菜单

人脸识别

2018年12月30日 - www.bway883.com

人脸识别,基于人脸部特征信息识别身份的生物体识别技术。录像机、录像头采集人脸图像或视频流,自动检测、跟踪图像中脸部,做脸部相关技能处理,人脸检测、人脸关键点检测、人脸验证等。《早稻田科技评价》(MIT
Technology
Review),二零一七年天下十大突破性技术榜单,支付宝“刷脸支付”(Paying with Your
Face)入围。

人脸识别优势,非强制性(采集格局不便于被发现,被识旁人脸图像可积极赢得)、非接触性(用户不需要与设施接触)、并发性(可同时三个人脸检测、跟踪、识别)。深度学习前,人脸识别两步骤:高维人工特征提取、降维。传统人脸识别技术基于可见光图像。深度学习+大数额(海量有标注人脸数据)为人脸识别领域主流技术路线。神经网络人脸识别技术,大量样本图像磨练识别模型,无需人工拔取特征,样本磨炼过程自行学习,识别准确率可以直达99%。

人脸识别技术流程。

人脸图像采集、检测。人脸图像采集,视频头把人脸图像采集下来,静态图像、动态图像、不同职务、不同表情。用户在收集设备拍报范围内,采集设置自动检索并拍摄。人脸检测属于目标检测(object
detection)。对要检测对象对象概率总结,得到待检测对象特征,建立目的检测模型。用模子匹配输入图像,输出匹配区域。人脸检测是人脸识别预处理,准确标定人脸在图像的职务大小。人脸图像情势特点丰富,直方图特征、颜色特征、模板特征、结构特征、哈尔(哈尔)特征(Haar-like
feature)。人脸检测挑出有用音信,用特色检测脸部。人脸检测算法,模板匹配模型、Adaboost模型,艾达boost模型速度。精度综合性能最好,训练慢、检测快,可达成视频流实时检测效果。

人脸图像预处理。基于人脸检测结果,处理图像,服务特征提取。系统拿到人脸图像遭到各个标准限制、随机烦扰,需缩放、旋转、拉伸、光线补偿、灰度变换、直方图均衡化、规范化、几何校正、过滤、锐化等图像预处理。

人脸图像特征提取。人脸图像新闻数字化,人脸图像转变为一串数字(特征向量)。如,眼睛左侧、嘴唇左侧、鼻子、下巴地方,特征点间欧氏距离、曲率、角度提取出特色分量,相关特征连接成长特征向量。

人脸图像匹配、识别。提取人脸图像特点数据与数据库存储人脸特征模板搜索匹配,遵照相似程度对地位音讯举办判定,设定阈值,相似度越过阈值,输出匹配结果。确认,一对一(1:1)图像相比,注解“你就是您”,金融核实身份、消息安全领域。辨认,一对多(1:N)图像匹配,“N人中找你”,录像流,人走进识别范围就到位辨认,安防领域。

人脸识别分类。

人脸检测。检测、定位图片人脸,重回高业饿啊人脸框坐标。对人脸分析、处理的第一步。“滑动窗口”,拔取图像矩形区域作滑动窗口,窗口中提取特征对图像区域描述,按照特征描述判断窗口是否人脸。不断遍历需要着眼窗口。

人脸关键点检测。定位、重回人脸五官、概略关键点坐标地方。人脸概略、眼睛、眉毛、嘴唇、鼻子概略。Face++提供高达106点关键点。人脸关键点定位技术,级联形回归(cascaded
shape regression,
CSR)。人脸识别,基于DeepID网络布局。DeepID网络布局类似卷积神经网络布局,倒数第二层,有DeepID层,与卷积层4、最大池化层3相连,卷积神经网络层数越高视野域越大,既考虑部分特征,又考虑全局特征。输入层
31x39x1、卷积层1 28x36x20(卷积核4x4x1)、最大池化层1
12x18x20(过滤器2×2)、卷积层2 12x16x20(卷积核3x3x20)、最大池化层2
6x8x40(过滤器2×2)、卷积层3 4x6x60(卷积核3x3x40)、最大池化层2
2x3x60(过滤器2×2)、卷积层4 2x2x80(卷积核2x2x60)、DeepID层
1×160、全连接层 Softmax。《Deep Learning Face Representation from
Predicting 10000 Classes》
http://mmlab.ie.cuhk.edu.hk/pdf/YiSun\_CVPR14.pdf

人脸验证。分析两张人脸同一人可能大小。输入两张人脸,得到置信度分类、相应阈值,评估相似度。

人脸属性检测。人脸属性辩识、人脸心情分析。https://www.betaface.com/wpa/
在线人脸识别测试。给出人年龄、是否有胡子、心境(如沐春风、正常、生气、愤怒)、性别、是否带眼镜、肤色。

