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Colombo
parent
8153e90ba3
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3 changed files with 103 additions and 101 deletions
4
main.py
4
main.py
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@ -66,8 +66,8 @@ if __name__ == "__main__":
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def process_dev_extract_umd_csv(arguments):
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def process_dev_extract_umd_csv(arguments):
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os_utils.set_process_lowest_prio()
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os_utils.set_process_lowest_prio()
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from mainscripts import Extractor
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from mainscripts import dev_misc
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Extractor.extract_umd_csv( arguments.input_csv_file,
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dev_misc.extract_umd_csv( arguments.input_csv_file,
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device_args={'cpu_only' : arguments.cpu_only,
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device_args={'cpu_only' : arguments.cpu_only,
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'multi_gpu' : arguments.multi_gpu,
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'multi_gpu' : arguments.multi_gpu,
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}
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}
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@ -789,105 +789,6 @@ def main(input_dir,
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faces_detected += sum([d.faces_detected for d in fix_data])
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faces_detected += sum([d.faces_detected for d in fix_data])
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io.log_info ('-------------------------')
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io.log_info ('Images found: %d' % (images_found) )
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io.log_info ('Faces detected: %d' % (faces_detected) )
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io.log_info ('-------------------------')
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#unused in end user workflow
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def extract_fanseg(input_dir, device_args={} ):
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multi_gpu = device_args.get('multi_gpu', False)
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cpu_only = device_args.get('cpu_only', False)
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input_path = Path(input_dir)
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if not input_path.exists():
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raise ValueError('Input directory not found. Please ensure it exists.')
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paths_to_extract = []
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for filename in Path_utils.get_image_paths(input_path) :
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filepath = Path(filename)
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dflimg = DFLIMG.load ( filepath )
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if dflimg is not None:
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paths_to_extract.append (filepath)
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paths_to_extract_len = len(paths_to_extract)
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if paths_to_extract_len > 0:
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io.log_info ("Performing extract fanseg for %d files..." % (paths_to_extract_len) )
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data = ExtractSubprocessor ([ ExtractSubprocessor.Data(filename) for filename in paths_to_extract ], 'fanseg', multi_gpu=multi_gpu, cpu_only=cpu_only).run()
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#unused in end user workflow
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def extract_umd_csv(input_file_csv,
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image_size=256,
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face_type='full_face',
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device_args={} ):
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#extract faces from umdfaces.io dataset csv file with pitch,yaw,roll info.
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multi_gpu = device_args.get('multi_gpu', False)
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cpu_only = device_args.get('cpu_only', False)
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face_type = FaceType.fromString(face_type)
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input_file_csv_path = Path(input_file_csv)
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if not input_file_csv_path.exists():
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raise ValueError('input_file_csv not found. Please ensure it exists.')
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input_file_csv_root_path = input_file_csv_path.parent
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output_path = input_file_csv_path.parent / ('aligned_' + input_file_csv_path.name)
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io.log_info("Output dir is %s." % (str(output_path)) )
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if output_path.exists():
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output_images_paths = Path_utils.get_image_paths(output_path)
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if len(output_images_paths) > 0:
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io.input_bool("WARNING !!! \n %s contains files! \n They will be deleted. \n Press enter to continue." % (str(output_path)), False )
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for filename in output_images_paths:
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Path(filename).unlink()
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else:
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output_path.mkdir(parents=True, exist_ok=True)
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try:
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with open( str(input_file_csv_path), 'r') as f:
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csv_file = f.read()
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except Exception as e:
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io.log_err("Unable to open or read file " + str(input_file_csv_path) + ": " + str(e) )
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return
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strings = csv_file.split('\n')
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keys = strings[0].split(',')
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keys_len = len(keys)
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csv_data = []
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for i in range(1, len(strings)):
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values = strings[i].split(',')
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if keys_len != len(values):
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io.log_err("Wrong string in csv file, skipping.")
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continue
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csv_data += [ { keys[n] : values[n] for n in range(keys_len) } ]
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data = []
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for d in csv_data:
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filename = input_file_csv_root_path / d['FILE']
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pitch, yaw, roll = float(d['PITCH']), float(d['YAW']), float(d['ROLL'])
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if pitch < -90 or pitch > 90 or yaw < -90 or yaw > 90 or roll < -90 or roll > 90:
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continue
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pitch_yaw_roll = pitch/90.0, yaw/90.0, roll/90.0
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x,y,w,h = float(d['FACE_X']), float(d['FACE_Y']), float(d['FACE_WIDTH']), float(d['FACE_HEIGHT'])
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data += [ ExtractSubprocessor.Data(filename=filename, rects=[ [x,y,x+w,y+h] ], pitch_yaw_roll=pitch_yaw_roll) ]
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images_found = len(data)
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faces_detected = 0
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if len(data) > 0:
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io.log_info ("Performing 2nd pass from csv file...")
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data = ExtractSubprocessor (data, 'landmarks', multi_gpu=multi_gpu, cpu_only=cpu_only).run()
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io.log_info ('Performing 3rd pass...')
