for( int i = 0; i < I.rows; ++i)
for( int j = 0; j < I.cols; ++j )
I.at<uchar>(i,j) = 255 － I.at<uchar>(i,j);
for( i = 0; i < nRows; ++i){
p = I.ptr<uchar>(i);
for ( j = 0; j < nCols; ++j){
p[j] = 255 － p[j];
}
}MatIterator_<uchar> it, end;
for( it = I.begin<uchar>(), end = I.end<uchar>(); it != end; ++it)
*it = 255 － *it;#include "opencv2/highgui/highgui.hpp"
#include "opencv2/imgproc/imgproc.hpp"
using namespace cv;
Mat applyLookUp(const cv::Mat& image,const cv::Mat& lookup) {
Mat result;
cv::LUT(image,lookup,result);
return result;
}
int main( int, char** argv )
{
Mat image,gray;
image = imread( argv[1], 1 );
if( !image.data )
return -1;
cvtColor(image, gray, CV_BGR2GRAY);
Mat lut(1,256,CV_8U);
for (int i=0; i<256; i++) {
lut.at<uchar>(i)= 255-i;
}
Mat out = applyLookUp(gray,lut);
namedWindow("sample");
imshow("sample",out);
waitKey(0);
return 0;
}
看以下这个确定的过程：
Mat hist= getHistogram(image);
int imin= 0;
for( ; imin < histSize[0]; imin++ )
if (hist.at<float>(imin) > minValue)
break;
int imax= histSize[0]-1;
for( ; imax >= 0; imax-- )
if (hist.at<float>(imax) > minValue)
break;Mat lookup(1, 256, CV_8U);
for (int i=0; i<256; i++) {
if (i < imin) lookup.at<uchar>(i)= 0;
else if (i > imax) lookup.at<uchar>(i)= 255;
else lookup.at<uchar>(i)= static_cast<uchar>(255.0*(i-imin)/(imax-imin)+0.5);
}假设我有灰度级255的图像，可是都是属于［100，110］的灰度，图像对照度就非常低。我应该尽可能拉到整个［0。255］
public int[][] Histogram_Equalization(int[][] oldmat)
{
int[][] new_mat = new int[height][width];
int[] tmp = new int[256];
for(int i = 0;i < width;i++){
for(int j = 0;j < height;j++){
//System.out.println(oldmat[j][i]);
int index = oldmat[j][i];
tmp[index]++;
}
}
float[] C = new float[256];
int total = width*height;
//计算累积函数
for(int i = 0;i < 256 ; i++){
if(i == 0)
C[i] = 1.0f * tmp[i] / total;
else
C[i] = C[i-1] + 1.0f * tmp[i] / total;
}
for(int i = 0;i < width;i++){
for(int j = 0;j < height;j++){
new_mat[j][i] = (int)(C[oldmat[j][i]] * 255);
new_mat[j][i] = new_mat[j][i] + (new_mat[j][i] << 8) + (new_mat[j][i] << 16);
//System.out.println(new_mat[j][i]);
}
}
return new_mat;
} 
因此，该算法更适合于改进图像的局部对照度以及获得很多其它的图像细节。
/*
* CLAHE
* 自适应直方图均衡化
*/
public int[][] AHE(int[][] oldmat,int pblock)
{
int block = pblock;
//将图像均匀分成等矩形大小，8行8列64个块是经常使用的选择
int width_block = width/block;
int height_block = height/block;
//存储各个直方图
int[][] tmp = new int[block*block][256];
//存储累积函数
float[][] C = new float[block*block][256];
//计算累积函数
for(int i = 0 ; i < block ; i ++)
{
for(int j = 0 ; j < block ; j++)
{
int start_x = i * width_block;
int end_x = start_x + width_block;
int start_y = j * height_block;
int end_y = start_y + height_block;
int num = i+block*j;
int total = width_block * height_block;
for(int ii = start_x ; ii < end_x ; ii++)
{
for(int jj = start_y ; jj < end_y ; jj++)
{
int index = oldmat[jj][ii];
tmp[num][index]++;
}
}
//裁剪操作
int average = width_block * height_block / 255;
int LIMIT = 4 * average;
int steal = 0;
for(int k = 0 ; k < 256 ; k++)
{
if(tmp[num][k] > LIMIT){
steal += tmp[num][k] - LIMIT;
tmp[num][k] = LIMIT;
}
}
int bonus = steal/256;