人脸识别应用,美图秀秀美颜应用、世纪佳缘查看地下配偶“面相”相似度,支付领域“刷脸支付”,安防领域“人脸鉴权”。Face++、商汤科技,提供人脸识别SDK。

人脸检测。https://github.com/davidsandberg/facenet

Florian Schroff、Dmitry Kalenichenko、James Philbin论文《FaceNet: A
Unified Embedding for Face Recognition and Clustering》
https://arxiv.org/abs/1503.03832
https://github.com/davidsandberg/facenet/wiki/Validate-on-lfw

LFW(Labeled Faces in the Wild
Home)数据集。http://vis-www.cs.umass.edu/lfw/
。美利哥加利福尼亚高校阿姆斯特分校统计机视觉实验室整理。13233张图片,5749人。4096人只有一张图纸,1680人多于一张。每张图片尺寸250×250。人脸图片在各类人物名字文件夹下。

数量预处理。校准代码
https://github.com/davidsandberg/facenet/blob/master/src/align/align\_dataset\_mtcnn.py

检测所用数据集校准为和预磨练模型所用数据集大小相同。
安装环境变量

export PYTHONPATH=[…]/facenet/src

校准命令

for N in {1..4}; do python src/align/align_dataset_mtcnn.py
~/datasets/lfw/raw ~/datasets/lfw/lfw_mtcnnpy_160 –image_size 160
–margin 32 –random_order –gpu_memory_fraction 0.25 & done

预磨炼模型20170216-091149.zip
https://drive.google.com/file/d/0B5MzpY9kBtDVZ2RpVDYwWmxoSUk
训练集 MS-Celeb-1M数据集
https://www.microsoft.com/en-us/research/project/ms-celeb-1m-challenge-recognizing-one-million-celebrities-real-world/
。微软人脸识别数据库,有名的人榜选取前100万有名气的人,搜索引擎采集每个有名的人100张人脸图片。预磨炼模型准确率0.993+-0.004。

检测。python src/validate_on_lfw.py datasets/lfw/lfw_mtcnnpy_160
models
原则相比较,采用facenet/data/pairs.txt,官方随机生成多少,匹配和不匹配人名和图片编号。

十折交叉验证(10-fold cross
validation),精度测试方法。数据集分成10份,轮流将其中9份做磨练集,1份做测试保,10次结果均值作算法精度估算。一般需要频繁10折交叉验证求均值。

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow as tf
import numpy as np
import argparse
import facenet
import lfw
import os
import sys
import math
from sklearn import metrics
from scipy.optimize import brentq
from scipy import interpolate

def main(args):
with tf.Graph().as_default():
with tf.Session() as sess:

# Read the file containing the pairs used for testing
# 1. 读入此前的pairs.txt文件
# 读入后如[[‘Abel_Pacheco’,’1′,’4′]]
pairs = lfw.read_pairs(os.path.expanduser(args.lfw_pairs))
# Get the paths for the corresponding images
# 获取文件路径和是否配合关系对
paths, actual_issame =
lfw.get_paths(os.path.expanduser(args.lfw_dir), pairs,
args.lfw_file_ext)
# Load the model
# 2. 加载模型
facenet.load_model(args.model)

# Get input and output tensors
# 获取输入输出张量
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”)

#image_size = images_placeholder.get_shape()[1] # For some reason
this doesn’t work for frozen graphs
image_size = args.image_size
embedding_size = embeddings.get_shape()[1]

# Run forward pass to calculate embeddings
# 3. 使用前向传来验证
print(‘Runnning forward pass on LFW images’)
batch_size = args.lfw_batch_size
nrof_images = len(paths)
nrof_batches = int(math.ceil(1.0*nrof_images / batch_size)) #
总共批次数
emb_array = np.zeros((nrof_images, embedding_size))
for i in range(nrof_batches):
start_index = i*batch_size
end_index = min((i+1)*batch_size, nrof_images)
paths_batch = paths[start_index:end_index]
images = facenet.load_data(paths_batch, False, False, image_size)
feed_dict = { images_placeholder:images,
phase_train_placeholder:False }
emb_array[start_index:end_index,:] = sess.run(embeddings,
feed_dict=feed_dict)

# 4. 计量准确率、验证率,十折交叉验证措施
tpr, fpr, accuracy, val, val_std, far = lfw.evaluate(emb_array,
actual_issame, nrof_folds=args.lfw_nrof_folds)
print(‘Accuracy: %1.3f+-%1.3f’ % (np.mean(accuracy),
np.std(accuracy)))
print(‘Validation rate: %2.5f+-%2.5f @ FAR=%2.5f’ % (val, val_std,
far))
# 得到auc值
auc = metrics.auc(fpr, tpr)
print(‘Area Under Curve (AUC): %1.3f’ % auc)
# 1得到错误率(eer)
eer = brentq(lambda x: 1. – x – interpolate.interp1d(fpr, tpr)(x), 0.,
1.)
print(‘Equal Error Rate (EER): %1.3f’ % eer)

