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一、首先了解cv::Mat结构体

cv::Mat::step与QImage转换有着较大的关系。

step的几个类别区分:

• step:矩阵第一行元素的字节数
• step[0]:矩阵第一行元素的字节数
• step[1]:矩阵中一个元素的字节数
• step1(0):矩阵中一行有几个通道数
• step1(1):一个元素有几个通道数(channel())

    cv::Mat cvmat(3, 4, CV_16UC4, cv::Scalar_<uchar>(1, 2, 3, 4));std::cout << cvmat<< std::endl;std::cout << "step:" << cvmat.step << std::endl;std::cout << "step[0]:" << cvmat.step[0] << std::endl;std::cout << "step[1]:" << cvmat.step[1] << std::endl;std::cout << "step1(0):" << cvmat.step1(0) << std::endl;std::cout << "step1(1):" << cvmat.step1(1) << std::endl;

运行结果：

分析:
创建了一个3∗4的16位4通道的矩阵；

每一个元素赋值为1,2,3,4；可以看到生成了3*4*4的矩阵；

因为创建的是16位的，所以每一个通道是2个字节数；

所以一行共有4*4*2=32个字节数，故step和step[0]都为32；

因为一个元素有4个通道，每个通道2个字节，所以1个元素的字节数step[1]为4*2=8；

一行是4个元素，每个元素是4个通道，所以一行的通道数，step1(0)为4*4=16，step1(1)为4；

二、cv::Mat转QImage

代码示例为拷贝转换：

QImage cvMat2QImage(const cv::Mat& mat) 
{if (mat.empty()){return QImage();}QImage image;switch (mat.type()){case CV_8UC1:{image = QImage((const uchar*)(mat.data),mat.cols, mat.rows, mat.step,QImage::Format_Grayscale8);return image.copy();}case CV_8UC2:{mat.convertTo(mat, CV_8UC1);image = QImage((const uchar*)(mat.data),mat.cols, mat.rows, mat.step,QImage::Format_Grayscale8);return image.copy();}case CV_8UC3:{// Copy input Matconst uchar *pSrc = (const uchar*)mat.data;// Create QImage with same dimensions as input MatQImage image(pSrc, mat.cols, mat.rows, mat.step, QImage::Format_RGB888);return image.rgbSwapped();}case CV_8UC4:{// Copy input Matconst uchar *pSrc = (const uchar*)mat.data;// Create QImage with same dimensions as input MatQImage image(pSrc, mat.cols, mat.rows, mat.step, QImage::Format_ARGB32);return image.copy();}case CV_32FC1:{Mat normalize_mat;normalize(mat, normalize_mat, 0, 255, NORM_MINMAX, -1);normalize_mat.convertTo(normalize_mat, CV_8U);const uchar *pSrc = (const uchar*)normalize_mat.data;QImage image(pSrc, normalize_mat.cols, normalize_mat.rows, normalize_mat.step, QImage::Format_Grayscale8);return image.copy();}case CV_32FC3:{Mat normalize_mat;normalize(mat, normalize_mat, 0, 255, NORM_MINMAX,-1);normalize_mat.convertTo(normalize_mat, CV_8U);const uchar *pSrc = (const uchar*)normalize_mat.data;// Create QImage with same dimensions as input MatQImage image(pSrc, normalize_mat.cols, normalize_mat.rows, normalize_mat.step, QImage::Format_RGB888);return image.rgbSwapped();}case CV_64FC1:{Mat normalize_mat;normalize(mat, normalize_mat, 0, 255, NORM_MINMAX, -1);normalize_mat.convertTo(normalize_mat, CV_8U);const uchar *pSrc = (const uchar*)normalize_mat.data;QImage image(pSrc, normalize_mat.cols, normalize_mat.rows, normalize_mat.step, QImage::Format_Grayscale8);return image.copy();}case CV_64FC3:{Mat normalize_mat;normalize(mat, normalize_mat, 0, 255, NORM_MINMAX, -1);normalize_mat.convertTo(normalize_mat, CV_8U);const uchar *pSrc = (const uchar*)normalize_mat.data;// Create QImage with same dimensions as input MatQImage image(pSrc, normalize_mat.cols, normalize_mat.rows, normalize_mat.step, QImage::Format_RGB888);return