版权声明：本文为博主原创文章，未经博主允许不得转载。 https://blog.csdn.net/qq_29540745/article/details/74681853
本博客借用了不少其他博客，相当于知识整理
一、光照补偿
1.直方图均衡化
#include "stdafx.h"
#include<opencv2/opencv.hpp>
#include<iostream>
using namespace std;
using namespace cv;
int main(int argc, char *argv[])
{
Mat image = imread("D://vvoo//123.jpg", 1);
if (!image.data)
{
cout << "image loading error" <<endl;
return -1;
}
Mat imageRGB[3];
split(image, imageRGB);
for (int i = 0; i < 3; i++)
{
equalizeHist(imageRGB[i], imageRGB[i]);
}
merge(imageRGB, 3, image);
imshow("equalizeHist", image);
waitKey();
return 0;
}
2.gamma corection:
http://www.cambridgeincolour.com/tutorials/gamma-correction.htm
人眼是按照gamma < 1的曲线对输入图像进行处理的。
原图
#include<opencv2/opencv.hpp>
#include<iostream>
using namespace std;
using namespace cv;
// Normalizes a given image into a value range between 0 and 255.
Mat norm(const Mat& src) {
// Create and return normalized image:
Mat dst;
switch (src.channels()) {
case 1:
cv::normalize(src, dst, 0, 255, NORM_MINMAX, CV_8UC1);
break;
case 3:
cv::normalize(src, dst, 0, 255, NORM_MINMAX, CV_8UC3);
break;
default:
src.copyTo(dst);
break;
}
return dst;
}
int main()
{
Mat image,X,I;
VideoCapture cap(0);
while (1)
{
cap >> image;
image.convertTo(X, CV_32FC1); //转换格式
float gamma = 4;
pow(X, gamma, I);
imshow("Original Image", image);
imshow("Gamma correction image", norm(I));
char key = waitKey(30);
if (key=='q' )
break;
}
return 0;
}3.拉普拉斯算子增强
int main(int argc, char *argv[])
{
Mat image = imread("D://vvoo//123.jpg", 1);
if (!image.data)
{
cout << "image loading error" <<endl;
return -1;
}
imshow("原图", image);
Mat imageEnhance;
Mat kernel = (Mat_<float>(3, 3) << 0, -1, 0, 0, 7, 0, 0, -1, 0);
filter2D(image, imageEnhance, CV_8UC3, kernel);
imshow("拉普拉斯算子图像增强效果", imageEnhance);
imwrite("C://Users//TOPSUN//Desktop//123.jpg",imageEnhance);
waitKey();
return 0;
}
4.对数变换
对数图像增强是图像增强的一种常见方法，其公式为： S = c log(r+1)，其中c是常数（以下算法c=255/(log(256)），这样可以实现整个画面的亮度增大此时默认v=e，即 S = c ln(r+1)。
如下图，对数使亮度比较低的像素转换成亮度比较高的，而亮度较高的像素则几乎没有变化，这样就使图片整体变亮。
int main(int argc, char *argv[])
{
double temp = 255 / log(256);
cout << "doubledouble temp ="<< temp<<endl;
Mat image = imread("D://vvoo//123.jpg", 1);
if (!image.data)
{
cout << "image loading error" <<endl;
return -1;
}
imshow("原图", image);
Mat imageLog(image.size(), CV_32FC3);
for (int i = 0; i < image.rows; i++)
{
for (int j = 0; j < image.cols; j++)
{
imageLog.at<Vec3f>(i, j)[0] = temp* log(1 + image.at<Vec3b>(i, j)[0]);
imageLog.at<Vec3f>(i, j)[1] = temp*log(1 + image.at<Vec3b>(i, j)[1]);
imageLog.at<Vec3f>(i, j)[2] = temp*log(1 + image.at<Vec3b>(i, j)[2]);
}
}
//归一化到0~255
normalize(imageLog, imageLog, 0, 255, CV_MINMAX);
//转换成8bit图像显示
convertScaleAbs(imageLog, imageLog);
int channel = image.channels();
cout << channel << endl;
imshow("Soure", image);
imshow("after", imageLog);
imwrite("C://Users//TOPSUN//Desktop//123.jpg", imageLog);
waitKey();
return 0;
}二、去除光照
5.RGB归一化
据说能消除光照，自己实现出来好垃圾啊
int main(int argc, char *argv[])
{
//double temp = 255 / log(256);
//cout << "doubledouble temp ="<< temp<<endl;
Mat image = imread("D://vvoo//sun_face.jpg", 1);
if (!image.data)
{
cout << "image loading error" <<endl;
return -1;
}
imshow("原图", image);
Mat src(image.size(), CV_32FC3);
for (int i = 0; i < image.rows; i++)
{
for (int j = 0; j < image.cols; j++)
{
src.at<Vec3f>(i, j)[0] = 255 * (float)image.at<Vec3b>(i, j)[0] / ((float)image.at<Vec3b>(i, j)[0] + (float)image.at<Vec3b>(i, j)[2] + (float)image.at<Vec3b>(i, j)[1]+0.01);
src.at<Vec3f>(i, j)[1] = 255 * (float)image.at<Vec3b>(i, j)[1] / ((float)image.at<Vec3b>(i, j)[0] + (float)image.at<Vec3b>(i, j)[2] + (float)image.at<Vec3b>(i, j)[1]+0.01);
