How i can take the average of 100 image using opencv?

Question

i have 100 image, each one is 598 * 598 pixels, and i want to remove the pictorial and noise by taking the average of pixels, but if i want to use Adding for "pixel by pixel"then dividing i will write a loop until 596*598 repetitions for one image, and 598*598*100 for hundred of image.

is there a method to help me in this operation?

Answer 1

You need to loop over each image, and accumulate the results. Since this is likely to cause overflow, you can convert each image to a CV_64FC3 image, and accumualate on a CV_64FC3 image. You can use also CV_32FC3 or CV_32SC3 for this, i.e. using float or integer instead of double.

Once you have accumulated all values, you can use convertTo to both:

• make the image a CV_8UC3
• divide each value by the number of image, to get the actual mean.

This is a sample code that creates 100 random images, and computes and shows the mean:

#include <opencv2\opencv.hpp>
using namespace cv;

Mat3b getMean(const vector<Mat3b>& images)
{
    if (images.empty()) return Mat3b();

    // Create a 0 initialized image to use as accumulator
    Mat m(images[0].rows, images[0].cols, CV_64FC3);
    m.setTo(Scalar(0,0,0,0));

    // Use a temp image to hold the conversion of each input image to CV_64FC3
    // This will be allocated just the first time, since all your images have
    // the same size.
    Mat temp;
    for (int i = 0; i < images.size(); ++i)
    {
        // Convert the input images to CV_64FC3 ...
        images[i].convertTo(temp, CV_64FC3);

        // ... so you can accumulate
        m += temp;
    }

    // Convert back to CV_8UC3 type, applying the division to get the actual mean
    m.convertTo(m, CV_8U, 1. / images.size());
    return m;
}

int main()
{
    // Create a vector of 100 random images
    vector<Mat3b> images;
    for (int i = 0; i < 100; ++i)
    {
        Mat3b img(598, 598);
        randu(img, Scalar(0), Scalar(256));

        images.push_back(img);
    }

    // Compute the mean
    Mat3b meanImage = getMean(images);

    // Show result
    imshow("Mean image", meanImage);
    waitKey();

    return 0;
}

Answer 2

Suppose that the images will not need to undergo transformations (gamma, color space, or alignment). The numpy package lets you do this quickly and succinctly.

# List of images, all must be the same size and data type.
images=[img0, img1, ...]
avg_img = np.mean(images, axis=0)

This will auto-promote the elements to float. If you want the as BGR888, then:

avg_img = avg_img.astype(np.uint8)

Could also do uint16 for 16 bits per channel. If you are dealing with 8 bits per channel, you almost certainly won't need 100 images.