前言
本文将使用OpenCV C++ 在案例实战二的基础上，编写新算法生成蒙太奇图像。
一、基于直方图比较
原图如图所示。
double calmyHist(Mat src, Mat temp)
{
//灰度图
if (src.channels() == 1)
{
int histSize = 256;
float range[] = {
0,256 };
const float*histRange = {
range };
Mat src_hist, temp_hist;
//计算直方图
calcHist(&src, 1, 0, Mat(), src_hist, 1, &histSize, &histRange);
calcHist(&temp, 1, 0, Mat(), temp_hist, 1, &histSize, &histRange);
//归一化
normalize(src_hist, src_hist, 0, 1, NORM_MINMAX);
normalize(temp_hist, temp_hist, 0, 1, NORM_MINMAX);
//直方图比较
double dis = compareHist(src_hist, temp_hist, HISTCMP_CORREL);
return dis;
}
//彩色图
else
{
//使用split进行通道分离
vector<Mat>src_bgr, temp_bgr;
split(src, src_bgr);
split(temp, temp_bgr);
int histSize = 256;
float range[] = {
0,256 };
const float*histRange = {
range };
//计算直方图
Mat src_hist_b, src_hist_g, src_hist_r, temp_hist_b, temp_hist_g, temp_hist_r;
calcHist(&src_bgr[0], 1, 0, Mat(), src_hist_b, 1, &histSize, &histRange);
calcHist(&src_bgr[1], 1, 0, Mat(), src_hist_g, 1, &histSize, &histRange);
calcHist(&src_bgr[2], 1, 0, Mat(), src_hist_r, 1, &histSize, &histRange);
calcHist(&temp_bgr[0], 1, 0, Mat(), temp_hist_b, 1, &histSize, &histRange);
calcHist(&temp_bgr[1], 1, 0, Mat(), temp_hist_g, 1, &histSize, &histRange);
calcHist(&temp_bgr[2], 1, 0, Mat(), temp_hist_r, 1, &histSize, &histRange);
//归一化
normalize(src_hist_b, src_hist_b, 0, 1, NORM_MINMAX);
normalize(src_hist_g, src_hist_g, 0, 1, NORM_MINMAX);
normalize(src_hist_r, src_hist_r, 0, 1, NORM_MINMAX);
normalize(temp_hist_b, temp_hist_b, 0, 1, NORM_MINMAX);
normalize(temp_hist_g, temp_hist_g, 0, 1, NORM_MINMAX);
normalize(temp_hist_r, temp_hist_r, 0, 1, NORM_MINMAX);
vector<Mat>src_Mat = {
src_hist_b ,src_hist_g ,src_hist_r };
vector<Mat>temp_Mat = {
temp_hist_b ,temp_hist_g ,temp_hist_r };
//将b、g、r三通道进行合并
Mat src_hist, temp_hist;
merge(src_Mat, src_hist);
merge(temp_Mat, temp_hist);
//直方图比较
double dis = compareHist(src_hist, temp_hist, HISTCMP_CORREL);
return dis;
}
}
上述代码块可以帮助我们比较两幅图像直方图，以此来判断两幅图像的相似程度。关于compareHist API使用请自行百度。
效果
生成的蒙版图。
像素加权效果图。
二、基于均方误差（MSE）比较
//计算均方误差MSE
double getMSE(Mat src, Mat dst)
{
double mse = 0.0;
if (src.channels() == 1)
{
for (int i = 0; i < src.rows; i++)
{
for (int j = 0; j < src.cols; j++)
{
double diff = pow(src.at<uchar>(i, j) - dst.at<uchar>(i, j), 2);
mse += diff;
}
}
}
else
{
for (int i = 0; i < src.rows; i++)
{
for (int j = 0; j < src.cols; j++)
{
double b = pow(src.at<Vec3b>(i, j)[0] - dst.at<Vec3b>(i, j)[0], 2);
double g = pow(src.at<Vec3b>(i, j)[1] - dst.at<Vec3b>(i, j)[1], 2);
double r = pow(src.at<Vec3b>(i, j)[2] - dst.at<Vec3b>(i, j)[2], 2);
double diff = b + g + r;
mse += diff;
}
}
}
double MSE = mse / (src.rows*src.cols); //均方误差
return MSE;
}
我们通过计算MSE可以比较两幅图像的相似程度，MSE越小，表示两幅图像越相似；反之，MSE越大，则表示两幅图像越不相似。
效果
生成的蒙版图。
像素加权效果图。
三、源码
#include <iostream>
#include<opencv2/opencv.hpp>
using namespace std;
using namespace cv;
const int step_x = 20;
const int step_y = 20;
int getImagePathList(string folder, vector<String> &imagePathList)
{
glob(folder, imagePathList);
return 0;
}
double calmyHist(Mat src, Mat temp)
{
//灰度图
if (src.channels() == 1)
{
cvtColor(temp, temp, COLOR_BGR2GRAY);
int histSize = 256;
float range[] = {
0,256 };
const float*histRange = {
range };
Mat src_hist, temp_hist;
//计算直方图
calcHist(&src, 1, 0, Mat(), src_hist, 1, &histSize, &histRange);
calcHist(&temp, 1, 0, Mat(), temp_hist, 1, &histSize, &histRange);
//归一化
normalize(src_hist, src_hist, 0, 1, NORM_MINMAX);
normalize(temp_hist, temp_hist, 0, 1, NORM_MINMAX);
//直方图比较
double dis = compareHist(src_hist, temp_hist, HISTCMP_CORREL);
return dis;
}
//彩色图
else
{
//使用split进行通道分离
vector<Mat>src_bgr, temp_bgr;
split(src, src_bgr);
split(temp, temp_bgr);
int histSize = 256;
float range[] = {
0,256 };
const float*histRange = {
range };
//计算直方图
Mat src_hist_b, src_hist_g, src_hist_r, temp_hist_b, temp_hist_g, temp_hist_r;
calcHist(&src_bgr[0], 1, 0, Mat(), src_hist_b, 1, &histSize, &histRange);
calcHist(&src_bgr[1], 1, 0, Mat(), src_hist_g, 1, &histSize, &histRange);
calcHist(&src_bgr[2], 1, 0, Mat(), src_hist_r, 1, &histSize, &histRange);
calcHist(&temp_bgr[0], 1, 0, Mat(), temp_hist_b, 1, &histSize, &histRange);
calcHist(&temp_bgr[1], 1, 0, Mat(), temp_hist_g, 1, &histSize, &histRange);
calcHist(&temp_bgr[2], 1, 0, Mat(), temp_hist_r, 1, &histSize, &histRange);
//归一化
normalize(src_hist_b, src_hist_b, 0, 1, NORM_MINMAX);
normalize(src_hist_g, src_hist_g, 0, 1, NORM_MINMAX);
normalize(src_hist_r, src_hist_r, 0, 1, NORM_MINMAX);
normalize(temp_hist_b, temp_hist_b, 0, 1, NORM_MINMAX);
normalize(temp_hist_g, temp_hist_g, 0, 1, NORM_MINMAX);
normalize(temp_hist_r, temp_hist_r, 0, 1, NORM_MINMAX);
vector<Mat>src_Mat = {
src_hist_b ,src_hist_g ,src_hist_r };
vector<Mat>temp_Mat = {
temp_hist_b ,temp_hist_g ,temp_hist_r };
//将b、g、r三通道进行合并
Mat src_hist, temp_hist;
merge(src_Mat, src_hist);
merge(temp_Mat, temp_hist);
//直方图比较
double dis = compareHist(src_hist, temp_hist, HISTCMP_CORREL);
return dis;
}
}
//计算均方误差MSE
double getMSE(Mat src, Mat dst)
{
double mse = 0.0;
if (src.channels() == 1)
{
cvtColor(dst, dst, COLOR_BGR2GRAY);
for (int i = 0; i < src.rows; i++)
