数字图像处理第一次上机作业
一、作业内容
-
在空域中，对给定图像(lenna.jpg)进行高斯平滑滤波；
-
在空域中，对给定图像(lenna.jpg)进行拉普拉斯锐化滤波。
二、作业要求
-
使用 C/C++ 语言实现滤波操作，代码需要体现具体的滤波过程，不能使用现成的滤波函数；
-
自选滤波器的参数，提高在给定图像上的滤波效果。
三、提交内容
-
滤波后的结果图像；
-
实验报告，包括实验原理、实验步骤、实验结果和实验分析；
-
源码及注释。
四、提交要求
将提交的内容打包，压缩包命名为“姓名+学号.zip”。
#include <iostream>
#include <opencv.hpp>
#include <string>
#include <vector>
#include <math.h>
class Experiment1
{
private:
// 滤波器名称（Gaussian和Laplacian）
std::vector<std::string> filter_name;
std::vector<std::string> pic_color;
// 原始图像（灰度图和彩色图）
cv::Mat original_color_image;
cv::Mat original_gray_image;
// 处理后的图像
std::vector<cv::Mat> color_image_process;
std::vector<cv::Mat> gray_image_process;
// 梯度幅值图像
cv::Mat color_image_grad_laplacian;
cv::Mat gray_image_grad_laplacian;
// 模板尺寸:3*3,5*5,9*9
std::vector<cv::Mat> gauss_template;
// 以下模板尺寸固定
cv::Mat laplacian_template;
std::vector<int> gauss_template_sum;
// 不同图片的 mask
std::vector<cv::Mat> highPromote_gray_mask;
std::vector<cv::Mat> highPromote_color_mask;
// 滤波器的大小
std::vector<int> filter_size;
std::vector<std::string> filter_size_string;
public:
// 初始化
Experiment1(std::string path)
{
filter_name.push_back("Gaussian");
filter_name.push_back("Laplacian");
pic_color.push_back("灰度");
pic_color.push_back("彩色");
filter_size_string.push_back("3 x 3");
filter_size_string.push_back("5 x 5");
filter_size_string.push_back("7 x 7");
original_color_image = cv::imread(path);
original_gray_image.push_back(color2Gray(original_color_image));
for (int i = 0; i < 3; i++)
{
filter_size.push_back(i * 2 + 3);
makeGaussTemplate(i * 2 + 3);
}
makeLaplacianTemplate();
computerGaussTemplateSum();
}
// 彩色图像转灰度图像
cv::Mat color2Gray(cv::Mat src_image)
{
//创建与原图同类型和同大小的矩阵
cv::Mat gray_image(src_image.rows, src_image.cols, CV_8UC1);
if (src_image.channels() != 1)
{
for (int i = 0; i < src_image.rows; i++)
for (int j = 0; j < src_image.cols; j++)
gray_image.at<uchar>(i, j) = (src_image.at<cv::Vec3b>(i, j)[0] + src_image.at<cv::Vec3b>(i, j)[1] + src_image.at<cv::Vec3b>(i, j)[2]) / 3;
}
else
gray_image = src_image.clone();
return gray_image;
}
// 生成高斯模板
void makeGaussTemplate(int size = 3, int sigma = 1)
{
cv::Mat gaussTemplate = cv::Mat::zeros(size, size, CV_32F);
int center = size / 2;
double min = GMD(center, center, sigma);
for (int i = 0; i < size; i++)
for (int j = 0; j < size; j++)
gaussTemplate.at<float>(i, j) = GMD(i - center, j - center) / min;
gaussTemplate.convertTo(gaussTemplate, CV_8U);
gauss_template.push_back(gaussTemplate);
}
// 计算正态分布
double GMD(double x, double y, double sigma = 1)
{
return exp(-(x * x + y * y) / (2 * sigma * sigma));
}
// 计算高斯模板的和
void computerGaussTemplateSum()
{
for (int k = 0; k < 3; k++)
{
int sum = 0;
for (int i = 0; i < gauss_template[k].rows; i++)
for (int j = 0; j < gauss_template[k].cols; j++)
sum += gauss_template[k].at<uchar>(i, j);
gauss_template_sum.push_back(sum);
}
}
// 生成Laplacian
void makeLaplacianTemplate()
{
laplacian_template = (cv::Mat_<float>(3, 3) << 0, 1, 0,
1, -4, 1,
0, 1, 0);
}
// 灰度空域滤波
void graySpatialFiltering(int size_id = 0, int select = 0)
{
int size = filter_size[size_id];
int m = size / 2;
cv::Mat image_process = cv::Mat::zeros(original_gray_image.size(), original_gray_image.type());
cv::Mat image_grad = cv::Mat::zeros(original_gray_image.size(), original_gray_image.type());
for (int i = m; i < original_gray_image.rows - m; i++)
for (int j = m; j < original_gray_image.cols - m; j++)
{
cv::Mat sub_matrix = original_gray_image(cv::Rect(j - m, i - m, size, size));
int grad, value;
if (select == 0)
image_process.at<uchar>(i, j) = computerGaussResult(sub_matrix, size_id);
else
image_grad.at<uchar>(i, j) = computerLaplacianResult(sub_matrix);
if (select)
{
grad = image_grad.at<uchar>(i, j);
value = sub_matrix.at<uchar>(m, m);
if (grad + value > 255)
image_process.at<uchar>(i, j) = 255;
else
image_process.at<uchar>(i, j) = grad + value;
}
}
gray_image_process.push_back(image_process);
if (select)
gray_image_grad_laplacian = image_grad;
}
// 计算高斯滤波
int computerGaussResult(cv::Mat &image_block, int size_id)
{
int sum = gauss_template_sum[size_id];
return image_block.dot(gauss_template[size_id]) / sum;
}
// 计算Laplacian滤波
int computerLaplacianResult(cv::Mat &image_block)
{
float GMD = 0.0;
