HOGDescriptionr OpenCV dsize.area(）断言失败

HOGDescriptor OpenCV dsize.area() assertion failed

本文关键字：断言 失败 area OpenCV dsize HOGDescriptionr      更新时间：2023-10-16

我正在尝试训练SVM，并在OpenCV的HOGDescrpitor中使用它。

HOGDescriptionr成功地生成并加载了xml文件，但当我尝试检测某个对象时，就会出现断言：

OpenCV错误：断言失败（dsize.area（）||（inv_scale_x>0&&inv_scale_y>0）），文件/build/buildd/opencv-2.48+dfsg1/modules/imgproc/src/imgwarp.cpp，行1825在抛出的实例后终止调用'tbb:：captured_exception'what（）：/build/buildd/opencv-2.48+dfsg1/modules/imgproc/src/imgwarp.cpp:1825:错误：（-215）中的dsize.area（）||（inv_scale_x>0&&inv_scale_y>0）函数调整大小

为了实现SVM训练器，我使用了使用OpenCV和带有图像的SVM的提示

生成的XML文件大约有144K字节。对于阳性和阴性样本，我使用了尺寸为64x128的图像（2000用于阳性，2000用于阴性）

SVM训练器参数：

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);
}

XML:

<?xml version="1.0"?>
<opencv_storage>
<my_svm type_id="opencv-ml-svm">
  <svm_type>C_SVC</svm_type>
  <kernel><type>LINEAR</type></kernel>
  <C>1.</C>
  <term_criteria><epsilon>2.2204460492503131e-16</epsilon>
    <iterations>10000</iterations></term_criteria>
  <var_all>8192</var_all>
  <var_count>8192</var_count>
  <class_count>2</class_count>
  <class_labels type_id="opencv-matrix">
    <rows>1</rows>
    <cols>2</cols>
    <dt>i</dt>
    <data>
      -1 1</data></class_labels>
  <sv_total>1</sv_total>
  <support_vectors>
    <_>
      -9.25376153e-05 -9.25376153e-05 -9.25376153e-05 -9.25376153e-05 ...and many, many...</_></support_vectors>
  <decision_functions>
    <_>
      <sv_count>1</sv_count>
      <rho>-1.</rho>
      <alpha>
        1.</alpha>
      <index>
        0</index></_></decision_functions></my_svm>
</opencv_storage>

有人能向我解释这个断言吗？或者也许能为这个问题提供一些解决方案？我花了将近3天的时间来解决这个问题，但没有任何成功。。。提前感谢！

这是我离。。。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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