本文介绍了一组图像处理实验，包括使用均值与高斯模板平滑灰度及彩色图像、利用Laplacian、Robert及Sobel算子进行边缘检测、采用高提升滤波算法增强图像等。通过OpenCV库实现了各种滤波器的应用，并展示了不同参数下的处理效果。
实验内容:
1、利用均值模板平滑灰度图像。
具体内容:利用 OpenCV 对图像像素进行操作，分别利用 33、55 和 9*9 尺寸的均值模板平滑灰度图像
2、利用高斯模板平滑灰度图像。
具体内容:利用 OpenCV 对图像像素进行操作，分别利用 33、55 和 9*9 尺寸的高斯模板平滑灰度图像
3、利用 Laplacian、Robert、Sobel 模板锐化灰度图像。
具体内容:利用 OpenCV 对图像像素进行操作，分别利用 Laplacian、Robert、Sobel 模板锐化灰度图像
4、利用高提升滤波算法增强灰度图像。
具体内容:利用 OpenCV 对图像像素进行操作，设计高提升滤波算法增强图像
5、利用均值模板平滑彩色图像。
具体内容:利用 OpenCV 分别对图像像素的 RGB 三个通道进行操作，利用 33、55 和 9*9 尺寸的均值模板平滑彩色图像
6、利用高斯模板平滑彩色图像。
具体内容:利用 OpenCV 分别对图像像素的 RGB 三个通道进行操作，分别利用 33、55 和 9*9 尺寸的高斯模板平滑彩色图像
7、利用 Laplacian、Robert、Sobel 模板锐化灰度图像。
具体内容:利用 OpenCV 分别对图像像素的 RGB 三个通道进行操作，分别利用 Laplacian、Robert、Sobel 模板锐化彩色图像
实验代码：
完整课程PPT和实验要求戳：https://github.com/MintLucas/Digital_image_process
（讲解用注释标注在代码中）
#include<iostream>
#include<string>
#include "Lab03_spacialFilter.hpp"
#include <opencv2/imgcodecs.hpp>
#include <opencv2/highgui.hpp>
#include <opencv2/imgproc/imgproc.hpp>
#include <opencv2/opencv.hpp>
#include <opencv2/core/types_c.h>
#include <opencv2/core/core_c.h>
using namespace std;
using namespace cv;
int spacialFilter(vector<float> f)
{
Mat image, res;
image = imread("/Users/zhipeng/ustc_term2/Opencv/Opencv/Opencv/Digital_imgae_process/CSet12/lena.png", IMREAD_COLOR); // Read the file
namedWindow("原图", WINDOW_AUTOSIZE); // Create a window for display.
imshow("原图", image); // Show our image inside it.
res.create(image.size(), image.type());//为输出图像分配内容
/*拉普拉斯滤波核3*3
0 -1 0
-1 5 -1
0 -1 0 */
//处理除最外围一圈外的所有像素值
for (int i = 1; i < image.rows - 1; i++)
{
const uchar * pre = image.ptr<const uchar>(i - 1);//前一行
const uchar * cur = image.ptr<const uchar>(i);//当前行，第i行
const uchar * next = image.ptr<const uchar>(i + 1);//下一行
uchar * output = res.ptr<uchar>(i);//输出图像的第i行
int ch = image.channels();//通道个数
int startCol = ch;//每一行的开始处理点
int endCol = (image.cols - 1)* ch;//每一行的处理结束点
for (int j = startCol; j < endCol; j++)
{
//输出图像的遍历指针与当前行的指针同步递增, 以每行的每一个像素点的每一个通道值为一个递增量, 因为要
//考虑到图像的通道数
//saturate_cast<uchar>保证结果在uchar范围内
if(f.size() == 9){
//*output++ = saturate_cast<uchar>(5 * cur[j] - pre[j] - next[j] - cur[j - ch] - cur[j + ch]);
*output++ = saturate_cast<uchar>(f[0]*pre[j-ch] + f[1]*pre[j] + f[2]*pre[j+ch] + f[3]*cur[j-ch] + f[4]*cur[j] + f[5]*cur[j+ch] + f[6]*next[j-ch] + f[7]*next[j] + f[8]*next[j+ch]);
}
}
}
//将最外围一圈的像素值设为0
res.row(0).setTo(Scalar(0));
res.row(res.rows - 1).setTo(Scalar(0));
res.col(0).setTo(Scalar(0));
res.col(res.cols - 1).setTo(Scalar(0));
/*/或者也可以尝试将最外围一圈设置为原图的像素值
image.row(0).copyTo(result.row(0));
image.row(image.rows-1).copyTo(result.row(result.rows-1));
image.col(0).copyTo(result.col(0));
image.col(image.cols-1).copyTo(result.col(result.cols-1));*/
namedWindow("拉普拉斯模板-手写", WINDOW_AUTOSIZE); // Create a window for display.