{
for (int j = 0; j < src.cols; j++)
{
double diff = pow(src.at<uchar>(i, j) - dst.at<uchar>(i, j), 2);
mse += diff;
}
}
}
else
{
for (int i = 0; i < src.rows; i++)
{
for (int j = 0; j < src.cols; j++)
{
double b = pow(src.at<Vec3b>(i, j)[0] - dst.at<Vec3b>(i, j)[0], 2);
double g = pow(src.at<Vec3b>(i, j)[1] - dst.at<Vec3b>(i, j)[1], 2);
double r = pow(src.at<Vec3b>(i, j)[2] - dst.at<Vec3b>(i, j)[2], 2);
double diff = b + g + r;
mse += diff;
}
}
}
double MSE = mse / (src.rows*src.cols); //均方误差
return MSE;
}
int main()
{
Mat src = imread("Taylor.jpg");
if (src.empty())
{
cout << "No image!" << endl;
system("pause");
return 0;
}
resize(src, src, Size(step_x*30, step_y*30), 1, 1, INTER_CUBIC);
vector<Mat>images;
string filename = "images/";
cout << "loading..." << endl;
//素材照片
vector<String> imagePathList;
getImagePathList(filename, imagePathList);
for (int i = 0; i < imagePathList.size(); i++)
{
Mat img = imread(imagePathList[i]);
resize(img, img, Size(step_x, step_y), 1, 1, INTER_AREA);
images.push_back(img);
}
cout << "size:" << images.size() << endl;
cout << "done!" << endl;
int rows = src.rows;
int cols = src.cols;
//height:表示生成的蒙太奇图像需要多少张素材图像填充rows
//width:表示生成的蒙太奇图像需要多少张素材图像填充cols
int height = rows / step_y, width = cols / step_x;
Mat dst = Mat(src.size(), CV_8UC3, Scalar(255, 255, 255));
Mat temp;
for (int i = 0; i < height; i++)
{
for (int j = 0; j < width; j++)
{
//计算当前ROI区域与图库中所有图片的直方图，找出最相似的一张作为填充ROI区域图片
Mat ROI = src(Rect(j * step_x, i * step_y, step_x, step_y));
double min =1000000.0;
Mat result;
for (int z = 0; z < images.size(); z++)
{
double dis = getMSE(ROI, images[z]);
if (dis < min)
{
min = dis;
result = images[z];
}
}
result.copyTo(temp);
//将temp图像赋值给需要生成的蒙太奇图像对应区域
temp = dst(Rect(j * step_x, i * step_y, step_x, step_y));
//index表示当前ROI索引
int index = i * width + j;
cout << "匹配成功：" << index << endl;
}
}
namedWindow("dst", WINDOW_NORMAL);
imwrite("蒙版.jpg", dst);
imshow("dst", dst);
for (int i = 0; i < rows; ++i)
{
for (int j = 0; j < cols; ++j)
{
//像素RGB值修改
dst.at<Vec3b>(i, j)[0] = 0.312*dst.at<Vec3b>(i, j)[0] + 0.688*src.at<Vec3b>(i, j)[0];
dst.at<Vec3b>(i, j)[1] = 0.312*dst.at<Vec3b>(i, j)[1] + 0.688*src.at<Vec3b>(i, j)[1];
dst.at<Vec3b>(i, j)[2] = 0.312*dst.at<Vec3b>(i, j)[2] + 0.688*src.at<Vec3b>(i, j)[2];
}
}
namedWindow("蒙太奇图像", WINDOW_NORMAL);
imwrite("蒙太奇图像.jpg", dst);
imshow("蒙太奇图像", dst);
waitKey(0);
system("pause");
return 0;
}
总结
本文使用OpenCV C++生成蒙太奇图像，关键步骤有以下几点。
1、基于直方图比较：找到与待填充区域（ROI）最相似的图片进行填充。
2、基于MSE比较：找到与待填充区域（ROI）最相似的图片进行填充。
对比以上两种方法，从效果上看，个人觉得使用MSE方法比较两幅图相似效果会更好。