【iOS干货】☞ 初识 Socket 网络通信

人脸识别,基于人脸部特征信息识别身份的古生物识别技术。录像机、录像头采集人脸图像或录像流,自动检测、跟踪图像中脸部,做脸部相关技术处理,人脸检测、人脸关键点检测、人脸验证等。《复旦科技评价》(MIT
Technology
Review),前年全世界十大突破性技术榜单,支付宝“刷脸支付”(Paying with Your
Face)入围。

一、概念

  • Socket 字面意思又称“套接字”

  • 网络上的五个程序(如,客户端和劳动器端)通过一个双向的通信连接实现多少的交流,那么些连续的一端称为一个Socket。

  • 应用程序一般是先经过Socket来确立一个通信连接,再向网络发出请求或响应网络请求。

  图片 1

  说明:

    ☞ 客户端向服务器端发送网络请求前,必须要先在底部建立一个通信连接(通信管道),才能发送网络请求。

客户端向劳动器端发送http请求,服务器再次回到数据,这一个进程就是一个数据互换的经过。

客户端与服务器端举办数据交流,需要先成立一个双向的通信连接(即一条线、一个通道)

    ☞ 客户端和劳动端
两端都有一个Socket,通过Socket建立一个接连(双向通信管道),有了管道就足以展开数据传输。

    ☞ Socket 就是通信管道的多少个端口,可以了解为管道的入口/出口。

人脸识别优势,非强制性(采集情势不便于被发现,被识旁人脸图像可积极获取)、非接触性(用户不需要与装备接触)、并发性(可同时多少人脸检测、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+大数据(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,大量样书图像锻练识别模型,无需人工选拔特征,样本锻练过程自行学习,识别准确率可以达标99%。

二、网络通信的元素

  网络上的乞求就是通过Socket来树立连接然后互相通信

  1.
IP地址(网络上主机设备的绝无仅有标识)——>寻找服务器主机

  2. 端口号(定位程序) ——> 寻找程序

    • 用于标示进程的逻辑地址,不同进程的标记
    • 实惠端口:0~65535,其中0~1024由系统采纳或者封存端口,开发中提出采用1024之上的端口

  3.
传输协议(就是用什么的不二法门开展交互)

  • 报导的条条框框
  • 普遍协议:TCP、UDP

人脸识别技术流程。

三、传输协议 TCP/UDP

  TCP和UDP:数据传输的两种方法,即把数据从一端传到另一端的二种办法

  1.
TCP(传输控制协议)
—>要建立连接(如:发送HTTP请求,客户端向服务端发送网络请求)

☞ 建立连接,形成传输数据的康庄大道

☞ 在一连中举办大数额传输(数据大小不受限制)

☞ 通过两回握手完成连接,是牢靠协议,安全送达

        表明:在创立通信连接(打通管道)在此之前有一次握手,目标是为着多少的安全性和可靠性(让数据安全可靠的传输到对方)。

        举例:打电话 (精晓五次握手)

首先次握手:拿起电话,举行拨号。这些拨号的过程称为第一次握手。【起始准备连接】

其次次握手:拨通了,对方”喂”了一声(响应了一声),我听见了,称为第二次握手。【表明自身总是你 没问题】

其一次握手:我听到了对方”喂”了一声(响应了一声),我也习惯性的”喂”了一声,对方听到了。【表达你总是我 没问题】

即使这四个经过都未曾问题,就足以确定通话连接建立成功。

    ☞ 必须建立连接,效用会稍低。(每趟请求都要建立连接)

  2.
UDP(用户数量报协议)—>不创立连接 (如:广播用那些,不断的出殡数据包)

    ☞ 将 数据 及 源 和 目的 封装成数据包中,不需要建立连接

    ☞ 每个数据报的深浅限制在64KB之内

    ☞ 因为无需连续,因此是不可靠协议

      举例:看师资广播讲课,网络卡主了,再来看的是时尚的视频内容,不可以跟着看,可能失掉了一部分情节。

    ☞ 不需要树立连接,速度快 (省掉了五回握手操作)

