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

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.

Answer 3

Firstly- convert images to floats. You have N=100 images. Imagine that a single image is an array of average pixel values of 1 image. You need to calculate an array of average pixel values of N images.
Let A- array of average pixel values of X images, B - array of average pixel values of Y images. Then C = (A * X + B * Y) / (X + Y) - array of average pixel values of X + Y images. To get better accuracy in floating point operations X and Y should be approximately equal
You may merge all you images like subarrays in merge sort. In you case merge operation is C = (A * X + B * Y) / (X + Y) where A and B are arrays of average pixel values of X and Y images