From 7d28c79ebede211a33e5c6860d83ccb80586a7a0 Mon Sep 17 00:00:00 2001 From: pjc97uv2f <1291955673@qq.com> Date: Sun, 1 Dec 2024 22:24:44 +0800 Subject: [PATCH] Add 2 --- 2 | 106 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 106 insertions(+) create mode 100644 2 diff --git a/2 b/2 new file mode 100644 index 0000000..a71aacd --- /dev/null +++ b/2 @@ -0,0 +1,106 @@ +#include +#include + +// 应用优化后的可分离高斯模糊到给定的二维数组src上，并将结果存到二维数组dst中 +void applySeparableGaussianBlur(float src[][5], float dst[][5], int h, int w, float kx[3], float ky[3]) { + float buf[3][3]; // 提前声明buf数组，符合C89要求 + int i, j; // 提前声明循环中使用的变量，符合C89要求 + + // 初始化buf数组为0 + for (i = 0; i < 3; ++i) { + for (j = 0; j < 3; ++j) { + buf[i][j] = 0; + } + } + + // 宏定义，用于循环利用buf数组的3个行缓冲区，通过模运算确保索引值在0~2之间 +#define IDX(n) ((n) % 3) + + // 循环计算前两行的行内卷积并存储在buf中 + for (i = 0; i < 2; ++i) { + for (j = 1; j < w - 1; ++j) { + buf[i][j] = src[i][j - 1] * kx[0] + src[i][j] * kx[1] + src[i][j + 1] * kx[2]; + } + } + + // 开始进行可分离卷积 + for (i = 1; i < h - 1; ++i) { + // 计算当前行的行内卷积 + for (j = 1; j < w - 1; ++j) { + buf[IDX(i)][j] = src[i][j - 1] * kx[0] + src[i][j] * kx[1] + src[i][j + 1] * kx[2]; + } + + // 进行行间的卷积得到最终像素值 + for (j = 1; j < w - 1; ++j) { + dst[i][j] = buf[IDX(i - 1)][j] * ky[0] + buf[IDX(i)][j] * ky[1] + buf[IDX(i + 1)][j] * ky[2]; + } + } +#undef IDX +} + +// 原始的应用3x3 GaussianBlur函数（与之前代码中的类似，方便对比用时） +void applyGaussianBlur(float src[][5], float dst[][5], int h, int w, float kernel[3][3]) { + int i, j; // 提前声明循环变量，符合C89要求 + for (i = 1; i < h - 1; ++i) { + for (j = 1; j < w - 1; ++j) { + dst[i][j] = + src[i - 1][j - 1] * kernel[0][0] + src[i - 1][j] * kernel[0][1] + src[i - 1][j + 1] * kernel[0][2] + + src[i][j - 1] * kernel[1][0] + src[i][j] * kernel[1][1] + src[i][j + 1] * kernel[1][2] + + src[i + 1][j - 1] * kernel[2][0] + src[i + 1][j] * kernel[2][1] + src[i + 1][j + 1] * kernel[2][2]; + } + } +} + +int main() { + float inputImage[5][5] = { + {1, 2, 3, 4, 5}, + {6, 7, 8, 9, 10}, + {11, 12, 13, 14, 15}, + {16, 17, 18, 19, 20}, + {21, 22, 23, 24, 25} + }; + float kernel[3][3] = { + {1.0f / 16, 2.0f / 16, 1.0f / 16}, + {2.0f / 16, 4.0f / 16, 2.0f / 16}, + {1.0f / 16, 2.0f / 16, 1.0f / 16} + }; + float kx[3] = { 0.25f, 0.5f, 0.25f }; + float ky[3] = { 0.25f, 0.5f, 0.25f }; + float outputImage1[5][5]; // 存放原始GaussianBlur结果 + float outputImage2[5][5]; // 存放优化后的可分离GaussianBlur结果 + int i, j; // 提前声明循环变量，符合C89要求 + + // 原始GaussianBlur计时开始 + clock_t start_time1 = clock(); + applyGaussianBlur(inputImage, outputImage1, 5, 5, kernel); + clock_t end_time1 = clock(); + double elapsed_time1 = ((double)(end_time1 - start_time1)) / CLOCKS_PER_SEC; + + // 优化后的可分离GaussianBlur计时开始 + clock_t start_time2 = clock(); + applySeparableGaussianBlur(inputImage, outputImage2, 5, 5, kx, ky); + clock_t end_time2 = clock(); + double elapsed_time2 = ((double)(end_time2 - start_time2)) / CLOCKS_PER_SEC; + + // 输出原始GaussianBlur结果 + printf("原始GaussianBlur后的图像矩阵：\n"); + for (i = 0; i < 5; ++i) { + for (j = 0; j < 5; ++j) { + printf("%.2f ", outputImage1[i][j]); + } + printf("\n"); + } + printf("原始GaussianBlur运行时间：%.6f 秒\n", elapsed_time1); + + // 输出优化后的可分离GaussianBlur结果 + printf("优化后的可分离GaussianBlur后的图像矩阵：\n"); + for (i = 0; i < 5; ++i) { + for (j = 0; j < 5; ++j) { + printf("%.2f ", outputImage2[i][j]); + } + printf("\n"); + } + printf("优化后的可分离GaussianBlur运行时间：%.6f 秒\n", elapsed_time2); + + return 0; +} \ No newline at end of file