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。用户在采访设备拍报范围内,采集设置自动搜索并拍摄。人脸检测属于目的检测(object
detection)。对要检测对象对象概率总括,获得待检测对象特征,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岗位大小。人脸图像形式特点充裕,直方图特征、颜色特征、模板特征、结构特征、哈尔特征(Haar-like
feature)。人脸检测挑出有用音讯,用特色检测脸部。人脸检测算法,模板匹配模型、Adaboost模型,Adaboost模型速度。精度综合性能最好,磨练慢、检测快,可达成视频流实时检测效果。

四、Socket 通信流程图

图片 2

☞ bind():绑定端口 (80、3306)

☞ listen():监听端口(服务器监听客户端有没有连接到这么些端口来)

☞ accept():假使有连接到这多少个端口,就收到这些连续。(通信管道开挖,接下去就足以传输数据了)

☞ write():发请求/写请求/发数据

☞ read():读请求/读数据

  • HTTP底层就是Socket通信,通过Socket建立连接(通信管道),实现数量传输,连接的主意(数据传输的法门)是TCP。
  • HTTP是一个TCP的传输协议(格局),它是一个保险、安全的商事。

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样标准限制、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。

五、体验 Socket

  实现Socket服务端监听:

1)使用C语言实现。

2)使用 CocoaAsyncSocket 第三方框架(OC),内部是对C的卷入。

    telnet命令:是接连服务器上的某部端口对应的劳务。

    telnet命令:telnet
host port 

      如:telnet www.baidu.com 80  (IP地址和域名一样,都能找到主机。)

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,眼睛右侧、嘴唇左侧、鼻子、下巴地点,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。

1. 【案例】写个10086劳务,体验客户端与服务端的Socket通信


自己写一个服务端,用极端代替客户端来演示

☞ 了解:通过Socket对象在服务器里怎么去接收数据和再次回到数据。

图片 3

图片 4

图片 5图片 6

/// ----- MyServiceListener.h -----
@interface MyServiceListener : NSObject
//开启服务
- (void)start;
@end

/// ----- MyServiceListener.m -----
#import "MyServiceListener.h"
#import "GCDAsyncSocket.h"
/**
 *  服务的监听者(服务端监听客户端连接)
 */
@interface MyServiceListener()<GCDAsyncSocketDelegate>
/** 保存服务端的Socket对象 */
@property (nonatomic, strong) GCDAsyncSocket *serviceSocket;
/** 保存客户端的所有Socket对象 */
@property (nonatomic, strong) NSMutableArray *clientSocketArr;

@end

@implementation MyServiceListener
- (GCDAsyncSocket *)serviceSocket {
    if (!_serviceSocket) {
        //1.创建一个Socket对象
                //serviceSocket 服务端的Socket只监听 有没有客户端请求连接
               //队列:代理的方法在哪个队列里调用 (子线程的队列)
        _serviceSocket = [[GCDAsyncSocket alloc]initWithDelegate:self delegateQueue:dispatch_get_global_queue(0, 0)];
    }
    return _serviceSocket;
}

- (NSMutableArray *)clientSocketArr {
    if(!_clientSocketArr) {
        _clientSocketArr = [NSMutableArray array];
    }
    return _clientSocketArr;
}

- (void)start {
    //开启10086服务:5288
    //2.绑定端口 + 开启监听
    NSError *error = nil;
    //框架里的这个方法做了两件事情:绑定端口和开启监听
    [self.serviceSocket acceptOnPort:5288 error:&error];
    if (!error) {
        NSLog(@"10086服务开启成功!");
    } else {
        //失败的原因是端口被其它程序占用
        NSLog(@"10086服务开启失败:%@", error);
    }    
}

