mirror of
https://github.com/iperov/DeepFaceLab.git
synced 2025-08-14 02:37:00 -07:00
fixed error "Failed to get convolution algorithm" on some systems
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
parent
582c974851
commit
b72d5a3f9a
15 changed files with 367 additions and 222 deletions
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@ -28,9 +28,9 @@ class ConvertSubprocessor(Subprocessor):
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self.device_idx = client_dict['device_idx']
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self.device_name = client_dict['device_name']
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self.converter = client_dict['converter']
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self.input_data = client_dict['input_data']
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self.output_path = Path(client_dict['output_dir']) if 'output_dir' in client_dict.keys() else None
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self.alignments = client_dict['alignments']
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self.avatar_image_paths = client_dict['avatar_image_paths']
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self.debug = client_dict['debug']
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#transfer and set stdin in order to work code.interact in debug subprocess
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@ -50,65 +50,30 @@ class ConvertSubprocessor(Subprocessor):
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#override
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def process_data(self, data):
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idx, filename = data
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filename_path = Path(filename)
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files_processed = 1
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faces_processed = 0
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idx, = data
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filename = self.input_data[idx][0]
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filename_path = Path(filename)
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output_filename_path = self.output_path / (filename_path.stem + '.png')
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image = None
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if self.converter.type == Converter.TYPE_FACE:
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if filename_path.stem not in self.alignments.keys():
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if not self.debug:
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self.log_info ( 'no faces found for %s, copying without faces' % (filename_path.name) )
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if (self.converter.type == Converter.TYPE_FACE or self.converter.type == Converter.TYPE_FACE_AVATAR ) \
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and filename_path.stem not in self.alignments.keys():
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if not self.debug:
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self.log_info ( 'no faces found for %s, copying without faces' % (filename_path.name) )
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if filename_path.suffix == '.png':
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shutil.copy ( str(filename_path), str(output_filename_path) )
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else:
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image = cv2_imread(str(filename_path))
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cv2_imwrite ( str(output_filename_path), image )
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else:
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image = (cv2_imread(str(filename_path)) / 255.0).astype(np.float32)
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image = normalize_channels (image, 3)
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if filename_path.suffix == '.png':
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shutil.copy ( str(filename_path), str(output_filename_path) )
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else:
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image = cv2_imread(str(filename_path))
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cv2_imwrite ( str(output_filename_path), image )
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else:
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image = (cv2_imread(str(filename_path)) / 255.0).astype(np.float32)
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image = normalize_channels (image, 3)
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if self.converter.type == Converter.TYPE_IMAGE:
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image = self.converter.cli_convert_image(image, None, self.debug)
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if self.debug:
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return (1, image)
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faces_processed = 1
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elif self.converter.type == Converter.TYPE_IMAGE_WITH_LANDMARKS:
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#currently unused
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if filename_path.suffix == '.png':
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dflimg = DFLPNG.load( str(filename_path) )
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elif filename_path.suffix == '.jpg':
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dflimg = DFLJPG.load ( str(filename_path) )
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else:
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dflimg = None
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if dflimg is not None:
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image_landmarks = dflimg.get_landmarks()
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image = self.converter.convert_image(image, image_landmarks, self.debug)
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if self.debug:
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raise NotImplementedError
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#for img in image:
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# io.show_image ('Debug convert', img )
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# cv2.waitKey(0)
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faces_processed = 1
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else:
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self.log_err ("%s is not a dfl image file" % (filename_path.name) )
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elif self.converter.type == Converter.TYPE_FACE or self.converter.type == Converter.TYPE_FACE_AVATAR:
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ava_face = None
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if self.converter.type == Converter.TYPE_FACE_AVATAR:
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ava_filename_path = self.avatar_image_paths[idx]
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ava_face = (cv2_imread(str(ava_filename_path)) / 255.0).astype(np.float32)
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ava_face = normalize_channels (ava_face, 3)
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faces = self.alignments[filename_path.stem]
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if self.debug:
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@ -120,9 +85,9 @@ class ConvertSubprocessor(Subprocessor):
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self.log_info ( '\nConverting face_num [%d] in file [%s]' % (face_num, filename_path) )
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if self.debug:
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debug_images += self.converter.cli_convert_face(image, image_landmarks, self.debug, avaperator_face_bgr=ava_face)
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debug_images += self.converter.cli_convert_face(image, image_landmarks, self.debug)
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else:
