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

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;
}

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.