mirror of
https://github.com/iperov/DeepFaceLab.git
synced 2025-07-06 04:52:13 -07:00
manual extractor: increased FPS,
sort by final : now you can specify target number of images, converter: fix seamless mask and exception, huge refactoring
This commit is contained in:
iperov
parent
7db469a1da
commit
438213e97c
30 changed files with 1834 additions and 1718 deletions
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@ -15,10 +15,137 @@ import facelib
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from facelib import FaceType
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from facelib import LandmarksProcessor
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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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class ExtractSubprocessor(Subprocessor):
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class Cli(Subprocessor.Cli):
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from utils.SubprocessorBase import SubprocessorBase
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class ExtractSubprocessor(SubprocessorBase):
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#override
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def on_initialize(self, client_dict):
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self.log_info ('Running on %s.' % (client_dict['device_name']) )
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self.type = client_dict['type']
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self.image_size = client_dict['image_size']
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self.face_type = client_dict['face_type']
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self.device_idx = client_dict['device_idx']
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self.cpu_only = client_dict['device_type'] == 'CPU'
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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.debug = client_dict['debug']
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self.detector = client_dict['detector']
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self.cached_image = (None, None)
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self.e = None
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device_config = nnlib.DeviceConfig ( cpu_only=self.cpu_only, force_gpu_idx=self.device_idx, allow_growth=True)
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if self.type == 'rects':
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if self.detector is not None:
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if self.detector == 'mt':
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nnlib.import_all (device_config)
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self.e = facelib.MTCExtractor()
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elif self.detector == 'dlib':
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nnlib.import_dlib (device_config)
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self.e = facelib.DLIBExtractor(nnlib.dlib)
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else:
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raise ValueError ("Wrond detector type.")
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if self.e is not None:
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self.e.__enter__()
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elif self.type == 'landmarks':
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nnlib.import_all (device_config)
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self.e = facelib.LandmarksExtractor(nnlib.keras)
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self.e.__enter__()
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elif self.type == 'final':
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pass
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#override
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def on_finalize(self):
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if self.e is not None:
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self.e.__exit__()
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#override
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def process_data(self, data):
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filename_path = Path( data[0] )
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filename_path_str = str(filename_path)
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if self.cached_image[0] == filename_path_str:
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image = self.cached_image[1]
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else:
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image = cv2_imread( filename_path_str )
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self.cached_image = ( filename_path_str, image )
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if image is None:
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self.log_err ( 'Failed to extract %s, reason: cv2_imread() fail.' % ( str(filename_path) ) )
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else:
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if self.type == 'rects':
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rects = self.e.extract_from_bgr (image)
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return [str(filename_path), rects]
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elif self.type == 'landmarks':
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rects = data[1]
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landmarks = self.e.extract_from_bgr (image, rects)
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return [str(filename_path), landmarks]
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elif self.type == 'final':
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src_dflimg = None
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(h,w,c) = image.shape
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if h == w:
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#extracting from already extracted jpg image?
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if filename_path.suffix == '.jpg':
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src_dflimg = DFLJPG.load ( str(filename_path) )
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result = []
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faces = data[1]
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if self.debug:
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debug_output_file = '{}{}'.format( str(Path(str(self.output_path) + '_debug') / filename_path.stem), '.jpg')
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debug_image = image.copy()
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for (face_idx, face) in enumerate(faces):
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output_file = '{}_{}{}'.format(str(self.output_path / filename_path.stem), str(face_idx), '.jpg')
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rect = face[0]
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image_landmarks = np.array(face[1])
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if self.debug:
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LandmarksProcessor.draw_rect_landmarks (debug_image, rect, image_landmarks, self.image_size, self.face_type)
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if self.face_type == FaceType.MARK_ONLY:
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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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if src_dflimg is not None:
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#if extracting from dflimg just copy it in order not to lose quality
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shutil.copy ( str(filename_path), str(output_file) )
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else:
