opencv用C++实现SSIM
时间: 2024-05-09 21:18:41 浏览: 18
以下是使用OpenCV库和C语言实现SSIM的示例代码:
```
#include <stdio.h>
#include <stdlib.h>
#include <math.h>
#include <opencv/cv.h>
#include <opencv/highgui.h>
double calculate_ssim(IplImage* original, IplImage* processed)
{
int i, j, k;
int width, height, channels;
double C1, C2, ssim;
double mu_original, mu_processed, sigma_original, sigma_processed, sigma_original_processed;
double* data_original = NULL;
double* data_processed = NULL;
double* temp_original = NULL;
double* temp_processed = NULL;
double* temp_original_processed = NULL;
double* temp_original_squared = NULL;
double* temp_processed_squared = NULL;
// 计算基本参数
width = original->width;
height = original->height;
channels = original->nChannels;
C1 = (0.01 * 255) * (0.01 * 255);
C2 = (0.03 * 255) * (0.03 * 255);
// 分配内存
data_original = (double*)malloc(width * height * channels * sizeof(double));
data_processed = (double*)malloc(width * height * channels * sizeof(double));
temp_original = (double*)malloc(width * height * channels * sizeof(double));
temp_processed = (double*)malloc(width * height * channels * sizeof(double));
temp_original_processed = (double*)malloc(width * height * channels * sizeof(double));
temp_original_squared = (double*)malloc(width * height * channels * sizeof(double));
temp_processed_squared = (double*)malloc(width * height * channels * sizeof(double));
// 将图像数据复制到double类型数组中
for (i = 0; i < height; i++) {
for (j = 0; j < width; j++) {
for (k = 0; k < channels; k++) {
data_original[i * width * channels + j * channels + k] = (double)(original->imageData[i * original->widthStep + j * channels + k]);
data_processed[i * width * channels + j * channels + k] = (double)(processed->imageData[i * processed->widthStep + j * channels + k]);
}
}
}
// 计算均值
cvSetData(original, temp_original, channels);
cvSetData(processed, temp_processed, channels);
cvSetData(original_processed, temp_original_processed, channels);
cvSetData(original_squared, temp_original_squared, channels);
cvSetData(processed_squared, temp_processed_squared, channels);
cvAvgSdv(original, &mu_original, &sigma_original, NULL);
cvAvgSdv(processed, &mu_processed, &sigma_processed, NULL);
// 计算方差和协方差
for (i = 0; i < height; i++) {
for (j = 0; j < width; j++) {
for (k = 0; k < channels; k++) {
temp_original[i * width * channels + j * channels + k] = data_original[i * width * channels + j * channels + k] - mu_original;
temp_processed[i * width * channels + j * channels + k] = data_processed[i * width * channels + j * channels + k] - mu_processed;
temp_original_squared[i * width * channels + j * channels + k] = temp_original[i * width * channels + j * channels + k] * temp_original[i * width * channels + j * channels + k];
temp_processed_squared[i * width * channels + j * channels + k] = temp_processed[i * width * channels + j * channels + k] * temp_processed[i * width * channels + j * channels + k];
temp_original_processed[i * width * channels + j * channels + k] = temp_original[i * width * channels + j * channels + k] * temp_processed[i * width * channels + j * channels + k];
}
}
}
cvAvgSdv(original_squared, &sigma_original, NULL, NULL);
cvAvgSdv(processed_squared, &sigma_processed, NULL, NULL);
cvAvgSdv(original_processed, &sigma_original_processed, NULL, NULL);
// 计算SSIM
ssim = ((2 * mu_original * mu_processed + C1) * (2 * sigma_original_processed + C2)) / ((mu_original * mu_original + mu_processed * mu_processed + C1) * (sigma_original + sigma_processed + C2));
// 释放内存
free(data_original);
free(data_processed);
free(temp_original);
free(temp_processed);
free(temp_original_processed);
free(temp_original_squared);
free(temp_processed_squared);
return ssim;
}
int main()
{
IplImage* original = NULL;
IplImage* processed = NULL;
double ssim = 0.0;
// 读取图像
original = cvLoadImage("original.jpg", CV_LOAD_IMAGE_COLOR);
processed = cvLoadImage("processed.jpg", CV_LOAD_IMAGE_COLOR);
// 计算SSIM
ssim = calculate_ssim(original, processed);
// 打印SSIM值
printf("SSIM: %f\n", ssim);
// 释放内存
cvReleaseImage(&original);
cvReleaseImage(&processed);
return 0;
}
```
在此示例中,我们使用OpenCV库和C语言来计算两个图像之间的结构相似性指数(SSIM)。我们首先将图像数据复制到double类型数组中,然后计算均值、方差和协方差。最后,我们将这些值用于计算SSIM指数。您可以通过更改原始图像和处理图像的文件名来测试此代码。
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