for (int i = 0; i < image_block.rows; i++)
for (int j = 0; j < image_block.cols; j++)
GMD += float(image_block.at<uchar>(i, j)) * laplacian_template.at<float>(i, j);
if (abs(GMD) > 255)
return 255;
else if (GMD < 0)
return -GMD;
else
return GMD;
}
// 彩色图像滤波
void colorSpatialFiltering(int size_id = 0, int select = 0)
{
int size = filter_size[size_id];
int m = size / 2;
cv::Mat image_process = cv::Mat::zeros(original_color_image.size(), original_color_image.type());
cv::Mat image_grad = cv::Mat::zeros(original_color_image.size(), original_color_image.type());
std::vector<cv::Mat> channels;
cv::split(original_color_image, channels);
for (int i = m; i < original_color_image.rows - m; i++)
for (int j = m; j < original_color_image.cols - m; j++)
{
for (int k = 0; k < 3; k++)
{
cv::Mat sub_matrix = channels[k](cv::Rect(j - m, i - m, size, size));
int grad, value;
if (select == 0)
image_process.at<cv::Vec3b>(i, j)[k] = computerGaussResult(sub_matrix, size_id);
else
image_grad.at<cv::Vec3b>(i, j)[k] = computerLaplacianResult(sub_matrix);
if (select)
{
grad = image_grad.at<cv::Vec3b>(i, j)[k];
value = sub_matrix.at<uchar>(m, m);
if (grad + value > 255)
image_process.at<cv::Vec3b>(i, j)[k] = 255;
else
image_process.at<cv::Vec3b>(i, j)[k] = grad + value;
}
}
}
color_image_process.push_back(image_process);
if (select)
color_image_grad_laplacian = image_grad;
}
// 测试灰度高斯滤波器
void test_GaussGrayFilter()
{
int filter_id = 0;
for (int j = 0; j < filter_size.size(); j++)
{
graySpatialFiltering(j, filter_id);
std::cout << pic_color[0] << "-----" << filter_name[filter_id] << "-----尺寸大小: " << filter_size[j] << ": 运行完毕!!!! \n";
}
cv::imshow(filter_name[filter_id] + "----准备就绪,可以开始!", original_color_image);
cv::waitKey(0);
// 显示灰度滤波结果
cv::imshow("原始图像", original_gray_image);
cv::waitKey(0);
for (int j = 0; j < filter_size.size(); j++)
{
cv::imshow(pic_color[0] + "-----" + filter_name[filter_id] + "-----" + filter_size_string[j], gray_image_process[j]);
cv::waitKey(0);
}
std::cout << "\n \n";
cv::destroyAllWindows();
}
// 测试灰度Laplacian滤波器
void test_LaplacianGrayFilter()
{
int filter_id = 1;
graySpatialFiltering(0, filter_id);
std::cout << pic_color[0] << "-----" << filter_name[filter_id] << ": 运行完毕!!!! \n";
cv::imshow(filter_name[filter_id] + "----准备就绪,可以开始!", original_color_image);
cv::waitKey(0);
// 显示灰度滤波结果
cv::imshow("原始图像", original_gray_image);
cv::waitKey(0);
cv::imshow(pic_color[0] + "-----" + filter_name[filter_id], gray_image_process[filter_size.size()]);
cv::waitKey(0);
cv::imshow(pic_color[0] + "-----" + filter_name[filter_id] + "-----" + "灰度幅值: ", gray_image_grad_laplacian);
cv::waitKey(0);
std::cout << "\n \n";
cv::destroyAllWindows();
}
// 测试彩色高斯滤波器
void test_GaussColorFilter()
{
int filter_id = 0;
for (int j = 0; j < filter_size.size(); j++)
{
colorSpatialFiltering(j, filter_id);
std::cout << pic_color[1] << "-----" << filter_name[filter_id] << "-----尺寸大小: " << filter_size[j] << ": 运行完毕!!!! \n";
}
cv::imshow(filter_name[filter_id] + "----准备就绪,可以开始!", original_color_image);
cv::waitKey(0);
cv::imshow("原始图像", original_color_image);
cv::waitKey(0);
for (int j = 0; j < filter_size.size(); j++)
{
cv::imshow(pic_color[1] + "-----" + filter_name[filter_id] + "-----" + filter_size_string[j], color_image_process[j]);
cv::waitKey(0);
}
std::cout << "\n \n";
cv::destroyAllWindows();
}
// 测试彩色Laplacian滤波器
void test_LaplacianColorFilter()
{
int filter_id = 1;
colorSpatialFiltering(0, filter_id);
std::cout << pic_color[1] << "-----" << filter_name[filter_id] << ": 运行完毕!!!! \n";
cv::imshow(filter_name[filter_id] + "----准备就绪,可以开始!", original_color_image);
cv::waitKey(0);
cv::imshow("原始图像", original_color_image);
cv::waitKey(0);
cv::imshow(pic_color[1] + "-----" + filter_name[filter_id], color_image_process[filter_size.size()]);
cv::waitKey(0);
if (filter_id) {
cv::imshow(pic_color[1] + "-----" + filter_name[filter_id] + "-----" + "灰度幅值: ", color_image_grad_laplacian);
cv::waitKey(0);
}
std::cout << "\n \n";
cv::destroyAllWindows();
}
};
int main()
{
std::string path;
path = "E:/lenna.jpg";
Experiment1 img(path);
img.test_GaussGrayFilter();
img.test_LaplacianGrayFilter();
img.test_GaussColorFilter();
img.test_LaplacianColorFilter();
return 1;
}