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

parser.add_argument(‘lfw_dir’, type=str,
help=’Path to the data directory containing aligned LFW face
patches.’)
parser.add_argument(‘–lfw_batch_size’, type=int,
help=’Number of images to process in a batch in the LFW test set.’,
default=100)
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_size’, type=int,
help=’Image size (height, width) in pixels.’, default=160)
parser.add_argument(‘–lfw_pairs’, type=str,
help=’The file containing the pairs to use for validation.’,
default=’data/pairs.txt’)
parser.add_argument(‘–lfw_file_ext’, type=str,
help=’The file extension for the LFW dataset.’, default=’png’,
choices=[‘jpg’, ‘png’])
parser.add_argument(‘–lfw_nrof_folds’, type=int,
help=’Number of folds to use for cross validation. Mainly used for
testing.’, default=10)
return parser.parse_args(argv)
if __name__ == ‘__main__’:
main(parse_arguments(sys.argv[1:]))

性别、年龄识别。https://github.com/dpressel/rude-carnie

Adience
数据集。http://www.openu.ac.il/home/hassner/Adience/data.html\#agegender
。26580张图片,2284类,年龄范围8个区段(0~2、4~6、8~13、15~20、25~32、38~43、48~53、60~),含有噪声、姿势、光照变化。aligned
# 经过剪裁对齐多少,faces #
原始数据。fold_0_data.txt至fold_4_data.txt
全体数额符号。fold_frontal_0_data.txt至fold_frontal_4_data.txt
仅用类似正面态度面部标记。数据结构 user_id
用户Flickr帐户ID、original_image 图片文件名、face_id
人标识符、age、gender、x、y、dx、dy 人脸边框、tilt_ang
切斜角度、fiducial_yaw_angle 基准偏移角度、fiducial_score
基准分数。https://www.flickr.com/

数量预处理。脚本把数量处理成TFRecords格式。https://github.com/dpressel/rude-carnie/blob/master/preproc.py
https://github.com/GilLevi/AgeGenderDeepLearning/tree/master/Folds文件夹,已经对训练集、测试集划分、标注。gender\_train.txt、gender\_val.txt
图片列表 Adience 数据集处理TFRecords文件。图片处理为大小256×256
JPEG编码RGB图像。tf.python_io.TFRecordWriter写入TFRecords文件,输出文件output_file。

构建模型。年龄、性别操练模型,Gil Levi、Tal Hassner散文《Age and Gender
Classification Using Convolutional Neural
Networks》http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.722.9654&rank=1
。模型 https://github.com/dpressel/rude-carnie/blob/master/model.py
。tenforflow.contrib.slim。

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from datetime import datetime
import time
import os
import numpy as np
import tensorflow as tf
from data import distorted_inputs
import re
from tensorflow.contrib.layers import *
from tensorflow.contrib.slim.python.slim.nets.inception_v3 import
inception_v3_base
TOWER_NAME = ‘tower’
def select_model(name):
if name.startswith(‘inception’):
print(‘selected (fine-tuning) inception model’)
return inception_v3
elif name == ‘bn’:
print(‘selected batch norm model’)
return levi_hassner_bn
print(‘selected default model’)
return levi_hassner
def get_checkpoint(checkpoint_path, requested_step=None,
basename=’checkpoint’):
if requested_step is not None:
model_checkpoint_path = ‘%s/%s-%s’ % (checkpoint_path, basename,
requested_step)
if os.path.exists(model_checkpoint_path) is None:
print(‘No checkpoint file found at [%s]’ % checkpoint_path)
exit(-1)
print(model_checkpoint_path)
print(model_checkpoint_path)
return model_checkpoint_path, requested_step
ckpt = tf.train.get_checkpoint_state(checkpoint_path)
if ckpt and ckpt.model_checkpoint_path:
# Restore checkpoint as described in top of this program
print(ckpt.model_checkpoint_path)
global_step =
ckpt.model_checkpoint_path.split(‘/’)[-1].split(‘-‘)[-1]
return ckpt.model_checkpoint_path, global_step
else:
print(‘No checkpoint file found at [%s]’ % checkpoint_path)
exit(-1)
def _activation_summary(x):
tensor_name = re.sub(‘%s_[0-9]*/’ % TOWER_NAME, ”, x.op.name)
tf.summary.histogram(tensor_name + ‘/activations’, x)
tf.summary.scalar(tensor_name + ‘/sparsity’, tf.nn.zero_fraction(x))
def inception_v3(nlabels, images, pkeep, is_training):
batch_norm_params = {
“is_training”: is_training,
“trainable”: True,
# Decay for the moving averages.
“decay”: 0.9997,
# Epsilon to prevent 0s in variance.
“epsilon”: 0.001,
# Collection containing the moving mean and moving variance.
“variables_collections”: {
“beta”: None,
“gamma”: None,
“moving_mean”: [“moving_vars”],
“moving_variance”: [“moving_vars”],
}
}
weight_decay = 0.00004
stddev=0.1
weights_regularizer =
tf.contrib.layers.l2_regularizer(weight_decay)
with tf.variable_scope(“InceptionV3”, “InceptionV3”, [images]) as
scope:
with tf.contrib.slim.arg_scope(
[tf.contrib.slim.conv2d, tf.contrib.slim.fully_connected],
weights_regularizer=weights_regularizer,
trainable=True):
with tf.contrib.slim.arg_scope(
[tf.contrib.slim.conv2d],
weights_initializer=tf.truncated_normal_initializer(stddev=stddev),
activation_fn=tf.nn.relu,
normalizer_fn=batch_norm,
normalizer_params=batch_norm_params):
net, end_points = inception_v3_base(images, scope=scope)
with tf.variable_scope(“logits”):
shape = net.get_shape()
net = avg_pool2d(net, shape[1:3], padding=”VALID”, scope=”pool”)
net = tf.nn.dropout(net, pkeep, name=’droplast’)
net = flatten(net, scope=”flatten”)