//hand out the steals averagely
for(int k = 0 ; k < 256 ; k++)
{
tmp[num][k] += bonus;
}
//计算累积分布直方图
for(int k = 0 ; k < 256 ; k++)
{
if( k == 0)
C[num][k] = 1.0f * tmp[num][k] / total;
else
C[num][k] = C[num][k-1] + 1.0f * tmp[num][k] / total;
}
}
}
int[][] new_mat = new int[height][width];
//计算变换后的像素值
//依据像素点的位置，选择不同的计算方法
for(int i = 0 ; i < width; i++)
{
for(int j = 0 ; j < height; j++)
{
//four coners
if(i <= width_block/2 && j <= height_block/2)
{
int num = 0;
new_mat[j][i] = (int)(C[num][oldmat[j][i]] * 255);
}else if(i <= width_block/2 && j >= ((block-1)*height_block + height_block/2)){
int num = block*(block-1);
new_mat[j][i] = (int)(C[num][oldmat[j][i]] * 255);
}else if(i >= ((block-1)*width_block+width_block/2) && j <= height_block/2){
int num = block-1;
new_mat[j][i] = (int)(C[num][oldmat[j][i]] * 255);
}else if(i >= ((block-1)*width_block+width_block/2) && j >= ((block-1)*height_block + height_block/2)){
int num = block*block-1;
new_mat[j][i] = (int)(C[num][oldmat[j][i]] * 255);
}
//four edges except coners
else if( i <= width_block/2 )
{
//线性插值
int num_i = 0;
int num_j = (j - height_block/2)/height_block;
int num1 = num_j*block + num_i;
int num2 = num1 + block;
float p = (j - (num_j*height_block+height_block/2))/(1.0f*height_block);
float q = 1-p;
new_mat[j][i] = (int)((q*C[num1][oldmat[j][i]]+ p*C[num2][oldmat[j][i]])* 255);
}else if( i >= ((block-1)*width_block+width_block/2)){
//线性插值
int num_i = block-1;
int num_j = (j - height_block/2)/height_block;
int num1 = num_j*block + num_i;
int num2 = num1 + block;
float p = (j - (num_j*height_block+height_block/2))/(1.0f*height_block);
float q = 1-p;
new_mat[j][i] = (int)((q*C[num1][oldmat[j][i]]+ p*C[num2][oldmat[j][i]])* 255);
}else if( j <= height_block/2 ){
//线性插值
int num_i = (i - width_block/2)/width_block;
int num_j = 0;
int num1 = num_j*block + num_i;
int num2 = num1 + 1;
float p = (i - (num_i*width_block+width_block/2))/(1.0f*width_block);
float q = 1-p;
new_mat[j][i] = (int)((q*C[num1][oldmat[j][i]]+ p*C[num2][oldmat[j][i]])* 255);
}else if( j >= ((block-1)*height_block + height_block/2) ){
//线性插值
int num_i = (i - width_block/2)/width_block;
int num_j = block-1;
int num1 = num_j*block + num_i;
int num2 = num1 + 1;
float p = (i - (num_i*width_block+width_block/2))/(1.0f*width_block);
float q = 1-p;
new_mat[j][i] = (int)((q*C[num1][oldmat[j][i]]+ p*C[num2][oldmat[j][i]])* 255);
}
//inner area
else{
int num_i = (i - width_block/2)/width_block;
int num_j = (j - height_block/2)/height_block;
int num1 = num_j*block + num_i;
int num2 = num1 + 1;
int num3 = num1 + block;
int num4 = num2 + block;
float u = (i - (num_i*width_block+width_block/2))/(1.0f*width_block);
float v = (j - (num_j*height_block+height_block/2))/(1.0f*height_block);
new_mat[j][i] = (int)((u*v*C[num4][oldmat[j][i]] +
(1-v)*(1-u)*C[num1][oldmat[j][i]] +
u*(1-v)*C[num2][oldmat[j][i]] +
v*(1-u)*C[num3][oldmat[j][i]]) * 255);
}
new_mat[j][i] = new_mat[j][i] + (new_mat[j][i] << 8) + (new_mat[j][i] << 16);
}
}
return new_mat;
} | ? |
|
|---|
