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pfzpyac3f 2 years ago
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# -*- coding: utf-8 -*-
from __future__ import print_function
import click
import os
import re
import face_recognition.api as face_recognition
import multiprocessing
import itertools
import sys
import PIL.Image
import numpy as np
def scan_known_people(known_people_folder):
known_names = []
known_face_encodings = []
for file in image_files_in_folder(known_people_folder):
basename = os.path.splitext(os.path.basename(file))[0]
img = face_recognition.load_image_file(file)
encodings = face_recognition.face_encodings(img)
if len(encodings) > 1:
click.echo("WARNING: More than one face found in {}. Only considering the first face.".format(file))
if len(encodings) == 0:
click.echo("WARNING: No faces found in {}. Ignoring file.".format(file))
else:
known_names.append(basename)
known_face_encodings.append(encodings[0])
return known_names, known_face_encodings
def print_result(filename, name, distance, show_distance=False):
if show_distance:
print("{},{},{}".format(filename, name, distance))
else:
print("{},{}".format(filename, name))
def test_image(image_to_check, known_names, known_face_encodings, tolerance=0.6, show_distance=False):
unknown_image = face_recognition.load_image_file(image_to_check)
# Scale down image if it's giant so things run a little faster
if max(unknown_image.shape) > 1600:
pil_img = PIL.Image.fromarray(unknown_image)
pil_img.thumbnail((1600, 1600), PIL.Image.LANCZOS)
unknown_image = np.array(pil_img)
unknown_encodings = face_recognition.face_encodings(unknown_image)
for unknown_encoding in unknown_encodings:
distances = face_recognition.face_distance(known_face_encodings, unknown_encoding)
result = list(distances <= tolerance)
if True in result:
[print_result(image_to_check, name, distance, show_distance) for is_match, name, distance in zip(result, known_names, distances) if is_match]
else:
print_result(image_to_check, "unknown_person", None, show_distance)
if not unknown_encodings:
# print out fact that no faces were found in image
print_result(image_to_check, "no_persons_found", None, show_distance)
def image_files_in_folder(folder):
return [os.path.join(folder, f) for f in os.listdir(folder) if re.match(r'.*\.(jpg|jpeg|png)', f, flags=re.I)]
def process_images_in_process_pool(images_to_check, known_names, known_face_encodings, number_of_cpus, tolerance, show_distance):
if number_of_cpus == -1:
processes = None
else:
processes = number_of_cpus
# macOS will crash due to a bug in libdispatch if you don't use 'forkserver'
context = multiprocessing
if "forkserver" in multiprocessing.get_all_start_methods():
context = multiprocessing.get_context("forkserver")
pool = context.Pool(processes=processes)
function_parameters = zip(
images_to_check,
itertools.repeat(known_names),
itertools.repeat(known_face_encodings),
itertools.repeat(tolerance),
itertools.repeat(show_distance)
)
pool.starmap(test_image, function_parameters)
@click.command()
@click.argument('known_people_folder')
@click.argument('image_to_check')
@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"')
@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.')
@click.option('--show-distance', default=False, type=bool, help='Output face distance. Useful for tweaking tolerance setting.')
def main(known_people_folder, image_to_check, cpus, tolerance, show_distance):
known_names, known_face_encodings = scan_known_people(known_people_folder)
# Multi-core processing only supported on Python 3.4 or greater
if (sys.version_info < (3, 4)) and cpus != 1:
click.echo("WARNING: Multi-processing support requires Python 3.4 or greater. Falling back to single-threaded processing!")
cpus = 1
if os.path.isdir(image_to_check):
if cpus == 1:
[test_image(image_file, known_names, known_face_encodings, tolerance, show_distance) for image_file in image_files_in_folder(image_to_check)]
else:
process_images_in_process_pool(image_files_in_folder(image_to_check), known_names, known_face_encodings, cpus, tolerance, show_distance)
else:
test_image(image_to_check, known_names, known_face_encodings, tolerance, show_distance)
if __name__ == "__main__":
main()
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