input_dst_face_mask_a_0 = cv2.resize (dst_face_mask_a_0, (self.predictor_input_size,self.predictor_input_size), cv2.INTER_CUBIC )
prd_inp_dst_face_mask_a = predictor_input_dst_face_mask_a_0[...,np.newaxis]
out_img = np.clip(prd_f, 0.0, 1.0)
prd_inp_avaperator_face_bgr = cv2.resize (avaperator_face_bgr, (self.predictor_input_size,self.predictor_input_size), cv2.INTER_CUBIC )
prd_face_bgr = self.predictor_func ( prd_inp_avaperator_face_bgr, prd_inp_dst_face_mask_a )
out_img = img_bgr.copy()
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 = np.clip(out_img, 0.0, 1.0)
out_img = np.concatenate ( [cv2.resize ( inp_f1, (prd_f.shape[1], prd_f.shape[0]) ),
out_img], axis=1 )
if debug:
debugs += [out_img.copy()]
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()]
return debugs if debug else out_img