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image = self.converter.cli_convert_face(image, image_landmarks, self.debug, avaperator_face_bgr=ava_face)
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image = self.converter.cli_convert_face(image, image_landmarks, self.debug)
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except Exception as e:
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e_str = traceback.format_exc()
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@ -135,29 +100,60 @@ class ConvertSubprocessor(Subprocessor):
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return (1, debug_images)
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faces_processed = len(faces)
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elif self.converter.type == Converter.TYPE_IMAGE:
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image = (cv2_imread(str(filename_path)) / 255.0).astype(np.float32)
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image = normalize_channels (image, 3)
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image = self.converter.cli_convert_image(image, None, self.debug)
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if not self.debug:
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cv2_imwrite (str(output_filename_path), (image*255).astype(np.uint8) )
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if self.debug:
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return (1, image)
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faces_processed = 1
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elif self.converter.type == Converter.TYPE_FACE_AVATAR:
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max_idx = len(self.input_data)-1
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i0 = max (idx-1, 0)
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i1 = idx
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i2 = min (max_idx, idx+1)
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f0 = (cv2_imread( self.input_data[i0][0] ) / 255.0).astype(np.float32)
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f0_lmrk = self.input_data[i0][1]
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f1 = (cv2_imread( self.input_data[i1][0] ) / 255.0).astype(np.float32)
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f1_lmrk = self.input_data[i1][1]
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f2 = (cv2_imread( self.input_data[i2][0] ) / 255.0).astype(np.float32)
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f2_lmrk = self.input_data[i2][1]
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f0, f1, f2 = [ normalize_channels (f, 3) for f in [f0,f1,f2] ]
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image = self.converter.cli_convert_face(f0, f0_lmrk, f1, f1_lmrk, f2, f2_lmrk, self.debug)
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output_filename_path = self.output_path / self.input_data[idx][2]
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if self.debug:
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return (1, image)
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faces_processed = 1
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if image is not None and not self.debug:
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cv2_imwrite (str(output_filename_path), (image*255).astype(np.uint8) )
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return (0, files_processed, faces_processed)
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#overridable
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def get_data_name (self, data):
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#return string identificator of your data
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idx, filename = data
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return filename
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idx, = data
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return self.input_data[idx][0]
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#override
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def __init__(self, converter, input_path_image_paths, output_path, alignments, avatar_image_paths=None, debug = False):
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def __init__(self, converter, input_data, output_path, alignments, debug = False):
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super().__init__('Converter', ConvertSubprocessor.Cli, 86400 if debug == True else 60)
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self.converter = converter
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self.input_data = self.input_path_image_paths = input_path_image_paths
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self.input_data = input_data
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self.input_data_idxs = [ *range(len(self.input_data)) ]
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self.output_path = output_path
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self.alignments = alignments
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self.avatar_image_paths = avatar_image_paths
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self.debug = debug
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self.files_processed = 0
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@ -171,9 +167,9 @@ class ConvertSubprocessor(Subprocessor):
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yield 'CPU%d' % (i), {}, {'device_idx': i,
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'device_name': 'CPU%d' % (i),
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'converter' : self.converter,
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'input_data' : self.input_data,
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'output_dir' : str(self.output_path),
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'alignments' : self.alignments,
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'avatar_image_paths' : self.avatar_image_paths,
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'debug': self.debug,
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'stdin_fd': sys.stdin.fileno() if self.debug else None
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}
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@ -196,12 +192,12 @@ class ConvertSubprocessor(Subprocessor):
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def get_data(self, host_dict):
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if len (self.input_data_idxs) > 0:
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idx = self.input_data_idxs.pop(0)
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return (idx, self.input_data[idx])
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return (idx, )
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return None
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#override
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def on_data_return (self, host_dict, data):
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idx, filename = data
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idx, = data
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self.input_data_idxs.insert(0, idx)
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#override
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@ -253,9 +249,9 @@ def main (args, device_args):
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converter = model.get_converter()
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input_path_image_paths = Path_utils.get_image_paths(input_path)
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alignments = None
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avatar_image_paths = None
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if converter.type == Converter.TYPE_FACE or converter.type == Converter.TYPE_FACE_AVATAR:
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if converter.type == Converter.TYPE_FACE:
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if aligned_dir is None:
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io.log_err('Aligned directory not found. Please ensure it exists.')