imshow("拉普拉斯模板-手写", res); // Show our image inside it.
waitKey(0); // Wait for a keystroke in the window
destroyAllWindows();
return 0;
}
int xGradient(Mat image, int x, int y)
{
int gx;
gx = image.at<uchar>(y-1,x+1) + 2*image.at<uchar>(y,x+1) + image.at<uchar>(y+1,x+1)
- image.at<uchar>(y-1,x-1) - 2*image.at<uchar>(y,x-1) -image.at<uchar>(y-1,x-1);
return gx;
}
int yGradient(Mat image, int x, int y)
{
int gy;
gy = image.at<uchar>(y-1,x+1) + 2*image.at<uchar>(y-1,x) + image.at<uchar>(y+1,x+1)
- image.at<uchar>(y-1,x-1) - 2*image.at<uchar>(y+1,x) -image.at<uchar>(y-1,x-1);
return gy;
}
int sobelXY()
{
int Gx, Gy, sum;
Mat img = imread("/Users/zhipeng/ustc_term2/Opencv/Opencv/Opencv/Digital_imgae_process/CSet12/lena.png", 0);
Mat dst = img.clone();
if(!img.data)
return -1;
for ( int y = 0; y < img.rows; y++)
{
for( int x = 0; x < img.cols; x++)
{
dst.at<uchar>(y,x) = 0.0;
}
}
for ( int y = 1; y < img.rows - 1; y++ )
{
for ( int x = 1; x < img.cols - 1 ; x++ )//错误原因，-1忘了，所以维数不匹配
{
Gx = xGradient(img, x, y);
Gy = yGradient(img, x, y);
sum = abs(Gx) + abs(Gy);
sum = sum > 255 ? 255 : sum;
sum = sum < 0 ? 0 : sum;
dst.at<uchar>(y,x) = sum;
}
}
imshow("Sobel",dst);
waitKey(0);
return 0;
}
int MeanFilter_Gray(int a,int b)
{
Mat image, meanRes;
image = imread("/Users/zhipeng/ustc_term2/Opencv/Opencv/Opencv/Digital_imgae_process/CSet12/lena.png", 0); // Read the file
namedWindow("原图", WINDOW_AUTOSIZE); // Create a window for display.
imshow("原图", image); // Show our image inside it.
blur(image, meanRes, Size(a, b)); //均值滤波
namedWindow("均值滤波", WINDOW_AUTOSIZE); // Create a window for display.
imshow("均值滤波", meanRes); // Show our image inside it.
waitKey(0); // Wait for a keystroke in the window
destroyAllWindows();
return 0;
}
int MeanFilter_Color(int a, int b)
{
Mat image, meanRes;
image = imread("/Users/zhipeng/ustc_term2/Opencv/Opencv/Opencv/Digital_imgae_process/CSet12/lena.png", 1); // Read the file
namedWindow("原图", WINDOW_AUTOSIZE); // Create a window for display.
imshow("原图", image); // Show our image inside it.
blur(image, meanRes, Size(a, b)); //均值滤波
namedWindow("均值滤波", WINDOW_AUTOSIZE); // Create a window for display.
imshow("均值滤波", meanRes); // Show our image inside it.
waitKey(0); // Wait for a keystroke in the window
destroyAllWindows();
return 0;
}
int GaussianFilter_Gray(int a, int b)
{
Mat image, res;
image = imread("/Users/zhipeng/ustc_term2/Opencv/Opencv/Opencv/Digital_imgae_process/CSet12/lena.png", 0); // Read the file
namedWindow("原图", WINDOW_AUTOSIZE); // Create a window for display.
imshow("原图", image); // Show our image inside it.