with tf.variable_scope(‘output’) as scope:

weights = tf.Variable(tf.truncated_normal([2048, nlabels], mean=0.0,
stddev=0.01), name=’weights’)
biases = tf.Variable(tf.constant(0.0, shape=[nlabels],
dtype=tf.float32), name=’biases’)
output = tf.add(tf.matmul(net, weights), biases, name=scope.name)
_activation_summary(output)
return output
def levi_hassner_bn(nlabels, images, pkeep, is_training):
batch_norm_params = {
“is_training”: is_training,
“trainable”: True,
# Decay for the moving averages.
“decay”: 0.9997,
# Epsilon to prevent 0s in variance.
“epsilon”: 0.001,
# Collection containing the moving mean and moving variance.
“variables_collections”: {
“beta”: None,
“gamma”: None,
“moving_mean”: [“moving_vars”],
“moving_variance”: [“moving_vars”],
}
}
weight_decay = 0.0005
weights_regularizer =
tf.contrib.layers.l2_regularizer(weight_decay)
with tf.variable_scope(“LeviHassnerBN”, “LeviHassnerBN”, [images]) as
scope:
with tf.contrib.slim.arg_scope(
[convolution2d, fully_connected],
weights_regularizer=weights_regularizer,
biases_initializer=tf.constant_initializer(1.),
weights_initializer=tf.random_normal_initializer(stddev=0.005),
trainable=True):
with tf.contrib.slim.arg_scope(
[convolution2d],
weights_initializer=tf.random_normal_initializer(stddev=0.01),
normalizer_fn=batch_norm,
normalizer_params=batch_www.bway883.com,norm_params):
conv1 = convolution2d(images, 96, [7,7], [4, 4], padding=’VALID’,
biases_initializer=tf.constant_initializer(0.), scope=’conv1′)
pool1 = max_pool2d(conv1, 3, 2, padding=’VALID’, scope=’pool1′)
conv2 = convolution2d(pool1, 256, [5, 5], [1, 1], padding=’SAME’,
scope=’conv2′)
pool2 = max_pool2d(conv2, 3, 2, padding=’VALID’, scope=’pool2′)
conv3 = convolution2d(pool2, 384, [3, 3], [1, 1], padding=’SAME’,
biases_initializer=tf.constant_initializer(0.), scope=’conv3′)
pool3 = max_pool2d(conv3, 3, 2, padding=’VALID’, scope=’pool3′)
# can use tf.contrib.layer.flatten
flat = tf.reshape(pool3, [-1, 384*6*6], name=’reshape’)
full1 = fully_connected(flat, 512, scope=’full1′)
drop1 = tf.nn.dropout(full1, pkeep, name=’drop1′)
full2 = fully_connected(drop1, 512, scope=’full2′)
drop2 = tf.nn.dropout(full2, pkeep, name=’drop2′)
with tf.variable_scope(‘output’) as scope:

