FaceNet 使用实例介绍
FaceNet将人脸图像映射到欧几里得空间，空间的距离代表了人脸图像的相似性。基于该映射空间可以实现人脸识别，验证和聚类等任务。
1. 安装依赖程序
a) 安装 Tensorflow b) 下载克隆 FaceNet repo 到本地 c) 安装 需要的Python模块
2. 下载已训练模型
facenet提供了两个预训练模型，分别是基于CASIA-WebFace和MS-Celeb-1M人脸库训练的，链接为：
| Model name | LFW accuracy | Training dataset | Architecture |
|---|---|---|---|
| 20170511-185253 | 0.987 | CASIA-WebFace | Inception ResNet v1 |
| 20170512-110547 | 0.992 | MS-Celeb-1M | Inception ResNet v1 |
将下载的文件夹解压到src\models\目录下面，目录 20170512-110547 将包含以下内容：
20170512-110547.pb
model-20170512-110547.ckpt-250000.data-00000-of-00001
model-20170512-110547.ckpt-250000.index
model-20170512-110547.meta
3. 对齐图片
文件src/align/align_dataset_mtcnn.py给我们提供了一个对齐图片中面部的实例。主要过程是：
- 读取指定目录中的图片
- 进行面部检测
- 然后对齐裁剪面部
- 另存到指定目录
这段代码：
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from scipy import misc
import sys
import os
import argparse
import tensorflow as tf
import numpy as np
import facenet
import align.detect_face
import random
from time import sleep
output_dir = 'ad/'
input_dir = 'presidents/'
gpu_memory_fraction=0.25 #每个会话允许使用最大25％的GPU总内存
margin = 32
image_size = 160
random_order = False
detect_multiple_faces = True
if not os.path.exists(output_dir):
os.makedirs(output_dir)
# Store some git revision info in a text file in the log directory
src_path,_ = os.path.split(os.path.realpath(__file__))
facenet.store_revision_info(src_path, output_dir, ' '.join(sys.argv))
dataset = facenet.get_dataset(input_dir)
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)
minsize = 80 # minimum size of face
threshold = [ 0.6, 0.8, 0.95 ] # three steps's threshold
factor = 0.709 # scale factor
# Add a random key to the filename to allow alignment using multiple processes
random_key = np.random.randint(0, high=99999)
bounding_boxes_filename = os.path.join(output_dir, 'bounding_boxes_%05d.txt' % random_key)
with open(bounding_boxes_filename, "w") as text_file:
nrof_images_total = 0
nrof_successfully_aligned = 0
if random_order:
random.shuffle(dataset)
for cls in dataset:
output_class_dir = os.path.join(output_dir, cls.name)
if not os.path.exists(output_class_dir):
os.makedirs(output_class_dir)
if random_order:
random.shuffle(cls.image_paths)
for image_path in cls.image_paths:
nrof_images_total += 1
filename = os.path.splitext(os.path.split(image_path)[1])[0]
output_filename = os.path.join(output_class_dir, filename+'.png')
print(image_path)
if not os.path.exists(output_filename):
try:
img = misc.imread(image_path)
except (IOError, ValueError, IndexError) as e:
errorMessage = '{}: {}'.format(image_path, e)
print(errorMessage)
else:
if img.ndim<2:
print('Unable to align "%s"' % image_path)
text_file.write('%s\n' % (output_filename))
continue
if img.ndim == 2:
img = facenet.to_rgb(img)
img = img[:,:,0:3]
bounding_boxes, _ = align.detect_face.detect_face(img, minsize, pnet, rnet, onet, threshold, factor)
nrof_faces = bounding_boxes.shape[0]
if nrof_faces>0:
det = bounding_boxes[:,0:4]
det_arr = []
img_size = np.asarray(img.shape)[0:2]
if nrof_faces>1:
if detect_multiple_faces:
for i in range(nrof_faces):
det_arr.append(np.squeeze(det[i]))
else:
bounding_box_size = (det[:,2]-det[:,0])*(det[:,3]-det[:,1])
img_center = img_size / 2
offsets = np.vstack([ (det[:,0]+det[:,2])/2-img_center[1], (det[:,1]+det[:,3])/2-img_center[0] ])
offset_dist_squared = np.sum(np.power(offsets,2.0),0)
index = np.argmax(bounding_box_size-offset_dist_squared*2.0) # some extra weight on the centering
det_arr.append(det[index,:])
else:
det_arr.append(np.squeeze(det))
for i, det in enumerate(det_arr):
det = np.squeeze(det)
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],:]
scaled = misc.imresize(cropped, (image_size, image_size), interp='bilinear')
nrof_successfully_aligned += 1
filename_base, file_extension = os.path.splitext(output_filename)
if detect_multiple_faces:
output_filename_n = "{}_{}{}".format(filename_base, i, file_extension)
else:
output_filename_n = "{}{}".format(filename_base, file_extension)
misc.imsave(output_filename_n, scaled)
text_file.write('%s %d %d %d %d\n' % (output_filename_n, bb[0], bb[1], bb[2], bb[3]))
else:
print('Unable to align "%s"' % image_path)
text_file.write('%s\n' % (output_filename))
print('Total number of images: %d' % nrof_images_total)
print('Number of successfully aligned images: %d' % nrof_successfully_aligned)
4. 人脸比对例子
facenet可以直接比对两个人脸经过它的网络映射之后的欧氏距离，运行程序为facenet-master\src\compare.py。
CMD下运行：
python compare.py models\20170511-185253 1.jpg 2.jpg