"""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
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") # 网络输入
embeddings = tf.get_default_graph().get_tensor_by_name("embeddings:0") # 输出
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(args.image_files) # 图片张数
print('Images:')
for i in range(nrof_images):
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,:])))) # 计算欧式距离
print(' %1.4f ' % dist, end='')
print('')
def load_and_align_data(image_paths, image_size, margin, gpu_memory_fraction):
# mtcnn 要用到的3个参数
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')
# 加载mtcnn模型
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 = []
# 遍历图片
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) # mtcnn人脸检测返回人脸边框shape(边框数,5),第二维前4个数是边框坐标,第5个数是score
# 如果没检测到人脸
if len(bounding_boxes) < 1:
image_paths.remove(image)
print("can't detect face, remove ", image)
continue
det = np.squeeze(bounding_boxes[0,0:4]) # 删除维度为1的那一维,即mtcnn返回的边框数那一维(第一维)
bb = np.zeros(4, dtype=np.int32)
bb[0] = np.maximum(det[0]-margin/2, 0) # 坐标往下移一点。mtcnn检测出来的只有人脸部分,扩展其范围以包含更多信息
bb[1] = np.maximum(det[1]-margin/2, 0) # 左移
bb[2] = np.minimum(det[2]+margin/2, img_size[1]) # 上移
bb[3] = np.minimum(det[3]+margin/2, img_size[0]) # 右移
cropped = img[bb[1]:bb[3],bb[0]:bb[2],:] # 从输入图片中裁剪处人脸部分
aligned = misc.imresize(cropped, (image_size, image_size), interp='bilinear') # resize为facenet网络输入大小160x160
prewhitened = facenet.prewhiten(aligned) # 图片的标准化处理,类似tf.image.per_img_standard()
img_list.append(prewhitened)
images = np.stack(img_list) # 将几张图片堆叠起来
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:]))
欧式距离公式:
n维空间点a(x11,x12,…,x1n)与b(x21,x22,…,x2n)间的欧氏距离(两个n维向量)
图片.png
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