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data = ExtractSubprocessor (data, 'final', image_size, face_type, None, multi_gpu=multi_gpu, cpu_only=cpu_only, manual=False, final_output_path=output_path).run()
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faces_detected += sum([d.faces_detected for d in data])
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io.log_info ('-------------------------')
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io.log_info ('-------------------------')
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io.log_info ('Images found: %d' % (images_found) )
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io.log_info ('Images found: %d' % (images_found) )
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io.log_info ('Faces detected: %d' % (faces_detected) )
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io.log_info ('Faces detected: %d' % (faces_detected) )
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@ -368,3 +368,104 @@ def apply_celebamaskhq(input_dir ):
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#import code
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#import code
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#code.interact(local=dict(globals(), **locals()))
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#code.interact(local=dict(globals(), **locals()))
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#unused in end user workflow
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def extract_fanseg(input_dir, device_args={} ):
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multi_gpu = device_args.get('multi_gpu', False)
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cpu_only = device_args.get('cpu_only', False)
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input_path = Path(input_dir)
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if not input_path.exists():
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raise ValueError('Input directory not found. Please ensure it exists.')
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paths_to_extract = []
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for filename in Path_utils.get_image_paths(input_path) :
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filepath = Path(filename)
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dflimg = DFLIMG.load ( filepath )
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if dflimg is not None:
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paths_to_extract.append (filepath)
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paths_to_extract_len = len(paths_to_extract)
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if paths_to_extract_len > 0:
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io.log_info ("Performing extract fanseg for %d files..." % (paths_to_extract_len) )
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data = ExtractSubprocessor ([ ExtractSubprocessor.Data(filename) for filename in paths_to_extract ], 'fanseg', multi_gpu=multi_gpu, cpu_only=cpu_only).run()
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#unused in end user workflow
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def extract_umd_csv(input_file_csv,
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image_size=256,
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face_type='full_face',
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device_args={} ):
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#extract faces from umdfaces.io dataset csv file with pitch,yaw,roll info.
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multi_gpu = device_args.get('multi_gpu', False)
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cpu_only = device_args.get('cpu_only', False)
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face_type = FaceType.fromString(face_type)
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input_file_csv_path = Path(input_file_csv)
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if not input_file_csv_path.exists():
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raise ValueError('input_file_csv not found. Please ensure it exists.')
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input_file_csv_root_path = input_file_csv_path.parent
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output_path = input_file_csv_path.parent / ('aligned_' + input_file_csv_path.name)
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io.log_info("Output dir is %s." % (str(output_path)) )
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if output_path.exists():
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output_images_paths = Path_utils.get_image_paths(output_path)
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if len(output_images_paths) > 0:
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io.input_bool("WARNING !!! \n %s contains files! \n They will be deleted. \n Press enter to continue." % (str(output_path)), False )
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for filename in output_images_paths:
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Path(filename).unlink()
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else:
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output_path.mkdir(parents=True, exist_ok=True)
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try:
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with open( str(input_file_csv_path), 'r') as f:
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csv_file = f.read()
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except Exception as e:
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io.log_err("Unable to open or read file " + str(input_file_csv_path) + ": " + str(e) )
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return
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strings = csv_file.split('\n')
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keys = strings[0].split(',')
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keys_len = len(keys)
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csv_data = []
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for i in range(1, len(strings)):
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values = strings[i].split(',')
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if keys_len != len(values):
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io.log_err("Wrong string in csv file, skipping.")
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continue
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csv_data += [ { keys[n] : values[n] for n in range(keys_len) } ]
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data = []
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for d in csv_data:
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filename = input_file_csv_root_path / d['FILE']
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#pitch, yaw, roll = float(d['PITCH']), float(d['YAW']), float(d['ROLL'])
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#if pitch < -90 or pitch > 90 or yaw < -90 or yaw > 90 or roll < -90 or roll > 90:
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# continue
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#
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#pitch_yaw_roll = pitch/90.0, yaw/90.0, roll/90.0
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x,y,w,h = float(d['FACE_X']), float(d['FACE_Y']), float(d['FACE_WIDTH']), float(d['FACE_HEIGHT'])
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data += [ ExtractSubprocessor.Data(filename=filename, rects=[ [x,y,x+w,y+h] ]) ]
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images_found = len(data)
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faces_detected = 0
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if len(data) > 0:
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io.log_info ("Performing 2nd pass from csv file...")
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data = ExtractSubprocessor (data, 'landmarks', multi_gpu=multi_gpu, cpu_only=cpu_only).run()
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io.log_info ('Performing 3rd pass...')
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data = ExtractSubprocessor (data, 'final', image_size, face_type, None, multi_gpu=multi_gpu, cpu_only=cpu_only, manual=False, final_output_path=output_path).run()
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faces_detected += sum([d.faces_detected for d in data])
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io.log_info ('-------------------------')
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io.log_info ('Images found: %d' % (images_found) )
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io.log_info ('Faces detected: %d' % (faces_detected) )
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io.log_info ('-------------------------')
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