#pragma mark -- 实现代理的方法 如果有客户端的Socket连接到服务器,就会调用这个方法。
- (void)socket:(GCDAsyncSocket *)serviceSocket didAcceptNewSocket:(GCDAsyncSocket *)clientSocket {
    static NSInteger index = 1;
    NSLog(@"客户端【%ld】已连接到服务器!", index++);
    //1.保存客户端的Socket(客户端的Socket被释放了,连接就会关闭)
    [self.clientSockets addObject:clientSocket];

    //提供服务(客户端一连接到服务器,就打印下面的内容)
    NSMutableString *serviceStr = [[NSMutableString alloc]init];
    [serviceStr appendString:@"========欢迎来到10086在线服务========\n"];
    [serviceStr appendString:@"请输入下面的数字选择服务...\n"];
    [serviceStr appendString:@" [0] 在线充值\n"];
    [serviceStr appendString:@" [1] 在线投诉\n"];
    [serviceStr appendString:@" [2] 优惠信息\n"];
    [serviceStr appendString:@" [3] special services\n"];
    [serviceStr appendString:@" [4] 退出\n"];
    [serviceStr appendString:@"=====================================\n"];
    // 服务端给客户端发送数据
    [clientSocket writeData:[serviceStr dataUsingEncoding:NSUTF8StringEncoding] withTimeout:-1 tag:0];

    //2.监听客户端有没有数据上传 (参数1:超时时间,-1代表不超时)
    /**
     *  timeout: 超时时间,-1 代表不超时
     *  tag:标识作用,现在不用就写0
     */
    [clientSocket readDataWithTimeout:-1 tag:0];
}

#pragma mark -- 服务器端 读取 客户端请求(发送)的数据。在服务端接收客户端数据,这个方法会被调用
- (void)socket:(GCDAsyncSocket *)clientSocket didReadData:(NSData *)data withTag:(long)tag {
    //1.获取客户端发送的数据
    NSString *str = [[NSString alloc]initWithData:data encoding:NSUTF8StringEncoding];
    NSInteger index = [self.clientSocketArr indexOfObject:clientSocket];
    NSLog(@"接收到客户端【%ld】发送的数据:%@", index + 1, str);
    //把字符串转成数字
    NSInteger num = [str integerValue];
    NSString *responseStr = nil;
    //服务器对应的处理的结果
    switch (num) {
        case 0:
            responseStr = @"在线充值服务暂停中...\n";
            break;
        case 1:
            responseStr = @"在线投诉服务暂停中...\n";
            break;
        case 2:
            responseStr = @"优惠信息没有\n";
            break;
        case 3:
            responseStr = @"没有特殊服务\n";
            break;
        case 4:
            responseStr = @"恭喜你退出成功!\n";
            break;
        default:
            break;
    }

    //2.服务端处理请求,返回数据(data)给客户端
    [clientSocket writeData:[responseStr dataUsingEncoding:NSUTF8StringEncoding] withTimeout:-1 tag:0];
    //写完数据后 判断
    if (num == 4) {
        //移除客户端,就会关闭连接
        [self.clientSockets removeObject:clientSocket];
    }

    //由于框架内部的实现,每次读完数据后,都要调用一次监听数据的方法(保证能接收到客户端第二次上传的数据)
    [clientSocket readDataWithTimeout:-1 tag:0];

}
@end

/// ----- ViewController.m -----
#import "ViewController.h"
#import "MyServiceListener.h"
@interface ViewController ()

@end

@implementation ViewController
- (void)viewDidLoad {
    [super viewDidLoad];
    //1.创建一个服务监听对象
    MyServiceListener *listener = [[MyServiceListener alloc]init];
    //2.开始监听
    [listener start];
    //3.开启主运行循环,让服务不能停(服务器一般要永久开启)
    [[NSRunLoop mainRunLoop] run];

}
@end


体验Socket通信-服务端简单实现代码:

Demo下载地址:https://github.com/borenfocus/Socket10086ServerDemo 

 