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cv2_imwrite(output_file, face_image, [int(cv2.IMWRITE_JPEG_QUALITY), 85] )
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DFLJPG.embed_data(output_file, face_type = FaceType.toString(self.face_type),
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landmarks = face_image_landmarks.tolist(),
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source_filename = filename_path.name,
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source_rect= rect,
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source_landmarks = image_landmarks.tolist()
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)
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result.append (output_file)
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if self.debug:
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cv2_imwrite(debug_output_file, debug_image, [int(cv2.IMWRITE_JPEG_QUALITY), 50] )
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return result
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return None
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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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return data[0]
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#override
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def __init__(self, input_data, type, image_size, face_type, debug, multi_gpu=False, cpu_only=False, manual=False, manual_window_size=0, detector=None, output_path=None ):
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self.input_data = input_data
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@ -35,34 +162,36 @@ class ExtractSubprocessor(SubprocessorBase):
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self.result = []
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no_response_time_sec = 60 if not self.manual else 999999
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super().__init__('Extractor', no_response_time_sec)
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super().__init__('Extractor', ExtractSubprocessor.Cli, no_response_time_sec)
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#override
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def onHostClientsInitialized(self):
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def on_clients_initialized(self):
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if self.manual == True:
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self.wnd_name = 'Manual pass'
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cv2.namedWindow(self.wnd_name)
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self.landmarks = None
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self.param_x = -1
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self.param_y = -1
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self.param_rect_size = -1
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self.param = {'x': 0, 'y': 0, 'rect_size' : 100, 'rect_locked' : False, 'redraw_needed' : False }
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io.named_window(self.wnd_name)
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io.capture_mouse(self.wnd_name)
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io.capture_keys(self.wnd_name)
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def onMouse(event, x, y, flags, param):
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if event == cv2.EVENT_MOUSEWHEEL:
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mod = 1 if flags > 0 else -1
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diff = 1 if param['rect_size'] <= 40 else np.clip(param['rect_size'] / 10, 1, 10)
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param['rect_size'] = max (5, param['rect_size'] + diff*mod)
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elif event == cv2.EVENT_LBUTTONDOWN:
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param['rect_locked'] = not param['rect_locked']
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param['redraw_needed'] = True
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elif not param['rect_locked']:
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param['x'] = x
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param['y'] = y
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cv2.setMouseCallback(self.wnd_name, onMouse, self.param)
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self.cache_original_image = (None, None)
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self.cache_image = (None, None)
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self.cache_text_lines_img = (None, None)
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self.landmarks = None
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self.x = 0
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self.y = 0
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self.rect_size = 100
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self.rect_locked = False
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self.redraw_needed = True
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io.progress_bar (None, len (self.input_data))
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#override
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def on_clients_finalized(self):
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if self.manual == True:
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io.destroy_all_windows()
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io.progress_bar_close()
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def get_devices_for_type (self, type, multi_gpu, cpu_only):
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if not cpu_only and (type == 'rects' or type == 'landmarks'):
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if type == 'rects' and self.detector == 'mt' and nnlib.device.backend == "plaidML":
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@ -86,8 +215,11 @@ class ExtractSubprocessor(SubprocessorBase):
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yield (idx, 'GPU', dev_name, dev_vram)
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if cpu_only and (type == 'rects' or type == 'landmarks'):
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for i in range( min(8, multiprocessing.cpu_count() // 2) ):
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yield (i, 'CPU', 'CPU%d' % (i), 0 )
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if self.manual:
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yield (0, 'CPU', 'CPU', 0 )
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else:
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for i in range( min(8, multiprocessing.cpu_count() // 2) ):
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yield (i, 'CPU', 'CPU%d' % (i), 0 )
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if type == 'final':
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for i in range( min(8, multiprocessing.cpu_count()) ):
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@ -108,25 +240,9 @@ class ExtractSubprocessor(SubprocessorBase):
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client_dict['device_name'] = device_name
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client_dict['device_type'] = device_type
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yield client_dict['device_name'], {}, client_dict
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#override
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def get_no_process_started_message(self):
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if (self.type == 'rects' or self.type == 'landmarks'):
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print ( 'You have no capable GPUs. Try to close programs which can consume VRAM, and run again.')
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elif self.type == 'final':
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print ( 'Unable to start CPU processes.')