weights = tf.Variable(tf.random_normal([512, nlabels], mean=0.0,
stddev=0.01), name=’weights’)
biases = tf.Variable(tf.constant(0.0, shape=[nlabels],
dtype=tf.float32), name=’biases’)
output = tf.add(tf.matmul(drop2, weights), biases, name=scope.name)
return output
def levi_hassner(nlabels, images, pkeep, is_training):
weight_decay = 0.0005
weights_regularizer =
tf.contrib.layers.l2_regularizer(weight_decay)
with tf.variable_scope(“LeviHassner”, “LeviHassner”, [images]) as
scope:
with tf.contrib.slim.arg_scope(
[convolution2d, fully_connected],
weights_regularizer=weights_regularizer,
biases_initializer=tf.constant_initializer(1.),
weights_initializer=tf.random_normal_initializer(stddev=0.005),
trainable=True):
with tf.contrib.slim.arg_scope(
[convolution2d],
weights_initializer=tf.random_normal_initializer(stddev=0.01)):
conv1 = convolution2d(images, 96, [7,7], [4, 4], padding=’VALID’,
biases_initializer=tf.constant_initializer(0.), scope=’conv1′)
pool1 = max_pool2d(conv1, 3, 2, padding=’VALID’, scope=’pool1′)
norm1 = tf.nn.local_response_normalization(pool1, 5, alpha=0.0001,
beta=0.75, name=’norm1′)
conv2 = convolution2d(norm1, 256, [5, 5], [1, 1], padding=’SAME’,
scope=’conv2′)
pool2 = max_pool2d(conv2, 3, 2, padding=’VALID’, scope=’pool2′)
norm2 = tf.nn.local_response_normalization(pool2, 5, alpha=0.0001,
beta=0.75, name=’norm2′)
conv3 = convolution2d(norm2, 384, [3, 3], [1, 1],
biases_initializer=tf.constant_initializer(0.), padding=’SAME’,
scope=’conv3′)
pool3 = max_pool2d(conv3, 3, 2, padding=’VALID’, scope=’pool3′)
flat = tf.reshape(pool3, [-1, 384*6*6], name=’reshape’)
full1 = fully_connected(flat, 512, scope=’full1′)
drop1 = tf.nn.dropout(full1, pkeep, name=’drop1′)
full2 = fully_connected(drop1, 512, scope=’full2′)
drop2 = tf.nn.dropout(full2, pkeep, name=’drop2′)
with tf.variable_scope(‘output’) as scope:

weights = tf.Variable(tf.random_normal([512, nlabels], mean=0.0,
stddev=0.01), name=’weights’)
biases = tf.Variable(tf.constant(0.0, shape=[nlabels],
dtype=tf.float32), name=’biases’)
output = tf.add(tf.matmul(drop2, weights), biases, name=scope.name)
return output

