如何克服 IOS 中实时摄像机视图的缓慢问题

Question

我正在尝试开发一个图像分割应用程序并在我的 coreml 模型中处理实时相机视图。但是我看到输出有些缓慢。带有掩码预测的相机视图速度较慢。下面是我的视觉管理器 class 预测像素缓冲区和调用此 class 的函数以在继续进行相机输出之前转换为颜色。以前有人遇到过这个问题吗？您是否发现我的代码中有导致运行缓慢的错误？

视觉管理器Class:

class VisionManager: NSObject {
static let shared = VisionManager()
static let MODEL = ba_224_segm().model

private lazy var predictionRequest: VNCoreMLRequest = {
    do{
        let model = try VNCoreMLModel(for: VisionManager.MODEL)
        let request = VNCoreMLRequest(model: model)
        request.imageCropAndScaleOption = VNImageCropAndScaleOption.centerCrop
        return request
    } catch {
        fatalError("can't load Vision ML Model")
    }
}()

func predict(pixelBuffer: CVImageBuffer, sampleBuffer: CMSampleBuffer, onResult: ((_ observations: [VNCoreMLFeatureValueObservation]) -> Void)) {
    var requestOptions: [VNImageOption: Any] = [:]
    if let cameraIntrinsicData = CMGetAttachment(sampleBuffer, key: kCMSampleBufferAttachmentKey_CameraIntrinsicMatrix, attachmentModeOut: nil) {
        requestOptions = [.cameraIntrinsics: cameraIntrinsicData]
    }
    
    let handler = VNImageRequestHandler(cvPixelBuffer: pixelBuffer, options: requestOptions)
    do {
        try handler.perform([predictionRequest])
    } catch {
        print("error handler")
    }

    guard let observations = predictionRequest.results as? [VNCoreMLFeatureValueObservation] else {
        fatalError("unexpected result type from VNCoreMLRequest")
    }
    onResult(observations)
}

预测相机输出函数：

func handleCameraOutput(pixelBuffer: CVImageBuffer, sampleBuffer: CMSampleBuffer, onFinish: @escaping ((_ image: UIImage?) -> Void)) {
    VisionManager.shared.predict(pixelBuffer: pixelBuffer, sampleBuffer: sampleBuffer) { [weak self ] (observations) in
        
        if let multiArray: MLMultiArray = observations[0].featureValue.multiArrayValue {
            
            mask = maskEdit.maskToRGBA(maskArray: MultiArray<Float32>(multiArray), rgba: (Float(r),Float(g),Float(b),Float(a)))!
            maskInverted = maskEdit.maskToRGBAInvert(maskArray: MultiArray<Float32>(multiArray), rgba: (r: 1.0, g: 1.0, b:1.0, a: 0.4))!
           
            
            let image = maskEdit.mergeMaskAndBackground( invertedMask: maskInverted, mask: mask, background: pixelBuffer, size: Int(size))
            
            
            DispatchQueue.main.async {
                onFinish(image)
            }
        }
    }

我在 viwDidAppear 下调用这些模型如下：

        CameraManager.shared.setDidOutputHandler { [weak self] (output, pixelBuffer, sampleBuffer, connection) in
            
            self!.maskColor.getRed(&self!.r, green:&self!.g, blue:&self!.b, alpha:&self!.a)
            self!.a = 0.5
            self?.handleCameraOutput(pixelBuffer: pixelBuffer, sampleBuffer: sampleBuffer, onFinish: { (image) in
            
         
            self?.predictionView.image = image
            })
        }

Answer 1

您的模型执行分割需要时间，然后将输出转换为图像也需要时间。除了使模型更小并确保输出 -> 图像转换代码尽可能快之外，您可以做很多事情来缩短此延迟。

Answer 2

我发现了关于不使用不同线程的问题。由于我是新开发人员，所以我不知道这些细节，并且仍在学习，感谢该领域的专家和他们共享的知识。请查看我的新旧 captureOutput 函数。使用不同的线程解决了我的问题：

旧状态：

    public func captureOutput(_ output: AVCaptureOutput, didOutput sampleBuffer: CMSampleBuffer, from connection: AVCaptureConnection) {
    guard let pixelBuffer = CMSampleBufferGetImageBuffer(sampleBuffer)
        else { return }


    self.handler?(output, pixelBuffer, sampleBuffer, connection)

    self.onCapture?(pixelBuffer, sampleBuffer)
    self.onCapture = nil

}

和新状态：

    public func captureOutput(_ output: AVCaptureOutput,
didOutput sampleBuffer: CMSampleBuffer,
                          from connection: AVCaptureConnection) {
    if currentBuffer == nil{
let pixelBuffer = CMSampleBufferGetImageBuffer(sampleBuffer)
    
currentBuffer = pixelBuffer
DispatchQueue.global(qos: .userInitiated).async {

    self.handler?(output, self.currentBuffer!, sampleBuffer, connection)
    
self.currentBuffer = nil

        }
        
}

}

如何克服 IOS 中实时摄像机视图的缓慢问题

How to overcome slowness of live camera view in IOS

avfoundation

image-segmentation

ios

swift

coreml