src.at<Vec3f>(i, j)[2] = 255 * (float)image.at<Vec3b>(i, j)[2] / ((float)image.at<Vec3b>(i, j)[0] + (float)image.at<Vec3b>(i, j)[2] + (float)image.at<Vec3b>(i, j)[1]+0.01);
}
}
normalize(src, src, 0, 255, CV_MINMAX);
convertScaleAbs(src,src);
imshow("rgb", src);
imwrite("C://Users//TOPSUN//Desktop//123.jpg", src);
waitKey(0);
return 0;
}
6.另一种去除光照的方法
void unevenLightCompensate(Mat &image, int blockSize)
{
if (image.channels() == 3) cvtColor(image, image, 7);
double average = mean(image)[0];
int rows_new = ceil(double(image.rows) / double(blockSize));
int cols_new = ceil(double(image.cols) / double(blockSize));
Mat blockImage;
blockImage = Mat::zeros(rows_new, cols_new, CV_32FC1);
for (int i = 0; i < rows_new; i++)
{
for (int j = 0; j < cols_new; j++)
{
int rowmin = i*blockSize;
int rowmax = (i + 1)*blockSize;
if (rowmax > image.rows) rowmax = image.rows;
int colmin = j*blockSize;
int colmax = (j + 1)*blockSize;
if (colmax > image.cols) colmax = image.cols;
Mat imageROI = image(Range(rowmin, rowmax), Range(colmin, colmax));
double temaver = mean(imageROI)[0];
blockImage.at<float>(i, j) = temaver;
}
}
blockImage = blockImage - average;
Mat blockImage2;
resize(blockImage, blockImage2, image.size(), (0, 0), (0, 0), INTER_CUBIC);
Mat image2;
image.convertTo(image2, CV_32FC1);
Mat dst = image2 - blockImage2;
dst.convertTo(image, CV_8UC1);
}
int main(int argc, char *argv[])
{
//double temp = 255 / log(256);
//cout << "doubledouble temp ="<< temp<<endl;
Mat image = imread("C://Users//TOPSUN//Desktop//2.jpg", 1);
if (!image.data)
{
cout << "image loading error" <<endl;
return -1;
}
imshow("原图", image);
unevenLightCompensate(image, 12);
imshow("rgb", image);
imwrite("C://Users//TOPSUN//Desktop//123.jpg", image);
waitKey(0);
return 0;
}
7.又找到一个
int highlight_remove_Chi(IplImage* src, IplImage* dst)
{
int height = src->height;
int width = src->width;
int step = src->widthStep;
int i = 0, j = 0;
unsigned char R, G, B, MaxC;
double alpha, beta, alpha_r, alpha_g, alpha_b, beta_r, beta_g, beta_b, temp = 0, realbeta = 0, minalpha = 0;
double gama, gama_r, gama_g, gama_b;
unsigned char* srcData;
unsigned char* dstData;
for (i = 0; i<height; i++)
{
srcData = (unsigned char*)src->imageData + i*step;
dstData = (unsigned char*)dst->imageData + i*step;
for (j = 0; j<width; j++)
{
R = srcData[j * 3];
G = srcData[j * 3 + 1];
B = srcData[j * 3 + 2];
alpha_r = (double)R / (double)(R + G + B);
alpha_g = (double)G / (double)(R + G + B);
alpha_b = (double)B / (double)(R + G + B);
alpha = max(max(alpha_r, alpha_g), alpha_b);
MaxC = max(max(R, G), B);// compute the maximum of the rgb channels
minalpha = min(min(alpha_r, alpha_g), alpha_b); beta_r = 1 - (alpha - alpha_r) / (3 * alpha - 1);
beta_g = 1 - (alpha - alpha_g) / (3 * alpha - 1);
beta_b = 1 - (alpha - alpha_b) / (3 * alpha - 1);
beta = max(max(beta_r, beta_g), beta_b);//将beta当做漫反射系数，则有 // gama is used to approximiate the beta
gama_r = (alpha_r - minalpha) / (1 - 3 * minalpha);
gama_g = (alpha_g - minalpha) / (1 - 3 * minalpha);
gama_b = (alpha_b - minalpha) / (1 - 3 * minalpha);
gama = max(max(gama_r, gama_g), gama_b);
temp = (gama*(R + G + B) - MaxC) / (3 * gama - 1);
//beta=(alpha-minalpha)/(1-3*minalpha)+0.08;
//temp=(gama*(R+G+B)-MaxC)/(3*gama-1);
dstData[j * 3] = R - (unsigned char)(temp + 0.5);
dstData[j * 3 + 1] = G - (unsigned char)(temp + 0.5);
dstData[j * 3 + 2] = B - (unsigned char)(temp + 0.5);
}
}
cvShowImage("src", src);
cvShowImage("dst", dst);
return 1;
}
void main()
{
IplImage *src = cvLoadImage("C://Users//TOPSUN//Desktop//2.jpg");
IplImage *dst = cvCreateImage(cvSize(src->width, src->height), src->depth, 3);
if (!src)
{
printf("请确保图像输入正确；");
return;
}
highlight_remove_Chi(src, dst);
cvSaveImage("C://Users//TOPSUN//Desktop//123.jpg", dst);
cvWaitKey(0);
}
未完待续。。。