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https://github.com/iperov/DeepFaceLab.git
synced 2025-07-05 20:42:11 -07:00
Extractor:
added ‘Image size’ option. The higher image size, the worse face-enhancer works. Use higher than 512 value only if the source image is sharp enough and the face does not need to be enhanced. added ‘Jpeg quality’ option in range 1-100. The higher jpeg quality the larger the output file size
This commit is contained in:
Colombo
parent
d78ec338c6
commit
7da28b283d
2 changed files with 44 additions and 28 deletions
4
main.py
4
main.py
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@ -38,6 +38,8 @@ if __name__ == "__main__":
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manual_output_debug_fix = arguments.manual_output_debug_fix,
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manual_window_size = arguments.manual_window_size,
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face_type = arguments.face_type,
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image_size = arguments.image_size,
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jpeg_quality = arguments.jpeg_quality,
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cpu_only = arguments.cpu_only,
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force_gpu_idxs = [ int(x) for x in arguments.force_gpu_idxs.split(',') ] if arguments.force_gpu_idxs is not None else None,
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)
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@ -49,6 +51,8 @@ if __name__ == "__main__":
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p.add_argument('--output-debug', action="store_true", dest="output_debug", default=None, help="Writes debug images to <output-dir>_debug\ directory.")
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p.add_argument('--no-output-debug', action="store_false", dest="output_debug", default=None, help="Don't writes debug images to <output-dir>_debug\ directory.")
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p.add_argument('--face-type', dest="face_type", choices=['half_face', 'full_face', 'whole_face', 'head', 'mark_only'], default=None)
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p.add_argument('--image-size', type=int, dest="image_size", default=None, help="Output image size.")
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p.add_argument('--jpeg-quality', type=int, dest="jpeg_quality", default=None, help="Jpeg quality.")
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p.add_argument('--manual-fix', action="store_true", dest="manual_fix", default=False, help="Enables manual extract only frames where faces were not recognized.")
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p.add_argument('--manual-output-debug-fix', action="store_true", dest="manual_output_debug_fix", default=False, help="Performs manual reextract input-dir frames which were deleted from [output_dir]_debug\ dir.")
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p.add_argument('--manual-window-size', type=int, dest="manual_window_size", default=1368, help="Manual fix window size. Default: 1368.")
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@ -44,6 +44,7 @@ class ExtractSubprocessor(Subprocessor):
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def on_initialize(self, client_dict):
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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.jpeg_quality = client_dict['jpeg_quality']
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self.face_type = client_dict['face_type']
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self.max_faces_from_image = client_dict['max_faces_from_image']
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self.device_idx = client_dict['device_idx']
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@ -119,6 +120,7 @@ class ExtractSubprocessor(Subprocessor):
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image=image,
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face_type=self.face_type,
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image_size=self.image_size,
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jpeg_quality=self.jpeg_quality,
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extract_from_dflimg=extract_from_dflimg,
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output_debug_path=self.output_debug_path,
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final_output_path=self.final_output_path,
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@ -202,6 +204,7 @@ class ExtractSubprocessor(Subprocessor):
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image,
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face_type,
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image_size,
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jpeg_quality,
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extract_from_dflimg = False,
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output_debug_path=None,
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final_output_path=None,
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@ -266,7 +269,7 @@ class ExtractSubprocessor(Subprocessor):
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shutil.copy ( str(filepath), str(output_filepath) )
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else:
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output_filepath = output_path / f"{filepath.stem}_{face_idx}.jpg"
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cv2_imwrite(output_filepath, face_image, [int(cv2.IMWRITE_JPEG_QUALITY), 90] )
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cv2_imwrite(output_filepath, face_image, [int(cv2.IMWRITE_JPEG_QUALITY), jpeg_quality ] )
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dflimg = DFLJPG.load(output_filepath)
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dflimg.set_face_type(FaceType.toString(face_type))
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@ -325,7 +328,7 @@ class ExtractSubprocessor(Subprocessor):
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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())) if not DEBUG else [0]) ]
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def __init__(self, input_data, type, image_size=None, face_type=None, output_debug_path=None, manual_window_size=0, max_faces_from_image=0, final_output_path=None, device_config=None):
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def __init__(self, input_data, type, image_size=None, jpeg_quality=None, face_type=None, output_debug_path=None, manual_window_size=0, max_faces_from_image=0, final_output_path=None, device_config=None):
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if type == 'landmarks-manual':
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for x in input_data:
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x.manual = True
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@ -334,6 +337,7 @@ class ExtractSubprocessor(Subprocessor):
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self.type = type
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self.image_size = image_size
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self.jpeg_quality = jpeg_quality
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self.face_type = face_type
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self.output_debug_path = output_debug_path
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self.final_output_path = final_output_path
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@ -381,6 +385,7 @@ class ExtractSubprocessor(Subprocessor):
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def process_info_generator(self):
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base_dict = {'type' : self.type,
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'image_size': self.image_size,
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'jpeg_quality' : self.jpeg_quality,
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'face_type': self.face_type,
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'max_faces_from_image':self.max_faces_from_image,
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'output_debug_path': self.output_debug_path,
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@ -725,6 +730,8 @@ def main(detector=None,
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manual_output_debug_fix=False,
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manual_window_size=1368,
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face_type='full_face',
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image_size=None,
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jpeg_quality=None,
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max_faces_from_image=0,
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cpu_only = False,
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force_gpu_idxs = None,
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@ -745,28 +752,6 @@ def main(detector=None,
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if dflimg is not None and dflimg.has_data():
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face_type = FaceType.fromString ( dflimg.get_face_type() )
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if face_type is None:
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face_type = io.input_str ("Face type", 'wf', ['f','wf','head'], help_message="Full face / whole face / head. 'Whole face' covers full area of face include forehead. 'head' covers full head, but requires XSeg for src and dst faceset.").lower()
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face_type = {'f' : FaceType.FULL,
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'wf' : FaceType.WHOLE_FACE,
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'head' : FaceType.HEAD}[face_type]
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image_size = 512 if face_type < FaceType.HEAD else 768
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if detector is None:
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io.log_info ("Choose detector type.")
