cv::Mat
is preferred over IplImage
because it simplifies your code
cv::Mat img = cv::imread("lenna.png");
for(int i=0; i<img.rows; i++)
for(int j=0; j<img.cols; j++)
// You can now access the pixel value with cv::Vec3b
std::cout << img.at<cv::Vec3b>(i,j)[0] << " " << img.at<cv::Vec3b>(i,j)[1] << " " << img.at<cv::Vec3b>(i,j)[2] << std::endl;
This assumes that you need to use the RGB values together. If you don't, you can uses cv::split to get each channel separately. See etarion's answer for the link with example.
Also, in my cases, you simply need the image in gray-scale. Then, you can load the image in grayscale and access it as an array of uchar.
cv::Mat img = cv::imread("lenna.png",0);
for(int i=0; i<img.rows; i++)
for(int j=0; j<img.cols; j++)
std::cout << img.at<uchar>(i,j) << std::endl;
UPDATE: Using split to get the 3 channels
cv::Mat img = cv::imread("lenna.png");
std::vector<cv::Mat> three_channels = cv::split(img);
// Now I can access each channel separately
for(int i=0; i<img.rows; i++)
for(int j=0; j<img.cols; j++)
std::cout << three_channels[0].at<uchar>(i,j) << " " << three_channels[1].at<uchar>(i,j) << " " << three_channels[2].at<uchar>(i,j) << std::endl;
// Similarly for the other two channels
UPDATE: Thanks to entarion for spotting the error I introduced when copying and pasting from the cv::Vec3b example.
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