opencv03空域滤波

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;
}