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,按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)图像相比较,声明“你就是您”,金融核实身份、信息安全世界。辨认,一对多(1:N)图像匹配,“N人中找你”,视频流,人走进识别范围就到位辨认,安防领域。

2. 【案例扩展】写个换车信息服务(群聊服务端)

  • 多个客户端连接到服务器。
  • 当一个客户端发送音讯给服务器时,服务器转发给此外已经连续的客户端。
  • 一定于一个群聊的雏形。

  图片 7

图片 8图片 9

/// MyService.h
#import <Foundation/Foundation.h>

@interface MyService : NSObject
/** 开启服务 */
- (void)startService;

@end

/// MyService.m
#import "MyService.h"
#import "GCDAsyncSocket.h"

@interface MyService ()<GCDAsyncSocketDelegate>
/** 保存服务端的Socket对象 */
@property (nonatomic, strong) GCDAsyncSocket *serviceSocket;
/** 保存客户端的所有Socket对象 */
@property (nonatomic, strong) NSMutableArray *clientSocketArr;

@end

@implementation MyService

//开启10086服务:5288
- (void)startService {
    NSError *error = nil;
    // 绑定端口 + 开启监听
    [self.serviceSocket acceptOnPort:5288 error:&error];
    if (!error) {
        NSLog(@"服务开启成功!");
    } else {
        NSLog(@"服务开启失败!");
    }
}

#pragma mark -- 实现代理的方法 如果有客户端的Socket连接到服务器,就会调用这个方法。
- (void)socket:(GCDAsyncSocket *)serviceSocket didAcceptNewSocket:(GCDAsyncSocket *)clientSocket {
    // 客户端的端口号是系统分配的,服务端的端口号是我们自己分配的
    NSLog(@"客户端【Host:%@, Port:%d】已连接到服务器!", clientSocket.connectedHost, clientSocket.connectedPort);
    //1.保存客户端的Socket(客户端的Socket被释放了,连接就会关闭)
    [self.clientSocketArr addObject:clientSocket];

    //2.监听客户端有没有数据上传 (参数1:超时时间,-1代表不超时;参数2:标识作用,现在不用就写0)
    [clientSocket readDataWithTimeout:-1 tag:0];
}

#pragma mark -- 服务器端 读取 客户端请求(发送)的数据。在服务端接收客户端数据,这个方法会被调用
- (void)socket:(GCDAsyncSocket *)clientSocket didReadData:(NSData *)data withTag:(long)tag {
    //1.获取客户端发送的数据
    NSString *messageStr = [[NSString alloc]initWithData:data encoding:NSUTF8StringEncoding];
    NSLog(@"接收到客户端【Host:%@, Port:%d】发送的数据:%@",  clientSocket.connectedHost, clientSocket.connectedPort, messageStr);
    // 遍历客户端数组
    for (GCDAsyncSocket *socket in self.clientSocketArr) {
        if (socket != clientSocket) { // 不转发给自己
            //2.服务端把收到的消息转发给其它客户端
            [socket writeData:data withTimeout:-1 tag:0];
        }
    }
    //由于框架内部的实现,每次读完数据后,都要调用一次监听数据的方法(保证能接收到客户端第二次上传的数据)
    [clientSocket readDataWithTimeout:-1 tag:0];
}

- (GCDAsyncSocket *)serviceSocket {
    if (!_serviceSocket) {
        // 1.创建一个Socket对象
        // serviceSocket 服务端的Socket只监听 有没有客户端请求连接
        // 队列:代理的方法在哪个队列里调用 (子线程的队列)
        _serviceSocket = [[GCDAsyncSocket alloc]initWithDelegate:self delegateQueue:dispatch_get_global_queue(0, 0)];
    }
    return _serviceSocket;
}

- (NSMutableArray *)clientSocketArr {
    if (!_clientSocketArr) {
        _clientSocketArr = [[NSMutableArray alloc]init];
    }
    return _clientSocketArr;
}

