120 lines
4.6 KiB
Python
120 lines
4.6 KiB
Python
# -*- coding: utf-8 -*-
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from __future__ import print_function
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import click
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import os
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import re
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import face_recognition.api as face_recognition
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import multiprocessing
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import itertools
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import sys
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import PIL.Image
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import numpy as np
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def scan_known_people(known_people_folder):
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known_names = []
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known_face_encodings = []
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for file in image_files_in_folder(known_people_folder):
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basename = os.path.splitext(os.path.basename(file))[0]
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img = face_recognition.load_image_file(file)
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encodings = face_recognition.face_encodings(img)
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if len(encodings) > 1:
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click.echo("WARNING: More than one face found in {}. Only considering the first face.".format(file))
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if len(encodings) == 0:
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click.echo("WARNING: No faces found in {}. Ignoring file.".format(file))
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else:
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known_names.append(basename)
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known_face_encodings.append(encodings[0])
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return known_names, known_face_encodings
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def print_result(filename, name, distance, show_distance=False):
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if show_distance:
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print("{},{},{}".format(filename, name, distance))
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else:
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print("{},{}".format(filename, name))
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def test_image(image_to_check, known_names, known_face_encodings, tolerance=0.6, show_distance=False):
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unknown_image = face_recognition.load_image_file(image_to_check)
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# Scale down image if it's giant so things run a little faster
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if max(unknown_image.shape) > 1600:
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pil_img = PIL.Image.fromarray(unknown_image)
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pil_img.thumbnail((1600, 1600), PIL.Image.LANCZOS)
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unknown_image = np.array(pil_img)
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unknown_encodings = face_recognition.face_encodings(unknown_image)
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for unknown_encoding in unknown_encodings:
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distances = face_recognition.face_distance(known_face_encodings, unknown_encoding)
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result = list(distances <= tolerance)
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if True in result:
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[print_result(image_to_check, name, distance, show_distance) for is_match, name, distance in zip(result, known_names, distances) if is_match]
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else:
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print_result(image_to_check, "unknown_person", None, show_distance)
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if not unknown_encodings:
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# print out fact that no faces were found in image
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print_result(image_to_check, "no_persons_found", None, show_distance)
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def image_files_in_folder(folder):
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return [os.path.join(folder, f) for f in os.listdir(folder) if re.match(r'.*\.(jpg|jpeg|png)', f, flags=re.I)]
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def process_images_in_process_pool(images_to_check, known_names, known_face_encodings, number_of_cpus, tolerance, show_distance):
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if number_of_cpus == -1:
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processes = None
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else:
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processes = number_of_cpus
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# macOS will crash due to a bug in libdispatch if you don't use 'forkserver'
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context = multiprocessing
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if "forkserver" in multiprocessing.get_all_start_methods():
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context = multiprocessing.get_context("forkserver")
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pool = context.Pool(processes=processes)
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function_parameters = zip(
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images_to_check,
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itertools.repeat(known_names),
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itertools.repeat(known_face_encodings),
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itertools.repeat(tolerance),
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itertools.repeat(show_distance)
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)
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pool.starmap(test_image, function_parameters)
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@click.command()
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@click.argument('known_people_folder')
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@click.argument('image_to_check')
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@click.option('--cpus', default=1, help='number of CPU cores to use in parallel (can speed up processing lots of images). -1 means "use all in system"')
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@click.option('--tolerance', default=0.6, help='Tolerance for face comparisons. Default is 0.6. Lower this if you get multiple matches for the same person.')
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@click.option('--show-distance', default=False, type=bool, help='Output face distance. Useful for tweaking tolerance setting.')
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def main(known_people_folder, image_to_check, cpus, tolerance, show_distance):
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known_names, known_face_encodings = scan_known_people(known_people_folder)
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# Multi-core processing only supported on Python 3.4 or greater
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if (sys.version_info < (3, 4)) and cpus != 1:
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click.echo("WARNING: Multi-processing support requires Python 3.4 or greater. Falling back to single-threaded processing!")
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cpus = 1
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if os.path.isdir(image_to_check):
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if cpus == 1:
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[test_image(image_file, known_names, known_face_encodings, tolerance, show_distance) for image_file in image_files_in_folder(image_to_check)]
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else:
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process_images_in_process_pool(image_files_in_folder(image_to_check), known_names, known_face_encodings, cpus, tolerance, show_distance)
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else:
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test_image(image_to_check, known_names, known_face_encodings, tolerance, show_distance)
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if __name__ == "__main__":
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main()
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