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io.log_info ("[0] S3FD")
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io.log_info ("[1] manual")
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detector = {0:'s3fd', 1:'manual'}[ io.input_int("", 0, [0,1]) ]
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device_config = nn.DeviceConfig.GPUIndexes( force_gpu_idxs or nn.ask_choose_device_idxs(choose_only_one=detector=='manual', suggest_all_gpu=True) ) \
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if not cpu_only else nn.DeviceConfig.CPU()
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output_debug_path = output_path.parent / (output_path.name + '_debug')
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if output_debug is None:
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output_debug = io.input_bool (f"Write debug images to {output_debug_path.name}?", False)
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if output_path.exists():
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if not manual_output_debug_fix and input_path != output_path:
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output_images_paths = pathex.get_image_paths(output_path)
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@ -777,6 +762,32 @@ def main(detector=None,
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else:
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output_path.mkdir(parents=True, exist_ok=True)
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device_config = nn.DeviceConfig.GPUIndexes( force_gpu_idxs or nn.ask_choose_device_idxs(choose_only_one=detector=='manual', suggest_all_gpu=True) ) \
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if not cpu_only else nn.DeviceConfig.CPU()
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if face_type is None:
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face_type = io.input_str ("Face type", 'wf', ['f','wf','head'], help_message="Full face / whole face / head. 'Whole face' covers full area of face include forehead. 'head' covers full head, but requires XSeg for src and dst faceset.").lower()
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face_type = {'f' : FaceType.FULL,
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'wf' : FaceType.WHOLE_FACE,
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'head' : FaceType.HEAD}[face_type]
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if image_size is None:
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image_size = io.input_int(f"Image size", 512 if face_type < FaceType.HEAD else 768, valid_range=[256,2048], help_message="Output image size. The higher image size, the worse face-enhancer works. Use higher than 512 value only if the source image is sharp enough and the face does not need to be enhanced.")
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if jpeg_quality is None:
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jpeg_quality = io.input_int(f"Jpeg quality", 90, valid_range=[1,100], help_message="Jpeg quality. The higher jpeg quality the larger the output file size.")
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if detector is None:
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io.log_info ("Choose detector type.")
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io.log_info ("[0] S3FD")
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io.log_info ("[1] manual")
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detector = {0:'s3fd', 1:'manual'}[ io.input_int("", 0, [0,1]) ]
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output_debug_path = output_path.parent / (output_path.name + '_debug')
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if output_debug is None:
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output_debug = io.input_bool (f"Write debug images to {output_debug_path.name}?", False)
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input_path_image_paths = pathex.get_image_unique_filestem_paths(input_path, verbose_print_func=io.log_info)
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if manual_output_debug_fix:
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@ -802,16 +813,17 @@ def main(detector=None,
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if images_found != 0:
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if detector == 'manual':
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io.log_info ('Performing manual extract...')
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data = ExtractSubprocessor ([ ExtractSubprocessor.Data(Path(filename)) for filename in input_path_image_paths ], 'landmarks-manual', image_size, face_type, output_debug_path if output_debug else None, manual_window_size=manual_window_size, device_config=device_config).run()
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data = ExtractSubprocessor ([ ExtractSubprocessor.Data(Path(filename)) for filename in input_path_image_paths ], 'landmarks-manual', image_size, jpeg_quality, face_type, output_debug_path if output_debug else None, manual_window_size=manual_window_size, device_config=device_config).run()
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io.log_info ('Performing 3rd pass...')
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data = ExtractSubprocessor (data, 'final', image_size, face_type, output_debug_path if output_debug else None, final_output_path=output_path, device_config=device_config).run()
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data = ExtractSubprocessor (data, 'final', image_size, jpeg_quality, face_type, output_debug_path if output_debug else None, final_output_path=output_path, device_config=device_config).run()
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else:
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io.log_info ('Extracting faces...')
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data = ExtractSubprocessor ([ ExtractSubprocessor.Data(Path(filename)) for filename in input_path_image_paths ],
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'all',
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image_size,
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jpeg_quality,
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face_type,
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output_debug_path if output_debug else None,
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max_faces_from_image=max_faces_from_image,
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@ -826,8 +838,8 @@ def main(detector=None,
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else:
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fix_data = [ ExtractSubprocessor.Data(d.filepath) for d in data if d.faces_detected == 0 ]
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io.log_info ('Performing manual fix for %d images...' % (len(fix_data)) )
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fix_data = ExtractSubprocessor (fix_data, 'landmarks-manual', image_size, face_type, output_debug_path if output_debug else None, manual_window_size=manual_window_size, device_config=device_config).run()
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fix_data = ExtractSubprocessor (fix_data, 'final', image_size, face_type, output_debug_path if output_debug else None, final_output_path=output_path, device_config=device_config).run()
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fix_data = ExtractSubprocessor (fix_data, 'landmarks-manual', image_size, jpeg_quality, face_type, output_debug_path if output_debug else None, manual_window_size=manual_window_size, device_config=device_config).run()
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fix_data = ExtractSubprocessor (fix_data, 'final', image_size, jpeg_quality, face_type, output_debug_path if output_debug else None, final_output_path=output_path, device_config=device_config).run()
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faces_detected += sum([d.faces_detected for d in fix_data])
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