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 Mat
const uchar *pSrc = (const uchar*)mat.data;
// Create QImage with same dimensions as input Mat
QImage image(pSrc, mat.cols, mat.rows, mat.step, QImage::Format_RGB888);
return image.rgbSwapped();
}
case CV_8UC4:
{
// Copy input Mat
const uchar *pSrc = (const uchar*)mat.data;
// Create QImage with same dimensions as input Mat
QImage 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 Mat
QImage 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 Mat
QImage 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 Mat
QImage 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 Mat
QImage 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;
}
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