GaussianBlur(image, res, Size(a, b), 1);
namedWindow("高斯滤波", WINDOW_AUTOSIZE); // Create a window for display.
imshow("高斯滤波", res); // Show our image inside it.
waitKey(0); // Wait for a keystroke in the window
destroyAllWindows();
return 0;
}
int GaussianFilter_Color(int a, int b)
{
Mat image, res;
image = imread("/Users/zhipeng/ustc_term2/Opencv/Opencv/Opencv/Digital_imgae_process/CSet12/lena.png", 1); // Read the file
namedWindow("原图", WINDOW_AUTOSIZE); // Create a window for display.
imshow("原图", image); // Show our image inside it.
GaussianBlur(image, res, Size(a, b), 1);
namedWindow("高斯滤波", WINDOW_AUTOSIZE); // Create a window for display.
imshow("高斯滤波", res); // Show our image inside it.
waitKey(0); // Wait for a keystroke in the window
destroyAllWindows();
return 0;
}
int Sobel()
{
Mat grad_x, grad_y;
Mat abs_grad_x, abs_grad_y;
Mat image, res;
image = imread("/Users/zhipeng/ustc_term2/Opencv/Opencv/Opencv/Digital_imgae_process/CSet12/lena.png", 0); // Read the file
namedWindow("原图", WINDOW_AUTOSIZE); // Create a window for display.
imshow("原图", image); // Show our image inside it.
Sobel(image, grad_x, image.depth(), 1, 0, 3, 1, 1, BORDER_DEFAULT);
convertScaleAbs(grad_x, abs_grad_x);
imshow("【效果图】 X方向Sobel", abs_grad_x);
Sobel(image, grad_y, image.depth(), 0, 1, 3, 1, 1, BORDER_DEFAULT);
convertScaleAbs(grad_y, abs_grad_y);
imshow("【效果图】Y方向Sobel", abs_grad_y);
//【5】合并梯度(近似)
addWeighted(abs_grad_x, 0.5, abs_grad_y, 0.5, 0, res);
imshow("【效果图】整体方向Sobel", res);
waitKey(0); // Wait for a keystroke in the window
destroyAllWindows();
return 0;
}
int Sobel_Color()
{
Mat grad_x, grad_y;
Mat abs_grad_x, abs_grad_y;
Mat image, res;
image = imread("/Users/zhipeng/ustc_term2/Opencv/Opencv/Opencv/Digital_imgae_process/CSet12/lena.png", 1); // Read the file
namedWindow("原图", WINDOW_AUTOSIZE); // Create a window for display.
imshow("原图", image); // Show our image inside it.
Sobel(image, grad_x, image.depth(), 1, 0, 3, 1, 1, BORDER_DEFAULT);
convertScaleAbs(grad_x, abs_grad_x);
imshow("【效果图】 X方向Sobel", abs_grad_x);
Sobel(image, grad_y, image.depth(), 0, 1, 3, 1, 1, BORDER_DEFAULT);
convertScaleAbs(grad_y, abs_grad_y);
imshow("【效果图】Y方向Sobel", abs_grad_y);
//【5】合并梯度(近似)
addWeighted(abs_grad_x, 0.5, abs_grad_y, 0.5, 0, res);
imshow("【效果图】整体方向Sobel", res);
waitKey(0); // Wait for a keystroke in the window
destroyAllWindows();
return 0;
}
//拉普拉斯模板
int Laplacian_Color()
{
Mat image, res;
image = imread("/Users/zhipeng/ustc_term2/Opencv/Opencv/Opencv/Digital_imgae_process/CSet12/lena.png", IMREAD_COLOR); // Read the file
namedWindow("原图", WINDOW_AUTOSIZE); // Create a window for display.
imshow("原图", image); // Show our image inside it.
Mat kernel = (Mat_<float>(3, 3) << 0, -1, 0, -1, 5, -1, 0, -1, 0);
filter2D(image, res, image.depth(), kernel);
namedWindow("拉普拉斯模板", WINDOW_AUTOSIZE); // Create a window for display.
imshow("拉普拉斯模板", res); // Show our image inside it.
waitKey(0); // Wait for a keystroke in the window
destroyAllWindows();
return 0;
}
int Laplacian_Gray()
{
Mat image, res;
image = imread("/Users/zhipeng/ustc_term2/Opencv/Opencv/Opencv/Digital_imgae_process/CSet12/lena.png", 0); // Read the file
namedWindow("原图", WINDOW_AUTOSIZE); // Create a window for display.
imshow("原图", image); // Show our image inside it.
