iOS 11之Vision人脸检测
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) {
//boundingBox
CGRect 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1
sPoints[1] = CGPointMake(rect.origin.x + rect.size.width, rect.origin.y);//坐标2
sPoints[2] = CGPointMake(rect.origin.x + rect.size.width, rect.origin.y + rect.size.height);//坐标3
sPoints[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) {
//boundingBox
CGRect 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) {
//boundingBox
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];
}
});
}];
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x
NSArray *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
注：本文著作权归作者，由demo大师代发，拒绝转载，转载需要作者授权