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return
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@ -287,21 +283,15 @@ def main (args, device_args):
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alignments[ source_filename_stem ] = []
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alignments[ source_filename_stem ].append (dflimg.get_source_landmarks())
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if converter.type == Converter.TYPE_FACE_AVATAR:
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if avaperator_aligned_dir is None:
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io.log_err('Avatar operator aligned directory not found. Please ensure it exists.')
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return
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#avatar_alignments += [ ( str(filepath), dflimg.get_source_landmarks(), dflimg.get_source_filename() ) ]
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input_data = [ (p,) for p in input_path_image_paths ]
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elif converter.type == Converter.TYPE_FACE_AVATAR:
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input_data = []
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for filepath in io.progress_bar_generator(input_path_image_paths, "Collecting info"):
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filepath = Path(filepath)
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avaperator_aligned_path = Path(avaperator_aligned_dir)
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if not avaperator_aligned_path.exists():
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io.log_err('Avatar operator aligned directory not found. Please ensure it exists.')
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return
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avatar_image_paths = []
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for filename in io.progress_bar_generator( Path_utils.get_image_paths(avaperator_aligned_path) , "Sorting avaperator faces"):
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filepath = Path(filename)
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if filepath.suffix == '.png':
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dflimg = DFLPNG.load( str(filepath) )
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elif filepath.suffix == '.jpg':
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@ -310,22 +300,19 @@ def main (args, device_args):
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dflimg = None
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if dflimg is None:
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io.log_err ("Fatal error: %s is not a dfl image file" % (filepath.name) )
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return
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avatar_image_paths += [ (filename, dflimg.get_source_filename() ) ]
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avatar_image_paths = [ p[0] for p in sorted(avatar_image_paths, key=operator.itemgetter(1)) ]
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io.log_err ("%s is not a dfl image file" % (filepath.name) )
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continue
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input_data += [ ( str(filepath), dflimg.get_landmarks(), dflimg.get_source_filename() ) ]
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if len(input_path_image_paths) < len(avatar_image_paths):
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io.log_err("Input faces count must be >= avatar operator faces count.")
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return
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input_data = sorted(input_data, key=operator.itemgetter(2))
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else:
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input_data = [ (p,) for p in input_path_image_paths ]
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files_processed, faces_processed = ConvertSubprocessor (
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converter = converter,
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input_path_image_paths = input_path_image_paths,
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input_data = input_data,
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output_path = output_path,
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alignments = alignments,
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avatar_image_paths = avatar_image_paths,
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debug = args.get('debug',False)
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).run()
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@ -389,3 +376,29 @@ if model_name == 'AVATAR':
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# new_points = np.concatenate( [np.expand_dims(p1,-1),np.expand_dims(p2,-1)], -1 )
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#
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# alignments[ a[i] ][0] = LandmarksProcessor.transform_points (new_points, m0, True).astype(np.int32)
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"""
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elif self.converter.type == Converter.TYPE_IMAGE_WITH_LANDMARKS:
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#currently unused
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if filename_path.suffix == '.png':
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dflimg = DFLPNG.load( str(filename_path) )
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elif filename_path.suffix == '.jpg':
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dflimg = DFLJPG.load ( str(filename_path) )
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else:
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dflimg = None
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if dflimg is not None:
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image_landmarks = dflimg.get_landmarks()
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image = self.converter.convert_image(image, image_landmarks, self.debug)
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if self.debug:
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raise NotImplementedError
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#for img in image:
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# io.show_image ('Debug convert', img )
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# cv2.waitKey(0)
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faces_processed = 1
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else:
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self.log_err ("%s is not a dfl image file" % (filename_path.name) )
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"""
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@ -6,6 +6,7 @@ import multiprocessing
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import shutil
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from pathlib import Path
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import numpy as np
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import math
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import mathlib
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import imagelib
