图像处理的基本操作就是矩阵运算了吧，也算是稍微理解下那些算法是怎么操作的
不过后来又看了一下，发现自己写的很幼稚。不管了，先摆出来，以后有时间再看。
#include <iostream> #include <assert.h> using namespace std; #define MAX 100//矩阵最大阶 double Kernel[MAX][MAX] = { 0 }; //卷积核 class Pixel_INMAGE { public: double Pixel[MAX][MAX]; //像素阵 double Result[MAX][MAX]; //变换结果矩阵 //构造 Pixel_INMAGE() { memset(Pixel, 0, sizeof(Pixel)); memset(Result, 0, sizeof(Result)); } //大小比较 inline int max(int a, int b){ return a > b ? a : b; } //********************************************************* //函 数 名：Convolution_One //参 数： k n m // 卷积核 行 列 //返 回 值：无 //备 注：压缩卷积 //********************************************************* void Convolution_One(int k, int n, int m) //flag为1 { assert(k <= m && k <= n); //压缩矩阵 for (int ROW_OF_RESULT = k / 2; ROW_OF_RESULT < n - k / 2; ROW_OF_RESULT++) { for (int COL_OF_RESULT = k / 2; COL_OF_RESULT < m - k / 2; COL_OF_RESULT++) { for (int ROW_OF_PIXEL = ROW_OF_RESULT - k / 2; ROW_OF_PIXEL <= ROW_OF_RESULT + k / 2; ROW_OF_PIXEL++)//像素矩阵行,等于号问题也是根据示例调出来的。 { int ROW_OF_KERNEL = ROW_OF_PIXEL - ROW_OF_RESULT + k / 2; for (int COL_OF_PIXEL = COL_OF_RESULT - k / 2; COL_OF_PIXEL <= COL_OF_RESULT + k / 2; COL_OF_PIXEL++) { int COL_OF_KERNEL = COL_OF_PIXEL - COL_OF_RESULT + k / 2; //我自己都佩服我自己 Result[ROW_OF_RESULT][COL_OF_RESULT] += Pixel[ROW_OF_PIXEL][COL_OF_PIXEL] * Kernel[ROW_OF_KERNEL][COL_OF_KERNEL]; } } } } } //********************************************************* //函 数 名：Convolution_Two //参 数： k n m // 卷积核 行 列 //返 回 值：无 //备 注：本来是要分情况考虑的，太碎了，还不如来一个整体的。我认为难点在于等于号的取舍 //********************************************************* void Convolution_Two(int k, int n, int m) //flag为2 { for (int ROW_OF_RESULT = 0; ROW_OF_RESULT < n; ROW_OF_RESULT++) { for (int COL_OF_RESULT = 0; COL_OF_RESULT < m; COL_OF_RESULT++) { if (ROW_OF_RESULT - k / 2 >= 0 && COL_OF_RESULT - k / 2 >= 0)//卷积后的result[ROW_OF_RESULT][COL_OF_RESULT]对应的卷积核在Piexl之内 { for (int ROW_OF_PIXEL = ROW_OF_RESULT - k / 2; ROW_OF_PIXEL <= ROW_OF_RESULT + k / 2; ROW_OF_PIXEL++)//像素矩阵行 { int ROW_OF_KERNEL = ROW_OF_PIXEL - ROW_OF_RESULT + k / 2; for (int COL_OF_PIXEL = COL_OF_RESULT - k / 2; COL_OF_PIXEL <= COL_OF_RESULT + k / 2; COL_OF_PIXEL++) { int COL_OF_KERNEL = COL_OF_PIXEL - COL_OF_RESULT + k / 2; Result[ROW_OF_RESULT][COL_OF_RESULT] += Pixel[ROW_OF_PIXEL][COL_OF_PIXEL] * Kernel[ROW_OF_KERNEL][COL_OF_KERNEL]; } } } else if (ROW_OF_RESULT - k / 2 >= 0 && COL_OF_RESULT - k / 2 < 0)//若是像素阵列数为负 { for (int ROW_OF_PIXEL = ROW_OF_RESULT - k / 2; ROW_OF_PIXEL <= ROW_OF_RESULT + k / 2; ROW_OF_PIXEL++) { int ROW_OF_KERNEL = ROW_OF_PIXEL - ROW_OF_RESULT + k / 2; for (int COL_OF_PIXEL = 0; COL_OF_PIXEL <= COL_OF_RESULT + k / 2; COL_OF_PIXEL++) { int COL_OF_KERNEL = k / 2 + COL_OF_PIXEL; Result[ROW_OF_RESULT][COL_OF_RESULT] += Pixel[ROW_OF_PIXEL][COL_OF_PIXEL] * Kernel[ROW_OF_KERNEL][COL_OF_KERNEL]; } } } else if (ROW_OF_RESULT - k / 2 < 0 && COL_OF_RESULT - k / 2 >= 0)//行数为负 { for (int ROW_OF_PIXEL = 0; ROW_OF_PIXEL <= ROW_OF_RESULT + k / 2; ROW_OF_PIXEL++) { int ROW_OF_KERNEL = k / 2 + ROW_OF_PIXEL; for (int COL_OF_PIXEL = COL_OF_RESULT - k / 2; COL_OF_PIXEL <= COL_OF_RESULT + k / 2; COL_OF_PIXEL++) { int COL_OF_KERNEL = COL_OF_PIXEL - COL_OF_RESULT + k / 2; Result[ROW_OF_RESULT][COL_OF_RESULT] += Pixel[ROW_OF_PIXEL][COL_OF_PIXEL] * Kernel[ROW_OF_KERNEL][COL_OF_KERNEL]; } } } else //行数列数全为负 { for (int ROW_OF_PIXEL = 0; ROW_OF_PIXEL <= ROW_OF_RESULT + k / 2; ROW_OF_PIXEL++) { int ROW_OF_KERNEL = k / 2 + ROW_OF_PIXEL; for (int COL_OF_PIXEL = 0; COL_OF_PIXEL <= COL_OF_RESULT + k / 2; COL_OF_PIXEL++) { int COL_OF_KERNEL = k / 2 + COL_OF_PIXEL; Result[ROW_OF_RESULT][COL_OF_RESULT] += Pixel[ROW_OF_PIXEL][COL_OF_PIXEL] * Kernel[ROW_OF_KERNEL][COL_OF_KERNEL]; } } } } } } //********************************************************* //函 数 名：print //参 数： k n m CONVOLUTIONAl_FLAG // 卷积核 行 列 flag //返 回 值：无 //备 注：输出答案 //********************************************************* void print(int k, int n, int m, int CONVOLUTIONAl_FLAG) { if (CONVOLUTIONAl_FLAG == 1) //类型一输出，按照计算的时候输出 { for (int ROW_OF_RESULT = k / 2; ROW_OF_RESULT < n - k / 2; ROW_OF_RESULT++) { for (int COL_OF_RESULT = k / 2; COL_OF_RESULT < m - k / 2; COL_OF_RESULT++) { cout << (int)Result[ROW_OF_RESULT][COL_OF_RESULT] << ' '; } cout << endl; } } else //类型二输出，图片无压缩 { for (int ROW_OF_RESULT = 0; ROW_OF_RESULT < n; ROW_OF_RESULT++) { for (int COL_OF_RESULT = 0; COL_OF_RESULT < m; COL_OF_RESULT++) { cout << (int)Result[ROW_OF_RESULT][COL_OF_RESULT] << ' '; } cout << endl; } } Pixel[MAX][MAX] = { 0 }; Result[MAX][MAX] = { 0 };//矩阵重置为零 Kernel[MAX][MAX] = { 0 }; } //********************************************************* //函 数 名：Picture_Input //参 数： n m // 行 列 //返 回 值：无 //备 注：输入图像 //********************************************************* void Picture_Input(int n, int m) { for (int ROW_OF_PIXEL = 0; ROW_OF_PIXEL < n; ROW_OF_PIXEL++)//像素矩阵。 { for (int COL_OF_PIXEL = 0; COL_OF_PIXEL < m; COL_OF_PIXEL++) cin >> Pixel[ROW_OF_PIXEL][COL_OF_PIXEL]; } } }; //********************************************************* //函 数 名：Kernel_Input //参 数： k 卷积核 //返 回 值：无 //备 注：输入卷积核 //********************************************************* void Kernel_Input(int k) { for (int ROW_OF_KERNEL = 0; ROW_OF_KERNEL < k; ROW_OF_KERNEL++)//卷积核。 { for (int COL_OF_KERNEL = 0; COL_OF_KERNEL < k; COL_OF_KERNEL++) { cin >> Kernel[ROW_OF_KERNEL][COL_OF_KERNEL]; } } } int main() { Pixel_INMAGE Picture; int k, n, m, CONVOLUTIONAl_FLAG;//k是k阶卷积核矩阵且k一定是奇数。n是行数，m是列数 cin >> k >> n >> m >> CONVOLUTIONAl_FLAG; assert(k % 2 != 0); //k坚决不为二 Picture.Picture_Input(n, m); Kernel_Input(k); if (CONVOLUTIONAl_FLAG == 1)Picture.Convolution_One(k, n, m); else if (CONVOLUTIONAl_FLAG == 2)Picture.Convolution_Two(k, n, m); Picture.print(k, n, m, CONVOLUTIONAl_FLAG); return 0; }