iOS 11之Vision人脸检测

代码地址如下：
http://www.demodashi.com/demo/11783.html

大道如青天，我独不得出

前言

在上一篇iOS Core ML与Vision初识中，初步了解到了vision的作用，并在文章最后留了个疑问，就是类似下面的一些函数有什么用

- (instancetype)initWithCIImage:(CIImage *)image options:(NSDictionary<VNImageOption, id> *)options;- (instancetype)initWithCVPixelBuffer:(CVPixelBufferRef)pixelBuffer options:(NSDictionary<VNImageOption, id> *)options;

在查阅一些资料后，最终通过这些函数得到了如下的效果

对，没错，这就是通过initWithCVPixelBuffer函数来实现的。当然vision的作用远不于此，还有如下的效果
1、图像匹配（上篇文章中的效果）
2、矩形检测
3、二维码、条码检测
4、目标跟踪
5、文字检测
6、人脸检测
7、人脸面部特征检测
由于对人脸识别比较感兴趣，所以这里就主要简单了解了下人脸部分，下面就针对人脸检测和面部检测写写

Vision支持的图片类型

通过查看VNRequestHandler.h文件，我们可以看到里面的所有初始化函数，通过这些初始化函数，我们可以了解到支持的类型有：
1、CVPixelBufferRef
2、CGImageRef
3、CIImage
4、NSURL
5、NSData

Vision使用

在使用vision的时候，我们首先需要明确自己需要什么效果，然后根据想要的效果来选择不同的类，最后实现自己的效果
1、需要一个RequestHandler，在创建RequestHandler的时候，需要一个合适的输入源，及图片类型
2、需要一个Request，在创建Request的时候，也需要根据实际情况来选择，Request大概有如下这么些

3、通过requestHandler将request联系起来，然后得到结果

[handler performRequests:@[requset] error:&error];

4、处理结果VNObservation，在VNRequest的results数组中，包含了VNObservation结果，VNObservation也分很多种，这和你Request的类型是相关联的

在完成上述步骤后，我们就可以根据结果来实现一些我们想要的效果

人脸矩形检测

这里我们需要用到VNDetectFaceRectanglesRequest

requset = [[VNDetectFaceRectanglesRequest alloc] initWithCompletionHandler:completionHandler];

在得到结果后，我们需要处理下坐标

    for (VNFaceObservation *faceObservation in observations) {//boundingBoxCGRect transFrame = [self convertRect:faceObservation.boundingBox imageSize:image.size];[rects addObject:[NSValue valueWithCGRect:transFrame]];}

// 转换Rect
- (CGRect)convertRect:(CGRect)boundingBox imageSize:(CGSize)imageSize{CGFloat w = boundingBox.size.width * imageSize.width;CGFloat h = boundingBox.size.height * imageSize.height;CGFloat x = boundingBox.origin.x * imageSize.width;CGFloat y = imageSize.height * (1 - boundingBox.origin.y - boundingBox.size.height);//- (boundingBox.origin.y * imageSize.height) - h;return CGRectMake(x, y, w, h);
}

在返回结果中的boundingBox中的坐标，我们并不能立即使用，而是需要进行转换，因为这里是相对于image的一个比例，这里需要注意的是y坐标的转换，因为坐标系的y轴和UIView的y轴是相反的。
最后就是通过返回的坐标进行矩形的绘制

+ (UIImage *)gl_drawImage:(UIImage *)image withRects:(NSArray *)rects
{UIImage *newImage = nil;UIGraphicsBeginImageContextWithOptions(image.size, NO, [UIScreen mainScreen].scale);CGContextRef context = UIGraphicsGetCurrentContext();CGContextSetLineCap(context,kCGLineCapRound); //边缘样式CGContextSetLineJoin(context, kCGLineJoinRound);CGContextSetLineWidth(context,2); //线宽CGContextSetAllowsAntialiasing(context,YES); //打开抗锯齿CGContextSetStrokeColorWithColor(context, [UIColor redColor].CGColor);CGContextSetFillColorWithColor(context, [UIColor clearColor].CGColor);//绘制图片[image drawInRect:CGRectMake(0, 0,image.size.width, image.size.height)];CGContextBeginPath(context);for (int i = 0; i < rects.count; i ++) {CGRect rect = [rects[i] CGRectValue];CGPoint sPoints[4];//坐标点sPoints[0] = CGPointMake(rect.origin.x, rect.origin.y);//坐标1sPoints[1] = CGPointMake(rect.origin.x + rect.size.width, rect.origin.y);//坐标2sPoints[2] = CGPointMake(rect.origin.x + rect.size.width, rect.origin.y + rect.size.height);//坐标3sPoints[3] = CGPointMake(rect.origin.x , rect.origin.y + rect.size.height);CGContextAddLines(context, sPoints, 4);//添加线CGContextClosePath(context); //封闭}CGContextDrawPath(context, kCGPathFillStroke); //根据坐标绘制路径newImage = UIGraphicsGetImageFromCurrentImageContext();UIGraphicsEndImageContext();return newImage;
}

