MATLAB conv2卷积的实现
二维卷积的算法原理比較简单,參考随意一本数字信号处理的书籍,而matlab的conv2函数的滤波有个形状參数,用以下的一张图非常能说明问题:
这里给出一种最原始的实现方案。这样的实现对于数据矩阵大小为1000x1000,卷积核矩阵大小为20x20,在我的机器上须要大约1秒钟的时间。而matlab採用的MKL库最快仅仅须要将近0.1s的时间。
以下的代码用到了自己眼下开发的FastIV中的一些函数接口。详细代码例如以下:
#include "fiv_core.h" typedef enum{ FIV_CONV2_SHAPE_FULL, FIV_CONV2_SHAPE_SAME, FIV_CONV2_SHAPE_VALID }FIV_CONV_SHAPE; void fIv_conv2(fIvMat** dst_mat, fIvMat* src_mat, fIvMat* kernel_mat, FIV_CONV_SHAPE shape) { int src_row = src_mat->rows; int src_cols = src_mat->cols; int kernel_row = kernel_mat->rows; int kernel_cols = kernel_mat->cols; int dst_row = 0, dst_cols = 0, edge_row = 0, edge_cols = 0; int i,j, kernel_i,kernel_j,src_i,src_j; fIvMat* ptr_dst_mat = NULL; switch(shape){ case FIV_CONV2_SHAPE_FULL: dst_row = src_row + kernel_row - 1; dst_cols = src_cols + kernel_cols - 1; edge_row = kernel_row - 1; edge_cols = kernel_cols - 1; break; case FIV_CONV2_SHAPE_SAME: dst_row = src_row; dst_cols = src_cols; edge_row = (kernel_row - 1) / 2; edge_cols = (kernel_cols - 1) / 2; break; case FIV_CONV2_SHAPE_VALID: dst_row = src_row - kernel_row + 1; dst_cols = src_cols - kernel_cols + 1; edge_row = edge_cols = 0; break; } ptr_dst_mat = fIv_create_mat(dst_row, dst_cols, FIV_64FC1); *dst_mat = ptr_dst_mat; for (i = 0; i < dst_row; i++) { ivf64* ptr_dst_line_i = (ivf64* )fIv_get_mat_data_at_row(ptr_dst_mat, i); for (j = 0; j < dst_cols; j++) { ivf64 sum = 0; kernel_i = kernel_row - 1 - FIV_MAX(0, edge_row - i); src_i = FIV_MAX(0, i - edge_row); for (; kernel_i >= 0 && src_i < src_row; kernel_i--, src_i++) { ivf64* ptr_src_line_i,*ptr_kernel_line_i; kernel_j = kernel_cols - 1 - FIV_MAX(0, edge_cols - j); src_j = FIV_MAX(0, j - edge_cols); ptr_src_line_i = (ivf64*)fIv_get_mat_data_at_row(src_mat, src_i); ptr_kernel_line_i = (ivf64*)fIv_get_mat_data_at_row(kernel_mat, kernel_i); ptr_src_line_i += src_j; ptr_kernel_line_i += kernel_j; for (; kernel_j >= 0 && src_j < src_cols; kernel_j--, src_j++){ sum += *ptr_src_line_i++ * *ptr_kernel_line_i--; } } ptr_dst_line_i[j] = sum; } } } FIV_ALIGNED(16) ivf64 ker_data[4*4] = {0.1,0.2,0.3,0.4, 0.5,0.6,0.7,0.8, 0.9,1.0,1.1,1.2, 1.3,1.4,1.5,1.6}; void test_conv2() { fIvMat* src_mat = fIv_create_mat_magic(8, FIV_64FC1); // 8x8 magic matrix fIvMat* kernel_mat = fIv_create_mat_header(4, 4, FIV_64FC1); fIvMat* dst_mat = NULL; fIv_set_mat_data(kernel_mat, ker_data, (sizeof(ivf64)) * 4 * 4); fIv_conv2(&dst_mat, src_mat, kernel_mat, FIV_CONV2_SHAPE_FULL); fIv_export_matrix_data_file(dst_mat,"dst_mat_4x4-full.txt", 1); fIv_release_mat(&src_mat); fIv_release_mat(&kernel_mat); fIv_release_mat(&dst_mat); } int main() { test_conv2(); return 0; }
10月24日更新:
眼下FastIV中的实现已经经过优化,最高速度在我的机器上已经超越MATLAB。