不存在从cv::Mat到const CvArr的适当转换函数

在OpenCV中，不存在直接从cv::Mat到const CvArr的适当转换函数。这是因为cv::Mat是OpenCV 2.x版本引入的新的数据结构，而CvArr是OpenCV 1.x版本中使用的数据结构。cv::Mat提供了更加方便和灵活的图像处理功能，而CvArr则是为了与旧版本代码的兼容性而保留的数据结构。

如果你需要将cv::Mat转换为CvArr类型，可以使用以下方法之一：
1. 使用cv::cvarrToMat函数将CvArr转换为cv::Mat类型，然后再进行相应的处理。
2. 将cv::Mat数据复制到CvMat或IplImage类型的数据结构中，然后再进行相应的处理。

下面是一个示例代码，演示了如何将cv::Mat转换为CvArr类型：

cv::Mat image;  // 假设有一个cv::Mat类型的图像

// 方法1：使用cv::cvarrToMat函数将CvArr转换为cv::Mat类型
CvMat cvmat = image;
const CvArr* cvarr = &cvmat;

// 方法2：将cv::Mat数据复制到CvMat或IplImage类型的数据结构中
CvMat cvmat;
IplImage iplimage = image;
cvCopy(&iplimage, &cvmat);

// 现在你可以使用cvarr进行相应的处理了

相关问题

python中cv2.camshift源代码

cv2.camshift() 是 OpenCV 中用于实现 CAMShift 算法的函数，其源代码可以在 OpenCV 的 GitHub 库中找到。

以下是 cv2.camshift() 的源代码：

CV_IMPL int cvCamShift( CvArr* imgProb, CvRect  _window, CvTermCriteria criteria,
                        CvConnectedComp* _comp, CvBox2D* box )
{
    const int TOLERANCE = 10;
    int   k, row, col, pix, iter;
    int   max_iter = criteria.max_iter;
    float hsize[] = {0.25f, 0.25f, 0.5f, 0.5f, 0.75f, 1.f, 1.f, 1.f};
    float hranges[] = {-180, 180};
    float vmin = 0, vmax = 180, smin = 0;
    float hist_thresh;
    CvSize sz;
    IplImage stub, *prob = (IplImage*)imgProb, *mask = 0;
    uchar *mask_row = 0;
    int   mask_step = 0;
    CvHistogram* hist = 0;
    CvMoments   m;
    CvPoint2D32f center, old_center;
    float a, b, c, xscale, yscale, max_val;
    CvBox2D box0;

    CV_FUNCNAME( "cvCamShift" );

    __BEGIN__;

    if( !CV_IS_IMAGE( prob ))
        CV_ERROR( CV_StsBadArg, "The probability map is not a valid image" );

    if( prob->depth != IPL_DEPTH_32F || prob->nChannels != 1 )
        CV_ERROR( CV_StsUnsupportedFormat,
        "Only 32-bit floating-point, single-channel probability images are supported" );

    if( !CV_ARE_SIZES_EQ( prob, &_window ))
        CV_ERROR( CV_StsUnmatchedSizes, "The probability map size differs from the tracking window size" );

    if( criteria.type & CV_TERMCRIT_EPS )
        criteria.epsilon *= criteria.epsilon;
    else
        criteria.epsilon = 0;

    criteria.epsilon = MAX( criteria.epsilon, 1e-8f );
    criteria.max_iter = MAX( criteria.max_iter, 1 );

    if( criteria.type & CV_TERMCRIT_ITER )
        criteria.max_iter = MIN( criteria.max_iter, 100 );
    else
        criteria.max_iter = 100;

    // allocate images
    sz = cvGetSize( prob );
    mask = cvCreateImage( sz, IPL_DEPTH_8U, 1 );
    cvRectangle( mask, cvPoint(0,0), cvPoint(sz.width,sz.height), CV_RGB(255,255,255), -1 );
    cvSetImageROI( mask, _window );
    cvSet( mask, cvScalar(0) );
    cvResetImageROI( mask );

    hist = cvCreateHist( 1, &hist_size, CV_HIST_ARRAY, &h_ranges, 1 );
    cvCalcArrHist( &prob, hist, 0 );
    cvGetMinMaxHistValue( hist, 0, &max_val, 0, 0 );
    cvConvertScale( hist->bins, hist->bins, max_val? 255.0/max_val : 0, 0 );