教练模型。https://github.com/dpressel/rude-carnie/blob/master/train.py

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from six.moves import xrange
from datetime import datetime
import time
import os
import numpy as np
import tensorflow as tf
from data import distorted_inputs
from model import select_model
import json
import re
LAMBDA = 0.01
MOM = 0.9
tf.app.flags.DEFINE_string(‘pre_checkpoint_path’, ”,
“””If specified, restore this pretrained model “””
“””before beginning any training.”””)
tf.app.flags.DEFINE_string(‘train_dir’,
‘/home/dpressel/dev/work/AgeGenderDeepLearning/Folds/tf/test_fold_is_0’,
‘Training directory’)
tf.app.flags.DEFINE_boolean(‘log_device_placement’, False,
“””Whether to log device placement.”””)
tf.app.flags.DEFINE_integer(‘num_preprocess_threads’, 4,
‘Number of preprocessing threads’)
tf.app.flags.DEFINE_string(‘optim’, ‘Momentum’,
‘Optimizer’)
tf.app.flags.DEFINE_integer(‘image_size’, 227,
‘Image size’)
tf.app.flags.DEFINE_float(‘eta’, 0.01,
‘Learning rate’)
tf.app.flags.DEFINE_float(‘pdrop’, 0.,
‘Dropout probability’)
tf.app.flags.DEFINE_integer(‘max_steps’, 40000,
‘Number of iterations’)
tf.app.flags.DEFINE_integer(‘steps_per_decay’, 10000,
‘Number of steps before learning rate decay’)
tf.app.flags.DEFINE_float(‘eta_decay_rate’, 0.1,
‘Learning rate decay’)
tf.app.flags.DEFINE_integer(‘epochs’, -1,
‘Number of epochs’)
tf.app.flags.DEFINE_integer(‘batch_size’, 128,
‘Batch size’)
tf.app.flags.DEFINE_string(‘checkpoint’, ‘checkpoint’,
‘Checkpoint name’)
tf.app.flags.DEFINE_string(‘model_type’, ‘default’,
‘Type of convnet’)
tf.app.flags.DEFINE_string(‘pre_model’,
”,#’./inception_v3.ckpt’,
‘checkpoint file’)
FLAGS = tf.app.flags.FLAGS
# Every 5k steps cut learning rate in half
def exponential_staircase_decay(at_step=10000, decay_rate=0.1):
print(‘decay [%f] every [%d] steps’ % (decay_rate, at_step))
def _decay(lr, global_step):
return tf.train.exponential_decay(lr, global_step,
at_step, decay_rate, staircase=True)
return _decay
def optimizer(optim, eta, loss_fn, at_step, decay_rate):
global_step = tf.Variable(0, trainable=False)
optz = optim
if optim == ‘Adadelta’:
optz = lambda lr: tf.train.AdadeltaOptimizer(lr, 0.95, 1e-6)
lr_decay_fn = None
elif optim == ‘Momentum’:
optz = lambda lr: tf.train.MomentumOptimizer(lr, MOM)
lr_decay_fn = exponential_staircase_decay(at_step, decay_rate)
return tf.contrib.layers.optimize_loss(loss_fn, global_step, eta,
optz, clip_gradients=4., learning_rate_decay_fn=lr_decay_fn)
def loss(logits, labels):
labels = tf.cast(labels, tf.int32)
cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(
logits=logits, labels=labels, name=’cross_entropy_per_example’)
cross_entropy_mean = tf.reduce_mean(cross_entropy,
name=’cross_entropy’)
tf.add_to_collection(‘losses’, cross_entropy_mean)
losses = tf.get_collection(‘losses’)
regularization_losses =
tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES)
total_loss = cross_entropy_mean + LAMBDA *
sum(regularization_losses)
tf.summary.scalar(‘tl (raw)’, total_loss)
#total_loss = tf.add_n(losses + regularization_losses,
name=’total_loss’)
loss_averages = tf.train.ExponentialMovingAverage(0.9, name=’avg’)
loss_averages_op = loss_averages.apply(losses + [total_loss])
for l in losses + [total_loss]:
tf.summary.scalar(l.op.name + ‘ (raw)’, l)
tf.summary.scalar(l.op.name, loss_averages.average(l))
with tf.control_dependencies([loss_averages_op]):
total_loss = tf.identity(total_loss)
return total_loss
def main(argv=None):
with tf.Graph().as_default():
model_fn = select_model(FLAGS.model_type)
# Open the metadata file and figure out nlabels, and size of epoch
#
打开元数据文件md.json,这么些文件是在预处理多少时生成。找出nlabels、epoch大小
input_file = os.path.join(FLAGS.train_dir, ‘md.json’)
print(input_file)
with open(input_file, ‘r’) as f:
md = json.load(f)
images, labels, _ = distorted_inputs(FLAGS.train_dir,
FLAGS.batch_size, FLAGS.image_size, FLAGS.num_preprocess_threads)
logits = model_fn(md[‘nlabels’], images, 1-FLAGS.pdrop, True)
total_loss = loss(logits, labels)
train_op = optimizer(FLAGS.optim, FLAGS.eta, total_loss,
FLAGS.steps_per_decay, FLAGS.eta_decay_rate)
saver = tf.train.Saver(tf.global_variables())
summary_op = tf.summary.merge_all()
sess = tf.Session(config=tf.ConfigProto(
log_device_placement=FLAGS.log_device_placement))
tf.global_variables_initializer().run(session=sess)
# This is total hackland, it only works to fine-tune iv3
# 本例可以输入预练习模型Inception V3,可用来微调 Inception V3
if FLAGS.pre_model:
inception_variables = tf.get_collection(
tf.GraphKeys.VARIABLES, scope=”InceptionV3″)
restorer = tf.train.Saver(inception_variables)
restorer.restore(sess, FLAGS.pre_model)
if FLAGS.pre_checkpoint_path:
if tf.gfile.Exists(FLAGS.pre_checkpoint_path) is True:
print(‘Trying to restore checkpoint from %s’ %
FLAGS.pre_checkpoint_path)
restorer = tf.train.Saver()
tf.train.latest_checkpoint(FLAGS.pre_checkpoint_path)
print(‘%s: Pre-trained model restored from %s’ %
(datetime.now(), FLAGS.pre_checkpoint_path))
# 将ckpt文件存储在run-(pid)目录
run_dir = ‘%s/run-%d’ % (FLAGS.train_dir, os.getpid())
checkpoint_path = ‘%s/%s’ % (run_dir, FLAGS.checkpoint)
if tf.gfile.Exists(run_dir) is False:
print(‘Creating %s’ % run_dir)
tf.gfile.MakeDirs(run_dir)
tf.train.write_graph(sess.graph_def, run_dir, ‘model.pb’,
as_text=True)
tf.train.start_queue_runners(sess=sess)
summary_writer = tf.summary.FileWriter(run_dir, sess.graph)
steps_per_train_epoch = int(md[‘train_counts’] /
FLAGS.batch_size)
num_steps = FLAGS.max_steps if FLAGS.epochs < 1 else FLAGS.epochs
* steps_per_train_epoch
print(‘Requested number of steps [%d]’ % num_steps)

