def main (input_dir, output_dir, debug, detector='mt', multi_gpu=True, cpu_only=False, manual_fix=False, manual_window_size=0, image_size=256, face_type='full_face'):
print ("Running extractor.\r\n")
input_path = Path(input_dir)
output_path = Path(output_dir)
face_type = FaceType.fromString(face_type)
if not input_path.exists():
print('Input directory not found. Please ensure it exists.')
return
if output_path.exists():
for filename in Path_utils.get_image_paths(output_path):
Path(filename).unlink()
else:
output_path.mkdir(parents=True, exist_ok=True)
if debug:
debug_output_path = Path(str(output_path) + '_debug')
if debug_output_path.exists():
for filename in Path_utils.get_image_paths(debug_output_path):
Path(filename).unlink()
else:
debug_output_path.mkdir(parents=True, exist_ok=True)
input_path_image_paths = Path_utils.get_image_unique_filestem_paths(input_path, verbose=True)
images_found = len(input_path_image_paths)
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()
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()
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()
if manual_fix:
print ('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.')
else:
extracted_faces = ExtractSubprocessor (extracted_faces, 'landmarks', image_size, face_type, debug, manual=True, manual_window_size=manual_window_size).process()
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()
faces_detected = len(final_imgs_paths)
print('-------------------------')
print('Images found: %d' % (images_found) )
print('Faces detected: %d' % (faces_detected) )
print('-------------------------')
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():
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):
Path(filename).unlink()
else:
output_path.mkdir(parents=True, exist_ok=True)
if manual_output_debug_fix:
debug = True
detector = 'manual'
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_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():
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():
for filename in Path_utils.get_image_paths(debug_output_path):
Path(filename).unlink()
else:
debug_output_path.mkdir(parents=True, exist_ok=True)
images_found = len(input_path_image_paths)
faces_detected = 0
if images_found != 0:
if detector == 'manual':
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:
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()
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:
io.log_info('Performing manual fix...')
if all(
np.array(
[len(data[1]) > 0
for data in extracted_faces]) == True):
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).run()
if len(extracted_faces) > 0:
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)
io.log_info('-------------------------')
io.log_info('Images found: %d' % (images_found))
io.log_info('Faces detected: %d' % (faces_detected))
io.log_info('-------------------------')
def main(input_dir,
output_dir,
debug_dir=None,
detector='mt',
manual_fix=False,
manual_output_debug_fix=False,
manual_window_size=1368,
image_size=256,
face_type='full_face',
max_faces_from_image=0,
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():
raise ValueError('Input directory not found. Please ensure it exists.')
if output_path.exists():
if not manual_output_debug_fix and input_path != output_path:
output_images_paths = Path_utils.get_image_paths(output_path)
if len(output_images_paths) > 0:
io.input_bool(
"WARNING !!! \n %s contains files! \n They will be deleted. \n Press enter to continue."
% (str(output_path)), False)
for filename in output_images_paths:
Path(filename).unlink()
else:
output_path.mkdir(parents=True, exist_ok=True)
if manual_output_debug_fix:
if debug_dir is None:
raise ValueError('debug-dir must be specified')
detector = 'manual'
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_print_func=io.log_info)
if debug_dir is not None:
debug_output_path = Path(debug_dir)
if manual_output_debug_fix:
if not debug_output_path.exists():
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)
io.log_info('Found %d images.' % (len(input_path_image_paths)))
else:
if debug_output_path.exists():
for filename in Path_utils.get_image_paths(debug_output_path):
Path(filename).unlink()
else:
debug_output_path.mkdir(parents=True, exist_ok=True)
images_found = len(input_path_image_paths)
faces_detected = 0
if images_found != 0:
if detector == 'manual':
io.log_info('Performing manual extract...')
data = ExtractSubprocessor(
[
ExtractSubprocessor.Data(filename)
for filename in input_path_image_paths
],
'landmarks',
image_size,
face_type,
debug_dir,
cpu_only=cpu_only,
manual=True,
manual_window_size=manual_window_size).run()
else:
io.log_info('Performing 1st pass...')
data = ExtractSubprocessor(
[
ExtractSubprocessor.Data(filename)
for filename in input_path_image_paths
],
'rects-' + detector,
image_size,
face_type,
debug_dir,
multi_gpu=multi_gpu,
cpu_only=cpu_only,
manual=False,
max_faces_from_image=max_faces_from_image).run()
io.log_info('Performing 2nd pass...')
data = ExtractSubprocessor(data,
'landmarks',
image_size,
face_type,
debug_dir,
multi_gpu=multi_gpu,
cpu_only=cpu_only,
manual=False).run()
io.log_info('Performing 3rd pass...')
