Average Filter by MicMac on SO - basic refactor, cleanup, and tracing with mistakes left in.

#include <opencv2/opencv.hpp>

/*
Note: I split off this piece of code into a separate function in order
      to simplify the reasoning about the code.
      This is probably not ideal for performance, but it's too early to worry about that now.
*/
uint8_t filter_mean_at(cv::Mat const& image, int32_t x, int32_t y, int32_t framing)
{
    std::cout << "Applying filter at (" << x << "," << y << ")\n";
    int32_t sum{ 0 };
    for (int32_t dx = 0; dx < framing; ++dx) {
        for (int32_t dy = 0; dy < framing; ++dy) {
            int32_t const xx = x + dx;
            int32_t const yy = y + dy;
            std::cout << " * Processing pixel at  (" << xx << "," << yy << ")\n";
            sum += image.at<uint8_t>(xx, yy);
        }
    }

    int32_t mean = sum; // NB: Mistake intentionally left here.
    std::cout << " Result = " << mean << " (as uint8_t = "
        << static_cast<int32_t>(static_cast<uint8_t>(mean)) << ")\n";
    return mean;
}

cv::Mat filter_mean(cv::Mat image, int window_size)
{
    cv::Mat result = image.clone();
    int32_t const framing = window_size / 2;
    for (int32_t x = 0; x < image.rows; ++x) {
        for (int32_t y = 0; y < image.cols; ++y) {
            result.at<uint8_t>(x, y) = filter_mean_at(image, x, y, framing);
        }
    }
    return result;
}

int main()
{
    // Just some small simple 4x4 grayscale image to test this on...
    cv::Mat img = (cv::Mat1b(4, 4)
        << 0, 16, 32, 48
        , 64, 64, 64, 64
        , 128, 128, 128, 192
        , 255, 255, 255, 255
        );

    cv::Mat filtered_image = filter_mean(img, 3);

    std::cout << "Result:\n" << filtered_image << '\n';

    return 0;
}