基于TensorFlow深度学习人脸识别源码级项目实战

基于TensorFlow深度学习⼈脸识别源码级项⽬实战

⽂章⽬录充气按摩器

前⾔

⼈脸年龄识别属于⼈脸属性识别的范畴，⼈脸属性识别可对图⽚中的⼈脸进⾏检测定位，并识别出⼈脸的相关属性(如年龄、性别、表情、种族、颜值等)内容。不同属性识别的算法可以相同，也可以不同。rude-carnie是做年龄识别和性别识别的⼀个开源项⽬，基于TensorFlow，源代码⽹址:www.github/dpressel/rude-carnie。下⾯我们基于这个项⽬源码来讲年龄识别。

⼀、年龄识别核⼼代码guess.py

我们可以把年龄划分为⼏个段[’(0, 2)’,’(4, 6)’,’(8, 12)’,’(15, 20)’,’(25, 32)’,’(38, 43)’,’(48, 53)’,’(60, 100)’]，然后基于分类的思想来做年龄预测问题。⽤下⾯的脚本命令：

python3 guess.py --model_type inception --model_dir /home/hadoop/chongdianleme/ nianling/22801/inception/22801 --filename /home/hadoop/chongdianleme/data/myimages/baidu1.jpg

另外⼀个问题是，因为训练的模型⽤的是开源项⽬训练好的，是拿外国⼈的⼈脸数据训练，这样⽤来预

测我们中国⼈的年龄会有⼀些差异，最好的⽅式是拿我们中国⼈⾃⼰的⼈脸年龄数据做训练，这样预测才会更好。

针对年龄识别和性别识别都是⽤guess.py这个⽂件，年龄识别是多分类，性别是⼆分类。我们看下guess.py的源码，如代码8.5所⽰：

【代码8.5】 guess.py

代码如下（⽰例）：

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

#性别有两个

GENDER_LIST =['M','F']

#年龄是分段的，可以看成多分类任务

AGE_LIST =['(0, 2)','(4, 6)','(8, 12)','(15, 20)','(25, 32)','(38, 43)','(48, 53)','(60, 100)']

MAX_BATCH_SZ =128

#模型⽂件⽬录

tf.app.flags.DEFINE_string('model_dir','',

'Model directory (where training data lives)')

#性别和年龄都是⽤的这个，通过参数age|gender来区分

tf.app.flags.DEFINE_string('class_type','age',

'Classification type (age|gender)')

#⽤cpu还是⽤GPU来训练

tf.app.flags.DEFINE_string('device_id','/cpu:0',

模具石膏粉

'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')

#检查点

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 , 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([dswith('.'+ ty)for ty in types])

def resolve_file(fname):

if ists(fname):return fname

for suffix in ('.jpg','.png','.JPG','.PNG','.jpeg'):

cand = fname + suffix

if ists(cand):

return cand

return None

def classify_many_single_crop(sess, label_list, softmax_output, coder, images, image_files, writer):

try:

num_batches = il(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)

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 =','dswith('.csv')else'\t'

maop

reader = ader(csvfile, delimiter=delim)

dswith('.csv') dswith('.tsv'):

print('skipping header')

_ =next(reader)

return[row[0]for row in reader]

def main(argv=None): # pylint: disable=unused-argument

files =[]

print("target %s"% FLAGS.target)

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_del_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 = quested_step quested_step else None

checkpoint_path ='%s'%(del_dir)

model_checkpoint_path, global_step =get_checkpoint(checkpoint_path, requested_step, FLAGS.checkpoint)

saver = tf.train.Saver()

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([dswith('.'+ ty)for ty in ('jpg','png','JPG','PNG','jpeg')]):

print(abspath)

510自动发卡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([dswith('.'+ 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()

⼆、年龄识别Web⼯程化代码

年龄识别我们对外提供⼀个Web接⼝，guessAgeWeb_chongdianleme.py如代码8.6所⽰：【代码8.6】 guessAgeWeb_chongdianleme.py

代码如下（⽰例）：

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 csv

#pip3 install flask

from flask import Flask

from flask import request

import urllib

# pip3 install requests

import requests, quest

from scipy import misc

import argparse

import facenet

import align.detect_face

import json

RESIZE_FINAL =227

#性别有两个

GENDER_LIST =['M','F']

#年龄是分段的，可以看成多分类任务

AGE_LIST =['(0, 2)','(4, 6)','(8, 12)','(15, 20)','(25, 32)','(38, 43)','(48, 53)','(60, 100)']

MAX_BATCH_SZ =128

def one_of(fname, types):

return any([dswith('.'+ ty)for ty in types])

def resolve_file(fname):

if ists(fname):return fname

for suffix in ('.jpg','.png','.JPG','.PNG','.jpeg'):

cand = fname + suffix

if ists(cand):

return cand

return None

def list_images(srcfile):

with open(srcfile,'r') as csvfile:

delim =','dswith('.csv')else'\t'

reader = ader(csvfile, delimiter=delim)

dswith('.csv') dswith('.tsv'):

print('skipping header')

_ =next(reader)

return[row[0]for row in reader]

# 初始化

class_type ='age'

device_id ="/cpu:0"

model_type ="inception"

requested_step =""

#模型⽂件⽬录

model_dir ="/home/hadoop/chongdianleme/nianling/22801/inception/22801"

checkpoint ="checkpoint"

config = tf.ConfigProto(allow_soft_placement=True)

sess = tf.Session(config=config)

with sess.as_default():

label_list = AGE_LIST if class_type =='age'else GENDER_LIST

nlabels =len(label_list)

model_fn =select_model(model_type)

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 = requested_step if requested_step else None双面发光字

checkpoint_path ='%s'%(model_dir)

model_checkpoint_path, global_step =get_checkpoint(checkpoint_path, requested_step, checkpoint) saver = tf.train.Saver()

softmax_output = tf.nn.softmax(logits)

coder =ImageCoder()

def _is_png(filename):

return'.png' in filename

app =Flask(__name__)

@ute('/predictAge', methods=['GET','POST'])

def prediction():

start = time.time()

imageUrl = ("imageUrl")

#⽤户信息

device = ("device")

userid = ("userid")

urlType = ("urlType")

imageType = ("imageType")

files =[]

#⽀持本地图⽚和⽹络图⽚

if urlType=="local":

自动化洗碗机filename = imageUrl

else:

baseImageName = os.path.basename(imageUrl)

filename ="/home/hadoop/chongdianleme/ageimage/%s"% baseImageName

filename = filename+imageType

本文发布于:2024-09-26 02:20:42，感谢您对本站的认可！

本文链接：https://www.17tex.com/tex/3/187692.html

上一篇：人脸识别实践（1）-基本原理与设计思路

下一篇：智能感知时代下的定位与导航教学改进

标签：识别年龄性别分类

留言与评论（共有 0 条评论）