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import cv2
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@ -21,6 +22,8 @@ from nnlib import nnlib
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from joblib import Subprocessor
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from interact import interact as io
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DEBUG = False
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class ExtractSubprocessor(Subprocessor):
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class Data(object):
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def __init__(self, filename=None, rects=None, landmarks = None, landmarks_accurate=True, pitch_yaw_roll=None, final_output_files = None):
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@ -44,6 +47,11 @@ class ExtractSubprocessor(Subprocessor):
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self.cpu_only = client_dict['device_type'] == 'CPU'
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self.final_output_path = Path(client_dict['final_output_dir']) if 'final_output_dir' in client_dict.keys() else None
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self.debug_dir = client_dict['debug_dir']
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#transfer and set stdin in order to work code.interact in debug subprocess
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stdin_fd = client_dict['stdin_fd']
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if stdin_fd is not None and DEBUG:
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sys.stdin = os.fdopen(stdin_fd)
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self.cached_image = (None, None)
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@ -224,10 +232,12 @@ class ExtractSubprocessor(Subprocessor):
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rect = np.array(rect)
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if self.face_type == FaceType.MARK_ONLY:
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image_to_face_mat = None
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face_image = image
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face_image_landmarks = image_landmarks
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else:
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image_to_face_mat = LandmarksProcessor.get_transform_mat (image_landmarks, self.image_size, self.face_type)
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face_image = cv2.warpAffine(image, image_to_face_mat, (self.image_size, self.image_size), cv2.INTER_LANCZOS4)
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face_image_landmarks = LandmarksProcessor.transform_points (image_landmarks, image_to_face_mat)
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@ -239,8 +249,8 @@ class ExtractSubprocessor(Subprocessor):
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if landmarks_area > 4*rect_area: #get rid of faces which umeyama-landmark-area > 4*detector-rect-area
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continue
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if self.debug_dir is not None:
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LandmarksProcessor.draw_rect_landmarks (debug_image, rect, image_landmarks, self.image_size, self.face_type, transparent_mask=True)
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if self.debug_dir is not None:
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LandmarksProcessor.draw_rect_landmarks (debug_image, rect, image_landmarks, self.image_size, self.face_type, transparent_mask=True)
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if src_dflimg is not None and filename_path.suffix == '.jpg':
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#if extracting from dflimg and jpg copy it in order not to lose quality
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@ -296,7 +306,7 @@ class ExtractSubprocessor(Subprocessor):
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self.devices = ExtractSubprocessor.get_devices_for_config(self.manual, self.type, multi_gpu, cpu_only)
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no_response_time_sec = 60 if not self.manual else 999999
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no_response_time_sec = 60 if not self.manual and not DEBUG else 999999
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super().__init__('Extractor', ExtractSubprocessor.Cli, no_response_time_sec)
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#override
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@ -342,7 +352,8 @@ class ExtractSubprocessor(Subprocessor):
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'image_size': self.image_size,
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'face_type': self.face_type,
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'debug_dir': self.debug_dir,
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'final_output_dir': str(self.final_output_path)}
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'final_output_dir': str(self.final_output_path),
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'stdin_fd': sys.stdin.fileno() }
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for (device_idx, device_type, device_name, device_total_vram_gb) in self.devices:
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@ -620,7 +631,7 @@ class ExtractSubprocessor(Subprocessor):
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return [ (i, 'CPU', 'CPU%d' % (i), 0 ) for i in range( min(8, multiprocessing.cpu_count() // 2) ) ]
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elif type == 'final':
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return [ (i, 'CPU', 'CPU%d' % (i), 0 ) for i in range(min(8, multiprocessing.cpu_count())) ]
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return [ (i, 'CPU', 'CPU%d' % (i), 0 ) for i in (range(min(8, multiprocessing.cpu_count())) if not DEBUG else [0]) ]
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class DeletedFilesSearcherSubprocessor(Subprocessor):
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class Cli(Subprocessor.Cli):
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@ -423,14 +423,11 @@ def sort_by_hist_dissim(input_path):
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else:
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dflimg = None
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if dflimg is None:
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io.log_err ("%s is not a dfl image file" % (filepath.name) )
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trash_img_list.append ([str(filepath)])
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continue
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image = cv2_imread(str(filepath))
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face_mask = LandmarksProcessor.get_image_hull_mask (image.shape, dflimg.get_landmarks())
|
||||
image = (image*face_mask).astype(np.uint8)
|
||||
|
||||
if dflimg is not None:
|
||||
face_mask = LandmarksProcessor.get_image_hull_mask (image.shape, dflimg.get_landmarks())
|
||||
image = (image*face_mask).astype(np.uint8)
|
||||
|
||||
img_list.append ([str(filepath), cv2.calcHist([cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)], [0], None, [256], [0, 256]), 0 ])
|
||||
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue