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opencv实现图片HDR功能

简介

　　本篇主要是利用三张图片：过曝(相机设置exposure+1)、正常(相机设置exposure+0)、欠曝(相机设置exposure-1)，来合成一张在亮出和暗处细节都清晰
的图片，来简易实现图片的HDR功能。

具体实现

实现代码

#include <opencv2/core/core.hpp>                                                                                                     
#include <opencv2/highgui/highgui.hpp>
#include <math.h>
#include <string.h>
#include <opencv/cv.h>
#include <stdio.h>
#include "opencv2/photo/photo.hpp"
 
using namespace cv;
 
char highpicName[20];
char normalpicName[20];
char lowpicName[20];
Mat mat1, mat2, mat3, dst_mat, tmp_mat;
int highWidth, highHeight;
int normalWidth, normalHeight;
int lowWidth, lowHeight;
IplImage src1, src2, src3, dst_src, tmp_src;
double weight=0.5;
 
 
void hdrCale(Mat pic1, Mat pic2, Mat pic3){
	int i, j;
	CvScalar s1, s2, s3;
 
 
	src1 = pic1;
	src2 = pic2;
	src3 = pic3;
	dst_src = dst_mat;
	tmp_src = tmp_mat;
 
	cvCvtColor(&src2, &tmp_src, CV_BGR2GRAY);
	for(i=0; i< normalWidth; i++){
		for(j=0; j<normalHeight; j++){
			s1 = cvGet2D(&src1, i, j);
			s2 = cvGet2D(&tmp_src, i, j);
			s3 = cvGet2D(&src3, i, j);
			weight = 0.5 + (127 - s2.val[0]) * 0.002;
			s3.val[0] = (s1.val[0] * weight) + (s3.val[0] * (1-weight));
			s3.val[1] = (s1.val[1] * weight) + (s3.val[1] * (1-weight));
			s3.val[2] = (s1.val[2] * weight) + (s3.val[2] * (1-weight));
			cvSet2D(&dst_src, i, j, s3);
		}	
	}
}
 
 
int main(int argc, char *argv[]){
	if(argc < 4){
		printf("Please input high exposure/normal exposure/low exposure picture!\n");
		return -1;	
	}
	memcpy(highpicName, argv[1], sizeof(argv[1]));
	memcpy(normalpicName, argv[2], sizeof(argv[2]));
	memcpy(lowpicName, argv[3], sizeof(argv[3]));
	mat1 = imread(argv[1]);
	mat2 = imread(argv[2]);
	mat3 = imread(argv[3]);
	highWidth = mat1.rows;
	highHeight = mat1.cols;
	normalWidth = mat2.rows;
	normalHeight = mat2.cols;
	lowWidth = mat3.rows;
	lowHeight = mat3.cols;
	dst_mat = Mat(normalWidth, normalHeight, CV_8UC3, cv::Scalar(0, 0, 0));
	tmp_mat = Mat(normalWidth, normalHeight, CV_8UC1, cv::Scalar(0, 0, 0));
 
	hdrCale(mat1, mat2, mat3);
 
	imshow("normal", mat2);
	imshow("HDR", dst_mat);
	imwrite("HDR.jpg", dst_mat);
	cv::waitKey(0);
	return 0;
}

代码讲解

　　1、首先进行相对应的初始化操作：运行软件时候，需要传入三张图片，顺序上分别是：过曝、正常、欠曝。打开这三张图片，保存在mat1、mat2、mat3
中，注意这三张图片必须大小一致。接着获取到图片的width和height。最后创建两张空白图片：tmp_mat和dst_mat。

        if(argc < 4){
		printf("Please input high exposure/normal exposure/low exposure picture!\n");
		return -1;	
	}
	memcpy(highpicName, argv[1], sizeof(argv[1]));
	memcpy(normalpicName, argv[2], sizeof(argv[2]));
	memcpy(lowpicName, argv[3], sizeof(argv[3]));
	mat1 = imread(argv[1]);
	mat2 = imread(argv[2]);
	mat3 = imread(argv[3]);
	highWidth = mat1.rows;
	highHeight = mat1.cols;
	normalWidth = mat2.rows;
	normalHeight = mat2.cols;
	lowWidth = mat3.rows;
	lowHeight = mat3.cols;
	dst_mat = Mat(normalWidth, normalHeight, CV_8UC3, cv::Scalar(0, 0, 0));
	tmp_mat = Mat(normalWidth, normalHeight, CV_8UC1, cv::Scalar(0, 0, 0));

　　2、接着进入到HDR的算法处理：对应的处理很简单，主要就是根据就是权重，把过曝和欠曝图片合成到dst_mat中。
具体做法：循环依次打开三张图片的同一位置像素，用正常曝光图片像素，利用公式：weight = 0.5 + (127 - s2.val[0]) * 0.002;
          来获得使用过曝、欠曝像素合成到dst_mat中对应使用的权值。接着：s3.val[0] = (s1.val[0] * weight) + (s3.val[0] * (1-weight));
          计算出合成像素值之后，写入到dst_mat对应的坐标位置。进而生成HDR照片。

void hdrCale(Mat pic1, Mat pic2, Mat pic3){
	int i, j;
	CvScalar s1, s2, s3;
 
	src1 = pic1;
	src2 = pic2;
	src3 = pic3;
	dst_src = dst_mat;
	tmp_src = tmp_mat;
 
	cvCvtColor(&src2, &tmp_src, CV_BGR2GRAY);
	for(i=0; i< normalWidth; i++){
		for(j=0; j<normalHeight; j++){
			s1 = cvGet2D(&src1, i, j);
			s2 = cvGet2D(&tmp_src, i, j);
			s3 = cvGet2D(&src3, i, j);
			weight = 0.5 + (127 - s2.val[0]) * 0.002;
			s3.val[0] = (s1.val[0] * weight) + (s3.val[0] * (1-weight));
			s3.val[1] = (s1.val[1] * weight) + (s3.val[1] * (1-weight));
			s3.val[2] = (s1.val[2] * weight) + (s3.val[2] * (1-weight));
			cvSet2D(&dst_src, i, j, s3);
		}	
	}
}

　　3、最后将正常照片和HDR照片显示初恋，并将hdr照片保存下来。

        imshow("normal", mat2);
	imshow("HDR", dst_mat);
	imwrite("HDR.jpg", dst_mat);
	cv::waitKey(0);

效果演示

　对应的效果演示如下:
    过曝图像：
        
    正常图像
        
    欠曝图像：
        
    HDR图像

文章来自：http://blog.csdn.net/u011630458/article/details/46592013