Mat kernel = (Mat_<float>(3, 3) << 0, -1, 0, -1, 5, -1, 0, -1, 0);
filter2D(image, res, image.depth(), kernel);
namedWindow("拉普拉斯模板", WINDOW_AUTOSIZE); // Create a window for display.
imshow("拉普拉斯模板", res); // Show our image inside it.
waitKey(0); // Wait for a keystroke in the window
destroyAllWindows();
return 0;
}
int Lap2()
{
Mat image, res;
image = imread("/Users/zhipeng/ustc_term2/Opencv/Opencv/Opencv/Digital_imgae_process/CSet12/lena.png", IMREAD_COLOR); // Read the file
namedWindow("原图", WINDOW_AUTOSIZE); // Create a window for display.
imshow("原图", image); // Show our image inside it.
res.create(image.size(), image.type());//为输出图像分配内容
/*拉普拉斯滤波核3*3
0 -1 0
-1 5 -1
0 -1 0 */
//处理除最外围一圈外的所有像素值
for (int i = 1; i < image.rows - 1; i++)
{
const uchar * pre = image.ptr<const uchar>(i - 1);//前一行
const uchar * cur = image.ptr<const uchar>(i);//当前行，第i行
const uchar * next = image.ptr<const uchar>(i + 1);//下一行
uchar * output = res.ptr<uchar>(i);//输出图像的第i行
int ch = image.channels();//通道个数
int startCol = ch;//每一行的开始处理点
int endCol = (image.cols - 1)* ch;//每一行的处理结束点
for (int j = startCol; j < endCol; j++)
{
//输出图像的遍历指针与当前行的指针同步递增, 以每行的每一个像素点的每一个通道值为一个递增量, 因为要
//考虑到图像的通道数
//saturate_cast<uchar>保证结果在uchar范围内
*output++ = saturate_cast<uchar>(5 * cur[j] - pre[j] - next[j] - cur[j - ch] - cur[j + ch]);
}
}
//将最外围一圈的像素值设为0
res.row(0).setTo(Scalar(0));
res.row(res.rows - 1).setTo(Scalar(0));
res.col(0).setTo(Scalar(0));
res.col(res.cols - 1).setTo(Scalar(0));
/*/或者也可以尝试将最外围一圈设置为原图的像素值
image.row(0).copyTo(result.row(0));
image.row(image.rows-1).copyTo(result.row(result.rows-1));
image.col(0).copyTo(result.col(0));
image.col(image.cols-1).copyTo(result.col(result.cols-1));*/
namedWindow("拉普拉斯模板-手写", WINDOW_AUTOSIZE); // Create a window for display.
imshow("拉普拉斯模板-手写", res); // Show our image inside it.
waitKey(0); // Wait for a keystroke in the window
destroyAllWindows();
return 0;
}
int Robert_RGB()
{
Mat image, res;
image = imread("/Users/zhipeng/ustc_term2/Opencv/Opencv/Opencv/Digital_imgae_process/CSet12/lena.png", 1); // Read the file
namedWindow("原图", WINDOW_AUTOSIZE); // Create a window for display.
imshow("原图", image); // Show our image inside it.
res = image.clone();
CvScalar t1, t2, t3, t4, t;
IplImage res2 = IplImage(image);
for (int i = 0; i < res2.height-1; i++)
{
for (int j = 0; j < res2.width-1; j++)
{
t1 = cvGet2D(&res2, i, j);
t2 = cvGet2D(&res2, i+1, j+1);
t3 = cvGet2D(&res2, i, j + 1);
t4 = cvGet2D(&res2, i + 1, j);
for (int k = 0; k < 3; k++)
{
int t7 = (t1.val[k] - t2.val[k])*(t1.val[k] - t2.val[k])+ (t4.val[k] - t3.val[k])*(t4.val[k] - t3.val[k]);
t.val[k] = sqrt(t7);
}
cvSet2D(&res2, i, j, t);
}
}
res = cvarrToMat(&res2);
namedWindow("Robert_RGB滤波", WINDOW_AUTOSIZE); // Create a window for display.
imshow("Robert_RGB滤波", res); // Show our image inside it.
waitKey(0); // Wait for a keystroke in the window
destroyAllWindows();
return 0;
}
int Robert_G()
{
Mat image, res;
image = imread("/Users/zhipeng/ustc_term2/Opencv/Opencv/Opencv/Digital_imgae_process/CSet12/lena.png", 0); // Read the file
namedWindow("原图", WINDOW_AUTOSIZE); // Create a window for display.