效果如下

人脸特征识别

这里我们需要用到VNDetectFaceLandmarksRequest

            requset = [[VNDetectFaceLandmarksRequest alloc] initWithCompletionHandler:completionHandler];

处理结果

    for (VNFaceObservation *faceObservation in observations) {//boundingBoxCGRect transFrame = [self convertRect:faceObservation.boundingBox imageSize:image.size];[rects addObject:[NSValue valueWithCGRect:transFrame]];}pointModel.faceRectPoints = rects;return pointModel;
}- (GLDiscernPointModel *)handlerFaceLandMark:(NSArray *)observations image:(UIImage *)image
{GLDiscernPointModel *pointModel = [[GLDiscernPointModel alloc] init];NSMutableArray *rects = @[].mutableCopy;for (VNFaceObservation *faceObservation in observations) {VNFaceLandmarks2D *faceLandMarks2D = faceObservation.landmarks;[self getKeysWithClass:[VNFaceLandmarks2D class] block:^(NSString *key) {if ([key isEqualToString:@"allPoints"]) {return ;}VNFaceLandmarkRegion2D *faceLandMarkRegion2D = [faceLandMarks2D valueForKey:key];NSMutableArray *sPoints = [[NSMutableArray alloc] initWithCapacity:faceLandMarkRegion2D.pointCount];for (int i = 0; i < faceLandMarkRegion2D.pointCount; i ++) {CGPoint point = faceLandMarkRegion2D.normalizedPoints[i];CGFloat rectWidth = image.size.width * faceObservation.boundingBox.size.width;CGFloat rectHeight = image.size.height * faceObservation.boundingBox.size.height;CGPoint p = CGPointMake(point.x * rectWidth + faceObservation.boundingBox.origin.x * image.size.width, faceObservation.boundingBox.origin.y * image.size.height + point.y * rectHeight);[sPoints addObject:[NSValue valueWithCGPoint:p]];}[rects addObject:sPoints];}];}

在这里，我们需要注意到landmarks这个属性，这是一个VNFaceLandmarks2D类型的对象，里面包含着许多面部特征的VNFaceLandmarkRegion2D对象，如：faceContour，leftEye，nose….分别表示面部轮廓、左眼、鼻子。这些对象中，又包含下面这么一个属性

@property (readonly, assign, nullable) const CGPoint* normalizedPoints

这是一个包含该面部特征的的数组，所以我们可以通过下面的方式取出里面的坐标

CGPoint point = faceLandMarkRegion2D.normalizedPoints[i];

当然这里面也存在坐标的转换，见上面代码
最后也是画线，代码如下

+ (UIImage *)gl_drawImage:(UIImage *)image faceLandMarkPoints:(NSArray *)landMarkPoints
{UIImage * newImage = image;for (NSMutableArray *points in landMarkPoints) {CGPoint sPoints [points.count];for (int i = 0;i <points.count;i++) {NSValue *pointValue = points[i];CGPoint point = pointValue.CGPointValue;sPoints[i] = point;}//画线UIGraphicsBeginImageContextWithOptions(newImage.size, NO, [UIScreen mainScreen].scale);CGContextRef context = UIGraphicsGetCurrentContext();CGContextSetLineCap(context,kCGLineCapRound); //边缘样式CGContextSetLineJoin(context, kCGLineJoinRound);CGContextSetLineWidth(context,2); //线宽CGContextSetAllowsAntialiasing(context,YES); //打开抗锯齿// 设置翻转CGContextTranslateCTM(context, 0, newImage.size.height);CGContextScaleCTM(context, 1.0, -1.0);CGContextSetStrokeColorWithColor(context, [UIColor redColor].CGColor);CGContextSetFillColorWithColor(context, [UIColor clearColor].CGColor);CGContextDrawImage(context, CGRectMake(0, 0,newImage.size.width,newImage.size.height), newImage.CGImage);CGContextBeginPath(context);CGContextAddLines(context, sPoints,points.count);//添加线CGContextClosePath(context); //封闭CGContextDrawPath(context, kCGPathFillStroke); //根据坐标绘制路径newImage = UIGraphicsGetImageFromCurrentImageContext();UIGraphicsEndImageContext();}return newImage;
}

