[ WWDC2018 ] - 计算机视觉和物体追踪 Vision with Core ML and Object Tracking in Vision

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:
* Vision with Core ML
* Object Tracking in Vision

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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