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代码语言：javascript复制#include "stdafx.h"
#include <cmath>
#include <iostream>
#include <opencv2corecore.hpp>
#include <opencv2highguihighgui.hpp>
#include <opencv2imgprocimgproc.hpp>
using namespace cv;
using namespace std;
float get_Gamma_Value(Mat& gray_img);
void create_Gamma_Table(unsigned char* gama_table, float gama_value);
void Gamma_Correction(Mat& gray_img, Mat& dst_img, unsigned char* gama_table);
int _tmain(int argc, _TCHAR* argv[])
{
char input_image_name[100];
char output_image_name[100];
int image_num = 476;
for (int i = 1; i <= image_num; i )
{
sprintf(input_image_name, "../%s\%d.jpg","temp", i);
Mat input_image = imread(input_image_name);
if (input_image.empty())
{
cout << "Failed to load image !" << endl;
continue;;
}
Mat gray_image;
cvtColor(input_image, gray_image, CV_BGR2GRAY);
// Start a timer
double duration;
duration = static_cast<double>(cv::getTickCount());
float gama_value = get_Gamma_Value(gray_image);
unsigned char LUT[256];
create_Gamma_Table(LUT, gama_value);
Mat result_image(gray_image.rows, gray_image.cols, gray_image.type());
Gamma_Correction(gray_image, result_image, LUT);
// Calculate the time cost and print
duration = static_cast<double>(cv::getTickCount()) - duration;
duration /= cv::getTickFrequency();
std::cout << duration * 1000 << " ms" << std::endl;
imshow("Source_Image", input_image);
imshow("Gamma_Correction", result_image);
//imwrite("test6.bmp",result_image);
waitKey(1);
}
return 0;
}
/****************************************************
①当Gamma值比1大时，在输入值相同的情况下，输出值减小；
②当Gamma值为1时，输出值不变；
③当Gamma值比1小时，在输入值相同的情况下，输出值增加。
****************************************************/
//公式：gamma = log(y/range)/ log(x/range)，x是整幅图像像素的平均值，y是像素值最大范围的一半。
//先计算灰度图像的像素均值mean，将计算出来的均值带入 gammaVal = log(mean/255)/log(0.5) 这个公式中，就可以得到Gamma值了。
float get_Gamma_Value(Mat& gray_img)
{
if (gray_img.empty())
{
return -1.0;
}
cv::Scalar meam_value = cv::mean(gray_img);
float val = meam_value.val[0];
//float gamma_val = (log10(val / 255.0)) / (log10(0.5));
float gamma_val = (log10(0.5)) / (log10(val / 255.0));
return gamma_val;
}
void create_Gamma_Table(unsigned char* gama_table, float gama_value)
{
for (int i = 0; i < 256; i )
{
float f = (i 0.5f) / 255.0;
f = (float)(pow(f, gama_value));
gama_table[i] = saturate_cast<uchar>(f * 255.0f - 0.5f);
}
}
void Gamma_Correction(Mat& gray_img, Mat& dst_img, unsigned char* gama_table)
{
if(gray_img.channels() != dst_img.channels() || gray_img.cols != dst_img.cols || gray_img.rows != dst_img.rows)
{
return;
}
for (int i = 0; i < gray_img.rows; i )
{
for (int j = 0; j < gray_img.cols; j )
{
dst_img.at<uchar>(i, j) = gama_table[(int)(gray_img.at<uchar>(i, j))];
}
}
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