效果如下

动态人脸矩形检测

要动态来检测，那么我们肯定需要通过相机来实时取出资源，然后再实现，所以我们这里选择了AVCapture，关于相机的初始化及使用方法这里就不在累赘了，我们直接上代码
在AVCaptureVideoDataOutputSampleBufferDelegate中，通过下面的方法

- (void)captureOutput:(AVCaptureOutput *)output didOutputSampleBuffer:(CMSampleBufferRef)sampleBuffer fromConnection:(AVCaptureConnection *)connection

我们可以进行这么一个转换

CVPixelBufferRef cvpixeBufferRef = CMSampleBufferGetImageBuffer(sampleBuffer);

然后通过

    VNImageRequestHandler *handler = [[VNImageRequestHandler alloc] initWithCVPixelBuffer:cvpixeBufferRef options:@{}];

将相机返回的图片与request进行关联了。
后续操作如下

            request = [[VNDetectFaceRectanglesRequest alloc] initWithCompletionHandler:^(VNRequest * _Nonnull request, NSError * _Nullable error) {NSLog(@" 打印信息:%lu",request.results.count);NSArray *vnobservations = request.results;dispatch_async(dispatch_get_main_queue(), ^{//先移除之前的矩形框[self.rectLayers makeObjectsPerformSelector:@selector(removeFromSuperlayer)];AVCaptureDevicePosition position = [[self.avInput device] position];for (VNFaceObservation *faceObservation in vnobservations) {//boundingBoxCGRect transFrame = [[GLTools sharedInstance] convertRect:faceObservation.boundingBox imageSize:self.view.frame.size];//前置摄像头的时候 记得转换if (position == AVCaptureDevicePositionFront){transFrame.origin.x = self.view.frame.size.width - transFrame.origin.x - transFrame.size.width;}CALayer *rectLayer = [CALayer layer];rectLayer.frame = transFrame;rectLayer.borderColor = [UIColor purpleColor].CGColor;rectLayer.borderWidth = 2;[self.view.layer addSublayer:rectLayer];[self.rectLayers addObject:rectLayer];}});}];

在这里存在一个问题，就是摄像头分为前后摄像头，所以在前置摄像头和后置摄像头切换的时候，需要重新配置下

            //需要重新进行配置输出 特别是下面的输出方向AVCaptureConnection *captureConnection = [self.avOutput connectionWithMediaType:AVMediaTypeVideo];if ([captureConnection isVideoOrientationSupported]) {[captureConnection setVideoOrientation:AVCaptureVideoOrientationPortrait];}// 视频稳定设置if ([captureConnection isVideoStabilizationSupported]) {captureConnection.preferredVideoStabilizationMode = AVCaptureVideoStabilizationModeAuto;}// 设置输出图片方向captureConnection.videoOrientation = AVCaptureVideoOrientationPortrait;

还有个问题就是在坐标转化的时候，前置摄像头的x轴和UIView的x轴也是相反的，所以这里也需要在进行一次转化

 transFrame.origin.x = self.view.frame.size.width - transFrame.origin.x - transFrame.size.width;

效果如下

动态添加场景

关于动态添加场景，其实就像我们平时用的美颜相机那样，在适当的位置添加些帽子、眼镜等各种搞笑的图片。这里我们还是需要用到AVCapture，并且和动态添加矩形的方法类似，只是在request上和处理方式上不一样
下面我们先看代码

