C++ HOGDescriptor OpenCV dsize.area（）断言失败

我正在尝试训练SVM，并在OpenCV的HOGDescrpitor中使用它

xml文件已由HOGDescriptor成功生成并加载，但当我尝试检测某个对象时，发生了断言：

OpenCV错误：断言失败（dsize.area（）| |（inv_scale_x>0&& inv_scale_y>0）在调整大小、文件中 /build/buildd/opencv-2.4.8+dfsg1/modules/imgproc/src/imgwarp.cpp，第行 1825在抛出的实例后调用terminate 'tbb:：捕获的\u异常'what（）： /build/buildd/opencv-2.4.8+dfsg1/modules/imgproc/src/imgwarp.cpp:1825： 错误：（-215）dsize.area（）| |（inv_scale_x>0和&inv_scale_y>0）位于 函数调整大小

为了实现SVM训练器，我使用了

生成的XML文件大约有144KB。对于正片和负片样本，我使用了64x128大小的图像（正片为2000，负片为2000）

SVM训练器的参数：

CvSVMParams svmParams;
svmParams.svm_type = CvSVM::C_SVC;
svmParams.kernel_type = CvSVM::LINEAR;
svmParams.term_crit = cvTermCriteria( CV_TERMCRIT_ITER, 10000, 1e-6 );

检测代码：

int main()
{
    HOGDescriptor hog();
    if(!hog.load("/home/bin/hogdescriptor.xml"))
    {
        std::cout << "Failed to load file!" << std::endl;
        return -1;
    }

    VideoCapture cap(0);
    if(!cap.isOpened())
    {
        std::cout << "Error opening camera!" << std::endl;
        return 1;
    }

    Mat testImage;

    while ((cvWaitKey(30) & 255) != 27)
    {
        cap >> testImage;
        detectTest(hog, testImage);
        imshow("HOG custom detection", testImage);
    }

    return EXIT_SUCCESS;
}

void showDetections(const vector<Rect>& found, Mat& imageData) {
    for (const Rect& rect : found)
    {
        Point rectPoint1;
    rectPoint1.x = rect.x;
        rectPoint1.y = rect.y;

        Point rectPoint2;
        rectPoint2.x = rect.x + rect.width;
        rectPoint2.y = rect.y + rect.height;

        std::cout << "detection x: " << rect.x << ", y: " << rect.y << std::endl;

        rectangle(imageData, rectPoint1, rectPoint2, Scalar(0, 255, 0));
    }
}

void detectTest(const HOGDescriptor& hog, Mat& imageData)
{
    std::cout << "Trying to detect" << std::endl;

    vector<Rect> found;
    int groupThreshold = 2;
    Size padding(Size(32, 32));
    Size winStride(Size(8, 8));
    double hitThreshold = 0.; // tolerance
    hog.detectMultiScale(imageData, found, hitThreshold, winStride, padding, 1.05, groupThreshold);
//    hog.detectMultiScale(imageData, found);

    std::cout << "Trying to show detections" << std::endl;
    showDetections(found, imageData);
}

intmain（）
{
hog（）；
如果（！hog.load（“/home/bin/hogdescriptor.xml”））
{
std：：cout这是我得到的最接近的东西…仍在尝试使用此xml

private static void buscar_hog_svm() {

    if (clasificador == null) {
        clasificador = new CvSVM();
        clasificador.load(path_vectores);
    }

    Mat img_gray = new Mat();
    //gray  
    Imgproc.cvtColor(imag, img_gray, Imgproc.COLOR_BGR2GRAY);

    //Extract HogFeature  
    hog = new HOGDescriptor(
            _winSize //new Size(32, 16)
            , _blockSize, _blockStride, _cellSize, _nbins);
    MatOfFloat descriptorsValues = new MatOfFloat();
    MatOfPoint locations = new MatOfPoint();
    hog.compute(img_gray,
            descriptorsValues,
            _winSize,
            _padding, locations);


    Mat fm = descriptorsValues;

    System.out.println("tamano fm: " + fm.size());
    //Classification whether data is positive or negative 
    float result = clasificador.predict(fm);
    System.out.println("resultado= " + result);
}

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private static void buscar_hog_svm() {

    if (clasificador == null) {
        clasificador = new CvSVM();
        clasificador.load(path_vectores);
    }

    Mat img_gray = new Mat();
    //gray  
    Imgproc.cvtColor(imag, img_gray, Imgproc.COLOR_BGR2GRAY);

    //Extract HogFeature  
    hog = new HOGDescriptor(
            _winSize //new Size(32, 16)
            , _blockSize, _blockStride, _cellSize, _nbins);
    MatOfFloat descriptorsValues = new MatOfFloat();
    MatOfPoint locations = new MatOfPoint();
    hog.compute(img_gray,
            descriptorsValues,
            _winSize,
            _padding, locations);


    Mat fm = descriptorsValues;

    System.out.println("tamano fm: " + fm.size());
    //Classification whether data is positive or negative 
    float result = clasificador.predict(fm);
    System.out.println("resultado= " + result);
}