C语言实现积分图实现ace算法

以下是使用C语言实现积分图实现ACE算法的代码：

#include <stdio.h>
#include <stdlib.h>
#include <opencv2/opencv.hpp>

using namespace cv;

void integral_image(Mat &img, Mat &integral)
{
    int h = img.rows;
    int w = img.cols;
    integral.create(h+1, w+1, CV_32S);
    integral.setTo(0);

    for(int i=1; i<=h; i++)
    {
        for(int j=1; j<=w; j++)
        {
            integral.at<int>(i, j) = img.at<uchar>(i-1, j-1) + integral.at<int>(i-1, j) + integral.at<int>(i, j-1) - integral.at<int>(i-1, j-1);
        }
    }
}

void local_mean_and_variance(Mat &integral, int w_size, Mat &local_mean, Mat &local_variance)
{
    int h = integral.rows - 1;
    int w = integral.cols - 1;
    int half_w_size = w_size / 2;
    local_mean.create(h, w, CV_32F);
    local_variance.create(h, w, CV_32F);

    for(int i=0; i<h; i++)
    {
        for(int j=0; j<w; j++)
        {
            int top = i - half_w_size;
            int left = j - half_w_size;
            int bottom = i + half_w_size;
            int right = j + half_w_size;
            top = top < 0 ? 0 : top;
            left = left < 0 ? 0 : left;
            bottom = bottom >= h ? h-1 : bottom;
            right = right >= w ? w-1 : right;

            int area = (bottom - top + 1) * (right - left + 1);
            int sum = integral.at<int>(bottom+1, right+1) + integral.at<int>(top, left) - integral.at<int>(bottom+1, left) - integral.at<int>(top, right+1);
            float mean = (float)sum / area;
            local_mean.at<float>(i, j) = mean;

            int sum2 = integral.at<int>(bottom+1, right+1) + integral.at<int>(top, left) - integral.at<int>(bottom+1, left) - integral.at<int>(top, right+1);
            float variance = (float)sum2 / area - mean * mean;
            local_variance.at<float>(i, j) = variance;
        }
    }
}

void contrast_score(Mat &local_mean, Mat &local_variance, float k, float alpha, Mat &contrast)
{
    contrast.create(local_mean.rows, local_mean.cols, CV_32F);

    for(int i=0; i<local_mean.rows; i++)
    {
        for(int j=0; j<local_mean.cols; j++)
        {
            float mean = local_mean.at<float>(i, j);
            float variance = local_variance.at<float>(i, j);
            float score = k * fabs(mean) / sqrt(variance + alpha * alpha);
            contrast.at<float>(i, j) = score;
        }
    }
}

void contrast_enhancement(Mat &img, Mat &contrast, Mat &img_enhanced)
{
    img.convertTo(img_enhanced, CV_32F);
    float *pImg = img_enhanced.ptr<float>(0);
    float *pContrast = contrast.ptr<float>(0);
    int size = img_enhanced.rows * img_enhanced.cols;

    for(int i=0; i<size; i++)
    {
        pImg[i] = pImg[i] + pContrast[i] * (pImg[i] - mean(pImg, size));
        pImg[i] = pImg[i] < 0 ? 0 : pImg[i];
        pImg[i] = pImg[i] > 255 ? 255 : pImg[i];
    }

    img_enhanced.convertTo(img_enhanced, CV_8U);
}

int main(int argc, char **argv)
{
    if(argc < 2)
    {
        printf("Usage: %s image_path\n", argv[0]);
        return -1;
    }

    Mat img = imread(argv[1], IMREAD_GRAYSCALE);

    if(img.empty())
    {
        printf("Failed to load image: %s\n", argv[1]);
        return -1;
    }

    Mat integral, local_mean, local_variance, contrast, img_enhanced;
    integral_image(img, integral);
    local_mean_and_variance(integral, 21, local_mean, local_variance);
    contrast_score(local_mean, local_variance, 0.4, 1.0, contrast);
    contrast_enhancement(img, contrast, img_enhanced);

    imshow("original", img);
    imshow("enhanced", img_enhanced);
    waitKey(0);
    destroyAllWindows();

    return 0;
}

其中，img_path 是待处理的图像路径，w_size 是局部均值和局部方差计算时的窗口大小，k 和 alpha 是对比度得分计算时的参数。