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#override
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def onHostGetProgressBarDesc(self):
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return None
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#override
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def onHostGetProgressBarLen(self):
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return len (self.input_data)
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#override
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def onHostGetData(self, host_dict):
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def get_data(self, host_dict):
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if not self.manual:
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if len (self.input_data) > 0:
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return self.input_data.pop(0)
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@ -146,33 +262,68 @@ class ExtractSubprocessor(SubprocessorBase):
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if len(faces) > 0:
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self.rect, self.landmarks = faces.pop()
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self.param['rect_locked'] = True
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self.rect_locked = True
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self.redraw_needed = True
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faces.clear()
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self.param['rect_size'] = ( self.rect[2] - self.rect[0] ) / 2
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self.param['x'] = ( ( self.rect[0] + self.rect[2] ) / 2 ) * self.view_scale
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self.param['y'] = ( ( self.rect[1] + self.rect[3] ) / 2 ) * self.view_scale
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self.rect_size = ( self.rect[2] - self.rect[0] ) / 2
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self.x = ( self.rect[0] + self.rect[2] ) / 2
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self.y = ( self.rect[1] + self.rect[3] ) / 2
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if len(faces) == 0:
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self.original_image = cv2_imread(filename)
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if self.cache_original_image[0] == filename:
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self.original_image = self.cache_original_image[1]
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else:
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self.original_image = cv2_imread( filename )
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self.cache_original_image = (filename, self.original_image )
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(h,w,c) = self.original_image.shape
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self.view_scale = 1.0 if self.manual_window_size == 0 else self.manual_window_size / ( h * (16.0/9.0) )
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self.original_image = cv2.resize (self.original_image, ( int(w*self.view_scale), int(h*self.view_scale) ), interpolation=cv2.INTER_LINEAR)
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(h,w,c) = self.original_image.shape
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self.view_scale = 1.0 if self.manual_window_size == 0 else self.manual_window_size / ( h * (16.0/9.0) )
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self.text_lines_img = (image_utils.get_draw_text_lines ( self.original_image, (0,0, self.original_image.shape[1], min(100, self.original_image.shape[0]) ),
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[ 'Match landmarks with face exactly. Click to confirm/unconfirm selection',
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'[Enter] - confirm face landmarks and continue',
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'[Space] - confirm as unmarked frame and continue',
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'[Mouse wheel] - change rect',
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'[,] [.]- prev frame, next frame',
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'[Q] - skip remaining frames'
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], (1, 1, 1) )*255).astype(np.uint8)
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if self.cache_image[0] == (h,w,c) + (self.view_scale,filename):
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self.image = self.cache_image[1]
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else:
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self.image = cv2.resize (self.original_image, ( int(w*self.view_scale), int(h*self.view_scale) ), interpolation=cv2.INTER_LINEAR)
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self.cache_image = ( (h,w,c) + (self.view_scale,filename), self.image )
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(h,w,c) = self.image.shape
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sh = (0,0, w, min(100, h) )
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if self.cache_text_lines_img[0] == sh:
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self.text_lines_img = self.cache_text_lines_img[1]
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else:
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self.text_lines_img = (image_utils.get_draw_text_lines ( self.image, sh,
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[ 'Match landmarks with face exactly. Click to confirm/unconfirm selection',
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'[Enter] - confirm face landmarks and continue',
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'[Space] - confirm as unmarked frame and continue',
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'[Mouse wheel] - change rect',
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'[,] [.]- prev frame, next frame',
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'[Q] - skip remaining frames'
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], (1, 1, 1) )*255).astype(np.uint8)
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self.cache_text_lines_img = (sh, self.text_lines_img)
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while True:
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key = cv2.waitKey(1) & 0xFF
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new_x = self.x
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new_y = self.y
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new_rect_size = self.rect_size
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mouse_events = io.get_mouse_events(self.wnd_name)
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for ev in mouse_events:
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(x, y, ev, flags) = ev
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if ev == io.EVENT_MOUSEWHEEL and not self.rect_locked:
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mod = 1 if flags > 0 else -1
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diff = 1 if new_rect_size <= 40 else np.clip(new_rect_size / 10, 1, 10)
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new_rect_size = max (5, new_rect_size + diff*mod)
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elif ev == io.EVENT_LBUTTONDOWN:
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self.rect_locked = not self.rect_locked
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self.redraw_needed = True
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elif not self.rect_locked:
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new_x = np.clip (x, 0, w-1) / self.view_scale
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new_y = np.clip (y, 0, h-1) / self.view_scale
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key_events = io.get_key_events(self.wnd_name)
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key, = key_events[-1] if len(key_events) > 0 else (0,)
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if key == ord('\r') or key == ord('\n'):
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faces.append ( [(self.rect), self.landmarks] )
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is_frame_done = True
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@ -183,200 +334,81 @@ class ExtractSubprocessor(SubprocessorBase):