imshow("原图", image); // Show our image inside it.
res = image.clone();
for (int i = 0; i < image.rows - 1; i++) {
for (int j = 0; j < image.cols - 1; j++) {
//根据公式计算
int t1 = (image.at<uchar>(i, j) -
image.at<uchar>(i + 1, j + 1))*
(image.at<uchar>(i, j) -
image.at<uchar>(i + 1, j + 1));
int t2 = (image.at<uchar>(i + 1, j) -
image.at<uchar>(i, j + 1))*
(image.at<uchar>(i + 1, j) -
image.at<uchar>(i, j + 1));
//计算g（x,y）
res.at<uchar>(i, j) = (uchar)sqrt(t1 + t2);
}
}
namedWindow("Robert_G滤波", WINDOW_AUTOSIZE); // Create a window for display.
imshow("Robert_G滤波", res); // Show our image inside it.
waitKey(0); // Wait for a keystroke in the window
destroyAllWindows();
return 0;
}
void EnhanceFilter(Mat img, Mat &dst, double dProportion, int nTempH, int nTempW, int nTempMY, int nTempMX, float *pfArray, float fCoef)
{
int i, j, nHeight = img.rows, nWidth = img.cols;
vector<vector<int>> GrayMat1, GrayMat2, GrayMat3;//暂存按比例叠加图像，R,G,B三通道
vector<int> vecRow1(nWidth, 0), vecRow2(nWidth, 0), vecRow3(nWidth, 0);
for (i = 0; i < nHeight; i++)
{
GrayMat1.push_back(vecRow1);
GrayMat2.push_back(vecRow2);
GrayMat3.push_back(vecRow3);
}
//锐化图像，输出带符号响应，并与原图像按比例叠加
for (i = nTempMY; i < nHeight - (nTempH - nTempMY) + 1; i++)
{
for (j = nTempMX; j < nWidth - (nTempW - nTempMX) + 1; j++)
{
float fResult1 = 0;
float fResult2 = 0;
float fResult3 = 0;
for (int k = 0; k < nTempH; k++)
{
for (int l = 0; l < nTempW; l++)
{
//分别计算三通道加权和
fResult1 += img.at<Vec3b>(i, j)[0] * pfArray[k*nTempW + 1];
fResult2 += img.at<Vec3b>(i, j)[1] * pfArray[k*nTempW + 1];
fResult3 += img.at<Vec3b>(i, j)[2] * pfArray[k*nTempW + 1];
}
}
//三通道加权和分别乘以系数并限制响应范围，最后和原图像按比例混合
fResult1 *= fCoef;
if (fResult1 > 255)
fResult1 = 255;
if (fResult1 < -255)
fResult1 = -255;
GrayMat1[i][j] = dProportion * img.at<Vec3b>(i, j)[0] + fResult1 + 0.5;
fResult2 *= fCoef;
if (fResult2 > 255)
fResult2 = 255;
if (fResult2 < -255)
fResult2 = -255;
GrayMat2[i][j] = dProportion * img.at<Vec3b>(i, j)[1] + fResult2 + 0.5;
fResult3 *= fCoef;
if (fResult3 > 255)
fResult3 = 255;
if (fResult3 < -255)
fResult3 = -255;
GrayMat3[i][j] = dProportion * img.at<Vec3b>(i, j)[2] + fResult3 + 0.5;
}
}
int nMax1 = 0, nMax2 = 0, nMax3 = 0;//三通道最大灰度和值
int nMin1 = 65535, nMin2 = 65535, nMin3 = 65535;//三通道最小灰度和值
//分别统计三通道最大值最小值
for (i = nTempMY; i < nHeight - (nTempH - nTempMY) + 1; i++)
{
for (j = nTempMX; j < nWidth - (nTempW - nTempMX) + 1; j++)
{
if (GrayMat1[i][j] > nMax1)
nMax1 = GrayMat1[i][j];
if (GrayMat1[i][j] < nMin1)
nMin1 = GrayMat1[i][j];
if (GrayMat2[i][j] > nMax2)
nMax2 = GrayMat2[i][j];
if (GrayMat2[i][j] < nMin2)
nMin2 = GrayMat2[i][j];
if (GrayMat3[i][j] > nMax3)
nMax3 = GrayMat3[i][j];
if (GrayMat3[i][j] < nMin3)
nMin3 = GrayMat3[i][j];
}
}