#include "opencv2/highgui/highgui.hpp"
#include "opencv2/imgproc/imgproc.hpp"
using namespace cv;
Mat applyLookUp(const cv::Mat& image,const cv::Mat& lookup) {
Mat result;
cv::LUT(image,lookup,result);
return result;
}
class Histogram1D {
private:
int histSize[1]; // number of bins
float hranges[2]; // min and max pixel value
const float* ranges[1];
int channels[1];
public:
Histogram1D() {
histSize[0]= 256;
hranges[0]= 0.0;
hranges[1]= 255.0;
ranges[0]= hranges;
channels[0]= 0; // by default, we look at channel 0
}
Mat getHistogram(const cv::Mat &image) {
Mat hist;
calcHist(&image,1,channels,Mat(),hist,1,histSize,ranges);
return hist;
}
Mat getHistogramImage(const cv::Mat &image){
Mat hist= getHistogram(image);
double maxVal=0;
double minVal=0;
minMaxLoc(hist, &minVal, &maxVal, 0, 0);
Mat histImg(histSize[0], histSize[0],CV_8U,Scalar(255));
int hpt = static_cast<int>(0.9*histSize[0]);
for( int h = 0; h < histSize[0]; h++ ) {
float binVal = hist.at<float>(h);
int intensity = static_cast<int>(binVal*hpt/maxVal);
line(histImg,Point(h,histSize[0]),
Point(h,histSize[0]-intensity),
Scalar::all(0));
}
return histImg;
}
Mat stretch(const cv::Mat &image, int minValue=0) {
Mat hist= getHistogram(image);
int imin= 0;
for( ; imin < histSize[0]; imin++ )
if (hist.at<float>(imin) > minValue)
break;
int imax= histSize[0]-1;
for( ; imax >= 0; imax-- )
if (hist.at<float>(imax) > minValue)
break;
Mat lookup(1, 256, CV_8U);
for (int i=0; i<256; i++) {
if (i < imin) lookup.at<uchar>(i)= 0;
else if (i > imax) lookup.at<uchar>(i)= 255;
else lookup.at<uchar>(i)= static_cast<uchar>(255.0*(i-imin)/(imax-imin)+0.5);
}
Mat result;
result= applyLookUp(image,lookup);
return result;
}
};
int main( int, char** argv )
{
Mat image,gray;
image = imread( argv[1], 1 );
if( !image.data )
return -1;
cvtColor(image, gray, CV_BGR2GRAY);
namedWindow("original");
imshow("original",gray);
Histogram1D h;
Mat streteched = h.stretch(gray,100);
namedWindow("sample");
imshow("sample",streteched);
namedWindow("histogram1");
imshow("histogram1",h.getHistogramImage(gray));
namedWindow("histogram2");
imshow("histogram2",h.getHistogramImage(streteched));
waitKey(0);
return 0;
}#include "opencv2/highgui/highgui.hpp"
#include "opencv2/imgproc/imgproc.hpp"
#include <iostream>
#include <stdio.h>
using namespace cv;
using namespace std;
int main( int, char** argv )
{
Mat src, dst;
const char* source_window = "Source image";
const char* equalized_window = "Equalized Image";
/// Load image
src = imread( argv[1], 1 );
if( !src.data )
{ cout<<"Usage: ./Histogram_Demo <path_to_image>"<<endl;
return -1;
}
/// Convert to grayscale
cvtColor( src, src, CV_BGR2GRAY );
/// Apply Histogram Equalization
equalizeHist( src, dst );
/// Display results
namedWindow( source_window, CV_WINDOW_AUTOSIZE );
namedWindow( equalized_window, CV_WINDOW_AUTOSIZE );
imshow( source_window, src );
imshow( equalized_window, dst );
/// Wait until user exits the program
waitKey(0);
return 0;OpenCV2马拉松第9圈——再谈对照度(对照度拉伸，直方图均衡化)
原文：https://www.cnblogs.com/ldxsuanfa/p/10898596.html