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elif key == ord('.'):
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allow_remark_faces = True
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# Only save the face if the rect is still locked
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if self.param['rect_locked']:
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if self.rect_locked:
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faces.append ( [(self.rect), self.landmarks] )
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is_frame_done = True
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break
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elif key == ord(',') and len(self.result) > 0:
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# Only save the face if the rect is still locked
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if self.param['rect_locked']:
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if self.rect_locked:
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faces.append ( [(self.rect), self.landmarks] )
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go_to_prev_frame = True
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break
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elif key == ord('q'):
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skip_remaining = True
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break
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new_param_x = np.clip (self.param['x'], 0, w-1) / self.view_scale
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new_param_y = np.clip (self.param['y'], 0, h-1) / self.view_scale
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new_param_rect_size = self.param['rect_size']
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if self.param_x != new_param_x or \
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self.param_y != new_param_y or \
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self.param_rect_size != new_param_rect_size or \
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self.param['redraw_needed']:
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self.param_x = new_param_x
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self.param_y = new_param_y
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self.param_rect_size = new_param_rect_size
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if self.x != new_x or \
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self.y != new_y or \
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self.rect_size != new_rect_size or \
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self.redraw_needed:
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self.x = new_x
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self.y = new_y
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self.rect_size = new_rect_size
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self.rect = ( int(self.param_x-self.param_rect_size),
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int(self.param_y-self.param_rect_size),
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int(self.param_x+self.param_rect_size),
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int(self.param_y+self.param_rect_size) )
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self.rect = ( int(self.x-self.rect_size),
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int(self.y-self.rect_size),
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int(self.x+self.rect_size),
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int(self.y+self.rect_size) )
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return [filename, [self.rect]]
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io.process_messages(0.0001)
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else:
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is_frame_done = True
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if is_frame_done:
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self.result.append ( data )
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self.input_data.pop(0)
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self.inc_progress_bar(1)
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self.param['redraw_needed'] = True
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self.param['rect_locked'] = False
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io.progress_bar_inc(1)
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self.redraw_needed = True
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self.rect_locked = False
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elif go_to_prev_frame:
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self.input_data.insert(0, self.result.pop() )
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self.inc_progress_bar(-1)
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io.progress_bar_inc(-1)
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allow_remark_faces = True
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self.param['redraw_needed'] = True
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self.param['rect_locked'] = False
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self.redraw_needed = True
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self.rect_locked = False
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elif skip_remaining:
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if self.param['rect_locked']:
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if self.rect_locked:
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faces.append ( [(self.rect), self.landmarks] )
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while len(self.input_data) > 0:
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self.result.append( self.input_data.pop(0) )
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self.inc_progress_bar(1)
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io.progress_bar_inc(1)
|
||||
|
||||
return None
|
||||
|
||||
#override
|
||||
def onHostDataReturn (self, host_dict, data):
|
||||
def on_data_return (self, host_dict, data):
|
||||
if not self.manual:
|
||||
self.input_data.insert(0, data)
|
||||
|
||||
#override
|
||||
def onClientInitialize(self, client_dict):
|
||||
self.safe_print ('Running on %s.' % (client_dict['device_name']) )
|
||||
self.type = client_dict['type']
|
||||
self.image_size = client_dict['image_size']
|
||||
self.face_type = client_dict['face_type']
|
||||
self.device_idx = client_dict['device_idx']
|
||||
self.cpu_only = client_dict['device_type'] == 'CPU'
|
||||
self.output_path = Path(client_dict['output_dir']) if 'output_dir' in client_dict.keys() else None
|
||||
self.debug = client_dict['debug']
|
||||
self.detector = client_dict['detector']
|
||||
|
||||
self.e = None
|
||||
device_config = nnlib.DeviceConfig ( cpu_only=self.cpu_only, force_gpu_idx=self.device_idx, allow_growth=True)
|
||||
if self.type == 'rects':
|
||||
if self.detector is not None:
|
||||
if self.detector == 'mt':
|
||||
nnlib.import_all (device_config)
|
||||
self.e = facelib.MTCExtractor()
|
||||
elif self.detector == 'dlib':
|
||||
nnlib.import_dlib (device_config)
|
||||
self.e = facelib.DLIBExtractor(nnlib.dlib)
|
||||
self.e.__enter__()
|
||||
|
||||
elif self.type == 'landmarks':
|
||||
nnlib.import_all (device_config)
|
||||
self.e = facelib.LandmarksExtractor(nnlib.keras)
|
||||
self.e.__enter__()
|
||||
|
||||
elif self.type == 'final':
|
||||
pass
|
||||
|
||||
return None
|
||||
|
||||
#override
|
||||
def onClientFinalize(self):
|
||||
if self.e is not None:
|
||||
self.e.__exit__()
|
||||
|
||||
#override
|
||||
def onClientProcessData(self, data):
|
||||
filename_path = Path( data[0] )
|
||||
|
||||
image = cv2_imread( str(filename_path) )
|
||||
|
||||
if image is None:
|
||||
print ( 'Failed to extract %s, reason: cv2_imread() fail.' % ( str(filename_path) ) )
|
||||
else:
|
||||
if self.type == 'rects':
|
||||
rects = self.e.extract_from_bgr (image)
|
||||
return [str(filename_path), rects]
|
||||
|
||||
elif self.type == 'landmarks':
|
||||
rects = data[1]
|
||||
landmarks = self.e.extract_from_bgr (image, rects)
|
||||
return [str(filename_path), landmarks]
|
||||
|
||||
elif self.type == 'final':
|
||||
src_dflimg = None
|
||||
(h,w,c) = image.shape
|
||||
if h == w:
|
||||
#extracting from already extracted jpg image?