data = ExtractSubprocessor(data,
'final',
image_size,
face_type,
debug_dir,
multi_gpu=multi_gpu,
cpu_only=cpu_only,
manual=False,
final_output_path=output_path).run()
faces_detected += sum([d.faces_detected for d in data])
if manual_fix:
if all(np.array([d.faces_detected > 0 for d in data]) == True):
io.log_info('All faces are detected, manual fix not needed.')
else:
fix_data = [
ExtractSubprocessor.Data(d.filename) for d in data
if d.faces_detected == 0
]
io.log_info('Performing manual fix for %d images...' %
(len(fix_data)))
fix_data = ExtractSubprocessor(
fix_data,
'landmarks',
image_size,
face_type,
debug_dir,
manual=True,
manual_window_size=manual_window_size).run()
fix_data = ExtractSubprocessor(
fix_data,
'final',
image_size,
face_type,
debug_dir,
multi_gpu=multi_gpu,
cpu_only=cpu_only,
manual=False,
final_output_path=output_path).run()
faces_detected += sum([d.faces_detected for d in fix_data])
io.log_info('-------------------------')
io.log_info('Images found: %d' % (images_found))
io.log_info('Faces detected: %d' % (faces_detected))
io.log_info('-------------------------')
def main(input_dir,
output_dir,
debug_dir=None,
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():
raise ValueError('输入路径不存在，请先确认workspace目录下是否存在data_dst和data_src文件夹')
if output_path.exists():
if not manual_output_debug_fix and input_path != output_path:
output_images_paths = Path_utils.get_image_paths(output_path)
if len(output_images_paths) > 0:
io.input_bool("警告 !!! \n %s 已经有图片了! \n 继续执行会删除之前的图片. \n 按回车[Enter]继续" % (str(output_path)), False )
for filename in output_images_paths:
Path(filename).unlink()
else:
output_path.mkdir(parents=True, exist_ok=True)
if manual_output_debug_fix:
if debug_dir is None:
raise ValueError('debug-dir must be specified')
detector = 'manual'
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_print_func=io.log_info)
if debug_dir is not None:
debug_output_path = Path(debug_dir)
if manual_output_debug_fix:
if not debug_output_path.exists():
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)
io.log_info('Found %d images.' % (len(input_path_image_paths)))
else:
if debug_output_path.exists():
for filename in Path_utils.get_image_paths(debug_output_path):
Path(filename).unlink()
else:
debug_output_path.mkdir(parents=True, exist_ok=True)
images_found = len(input_path_image_paths)
faces_detected = 0
if images_found != 0:
if detector == 'manual':
io.log_info ('正在启动手动提取...')
data = ExtractSubprocessor ([ ExtractSubprocessor.Data(filename) for filename in input_path_image_paths ], 'landmarks', image_size, face_type, debug_dir, cpu_only=cpu_only, manual=True, manual_window_size=manual_window_size).run()
else:
io.log_info ('第一阶段...')
data = ExtractSubprocessor ([ ExtractSubprocessor.Data(filename) for filename in input_path_image_paths ], 'rects-'+detector, image_size, face_type, debug_dir, multi_gpu=multi_gpu, cpu_only=cpu_only, manual=False).run()
io.log_info ('第二阶段...')
data = ExtractSubprocessor (data, 'landmarks', image_size, face_type, debug_dir, multi_gpu=multi_gpu, cpu_only=cpu_only, manual=False).run()
io.log_info ('第三阶段...')
data = ExtractSubprocessor (data, 'final', image_size, face_type, debug_dir, multi_gpu=multi_gpu, cpu_only=cpu_only, manual=False, final_output_path=output_path).run()
faces_detected += sum([d.faces_detected for d in data])
if manual_fix:
if all ( np.array ( [ d.faces_detected > 0 for d in data] ) == True ):
io.log_info ('All faces are detected, manual fix not needed.')
else:
fix_data = [ ExtractSubprocessor.Data(d.filename) for d in data if d.faces_detected == 0 ]
io.log_info ('Performing manual fix for %d images...' % (len(fix_data)) )
fix_data = ExtractSubprocessor (fix_data, 'landmarks', image_size, face_type, debug_dir, manual=True, manual_window_size=manual_window_size).run()
fix_data = ExtractSubprocessor (fix_data, 'final', image_size, face_type, debug_dir, multi_gpu=multi_gpu, cpu_only=cpu_only, manual=False, final_output_path=output_path).run()
faces_detected += sum([d.faces_detected for d in fix_data])
io.log_info ('-------------------------')
io.log_info ('图片数量: %d' % (images_found) )
io.log_info ('人脸数量: %d' % (faces_detected) )
io.log_info ('-------------------------')