for step in xrange(num_steps):
start_time = time.time()
_, loss_value = sess.run([train_op, total_loss])
duration = time.time() – start_time
assert not np.isnan(loss_value), ‘Model diverged with loss = NaN’
# 每10步记录一遍摘要文件,保存一个检查点文件
if step % 10 == 0:
num_examples_per_step = FLAGS.batch_size
examples_per_sec = num_examples_per_step / duration
sec_per_batch = float(duration)

format_str = (‘%s: step %d, loss = %.3f (%.1f examples/sec; %.3f ‘
‘sec/batch)’)
print(format_str % (datetime.now(), step, loss_value,
examples_per_sec, sec_per_batch))
# Loss only actually evaluated every 100 steps?
if step % 100 == 0:
summary_str = sess.run(summary_op)
summary_writer.add_summary(summary_str, step)

if step % 1000 == 0 or (step + 1) == num_steps:
saver.save(sess, checkpoint_path, global_step=step)
if __name__ == ‘__main__’:
tf.app.run()

表达模型。https://github.com/dpressel/rude-carnie/blob/master/guess.py

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from datetime import datetime
import math
import time
from data import inputs
import numpy as np
import tensorflow as tf
from model import select_model, get_checkpoint
from utils import *
import os
import json
import csv
RESIZE_FINAL = 227
GENDER_LIST =[‘M’,’F’]
AGE_LIST = [‘(0, 2)’,'(4, 6)’,'(8, 12)’,'(15, 20)’,'(25, 32)’,'(38,
43)’,'(48, 53)’,'(60, 100)’]
MAX_BATCH_SZ = 128
tf.app.flags.DEFINE_string(‘model_dir’, ”,
‘Model directory (where training data lives)’)
tf.app.flags.DEFINE_string(‘class_type’, ‘age’,
‘Classification type (age|gender)’)
tf.app.flags.DEFINE_string(‘device_id’, ‘/cpu:0’,
‘What processing unit to execute inference on’)
tf.app.flags.DEFINE_string(‘filename’, ”,
‘File (Image) or File list (Text/No header TSV) to process’)
tf.app.flags.DEFINE_string(‘target’, ”,
‘CSV file containing the filename processed along with best guess and
score’)
tf.app.flags.DEFINE_string(‘checkpoint’, ‘checkpoint’,
‘Checkpoint basename’)
tf.app.flags.DEFINE_string(‘model_type’, ‘default’,
‘Type of convnet’)
tf.app.flags.DEFINE_string(‘requested_step’, ”, ‘Within the model
directory, a requested step to restore e.g., 9000’)
tf.app.flags.DEFINE_boolean(‘single_look’, False, ‘single look at the
image or multiple crops’)
tf.app.flags.DEFINE_string(‘face_detection_model’, ”, ‘Do frontal
face detection with model specified’)
tf.app.flags.DEFINE_string(‘face_detection_type’, ‘cascade’, ‘Face
detection model type (yolo_tiny|cascade)’)
FLAGS = tf.app.flags.FLAGS
def one_of(fname, types):
return any([fname.endswith(‘.’ + ty) for ty in types])
def resolve_file(fname):
if os.path.exists(fname): return fname
for suffix in (‘.jpg’, ‘.png’, ‘.JPG’, ‘.PNG’, ‘.jpeg’):
cand = fname + suffix
if os.path.exists(cand):
return cand
return None
def classify_many_single_crop(sess, label_list, softmax_output,
coder, images, image_files, writer):
try:
num_batches = math.ceil(len(image_files) / MAX_BATCH_SZ)
pg = ProgressBar(num_batches)
for j in range(num_batches):
start_offset = j * MAX_BATCH_SZ
end_offset = min((j + 1) * MAX_BATCH_SZ, len(image_files))

batch_image_files = image_files[start_offset:end_offset]
print(start_offset, end_offset, len(batch_image_files))
image_batch = make_multi_image_batch(batch_image_files, coder)
batch_results = sess.run(softmax_output,
feed_dict={images:image_batch.eval()})
batch_sz = batch_results.shape[0]
for i in range(batch_sz):
output_i = batch_results[i]
best_i = np.argmax(output_i)
best_choice = (label_list[best_i], output_i[best_i])
print(‘Guess @ 1 %s, prob = %.2f’ % best_choice)
if writer is not None:
f = batch_image_files[i]
writer.writerow((f, best_choice[0], ‘%.2f’ % best_choice[1]))
pg.update()
pg.done()
except Exception as e:
print(e)
print(‘Failed to run all images’)
def classify_one_multi_crop(sess, label_list, softmax_output,
coder, images, image_file, writer):
try:
print(‘Running file %s’ % image_file)
image_batch = make_multi_crop_batch(image_file, coder)
batch_results = sess.run(softmax_output,
feed_dict={images:image_batch.eval()})
output = batch_results[0]
batch_sz = batch_results.shape[0]