//将按比例叠加后的三通道图像取值范围重新归一化到[0,255]
int nSpan1 = nMax1 - nMin1, nSpan2 = nMax2 - nMin2, nSpan3 = nMax3 - nMin3;
for (i = nTempMY; i < nHeight - (nTempH - nTempMY) + 1; i++)
{
for (j = nTempMX; j < nWidth - (nTempW - nTempMX) + 1; j++)
{
int br, bg, bb;
if (nSpan1 > 0)
br = (GrayMat1[i][j] - nMin1) * 255 / nSpan1;
else if (GrayMat1[i][j] <= 255)
br = GrayMat1[i][j];
else
br = 255;
dst.at<Vec3b>(i, j)[0] = br;
if (nSpan2 > 0)
bg = (GrayMat2[i][j] - nMin2) * 255 / nSpan2;
else if (GrayMat2[i][j] <= 255)
bg = GrayMat2[i][j];
else
bg = 255;
dst.at<Vec3b>(i, j)[1] = bg;
if (nSpan3 > 0)
bb = (GrayMat3[i][j] - nMin3) * 255 / nSpan3;
else if (GrayMat3[i][j] <= 255)
bb = GrayMat3[i][j];
else
bb = 255;
dst.at<Vec3b>(i, j)[2] = bb;
}
}
}
int enhanceFilter()
{
Mat img = imread("/Users/zhipeng/ustc_term2/Opencv/Opencv/Opencv/Digital_imgae_process/CSet12/lena.png");
imshow("原图", img);
Mat dst = img.clone();
float Template_Laplacian2[9] = { -1, -1, -1, -1, 8, -1, -1, -1, -1 };//对45度各向同性
/*************************************************************************************************************
高提升滤波
dProportion：高提升滤波中原图像的混合比例
nTempH：模板高度，nTempW：模板宽度
nTempMY：模板中心元素坐标，nTempMX：模板中心元素坐标
fpArray：指向模板数组的指针，可以选取不同模板实现不同滤波的高提升版本
fCoef：模板系数
**************************************************************************************************************/
EnhanceFilter(img, dst, 1.8, 3, 3, 1, 1, Template_Laplacian2, 1);
imshow("高提升Laplacian", dst);
waitKey(0);
destroyAllWindows();
return 0;
}
int Lab03()
{
vector<float> Template_Smooth_Avg = { 1/9, 1/9, 1/9, 1/9, 1/9, 1/9, 1/9, 1/9, 1/9 };
//Gauss平滑1/16
vector<float> Template_Smooth_Gauss = { 1, 2, 1, 2, 4, 2, 1, 2, 1 };
//Sobel垂直边缘检测
vector<float> Template_Smooth_HSobel = { -1, 0, 1, -2, 0, 2, -1, 0, 1 };
//Sobel水平边缘检测
vector<float> Template_Smooth_VSobel = { -1, -2, -1, 0, 0, 0, 1, 2, 1 };
//LOG边缘检测
vector<float> Template_Log = { 0, 0, -1, 0, 0, 0, -1, -2, -1, 0, -1, -2, 16, -2, -1, 0, -1, -2, -1, 0, 0, 0, -1, 0, 0 };
//Laplacian边缘检测
vector<float> Template_Laplacian1 = { 0, -1, 0, -1, 5, -1, 0, -1, 0 };//对90度各向同性
vector<float> Template_Laplacian2 = { -1, -1, -1, -1, 8, -1, -1, -1, -1 };//对45度各向同性
// MeanFilter_Gray(3,3);
// MeanFilter_Gray(5, 5);
// MeanFilter_Gray(9, 9);
// GaussianFilter_Gray(3,3);
// GaussianFilter_Gray(5, 5);
// GaussianFilter_Gray(9, 9);
// Laplacian_Gray();
// Robert_G();
//Sobel();
MeanFilter_Color(3, 3);
MeanFilter_Color(5, 5);
MeanFilter_Color(9, 9);
GaussianFilter_Color(3, 3);
GaussianFilter_Color(5, 5);
GaussianFilter_Color(9, 9);
sobelXY();
spacialFilter(Template_Laplacian1);
spacialFilter(Template_Laplacian2);
Laplacian_Color();
enhanceFilter();
// Robert_RGB();
// Sobel_Color();
return 0;
}
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