@end


/// main.m
#import <Foundation/Foundation.h>
#import "MyService.h"

int main(int argc, const char * argv[]) {
    @autoreleasepool {
        //1.创建一个服务监听对象
        MyService *service = [[MyService alloc]init];
        //2.开始监听
        [service startService];
        //3.开启主运行循环,让服务不能停(服务器一般要永久开启)
        [[NSRunLoop mainRunLoop] run];
    }
    return 0;
}


体验Socket通信-群聊服务端实现代码:

 Demo下载地址:https://github.com/borenfocus/SocketGroupServerDemo

人脸识别分类。

3. 【案例】体验Socket通信-群聊客户端实现

  图片 10

  图片 11

图片 12图片 13

///  ViewController.m
#import "ViewController.h"
#import "GCDAsyncSocket.h"

@interface ViewController ()<UITableViewDataSource, GCDAsyncSocketDelegate>
@property (weak, nonatomic) IBOutlet UITableView *tableView;
@property (weak, nonatomic) IBOutlet UITextField *textField;
@property (nonatomic, strong) GCDAsyncSocket *clientSocket;

@property (nonatomic, strong) NSMutableArray *dataArr;

@end

@implementation ViewController

- (void)viewDidLoad {
    [super viewDidLoad];
    // 实现聊天室
    // 1. 连接到服务器
    NSError *error = nil;
    [self.clientSocket connectToHost:@"192.168.1.95" onPort:5288 error:&error];
    if (error) {
        NSLog(@"error:%@", error);
    }
}

#pragma mark - GCDAsyncSocketDelegate
- (void)socket:(GCDAsyncSocket *)clientSock didConnectToHost:(NSString *)host port:(uint16_t)port {
    NSLog(@"与服务器连接成功!");
    // 监听读取数据(在读数据的时候,要监听有没有数据可读,目的是保证数据读取到)
    [clientSock readDataWithTimeout:-1 tag:0];
}

- (void)socketDidDisconnect:(GCDAsyncSocket *)sock withError:(NSError *)err {
    NSLog(@"与服务器断开连接:%@", err);
}

// 读取数据(接收消息)
- (void)socket:(GCDAsyncSocket *)clientSock didReadData:(NSData *)data withTag:(long)tag {
    NSString *messageStr = [[NSString alloc]initWithData:data encoding:NSUTF8StringEncoding];
    NSLog(@"接收到消息:%@", messageStr);
    messageStr = [NSString stringWithFormat:@"【匿名】:%@", messageStr];
    [self.dataArr addObject:messageStr];
    // 刷新UI要在主线程
    dispatch_async(dispatch_get_main_queue(), ^{
        [self.tableView reloadData];
    });

    // 监听读取数据(读完数据后,继续监听有没有数据可读,目的是保证下一次数据可以读取到)
    [clientSock readDataWithTimeout:-1 tag:0];
}

#pragma mark - UITableViewDataSource
- (NSInteger)numberOfSectionsInTableView:(UITableView *)tableView {
    return 1;
}

- (NSInteger)tableView:(UITableView *)tableView numberOfRowsInSection:(NSInteger)section {
    return self.dataArr.count;
}

- (UITableViewCell *)tableView:(UITableView *)tableView cellForRowAtIndexPath:(NSIndexPath *)indexPath {
    UITableViewCell *cell = [tableView dequeueReusableCellWithIdentifier:@"cell"];
    cell.textLabel.text = self.dataArr[indexPath.row];
    return cell;
}

- (IBAction)clickSenderBtn:(UIButton *)sender {
    NSLog(@"发送消息");
    [self.view endEditing:YES];
    NSString *senderStr = self.textField.text;
    if (senderStr.length == 0) {
        return;
    }
    // 发送数据
    [self.clientSocket writeData:[senderStr dataUsingEncoding:NSUTF8StringEncoding] withTimeout:-1 tag:0];

    senderStr = [NSString stringWithFormat:@"【我】:%@", senderStr];
    [self.dataArr addObject:senderStr];
    [self.tableView reloadData];
}

