一、WWDC2018 Vision
去年IOS11出了Vision框架给开发者提供了使用简单的图像识别方式，本来期待在今年能够拥有更多的图像处理的功能，但是从WWDC2018看来，苹果此番针对Vision框架并没有进行大幅度的升级，功能未变，只是针对IOS12有增加一些修订含义的常量，比如：
- VNDetectFaceLandmarksRequestRevision1
- VNDetectFaceLandmarksRequestRevision2
- VNDetectHorizonRequestRevision1
而关于Vision框架的使用只有两个session的讲解，分别是两个场景下的使用：
场景的使用下的使用并不复杂，我们通过一个具体的Demo来看看。
二、Vision调用CoreML
苹果在大会上演示了一个Demo,Vision框架通过调用CoreML在相机实时的视频流检测识别出物体名称,我们这里也来实现一个。
1、通过AVFoundation构建一个相机
```
- (void)initAVCapturWritterConfig
{
self.session = [[AVCaptureSession alloc] init];
//视频
AVCaptureDevice *videoDevice = [AVCaptureDevice defaultDeviceWithMediaType:AVMediaTypeVideo];
if (videoDevice.isFocusPointOfInterestSupported && [videoDevice isFocusModeSupported:AVCaptureFocusModeContinuousAutoFocus]) {
[videoDevice lockForConfiguration:nil];
[videoDevice setFocusMode:AVCaptureFocusModeContinuousAutoFocus];
[videoDevice unlockForConfiguration];
}
AVCaptureDeviceInput *cameraDeviceInput = [[AVCaptureDeviceInput alloc] initWithDevice:videoDevice error:nil];
if ([self.session canAddInput:cameraDeviceInput]) {
[self.session addInput:cameraDeviceInput];
}
//视频
self.videoOutPut = [[AVCaptureVideoDataOutput alloc] init];
NSDictionary * outputSettings = [[NSDictionary alloc] initWithObjectsAndKeys:[NSNumber numberWithInt:kCVPixelFormatType_32BGRA],(id)kCVPixelBufferPixelFormatTypeKey, nil];
[self.videoOutPut setVideoSettings:outputSettings];
if ([self.session canAddOutput:self.videoOutPut]) {
[self.session addOutput:self.videoOutPut];
}
self.videoConnection = [self.videoOutPut connectionWithMediaType:AVMediaTypeVideo];
self.videoConnection.enabled = NO;
[self.videoConnection setVideoOrientation:AVCaptureVideoOrientationPortrait];
//初始化预览图层
self.previewLayer = [[AVCaptureVideoPreviewLayer alloc] initWithSession:self.session];
[self.previewLayer setVideoGravity:AVLayerVideoGravityResizeAspectFill];
}
```
2、引入CoreML的模型
coreml.png
3、初始化Vision框架的请求
```
//实物识别
VNCoreMLModel *vnModel = [VNCoreMLModel modelForMLModel:[MobileNet new].model error:nil];
self.coreMLRequest = [[VNCoreMLRequest alloc] initWithModel:vnModel completionHandler:^(VNRequest * _Nonnull request, NSError * _Nullable error) {
VNCoreMLRequest *coreR = (VNCoreMLRequest *)request;
VNClassificationObservation *firstObservation = [coreR.results firstObject];
dispatch_async(dispatch_get_main_queue(), ^{
if (firstObservation) {
self.googleLabel.text = firstObservation.identifier;
}
else {
self.googleLabel.text = @"";
}
});
}];
self.coreMLRequest.imageCropAndScaleOption = VNImageCropAndScaleOptionCenterCrop;
```
4、相机回调执行
```
- (void)captureOutput:(AVCaptureOutput *)captureOutput didOutputSampleBuffer:(CMSampleBufferRef)sampleBuffer fromConnection:(AVCaptureConnection *)connection
{
UIImage *image = [UIImage imageFromSampleBuffer:sampleBuffer];
UIImage *scaledImage = [image scaleToSize:CGSizeMake(224, 224)];
CVPixelBufferRef buffer = [image pixelBufferFromCGImage:scaledImage];
VNImageRequestHandler *handler = [[VNImageRequestHandler alloc] initWithCVPixelBuffer:buffer options:@{}];
NSError *error;
[handler performRequests:@[self.coreMLRequest] error:&error];
}
```
5、结果展示
当我获取的画面返回的时候就会通过MobileNet这个机器学习模型去识别，结果展示在左下角的标签里面。这样也就完成了Vision在CoreML上的调用。
model.gif
三、Vision实现物体追踪
1、人脸请求
这里我们没有使用VNTrackObjectRequest，这里使用了VNDetectFaceLandmarksRequest来实现一个脸部追踪贴纸的效果，调用是一样的。
在上面相机的基础上，我们新建一个脸部识别的请求
```
self.faceRequest = [[VNDetectFaceLandmarksRequest alloc] initWithCompletionHandler:^(VNRequest * _Nonnull request, NSError * _Nullable error) {
VNDetectFaceLandmarksRequest *faceRequest = (VNDetectFaceLandmarksRequest*)request;
VNFaceObservation *firstObservation = [faceRequest.results firstObject];
dispatch_async(dispatch_get_main_queue(), ^{
if (firstObservation) {
CGRect boundingBox = [firstObservation boundingBox];
CGRect rect = VNImageRectForNormalizedRect(boundingBox,self.realTimeView.frame.size.width,self.realTimeView.frame.size.height);
CGRect frame = CGRectMake(self.realTimeView.frame.size.width - rect.origin.x - rect.size.width, self.realTimeView.frame.size.height - rect.origin.y - rect.size.height, rect.size.width, rect.size.height);
self.maskView.frame = frame;
self.maskView.hidden = NO;
}
else {
self.maskView.hidden = YES;
}
})
}];
```
2、相机回调切换
在上面相机回调的基础上增加一个按钮切换请求模式即可
- (void)captureOutput:(AVCaptureOutput *)captureOutput didOutputSampleBuffer:(CMSampleBufferRef)sampleBuffer fromConnection:(AVCaptureConnection *)connection
{
if (self.coreMlMode) {
UIImage *image = [UIImage imageFromSampleBuffer:sampleBuffer];
UIImage *scaledImage = [image scaleToSize:CGSizeMake(224, 224)];
CVPixelBufferRef buffer = [image pixelBufferFromCGImage:scaledImage];
VNImageRequestHandler *handler = [[VNImageRequestHandler alloc] initWithCVPixelBuffer:buffer options:@{}];
NSError *error;
[handler performRequests:@[self.coreMLRequest] error:&error];
}
else {
CVImageBufferRef imageBuffer = CMSampleBufferGetImageBuffer(sampleBuffer);
VNImageRequestHandler *handler = [[VNImageRequestHandler alloc] initWithCVPixelBuffer:(CVPixelBufferRef)imageBuffer options:@{}];
NSError *error;
[handler performRequests:@[self.faceRequest] error:&error];
}
}
3、结果展示
我们把镜头放在同事的脸上，就会识别出同事的脸部位置，将预先插入的maskView的frame设置在对应的位置，就能让面具一直追踪脸部紧贴，当没有识别出脸部的时候，就会隐藏面具，效果如下。
face.gif
四、小结
Vision框架为我们封装的视觉处理一些场景下的功能，调用非常简单，但是正是由于调用的简单，对应就达不到一个复杂的功能，一般场景是可以实现的，期待苹果未来能够提供更为丰富的API，比如图片的风格变换等等，我们的应用也会越来越丰富。附带Demo地址，有兴趣的可以下载看看。iOS Vision in Video Streams
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