github/DeepFaceLab
1
0
Fork 0
mirror of https://github.com/iperov/DeepFaceLab.git synced 2025-07-06 21:12:07 -07:00
Code Issues Projects Releases Packages Wiki Activity Actions

fix

Browse source
This commit is contained in:
iperov 2019-03-18 10:58:03 +04:00
parent da7cf225db
commit bf831331e6
1 changed files with 66 additions and 59 deletions
Download patch file Download diff file Expand all files Collapse all files

13
mainscripts/Extractor.py
View file

@ -142,6 +142,14 @@ class ExtractSubprocessor(Subprocessor):
debug_output_file = str( Path(self.debug_dir) / (filename_path.stem+'.jpg') ) debug_output_file = str( Path(self.debug_dir) / (filename_path.stem+'.jpg') )
debug_image = image.copy() debug_image = image.copy()
if src_dflimg is not None and len(faces) != 1:
#if re-extracting from dflimg and more than 1 or zero faces detected - dont process and just copy it
print("src_dflimg is not None and len(faces) != 1", str(filename_path) )
output_file = str(self.output_path / filename_path.name)
if str(filename_path) != str(output_file):
shutil.copy ( str(filename_path), str(output_file) )
result.append (output_file)
else:
face_idx = 0 face_idx = 0
for face in faces: for face in faces:
rect = np.array(face[0]) rect = np.array(face[0])
@ -169,9 +177,6 @@ class ExtractSubprocessor(Subprocessor):
if self.debug_dir is not None: if self.debug_dir is not None:
LandmarksProcessor.draw_rect_landmarks (debug_image, rect, image_landmarks, self.image_size, self.face_type, transparent_mask=True) LandmarksProcessor.draw_rect_landmarks (debug_image, rect, image_landmarks, self.image_size, self.face_type, transparent_mask=True)
output_file = '{}_{}{}'.format(str(self.output_path / filename_path.stem), str(face_idx), '.jpg')
face_idx += 1
landmarks=face_image_landmarks.tolist() landmarks=face_image_landmarks.tolist()
source_filename = filename_path.name source_filename = filename_path.name
source_landmarks = image_landmarks.tolist() source_landmarks = image_landmarks.tolist()
@ -189,6 +194,7 @@ class ExtractSubprocessor(Subprocessor):
image_to_face_mat = mat image_to_face_mat = mat
source_landmarks = LandmarksProcessor.transform_points (landmarks, image_to_face_mat, True) source_landmarks = LandmarksProcessor.transform_points (landmarks, image_to_face_mat, True)
else: else:
output_file = '{}_{}{}'.format(str(self.output_path / filename_path.stem), str(face_idx), '.jpg')
cv2_imwrite(output_file, face_image, [int(cv2.IMWRITE_JPEG_QUALITY), 85] ) cv2_imwrite(output_file, face_image, [int(cv2.IMWRITE_JPEG_QUALITY), 85] )
DFLJPG.embed_data(output_file, face_type=FaceType.toString(self.face_type), DFLJPG.embed_data(output_file, face_type=FaceType.toString(self.face_type),
@ -200,6 +206,7 @@ class ExtractSubprocessor(Subprocessor):
) )
result.append (output_file) result.append (output_file)
face_idx += 1
if self.debug_dir is not None: if self.debug_dir is not None:
cv2_imwrite(debug_output_file, debug_image, [int(cv2.IMWRITE_JPEG_QUALITY), 50] ) cv2_imwrite(debug_output_file, debug_image, [int(cv2.IMWRITE_JPEG_QUALITY), 50] )