OpenCV进行图像相似度对比的几种办法

转载请注明出处：http://blog.csdn.net/wangyaninglm/article/details/43853435，
来自：shiter编写程序的艺术

对计算图像相似度的方法，本文做了如下总结，主要有三种办法：

1.PSNR峰值信噪比

PSNR（Peak Signal to Noise Ratio），一种全参考的图像质量评价指标。

简介：https://en.wikipedia.org/wiki/Peak_signal-to-noise_ratio

PSNR是最普遍和使用最为广泛的一种图像客观评价指标，然而它是基于对应像素点间的误差，即基于误差敏感的图像质量评价。由于并未考虑到人眼的视觉特性（人眼对空间频率较低的对比差异敏感度较高，人眼对亮度对比差异的敏感度较色度高，人眼对一个区域的感知结果会受到其周围邻近区域的影响等），因而经常出现评价结果与人的主观感觉不一致的情况。

SSIM（structural similarity）结构相似性，也是一种全参考的图像质量评价指标，它分别从亮度、对比度、结构三方面度量图像相似性。

SSIM取值范围[0,1]，值越大，表示图像失真越小.

在实际应用中，可以利用滑动窗将图像分块，令分块总数为N，考虑到窗口形状对分块的影响，采用高斯加权计算每一窗口的均值、方差以及协方差，然后计算对应块的结构相似度SSIM，最后将平均值作为两图像的结构相似性度量，即平均结构相似性MSSIM：

参考资料

[1] 峰值信噪比-维基百科

[2] 王宇庆，刘维亚，王勇. 一种基于局部方差和结构相似度的图像质量评价方法[J]. 光电子激光，2008。
[3]http://www.cnblogs.com/vincent2012/archive/2012/10/13/2723152.html

官方文档的说明，不过是GPU版本的，我们可以修改不用gpu不然还得重新编译

http://www.opencv.org.cn/opencvdoc/2.3.2/html/doc/tutorials/highgui/video-input-psnr-ssim/video-input-psnr-ssim.html#videoinputpsnrmssim
http://www.opencv.org.cn/opencvdoc/2.3.2/html/doc/tutorials/gpu/gpu-basics-similarity/gpu-basics-similarity.html?highlight=psnr

// PSNR.cpp : 定义控制台应用程序的入口点。
//
 
#include "stdafx.h"
 
#include <iostream>                   // Console I/O
#include <sstream>                    // String to number conversion
 
#include <opencv2/core/core.hpp>      // Basic OpenCV structures
#include <opencv2/imgproc/imgproc.hpp>// Image processing methods for the CPU
#include <opencv2/highgui/highgui.hpp>// Read images
#include <opencv2/gpu/gpu.hpp>        // GPU structures and methods
 
using namespace std;
using namespace cv;
 
double getPSNR(const Mat& I1, const Mat& I2);      // CPU versions
Scalar getMSSIM( const Mat& I1, const Mat& I2);
 
double getPSNR_GPU(const Mat& I1, const Mat& I2);  // Basic GPU versions
Scalar getMSSIM_GPU( const Mat& I1, const Mat& I2);
 
struct BufferPSNR                                     // Optimized GPU versions
{   // Data allocations are very expensive on GPU. Use a buffer to solve: allocate once reuse later.
    gpu::GpuMat gI1, gI2, gs, t1,t2;
 
    gpu::GpuMat buf;
};
double getPSNR_GPU_optimized(const Mat& I1, const Mat& I2, BufferPSNR& b);
 
struct BufferMSSIM                                     // Optimized GPU versions
{   // Data allocations are very expensive on GPU. Use a buffer to solve: allocate once reuse later.
    gpu::GpuMat gI1, gI2, gs, t1,t2;
 
    gpu::GpuMat I1_2, I2_2, I1_I2;
    vector<gpu::GpuMat> vI1, vI2;
 
    gpu::GpuMat mu1, mu2; 
    gpu::GpuMat mu1_2, mu2_2, mu1_mu2; 
 
    gpu::GpuMat sigma1_2, sigma2_2, sigma12; 
    gpu::GpuMat t3; 
 
    gpu::GpuMat ssim_map;
 
    gpu::GpuMat buf;
};
Scalar getMSSIM_GPU_optimized( const Mat& i1, const Mat& i2, BufferMSSIM& b);
 
void help()
{
    cout
        << "\n--------------------------------------------------------------------------" << endl
        << "This program shows how to port your CPU code to GPU or write that from scratch." << endl
        << "You can see the performance improvement for the similarity check methods (PSNR and SSIM)."  << endl
        << "Usage:"                                                               << endl
        << "./gpu-basics-similarity referenceImage comparedImage numberOfTimesToRunTest(like 10)." << endl
        << "--------------------------------------------------------------------------"   << endl
        << endl;
}
 
int main(int argc, char *argv[])
{
    help(); 
    Mat I1 = imread("swan1.jpg",1);           // Read the two images
    Mat I2 = imread("swan2.jpg",1);
 
    if (!I1.data || !I2.data)           // Check for success
    {
        cout << "Couldn't read the image";
        return 0;
    }
 
    BufferPSNR bufferPSNR;
    BufferMSSIM bufferMSSIM;
 
    int TIMES; 
    stringstream sstr("500"); 
    sstr >> TIMES;
    double time, result;
 
    //------------------------------- PSNR CPU ----------------------------------------------------
    time = (double)getTickCount();    
 
    for (int i = 0; i < TIMES; ++i)
        result = getPSNR(I1,I2);
 
    time = 1000*((double)getTickCount() - time)/getTickFrequency();
    time /= TIMES;
 
    cout << "Time of PSNR CPU (averaged for " << TIMES << " runs): " << time << " milliseconds."
        << " With result of: " <<  result << endl; 
 
    ------------------------------- PSNR GPU ----------------------------------------------------
    //time = (double)getTickCount();    
 
    //for (int i = 0; i < TIMES; ++i)
    //  result = getPSNR_GPU(I1,I2);
 
    //time = 1000*((double)getTickCount() - time)/getTickFrequency();
    //time /= TIMES;
 
    //cout << "Time of PSNR GPU (averaged for " << TIMES << " runs): " << time << " milliseconds."
    //  << " With result of: " <<  result << endl; 
/*
    //------------------------------- PSNR GPU Optimized--------------------------------------------
    time = (double)getTickCount();                                  // Initial call
    result = getPSNR_GPU_optimized(I1, I2, bufferPSNR);
    time = 1000*((double)getTickCount() - time)/getTickFrequency();
    cout << "Initial call GPU optimized:              " << time  <<" milliseconds."
        << " With result of: " << result << endl;
    time = (double)getTickCount();    
    for (int i = 0; i < TIMES; ++i)
        result = getPSNR_GPU_optimized(I1, I2, bufferPSNR);
    time = 1000*((double)getTickCount() - time)/getTickFrequency();
    time /= TIMES;
    cout << "Time of PSNR GPU OPTIMIZED ( / " << TIMES << " runs): &
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120