image.rgbSwapped();}case CV_32SC1:{Mat normalize_mat;normalize(mat, normalize_mat, 0, 255, NORM_MINMAX, -1);normalize_mat.convertTo(normalize_mat, CV_8U);const uchar *pSrc = (const uchar*)normalize_mat.data;QImage image(pSrc, normalize_mat.cols, normalize_mat.rows, normalize_mat.step, QImage::Format_Grayscale8);return image.copy();}case CV_32SC3:{Mat normalize_mat;normalize(mat, normalize_mat, 0, 255, NORM_MINMAX, -1);normalize_mat.convertTo(normalize_mat, CV_8U);const uchar *pSrc = (const uchar*)normalize_mat.data;// Create QImage with same dimensions as input MatQImage image(pSrc, normalize_mat.cols, normalize_mat.rows, normalize_mat.step, QImage::Format_RGB888);return image.rgbSwapped();}case CV_16SC1:{Mat normalize_mat;normalize(mat, normalize_mat, 0, 255, NORM_MINMAX, -1);normalize_mat.convertTo(normalize_mat, CV_8U);const uchar *pSrc = (const uchar*)normalize_mat.data;QImage image(pSrc, normalize_mat.cols, normalize_mat.rows, normalize_mat.step, QImage::Format_Grayscale8);return image.copy();}case CV_16SC3:{Mat normalize_mat;normalize(mat, normalize_mat, 0, 255, NORM_MINMAX, -1);normalize_mat.convertTo(normalize_mat, CV_8U);const uchar *pSrc = (const uchar*)normalize_mat.data;// Create QImage with same dimensions as input MatQImage image(pSrc, normalize_mat.cols, normalize_mat.rows, normalize_mat.step, QImage::Format_RGB888);return image.rgbSwapped();}case CV_8SC1:{//Mat normalize_mat;//normalize(mat, normalize_mat, 0, 255, NORM_MINMAX, -1);mat.convertTo(mat, CV_8U);const uchar *pSrc = (const uchar*)mat.data;QImage image(pSrc, mat.cols, mat.rows, mat.step, QImage::Format_Grayscale8);return image.copy();}case CV_8SC3:{mat.convertTo(mat, CV_8U);const uchar *pSrc = (const uchar*)mat.data;QImage image(pSrc, mat.cols, mat.rows, mat.step, QImage::Format_RGB888);return image.rgbSwapped();}default:mat.convertTo(mat, CV_8UC3);QImage image((const uchar*)mat.data, mat.cols, mat.rows, mat.step, QImage::Format_RGB888);return image.rgbSwapped();return QImage();break;}
}

三、QImage转cv::Mat

示例代码为共享内存转换：

cv::Mat QImage2cvMat(QImage& image)
{cv::Mat mat;//qDebug() << image.format();switch (image.format()){case QImage::Format_ARGB32:mat = cv::Mat(image.height(), image.width(), CV_8UC4, (void*)image.constBits(), image.bytesPerLine());break;case QImage::Format_RGB32:mat = cv::Mat(image.height(), image.width(), CV_8UC3, (void*)image.constBits(), image.bytesPerLine());//cv::cvtColor(mat, mat, CV_BGR2RGB);break;case QImage::Format_ARGB32_Premultiplied:mat = cv::Mat(image.height(), image.width(), CV_8UC4, (void*)image.constBits(), image.bytesPerLine());break;case QImage::Format_RGB888:mat = cv::Mat(image.height(), image.width(), CV_8UC3, (void*)image.constBits(), image.bytesPerLine());//cv::cvtColor(mat, mat, CV_BGR2RGB);break;case QImage::Format_Indexed8:mat = cv::Mat(image.height(), image.width(), CV_8UC1, (void*)image.constBits(), image.bytesPerLine());break;case QImage::Format_Grayscale8:mat = cv::Mat(image.height(), image.width(), CV_8UC1, (void*)image.constBits(), image.bytesPerLine());break;}return mat;
}