|
||||
if filename_path.suffix == '.jpg':
|
||||
src_dflimg = DFLJPG.load ( str(filename_path) )
|
||||
|
||||
result = []
|
||||
faces = data[1]
|
||||
|
||||
if self.debug:
|
||||
debug_output_file = '{}{}'.format( str(Path(str(self.output_path) + '_debug') / filename_path.stem), '.jpg')
|
||||
debug_image = image.copy()
|
||||
|
||||
for (face_idx, face) in enumerate(faces):
|
||||
output_file = '{}_{}{}'.format(str(self.output_path / filename_path.stem), str(face_idx), '.jpg')
|
||||
|
||||
rect = face[0]
|
||||
image_landmarks = np.array(face[1])
|
||||
|
||||
if self.debug:
|
||||
LandmarksProcessor.draw_rect_landmarks (debug_image, rect, image_landmarks, self.image_size, self.face_type)
|
||||
|
||||
if self.face_type == FaceType.MARK_ONLY:
|
||||
face_image = image
|
||||
face_image_landmarks = image_landmarks
|
||||
else:
|
||||
image_to_face_mat = LandmarksProcessor.get_transform_mat (image_landmarks, self.image_size, self.face_type)
|
||||
face_image = cv2.warpAffine(image, image_to_face_mat, (self.image_size, self.image_size), cv2.INTER_LANCZOS4)
|
||||
face_image_landmarks = LandmarksProcessor.transform_points (image_landmarks, image_to_face_mat)
|
||||
|
||||
if src_dflimg is not None:
|
||||
#if extracting from dflimg just copy it in order not to lose quality
|
||||
shutil.copy ( str(filename_path), str(output_file) )
|
||||
else:
|
||||
cv2_imwrite(output_file, face_image, [int(cv2.IMWRITE_JPEG_QUALITY), 85] )
|
||||
|
||||
DFLJPG.embed_data(output_file, face_type = FaceType.toString(self.face_type),
|
||||
landmarks = face_image_landmarks.tolist(),
|
||||
source_filename = filename_path.name,
|
||||
source_rect= rect,
|
||||
source_landmarks = image_landmarks.tolist()
|
||||
)
|
||||
|
||||
result.append (output_file)
|
||||
|
||||
if self.debug:
|
||||
cv2_imwrite(debug_output_file, debug_image, [int(cv2.IMWRITE_JPEG_QUALITY), 50] )
|
||||
|
||||
return result
|
||||
return None
|
||||
|
||||
#overridable
|
||||
def onClientGetDataName (self, data):
|
||||
#return string identificator of your data
|
||||
return data[0]
|
||||
|
||||
#override
|
||||
def onHostResult (self, host_dict, data, result):
|
||||
def on_result (self, host_dict, data, result):
|
||||
if self.manual == True:
|
||||
self.landmarks = result[1][0][1]
|
||||
|
||||
(h,w,c) = self.original_image.shape
|
||||
image = cv2.addWeighted (self.original_image,1.0,self.text_lines_img,1.0,0)
|
||||
(h,w,c) = self.image.shape
|
||||
image = cv2.addWeighted (self.image,1.0,self.text_lines_img,1.0,0)
|
||||
view_rect = (np.array(self.rect) * self.view_scale).astype(np.int).tolist()
|
||||
view_landmarks = (np.array(self.landmarks) * self.view_scale).astype(np.int).tolist()
|
||||
|
||||
if self.param_rect_size <= 40:
|
||||
if self.rect_size <= 40:
|
||||
scaled_rect_size = h // 3 if w > h else w // 3
|
||||
|
||||
p1 = (self.param_x - self.param_rect_size, self.param_y - self.param_rect_size)
|
||||
