git clone [email protected]:davidsandberg/facenet.git

# python はとりあえず 3.6.5でためしている
cd facenet pyenv local 3.6.5
pip install -r requirements.txt

# https://drive.google.com/file/d/1EXPBSXwTaqrSC0OhUdXNmKSh9qJUQ55-/view からmodelをdownloadしておく
mkdir models
# とかして置いておく

# 比べたいテスト画像を
mkdir images
# とかして置いておく

""Performs face alignment and calculates L2 distance between the embeddings of images."""

# MIT License
#
# Copyright (c) 2016 David Sandberg
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in all
# copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

from scipy import misc
import tensorflow as tf
import numpy as np
import sys
import os
import copy
import argparse
import facenet
import align.detect_face
import cv2
import functools


def main(args):

  images = load_and_align_data(args.image_files, args.image_size, args.margin, args.gpu_memory_fraction)
  with tf.Graph().as_default():
    with tf.Session() as sess:
      # Load the model
      facenet.load_model(args.model)

      # Get input and output tensors
      images_placeholder = tf.get_default_graph().get_tensor_by_name("input:0")
      embeddings = tf.get_default_graph().get_tensor_by_name("embeddings:0")
      phase_train_placeholder = tf.get_default_graph().get_tensor_by_name("phase_train:0")

      # Run forward pass to calculate embeddings
      feed_dict = {images_placeholder: images, phase_train_placeholder: False}
      emb = sess.run(embeddings, feed_dict=feed_dict)
      nrof_images = len(images)

      print('Images:')
      for i in range(len(args.image_files)):
        print('%1d: %s' % (i, args.image_files[i]))
      print('')

      # Print distance matrix
      print('Distance matrix')
      print('    ', end='')
      for i in range(nrof_images):
        print('    %1d     ' % i, end='')
      print('')
      for i in range(nrof_images):
        print('%1d  ' % i, end='')
        for j in range(nrof_images):
          dist = np.sqrt(np.sum(np.square(np.subtract(emb[i, :], emb[j, :]))))
          print('  %1.4f  ' % dist, end='')
        print('')
  cv2.waitKey(0)
  cv2.destroyAllWindows()


def load_and_align_data(image_paths, image_size, margin, gpu_memory_fraction):

  minsize = 20  # minimum size of face
  threshold = [0.6, 0.7, 0.7]  # three steps's threshold
  factor = 0.709  # scale factor

  print('Creating networks and loading parameters')
  with tf.Graph().as_default():
    gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=gpu_memory_fraction)
    sess = tf.Session(config=tf.ConfigProto(gpu_options=gpu_options, log_device_placement=False))
    with sess.as_default():
      pnet, rnet, onet = align.detect_face.create_mtcnn(sess, None)

  tmp_image_paths = copy.copy(image_paths)
  img_list = []
  img_no = 0
  for image in tmp_image_paths:
    img = misc.imread(os.path.expanduser(image), mode='RGB')
    img_size = np.asarray(img.shape)[0:2]
    bounding_boxes, _ = align.detect_face.detect_face(img, minsize, pnet, rnet, onet, threshold, factor)
    if len(bounding_boxes) < 1:
      image_paths.remove(image)
      print("can't detect face, remove ", image)
      continue
    img2 = cv2.imread(os.path.expanduser(image))
    for box in bounding_boxes:
      # この辺で顔に番号を振っている
      img2 = cv2.rectangle(img2, (int(box[0]), int(box[1])), (int(box[2]), int(box[3])), (0, 255, 0), 1)
      cv2.putText(img2, str(img_no), ((int(box[0]) + int(box[2])) // 2, (int(box[1]) + int(box[3])) // 2),
                  cv2.FONT_HERSHEY_PLAIN, 2, (255, 255, 0), 5)
      img_no += 1
    cv2.imshow(f'test{img_no}', img2)
    for i in range(len(bounding_boxes)):
      det = np.squeeze(bounding_boxes[i, 0:4])
      bb = np.zeros(4, dtype=np.int32)
      bb[0] = np.maximum(det[0] - margin / 2, 0)
      bb[1] = np.maximum(det[1] - margin / 2, 0)
      bb[2] = np.minimum(det[2] + margin / 2, img_size[1])
      bb[3] = np.minimum(det[3] + margin / 2, img_size[0])
      cropped = img[bb[1]:bb[3], bb[0]:bb[2], :]
      aligned = misc.imresize(cropped, (image_size, image_size), interp='bilinear')
      prewhitened = facenet.prewhiten(aligned)
      img_list.append(prewhitened)
  images = np.stack(img_list)
  return images


def parse_arguments(argv):
  parser = argparse.ArgumentParser()

  parser.add_argument('model', type=str,
                      help='Could be either a directory containing the meta_file and ckpt_file or a model protobuf (.pb) file')
  parser.add_argument('image_files', type=str, nargs='+', help='Images to compare')
  parser.add_argument('--image_size', type=int,
                      help='Image size (height, width) in pixels.', default=160)
  parser.add_argument('--margin', type=int,
                      help='Margin for the crop around the bounding box (height, width) in pixels.', default=44)
  parser.add_argument('--gpu_memory_fraction', type=float,
                      help='Upper bound on the amount of GPU memory that will be used by the process.', default=1.0)
  return parser.parse_args(argv)


if __name__ == '__main__':
  main(parse_arguments(sys.argv[1:]))

Creating networks and loading parameters
2019-05-29 00:27:43.585356: I tensorflow/core/platform/cpu_feature_guard.cc:140] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA
Model directory: models/20180402-114759
Metagraph file: model-20180402-114759.meta
Checkpoint file: model-20180402-114759.ckpt-275
Images:
0: images/nissinIMGL1032_TP_V4.jpg
1: images/saya12050I9A1675_TP_V4.jpg
2: images/akanesaya501IMGL3229_TP_V4.jpg

Distance matrix
        0         1         2
0    0.0000    1.0826    0.9648
1    1.0826    0.0000    0.6487
2    0.9648    0.6487    0.0000