            request = [[VNDetectFaceLandmarksRequest alloc] initWithCompletionHandler:^(VNRequest * _Nonnull request, NSError * _Nullable error) {NSArray *vnobservations = request.results;for (VNFaceObservation *faceObservation in vnobservations) {VNFaceLandmarks2D *faceLandMarks2D = faceObservation.landmarks;VNFaceLandmarkRegion2D *leftEyefaceLandMarkRegion2D = faceLandMarks2D.leftEye;VNFaceLandmarkRegion2D *rightEyefaceLandMarkRegion2D = faceLandMarks2D.rightEye;dispatch_async(dispatch_get_main_queue(), ^{//                        //先移除之前的矩形框
//                        [self.rectLayers makeObjectsPerformSelector:@selector(removeFromSuperlayer)];
//
//                        AVCaptureDevicePosition position = [[self.avInput device] position];
//
//                        CGRect transFrame = [[GLTools sharedInstance] convertRect:faceObservation.boundingBox imageSize:self.view.frame.size];
//                        //前置摄像头的时候 记得转换
//                        if (position == AVCaptureDevicePositionFront){
//                            transFrame.origin.x = self.view.frame.size.width - transFrame.origin.x - transFrame.size.width;
//                        }
//
//                        CALayer *rectLayer = [CALayer layer];
//                        rectLayer.frame = transFrame;
//                        rectLayer.borderColor = [UIColor purpleColor].CGColor;
//                        rectLayer.borderWidth = 2;
//                        [self.view.layer addSublayer:rectLayer];
//
//                        [self.rectLayers addObject:rectLayer];AVCaptureDevicePosition position = [[self.avInput device] position];CGPoint sPoints[leftEyefaceLandMarkRegion2D.pointCount + rightEyefaceLandMarkRegion2D.pointCount];NSMutableArray *pointXs = [[NSMutableArray alloc] init];NSMutableArray *pointYs = [[NSMutableArray alloc] init];for (int i = 0; i < leftEyefaceLandMarkRegion2D.pointCount; i ++) {CGPoint point = leftEyefaceLandMarkRegion2D.normalizedPoints[i];CGFloat rectWidth = self.view.bounds.size.width * faceObservation.boundingBox.size.width;CGFloat rectHeight = self.view.bounds.size.height * faceObservation.boundingBox.size.height;CGFloat boundingBoxY = self.view.bounds.size.height * (1 - faceObservation.boundingBox.origin.y - faceObservation.boundingBox.size.height);CGPoint p = CGPointZero;if (position == AVCaptureDevicePositionFront){CGFloat boundingX = self.view.frame.size.width - faceObservation.boundingBox.origin.x * self.view.bounds.size.width - rectWidth;p = CGPointMake(point.x * rectWidth + boundingX, boundingBoxY + (1-point.y) * rectHeight);}else{p = CGPointMake(point.x * rectWidth + faceObservation.boundingBox.origin.x * self.view.bounds.size.width, boundingBoxY + (1-point.y) * rectHeight);}sPoints[i] = p;[pointXs addObject:[NSNumber numberWithFloat:p.x]];[pointYs addObject:[NSNumber numberWithFloat:p.y]];}for (int j = 0; j < rightEyefaceLandMarkRegion2D.pointCount; j ++) {CGPoint point = rightEyefaceLandMarkRegion2D.normalizedPoints[j];CGFloat rectWidth = self.view.bounds.size.width * faceObservation.boundingBox.size.width;CGFloat rectHeight = self.view.bounds.size.height * faceObservation.boundingBox.size.height;CGFloat boundingBoxY = self.view.bounds.size.height * (1 - faceObservation.boundingBox.origin.y - faceObservation.boundingBox.size.height);CGPoint p = CGPointZero;if (position == AVCaptureDevicePositionFront){CGFloat boundingX = self.view.frame.size.width - faceObservation.boundingBox.origin.x * self.view.bounds.size.width - rectWidth;p = CGPointMake(point.x * rectWidth + boundingX, boundingBoxY + (1-point.y) * rectHeight);}else{p = CGPointMake(point.x * rectWidth + faceObservation.boundingBox.origin.x * self.view.bounds.size.width, boundingBoxY + (1-point.y) * rectHeight);}sPoints[leftEyefaceLandMarkRegion2D.pointCount + j] = p;[pointXs addObject:[NSNumber numberWithFloat:p.x]];[pointYs addObject:[NSNumber numberWithFloat:p.y]];}//                        for (UIView *view in self.view.subviews) {
//                            if ([view isKindOfClass:[UIImageView class]]) {
//                                [view removeFromSuperview];
//                            }
//                        }
//
//                        for (int i = 0; i < rightEyefaceLandMarkRegion2D.pointCount + leftEyefaceLandMarkRegion2D.pointCount; i++) {//                            CGFloat x = sPoints[i].x;
//                            CGFloat y = sPoints[i].y;
//                            UIImageView *view = [[UIImageView alloc] initWithFrame:CGRectMake(x, y, 2, 2)];
//                            view.backgroundColor = [UIColor redColor];
//                            [self.view addSubview:view];
//                        }//排序 得到最小的x和最大的xNSArray *sortPointXs = [pointXs sortedArrayWithOptions:NSSortStable usingComparator:^NSComparisonResult(id  _Nonnull obj1, id  _Nonnull obj2) {int value1 = [obj1 floatValue];int value2 = [obj2 floatValue];if (value1 > value2) {return NSOrderedDescending;}else if (value1 == value2){return NSOrderedSame;}else{return NSOrderedAscending;}}];NSArray *sortPointYs = [pointYs sortedArrayWithOptions:NSSortStable usingComparator:^NSComparisonResult(id  _Nonnull obj1, id  _Nonnull obj2) {int value1 = [obj1 floatValue];int value2 = [obj2 floatValue];if (value1 > value2) {return NSOrderedDescending;}else if (value1 == value2){return NSOrderedSame;}else{return NSOrderedAscending;}}];UIImage *image =[UIImage imageNamed:@"eyes"];CGFloat imageWidth = [sortPointXs.lastObject floatValue] - [sortPointXs.firstObject floatValue] + 40;CGFloat imageHeight = (imageWidth * image.size.height)/image.size.width;self.glassesImageView.frame = CGRectMake([sortPointXs.firstObject floatValue]-20, [sortPointYs.firstObject floatValue]-5, imageWidth, imageHeight);});}}];