for i in range(1, batch_sz):
output = output + batch_results[i]

output /= batch_sz
best = np.argmax(output) # 最可能性能分类
best_choice = (label_list[best], output[best])
print(‘Guess @ 1 %s, prob = %.2f’ % best_choice)

nlabels = len(label_list)
if nlabels > 2:
output[best] = 0
second_best = np.argmax(output)
print(‘Guess @ 2 %s, prob = %.2f’ % (label_list[second_best],
output[second_best]))
if writer is not None:
writer.writerow((image_file, best_choice[0], ‘%.2f’ %
best_choice[1]))
except Exception as e:
print(e)
print(‘Failed to run image %s ‘ % image_file)
def list_images(srcfile):
with open(srcfile, ‘r’) as csvfile:
delim = ‘,’ if srcfile.endswith(‘.csv’) else ‘\t’
reader = csv.reader(csvfile, delimiter=delim)
if srcfile.endswith(‘.csv’) or srcfile.endswith(‘.tsv’):
print(‘skipping header’)
_ = next(reader)

return [row[0] for row in reader]
def main(argv=None): # pylint: disable=unused-argument
files = []

if FLAGS.face_detection_model:
print(‘Using face detector (%s) %s’ % (FLAGS.face_detection_type,
FLAGS.face_detection_model))
face_detect = face_detection_model(FLAGS.face_detection_type,
FLAGS.face_detection_model)
face_files, rectangles = face_detect.run(FLAGS.filename)
print(face_files)
files += face_files
config = tf.ConfigProto(allow_soft_placement=True)
with tf.Session(config=config) as sess:
label_list = AGE_LIST if FLAGS.class_type == ‘age’ else
GENDER_LIST
nlabels = len(label_list)
print(‘Executing on %s’ % FLAGS.device_id)
model_fn = select_model(FLAGS.model_type)
with tf.device(FLAGS.device_id):

images = tf.placeholder(tf.float32, [None, RESIZE_FINAL,
RESIZE_FINAL, 3])
logits = model_fn(nlabels, images, 1, False)
init = tf.global_variables_initializer()

requested_step = FLAGS.requested_step if FLAGS.requested_step else
None

checkpoint_path = ‘%s’ % (FLAGS.model_dir)
model_checkpoint_path, global_step =
get_checkpoint(checkpoint_path, requested_step, FLAGS.checkpoint)

saver = tf.train.Saver()
saver.restore(sess, model_checkpoint_path)

softmax_output = tf.nn.softmax(logits)
coder = ImageCoder()
# Support a batch mode if no face detection model
if len(files) == 0:
if (os.path.isdir(FLAGS.filename)):
for relpath in os.listdir(FLAGS.filename):
abspath = os.path.join(FLAGS.filename, relpath)

if os.path.isfile(abspath) and any([abspath.endswith(‘.’ + ty) for ty
in (‘jpg’, ‘png’, ‘JPG’, ‘PNG’, ‘jpeg’)]):
print(abspath)
files.append(abspath)
else:
files.append(FLAGS.filename)
# If it happens to be a list file, read the list and clobber the
files
if any([FLAGS.filename.endswith(‘.’ + ty) for ty in (‘csv’, ‘tsv’,
‘txt’)]):
files = list_images(FLAGS.filename)

writer = None
output = None
if FLAGS.target:
print(‘Creating output file %s’ % FLAGS.target)
output = open(FLAGS.target, ‘w’)
writer = csv.writer(output)
writer.writerow((‘file’, ‘label’, ‘score’))
image_files = list(filter(lambda x: x is not None, [resolve_file(f)
for f in files]))
print(image_files)
if FLAGS.single_look:
classify_many_single_crop(sess, label_list, softmax_output, coder,
images, image_files, writer)
else:
for image_file in image_files:
classify_one_multi_crop(sess, label_list, softmax_output, coder,
images, image_file, writer)
if output is not None:
output.close()

if __name__ == ‘__main__’:
tf.app.run()

微软脸部图片识别性别、年龄网站 http://how-old.net/
。图片识别年龄、性别。按照题目查找图片。

参考资料:
《TensorFlow技术解析与实战》

迎接推荐香港机械学习工作机会,我的微信:qingxingfengzi

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