    // cam-shift iteration
    center.x = (_window.x + _window.width - 1)*0.5f;
    center.y = (_window.y + _window.height - 1)*0.5f;
    old_center = center;
    box0.center = center;
    box0.size.width = _window.width;
    box0.size.height = _window.height;
    box0.angle = 0;
    iter = 0;

    for(;;)
    {
        CvBox2D box1;
        float *row_ptr;
        float m00, m10, m01;

        if( center.x <= 0 || center.x >= sz.width-1 ||
            center.y <= 0 || center.y >= sz.height-1 )
            break;

        cvSetImageROI( prob, _window );
        cvSetImageROI( mask, _window );
        cvCalcArrHist( &prob, hist, 0 );
        cvGetMinMaxHistValue( hist, 0, &max_val, 0, 0 );
        hist_thresh = max_val * (1.f - criteria.epsilon);
        cvThreshHist( hist, hist_thresh );
        cvNormalizeHist( hist, 1 );

        // find the x and y gradients
        assert( CV_MAT_DEPTH( prob->type ) == CV_32F );
        cvSobel( prob, dx, 1, 0, 1 );
        cvSobel( prob, dy, 0, 1, 1 );

        // initialize the transition matrix H = J'J
        m00 = m10 = m01 = 0;
        row_ptr = (float*)(dx->imageData + dx->widthStep);
        for( row = 1; row <= _window.height; row++, row_ptr += dx->widthStep )
            for( col = 1; col <= _window.width; col++ )
            {
                pix = cvRound(row_ptr[col]);
                m00 += pix*pix;
                m10 += (float)col*pix;
                m01 += (float)row*pix;
            }

        H[0] = m00; H[1] = m10; H[2] = m01;
        H[3] = m10; H[4] = (float)_window.width*_window.width; H[5] = 0;
        H[6] = m01; H[7] = 0; H[8] = (float)_window.height*_window.height;

        // calculate the update step
        cvSolve( &H, &dh, &dp, CV_LU );

        if( fabs(dp.x) > (float)_window.width*TOLERANCE ||
            fabs(dp.y) > (float)_window.height*TOLERANCE )
            break;

        // update the window position
        center.x += dp.x;
        center.y += dp.y;
        _window.x = cvRound(center.x - _window.width*0.5f);
        _window.y = cvRound(center.y - _window.height*0.5f);

        if( iter >= max_iter )
            break;

        if( _window.x < 0 || _window.x + _window.width >= sz.width ||
            _window.y < 0 || _window.y + _window.height >= sz.height )
            break;

        box1.center = center;
        box1.size.width = _window.width;
        box1.size.height = _window.height;
        box1.angle = 0;

        a = (float)fabs( box0.size.width - box1.size.width );
        b = (float)fabs( box0.size.height - box1.size.height );
        c = (float)fabs( box0.angle - box1.angle );
        c = c >= 180 ? 360 - c : c;
        if( a < _window.width*0.05f &&
            b < _window.height*0.05f &&
            c < 2.0f )
            break;

        box0 = box1;
        iter++;
    }

    if( _comp )
    {
        memset( _comp, 0, sizeof(*_comp));
        _comp->rect.x = _window.x;
        _comp->rect.y = _window.y;
        _comp->rect.width = _window.width;
        _comp->rect.height = _window.height;
        _comp->area = 0;
        mask_row = (uchar*)(mask->imageData + mask->widthStep*(int)_window.y);
        mask_step = mask->widthStep;
    }

    if( box )
    {
        box->center = center;
        box->size.width = _window.width;
        box->size.height = _window.height;
        box->angle = 0;
    }

    if( _comp )
    {
        for( row = 0; row < _window.height; row++, mask_row += mask_step )
            for( col = 0; col < _window.width; col++ )
                if( mask_row[col] )
                    _comp->area++;

        if( _comp->area > 0 )
        {
            _comp->value = -1;
            _comp->rect.width--;
            _comp->rect.height--;
            cvCalcSubPixelMoment( &m, prob, &center );
            _comp->centroid.x = m.m10/m.m00 + _window.x;
            _comp->centroid.y = m.m01/m.m00 + _window.y;
        }
    }

    __END__;

    cvReleaseImage( &mask );
    cvReleaseHist( &hist );
    cvReleaseImage( &dx );
    cvReleaseImage( &dy );

    return cvGetErrStatus();
}

该函数的实现包括以下步骤：

1. 对输入的概率图像进行判断；
2. 分配内存；
3. 计算直方图；
4. CAMShift 迭代；
5. 返回结果。

在 CAMShift 迭代中，该函数会计算梯度，判断迭代是否终止，并更新窗口位置和大小。