Cycle through pixels with opencv

Question

How would I be able to cycle through an image using opencv as if it were a 2d array to get the rgb values of each pixel? Also, would a mat be preferable over an iplimage for this operation?

Answer 1

If you use C++, use the C++ interface of opencv and then you can access the members via http://docs.opencv.org/2.4/doc/tutorials/core/how_to_scan_images/how_to_scan_images.html#the-efficient-way or using cv::Mat::at(), for example.

Answer 2

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.

Answer 3

Since OpenCV 3.0, there are official and fastest way to run function all over the pixel in cv::Mat.

void cv::Mat::forEach (const Functor& operation)

If you use this function, operation is runs on multi core automatically.

Disclosure : I'm contributor of this feature.

Answer 4

The docs show a well written comparison of different ways to iterate over a Mat image here.

The fastest way is to use C style pointers. Here is the code copied from the docs:

Mat& ScanImageAndReduceC(Mat& I, const uchar* const table)
{
// accept only char type matrices
CV_Assert(I.depth() != sizeof(uchar));

int channels = I.channels();

int nRows = I.rows;
int nCols = I.cols * channels;

if (I.isContinuous())
{
    nCols *= nRows;
    nRows = 1;
}

int i,j;
uchar* p;
for( i = 0; i < nRows; ++i)
{
    p = I.ptr<uchar>(i);
    for ( j = 0; j < nCols; ++j)
    {
        p[j] = table[p[j]];
    }
}
return I;
}

Accessing the elements with the at is quite slow.

Note that if your operation can be performed using a lookup table, the built in function LUT is by far the fastest (also described in the docs).

Answer 5

Since OpenCV 3.3 (see changelog) it is also possible to use C++11 style for loops:

// Example 1
Mat_<Vec3b> img = imread("lena.jpg");
for( auto& pixel: img ) {
    pixel[0] = gamma_lut[pixel[0]];
    pixel[1] = gamma_lut[pixel[1]];
    pixel[2] = gamma_lut[pixel[2]];
}

// Example 2
Mat_<float> img2 = imread("float_image.exr", cv::IMREAD_UNCHANGED);
for(auto& p : img2) p *= 2;

Answer 6

If you want to modify RGB pixels one by one, the example below will help!

void LoopPixels(cv::Mat &img) {
    // Accept only char type matrices
    CV_Assert(img.depth() == CV_8U);

    // Get the channel count (3 = rgb, 4 = rgba, etc.)
    const int channels = img.channels();
    switch (channels) {
    case 1:
    {
        // Single colour
        cv::MatIterator_<uchar> it, end;
        for (it = img.begin<uchar>(), end = img.end<uchar>(); it != end; ++it)
            *it = 255;
        break;
    }
    case 3:
    {
        // RGB Color
        cv::MatIterator_<cv::Vec3b> it, end;
        for (it = img.begin<cv::Vec3b>(), end = img.end<cv::Vec3b>(); it != end; ++it) {
            uchar &r = (*it)[2];
            uchar &g = (*it)[1];
            uchar &b = (*it)[0];
            // Modify r, g, b values
            // E.g. r = 255; g = 0; b = 0;
        }
        break;
    }
    }
}

Answer 7

This is an old question but needs to get updated since opencv is being actively developed. Recently, OpenCV has introduce parallel_for_ which complies with c++11 lambda functions. Here is the example

parallel_for_(Range(0 , img.rows * img.cols), [&](const Range& range){
    for(int r = range.start; r<range.end; r++ )
    {
         int i = r / img.cols;
         int j = r % img.cols;
        img.ptr<uchar>(i)[j] = doSomethingWithPixel(img.at<uchar>(i,j));
    }
});

This is mention-worthy that this method uses the CPU cores in modern computer architectures.