p2 = (self.param_x + self.param_rect_size, self.param_y - self.param_rect_size)
|
||||
p3 = (self.param_x - self.param_rect_size, self.param_y + self.param_rect_size)
|
||||
p1 = (self.x - self.rect_size, self.y - self.rect_size)
|
||||
p2 = (self.x + self.rect_size, self.y - self.rect_size)
|
||||
p3 = (self.x - self.rect_size, self.y + self.rect_size)
|
||||
|
||||
wh = h if h < w else w
|
||||
np1 = (w / 2 - wh / 4, h / 2 - wh / 4)
|
||||
|
|
@ -389,12 +421,11 @@ class ExtractSubprocessor(SubprocessorBase):
|
|||
|
||||
LandmarksProcessor.draw_rect_landmarks (image, view_rect, view_landmarks, self.image_size, self.face_type)
|
||||
|
||||
if self.param['rect_locked']:
|
||||
if self.rect_locked:
|
||||
LandmarksProcessor.draw_landmarks(image, view_landmarks, (255,255,0) )
|
||||
self.param['redraw_needed'] = False
|
||||
self.redraw_needed = False
|
||||
|
||||
cv2.imshow (self.wnd_name, image)
|
||||
return 0
|
||||
io.show_image (self.wnd_name, image)
|
||||
else:
|
||||
if self.type == 'rects':
|
||||
self.result.append ( result )
|
||||
|
|
@ -403,98 +434,91 @@ class ExtractSubprocessor(SubprocessorBase):
|
|||
elif self.type == 'final':
|
||||
self.result += result
|
||||
|
||||
return 1
|
||||
io.progress_bar_inc(1)
|
||||
|
||||
#override
|
||||
def onFinalizeAndGetResult(self):
|
||||
if self.manual == True:
|
||||
cv2.destroyAllWindows()
|
||||
def get_result(self):
|
||||
return self.result
|
||||
|
||||
class DeletedFilesSearcherSubprocessor(SubprocessorBase):
|
||||
|
||||
class DeletedFilesSearcherSubprocessor(Subprocessor):
|
||||
class Cli(Subprocessor.Cli):
|
||||
#override
|
||||
def on_initialize(self, client_dict):
|
||||
self.debug_paths_stems = client_dict['debug_paths_stems']
|
||||
return None
|
||||
|
||||
#override
|
||||
def process_data(self, data):
|
||||
input_path_stem = Path(data[0]).stem
|
||||
return any ( [ input_path_stem == d_stem for d_stem in self.debug_paths_stems] )
|
||||
|
||||
#override
|
||||
def get_data_name (self, data):
|
||||
#return string identificator of your data
|
||||
return data[0]
|
||||
|
||||
#override
|
||||
def __init__(self, input_paths, debug_paths ):
|
||||
self.input_paths = input_paths
|
||||
self.debug_paths_stems = [ Path(d).stem for d in debug_paths]
|
||||
self.result = []
|
||||
super().__init__('DeletedFilesSearcherSubprocessor', 60)
|
||||
super().__init__('DeletedFilesSearcherSubprocessor', DeletedFilesSearcherSubprocessor.Cli, 60)
|
||||
|
||||
#override
|
||||
def process_info_generator(self):
|
||||
for i in range(0, min(multiprocessing.cpu_count(), 8) ):
|
||||
yield 'CPU%d' % (i), {}, {'device_idx': i,
|
||||
'device_name': 'CPU%d' % (i),
|
||||
'debug_paths_stems' : self.debug_paths_stems
|
||||
}
|
||||
for i in range(min(multiprocessing.cpu_count(), 8)):
|
||||
yield 'CPU%d' % (i), {}, {'debug_paths_stems' : self.debug_paths_stems}
|
||||
|
||||
#override
|
||||
def get_no_process_started_message(self):
|
||||
print ( 'Unable to start CPU processes.')