- (GCDAsyncSocket *)clientSocket {
    if (!_clientSocket) {
        _clientSocket = [[GCDAsyncSocket alloc]initWithDelegate:self delegateQueue:dispatch_get_global_queue(0, 0)];
    }
    return _clientSocket;
}

- (NSMutableArray *)dataArr {
    if (!_dataArr) {
        _dataArr = [[NSMutableArray alloc]init];
    }
    return _dataArr;
}

@end


体验Socket通信-群聊客户端实现:

Demo下载地址:https://github.com/borenfocus/SocketGroupClientDemo  

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”,采纳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,依照特征描述判断窗口是否人脸。不断遍历需要寓目窗口。

六、长连接和短连接

  长连接和短连接:是连续的一个封存意况(保存时间),长连接就是长日子总是,短连接就是短期总是。

  • http网络请求是短连接。
  • 长连接用在当时通信(实时聊天,要随时随地的发送音信,考虑到性能,用长连接)

人脸关键点检测。定位、重临人脸五官、概略关键点坐标地点。人脸概略、眼睛、眉毛、嘴唇、鼻子轮廓。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

七、Socket 层上的情商

  Socket层上的商谈:指的数码传输的格式。

  1. HTTP磋商:定义在网络上多少传输的一种格式。

    传输格式:假如:这是只要,实际http的格式不是这样的。

    http1.1,content-type:multipart/form-data,content-length:188,body:username=zhangsan&password=123456

  2.
XMPP协议:是一款即时通讯协议 (别人定义好的商议,大家通常拿来用)

    是遵照可扩张标记语言(XML)的商事,它用来即时新闻(IM)以及在线现场探测。

    传输格式:

      <from>zhangsan<from>

      <to>lisi<to>

      <body>一起吃早晨</body>

  3.
自定义即时通讯协议,json格式。

    {

      ”from”:
“zhangsan”,

      ”to”:
“lisi”,

      ”body”:
“下午伙同进餐”,

    }

  你做哪些操作,必须要有一个恒定的格式,这样服务器才知道您要做如何。

  

  举例:写一封信给上海市知音(区别 TCP/UDP 与 HTTP/XMMP)

  • 数码传输的艺术:TCP/UDP —》相当于 EMS/顺丰/申通/中通   

  • 数量传输的格式:HTTP/XMMP —》相当于 信的内容格式 (可以是中文/英文/…等)

 

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

人脸属性检测。人脸属性辩识、人脸心思分析。https://www.betaface.com/wpa/
在线人脸识别测试。给出人年龄、是否有胡子、心绪(称心快意、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/
。美利哥北达科他大学阿姆斯特分校总结机视觉实验室整理。13233张图纸,5749人。4096人只有一张图纸,1680人多于一张。每张图片尺寸250×250。人脸图片在每个人物名字文件夹下。

数码预处理。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锻炼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

预练习模型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,有名的人榜采取前100万球星,搜索引擎采集每个有名气的人100张人脸图片。预训练模型准确率0.993+-0.004。

检测。python src/validate_on_lfw.py datasets/lfw/lfw_mtcnnpy_160
models
标准化相比较,采取facenet/data/pairs.txt,官方随机生成多少,匹配和不匹配人名和图表编号。

十折交叉验证(10-fold cross
validation),精度测试方法。数据集分成10份,轮流将中间9份做操练集,1份做测试保,10次结果均值作算法精度预计。一般需要反复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. 读入从前的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匹配关系对
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. 乘除准确率、验证率,十折交叉验证形式
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张图纸,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
全部数额符号。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。脚本把数据处理成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、性别磨练模型,Gil Levi、Tal Hassner杂文《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_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数据时生成。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步记录三遍摘要文件,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()

表明模型。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。遵照问题查找图片。

参考资料:
《TensorFlow技术解析与实战》

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