由于时间关系，代码有点乱，将就将就

先说说思路，我是想动态添加一个眼镜的，所以我必须先得到两个眼睛的位置，然后在计算出两个眼睛的宽高，最后适当的调整眼镜的大小，再动态的添加上去

这里必须要说的一个问题，就是我在实现过程中遇到的—坐标

首先是y坐标，如果还是按照静态图片的那种获取方式，那么得到的结果将会是完全相反的。

faceObservation.boundingBox.origin.y * image.size.height + point.y * rectHeight

这里我做了一个假设，估计是由于摄像机成像的原因造成的，所以必须反其道而行，于是我如下改造了下

 CGFloat boundingBoxY = self.view.bounds.size.height * (1 - faceObservation.boundingBox.origin.y - faceObservation.boundingBox.size.height);p = CGPointMake(point.x * rectWidth + faceObservation.boundingBox.origin.x * self.view.bounds.size.width, boundingBoxY + (1-point.y) * rectHeight);

从中可以看到，所有的point.y都用1减去了，这个试验的过程有点恼火，我还没怎么相通，若有知道的，希望可以告诉我下，当然我也会再研究研究。
再说完y坐标后，就是x坐标了，x坐标在前置摄像头的时候一切正常，然而在切换成后置摄像头的时候，又反了。心累啊，所以没办法，我就只要加判断，然后进行测试，有了如下代码

 CGFloat boundingX = self.view.frame.size.width - faceObservation.boundingBox.origin.x * self.view.bounds.size.width - rectWidth;

最后终于大功告成！
效果就是文章最顶的那个效果

项目文件截图如下

注意

1、在使用过程中，我发现当检测图片的时候内存和cpu的消耗还是很高的，比如我的5s就成功的崩溃过…..
2、图片方向是有要求的….

- (instancetype)initWithCVPixelBuffer:(CVPixelBufferRef)pixelBuffer options:(NSDictionary<VNImageOption, id> *)options;/*!@brief initWithCVPixelBuffer:options creates a VNImageRequestHandler to be used for performing requests against the image passed in as buffer.@param pixelBuffer A CVPixelBuffer containing the image to be used for performing the requests. The content of the buffer cannot be modified for the lifetime of the VNImageRequestHandler.@param orientation The orientation of the image/buffer based on the EXIF specification. For details see kCGImagePropertyOrientation. The value has to be an integer from 1 to 8. This superceeds every other orientation information.@param options A dictionary with options specifying auxilary information for the buffer/image like VNImageOptionCameraIntrinsics*/
- (instancetype)initWithCVPixelBuffer:(CVPixelBufferRef)pixelBuffer orientation:(CGImagePropertyOrientation)orientation options:(NSDictionary<VNImageOption, id> *)options;

通过对比上面两个函数，我们可以发现，多了一个CGImagePropertyOrientation类型的参数，没错，这就是指定传入图片的方向，如果指定了方向，而图片方向却不一致，那么恭喜你，检测不出来….这里我用的都是第一个方法，及没有参数，好像默认是up的。
iOS 11之Vision人脸检测

代码地址如下：
http://www.demodashi.com/demo/11783.html

注：本文著作权归作者，由demo大师发表，拒绝转载，转载需要作者授权