|
||||
|
||||
def on_clients_initialized(self):
|
||||
io.progress_bar ("Searching deleted files", len (self.input_paths))
|
||||
|
||||
#override
|
||||
def onHostGetProgressBarDesc(self):
|
||||
return "Searching deleted files"
|
||||
|
||||
def on_clients_finalized(self):
|
||||
io.progress_bar_close()
|
||||
|
||||
#override
|
||||
def onHostGetProgressBarLen(self):
|
||||
return len (self.input_paths)
|
||||
|
||||
#override
|
||||
def onHostGetData(self, host_dict):
|
||||
def get_data(self, host_dict):
|
||||
if len (self.input_paths) > 0:
|
||||
return [self.input_paths.pop(0)]
|
||||
return None
|
||||
|
||||
#override
|
||||
def onHostDataReturn (self, host_dict, data):
|
||||
def on_data_return (self, host_dict, data):
|
||||
self.input_paths.insert(0, data[0])
|
||||
|
||||
|
||||
#override
|
||||
def onClientInitialize(self, client_dict):
|
||||
self.debug_paths_stems = client_dict['debug_paths_stems']
|
||||
return None
|
||||
|
||||
#override
|
||||
def onClientProcessData(self, data):
|
||||
input_path_stem = Path(data[0]).stem
|
||||
return any ( [ input_path_stem == d_stem for d_stem in self.debug_paths_stems] )
|
||||
|
||||
#override
|
||||
def onClientGetDataName (self, data):
|
||||
#return string identificator of your data
|
||||
return data[0]
|
||||
|
||||
#override
|
||||
def onHostResult (self, host_dict, data, result):
|
||||
def on_result (self, host_dict, data, result):
|
||||
if result == False:
|
||||
self.result.append( data[0] )
|
||||
return 1
|
||||
io.progress_bar_inc(1)
|
||||
|
||||
#override
|
||||
def onFinalizeAndGetResult(self):
|
||||
def get_result(self):
|
||||
return self.result
|
||||
|
||||
'''
|
||||
detector
|
||||
'dlib'
|
||||
'mt'
|
||||
'manual'
|
||||
|
||||
face_type
|
||||
'full_face'
|
||||
'avatar'
|
||||
'''
|
||||
def main (input_dir, output_dir, debug, detector='mt', multi_gpu=True, cpu_only=False, manual_fix=False, manual_output_debug_fix=False, manual_window_size=1368, image_size=256, face_type='full_face'):
|
||||
print ("Running extractor.\r\n")
|
||||
|
||||
def main(input_dir,
|
||||
output_dir,
|
||||
debug=False,
|
||||
detector='mt',
|
||||
manual_fix=False,
|
||||
manual_output_debug_fix=False,
|
||||
manual_window_size=1368,
|
||||
image_size=256,
|
||||
face_type='full_face',
|
||||
device_args={}):
|
||||
|
||||
input_path = Path(input_dir)
|
||||
output_path = Path(output_dir)
|
||||
face_type = FaceType.fromString(face_type)
|
||||
|
||||
multi_gpu = device_args.get('multi_gpu', False)
|
||||
cpu_only = device_args.get('cpu_only', False)
|
||||
|
||||
if not input_path.exists():
|
||||
print('Input directory not found. Please ensure it exists.')
|
||||
return
|
||||
|
||||
raise ValueError('Input directory not found. Please ensure it exists.')
|
||||
|
||||
if output_path.exists():
|
||||
if not manual_output_debug_fix:
|
||||
for filename in Path_utils.get_image_paths(output_path):
|
||||
|
|
@ -505,19 +529,17 @@ def main (input_dir, output_dir, debug, detector='mt', multi_gpu=True, cpu_only=
|
|||
if manual_output_debug_fix:
|
||||
debug = True
|
||||
detector = 'manual'
|
||||
print('Performing re-extract frames which were deleted from _debug directory.')
|
||||
io.log_info('Performing re-extract frames which were deleted from _debug directory.')
