iOS之人脸识别

最近做一个项目有人脸识别和自动拍照两个功能，先记录一下相关代码。
人脸识别这块用的第三方是face++，但客户端没有应用，只负责拍照传照片，数据由后台来分析，那么就此来讲实现这些功能其实和扫码类似。

首先获取设备，创建输入输出流，以及捕捉静态图片流，捕捉影像层，初始化AVCaptureSession对象。

//获取摄像设备
    device = [self cameraWithPosition:AVCaptureDevicePositionFront];
    //创建输入流
    input = [AVCaptureDeviceInput deviceInputWithDevice:device error:nil];
    if (!input) return;
    //创建输出流
    output = [[AVCaptureMetadataOutput alloc]init];
    //设置代理 在主线程里刷新
    [output setMetadataObjectsDelegate:self queue:dispatch_get_main_queue()];
    //初始化链接对象
    _session = [[AVCaptureSession alloc]init];
    //高质量采集率
    [_session setSessionPreset:AVCaptureSessionPresetHigh];
    [_session addInput:input];
    [_session addOutput:output];
    [_session addOutput:self.stillImageOutput];
    if ([_session canAddOutput:self.videoDataOutput]) {
        [_session addOutput:self.videoDataOutput];
    }
    //设置像素格式
    [_videoDataOutput setVideoSettings:[NSDictionary dictionaryWithObject:[NSNumber numberWithInt:kCVPixelFormatType_32BGRA] forKey:(id)kCVPixelBufferPixelFormatTypeKey]];
    //设置扫脸类型
    output.metadataObjectTypes=@[AVMetadataObjectTypeFace];
    layer = [AVCaptureVideoPreviewLayer layerWithSession:_session];
    layer.videoGravity=AVLayerVideoGravityResizeAspectFill;
    layer.frame=self.view.layer.bounds;
    [self.view.layer insertSublayer:layer atIndex:0];
    //开始捕获
    [_session startRunning];

设置AVCaptureMetadataOutputObjectsDelegate,AVCaptureVideoDataOutputSampleBufferDelegate代理，并且实现两个方法。

//AVCaptureVideoDataOutput获取实时图像，这个代理方法的回调频率很快，几乎与手机屏幕的刷新频率一样快
-(void)captureOutput:(AVCaptureOutput *)captureOutput didOutputSampleBuffer:(CMSampleBufferRef)sampleBuffer fromConnection:(AVCaptureConnection *)connection{

    [connection setVideoOrientation:AVCaptureVideoOrientationPortrait];
    constantImage = [self imageFromSampleBuffer:sampleBuffer];
    [self addFaceFrameWithImage:constantImage];
}
//CMSampleBufferRef转NSImage
-(UIImage *)imageFromSampleBuffer:(CMSampleBufferRef)sampleBuffer{
    // 为媒体数据设置一个CMSampleBuffer的Core Video图像缓存对象
    CVImageBufferRef imageBuffer = CMSampleBufferGetImageBuffer(sampleBuffer);
    // 锁定pixel buffer的基地址
    CVPixelBufferLockBaseAddress(imageBuffer, 0);
    // 得到pixel buffer的基地址
    void *baseAddress = CVPixelBufferGetBaseAddress(imageBuffer);
    // 得到pixel buffer的行字节数
    size_t bytesPerRow = CVPixelBufferGetBytesPerRow(imageBuffer);
    // 得到pixel buffer的宽和高
    size_t width = CVPixelBufferGetWidth(imageBuffer);
    size_t height = CVPixelBufferGetHeight(imageBuffer);
    // 创建一个依赖于设备的RGB颜色空间
    CGColorSpaceRef colorSpace = CGColorSpaceCreateDeviceRGB();
    // 用抽样缓存的数据创建一个位图格式的图形上下文（graphics context）对象
    CGContextRef context = CGBitmapContextCreate(baseAddress, width, height, 8, bytesPerRow, colorSpace, kCGBitmapByteOrder32Little | kCGImageAlphaPremultipliedFirst);
    // 根据这个位图context中的像素数据创建一个Quartz image对象
    CGImageRef quartzImage = CGBitmapContextCreateImage(context);
    // 解锁pixel buffer
    CVPixelBufferUnlockBaseAddress(imageBuffer,0);
    // 释放context和颜色空间
    CGContextRelease(context); CGColorSpaceRelease(colorSpace);
    // 用Quartz image创建一个UIImage对象image
    UIImage *image = [UIImage imageWithCGImage:quartzImage scale:1 orientation:UIImageOrientationUp];
    // 释放Quartz image对象
    CGImageRelease(quartzImage);
    return (image);
}
//人脸定位,添加框架
- (void)addFaceFrameWithImage:(UIImage *)images{
    CIContext * context = [CIContext contextWithOptions:nil ];
    CIImage * image = [CIImage imageWithCGImage:images.CGImage];
    NSDictionary * param = [NSDictionary dictionaryWithObject:CIDetectorAccuracyLow forKey:CIDetectorAccuracy];
    CIDetector * faceDetector = [CIDetector detectorOfType:CIDetectorTypeFace context:context options:param];
    NSArray * detectResult = [faceDetector featuresInImage:image];
    for(int j=0;m_highlitView[j]!=nil;j++){
        m_highlitView[j].hidden = YES;
    }
    int i=0;
    for(CIFaceFeature* faceObject in detectResult){
        CGRect modifiedFaceBounds = faceObject.bounds;
        modifiedFaceBounds.origin.y = images.size.height-faceObject.bounds.size.height -faceObject.bounds.origin.y;
        [self addSubViewWithFrame:modifiedFaceBounds index:i];
        i++;
    }
}
///自画图像
-(void)addSubViewWithFrame:(CGRect)frame  index:(int)_index{
    if(m_highlitView[_index]==nil) {
        m_highlitView[_index]= [[UIView alloc] initWithFrame:frame];
        m_highlitView[_index].layer.borderWidth = 2;
        m_highlitView[_index].layer.borderColor = [[UIColor redColor] CGColor];
        [self.view addSubview:m_highlitView[_index]];
        m_transform[_index] = m_highlitView[_index].transform;
    }
    frame.origin.x = frame.origin.x/2.5;
    frame.origin.y = frame.origin.y/2.5;
    frame.size.width = frame.size.width/1.8;
    frame.size.height = frame.size.height/1.8;
    m_highlitView[_index].frame = frame;
    
    ///根据头像大小缩放自画View
    float scale = frame.size.width/220;
    CGAffineTransform transform = CGAffineTransformScale(m_transform[_index], scale,scale);
    m_highlitView[_index].transform = transform;
    m_highlitView[_index].hidden = NO;
}

通过如下方法识别人脸

captureOutput:(AVCaptureOutput *)captureOutput didOutputMetadataObjects:(NSArray *)metadataObjects fromConnection:(AVCaptureConnection *)connection 

通过如下方法获取图片,最终上传服务器比较

captureStillImageAsynchronouslyFromConnection:(AVCaptureConnection *)connection completionHandler:(void (^)(CMSampleBufferRef imageDataSampleBuffer, NSError *error))handler

目前未解决的问题是，添加识别人脸的红框闪动频率很快，其他正常！