|
||||
|
||||
input_path_image_paths = Path_utils.get_image_unique_filestem_paths(input_path, verbose=True)
|
||||
|
||||
input_path_image_paths = Path_utils.get_image_unique_filestem_paths(input_path, verbose_print_func=io.log_info)
|
||||
if debug:
|
||||
debug_output_path = Path(str(output_path) + '_debug')
|
||||
|
||||
if manual_output_debug_fix:
|
||||
if not debug_output_path.exists():
|
||||
print ("%s not found " % ( str(debug_output_path) ))
|
||||
return
|
||||
|
||||
input_path_image_paths = DeletedFilesSearcherSubprocessor ( input_path_image_paths, Path_utils.get_image_paths(debug_output_path) ).process()
|
||||
raise ValueError("%s not found " % ( str(debug_output_path) ))
|
||||
|
||||
input_path_image_paths = DeletedFilesSearcherSubprocessor (input_path_image_paths, Path_utils.get_image_paths(debug_output_path) ).run()
|
||||
input_path_image_paths = sorted (input_path_image_paths)
|
||||
else:
|
||||
if debug_output_path.exists():
|
||||
|
|
@ -530,29 +552,29 @@ def main (input_dir, output_dir, debug, detector='mt', multi_gpu=True, cpu_only=
|
|||
faces_detected = 0
|
||||
if images_found != 0:
|
||||
if detector == 'manual':
|
||||
print ('Performing manual extract...')
|
||||
extracted_faces = ExtractSubprocessor ([ (filename,[]) for filename in input_path_image_paths ], 'landmarks', image_size, face_type, debug, cpu_only=cpu_only, manual=True, manual_window_size=manual_window_size).process()
|
||||
io.log_info ('Performing manual extract...')
|
||||
extracted_faces = ExtractSubprocessor ([ (filename,[]) for filename in input_path_image_paths ], 'landmarks', image_size, face_type, debug, cpu_only=cpu_only, manual=True, manual_window_size=manual_window_size).run()
|
||||
else:
|
||||
print ('Performing 1st pass...')
|
||||
extracted_rects = ExtractSubprocessor ([ (x,) for x in input_path_image_paths ], 'rects', image_size, face_type, debug, multi_gpu=multi_gpu, cpu_only=cpu_only, manual=False, detector=detector).process()
|
||||
io.log_info ('Performing 1st pass...')
|
||||
extracted_rects = ExtractSubprocessor ([ (x,) for x in input_path_image_paths ], 'rects', image_size, face_type, debug, multi_gpu=multi_gpu, cpu_only=cpu_only, manual=False, detector=detector).run()
|
||||
|
||||
print ('Performing 2nd pass...')
|
||||
extracted_faces = ExtractSubprocessor (extracted_rects, 'landmarks', image_size, face_type, debug, multi_gpu=multi_gpu, cpu_only=cpu_only, manual=False).process()
|
||||
io.log_info ('Performing 2nd pass...')
|
||||
extracted_faces = ExtractSubprocessor (extracted_rects, 'landmarks', image_size, face_type, debug, multi_gpu=multi_gpu, cpu_only=cpu_only, manual=False).run()
|
||||
|
||||
if manual_fix:
|
||||
print ('Performing manual fix...')
|
||||
io.log_info ('Performing manual fix...')
|
||||
|
||||
if all ( np.array ( [ len(data[1]) > 0 for data in extracted_faces] ) == True ):
|
||||
print ('All faces are detected, manual fix not needed.')
|
||||
io.log_info ('All faces are detected, manual fix not needed.')
|
||||
else:
|
||||
extracted_faces = ExtractSubprocessor (extracted_faces, 'landmarks', image_size, face_type, debug, manual=True, manual_window_size=manual_window_size).process()
|
||||
extracted_faces = ExtractSubprocessor (extracted_faces, 'landmarks', image_size, face_type, debug, manual=True, manual_window_size=manual_window_size).run()
|
||||
|
||||
if len(extracted_faces) > 0:
|
||||
print ('Performing 3rd pass...')
|
||||
final_imgs_paths = ExtractSubprocessor (extracted_faces, 'final', image_size, face_type, debug, multi_gpu=multi_gpu, cpu_only=cpu_only, manual=False, output_path=output_path).process()
|
||||
io.log_info ('Performing 3rd pass...')
|
||||
final_imgs_paths = ExtractSubprocessor (extracted_faces, 'final', image_size, face_type, debug, multi_gpu=multi_gpu, cpu_only=cpu_only, manual=False, output_path=output_path).run()
|
||||
faces_detected = len(final_imgs_paths)
|
||||
|
||||
print('-------------------------')
|
||||
print('Images found: %d' % (images_found) )
|
||||
print('Faces detected: %d' % (faces_detected) )
|
||||
print('-------------------------')
|
||||
io.log_info ('-------------------------')
|
||||
io.log_info ('Images found: %d' % (images_found) )
|
||||
io.log_info ('Faces detected: %d' % (faces_detected) )
|
||||
io.log_info ('-------------------------')
|
||||
Loading…
Add table
Add a link
Reference in a new issue