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Copy paththinning.cpp
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117 lines (101 loc) · 5.2 KB
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#include <vector>
#include <cstdint>
#include <chrono>
#include <opencv2/opencv.hpp>
#include <opencv2/ximgproc.hpp>
static void faster_thining(const cv::Mat& src, cv::Mat& dst) {
// 核心思想，查表法，列出周围8像素的所有情况下，针对每一种情况直接判断是否需要删除，
// 每次删除，一共迭代两轮，其中第一轮是删除横纵方向上的情况，第二轮删除斜对角方向上的情况
// lut 0代表不能删除，1代表横纵方向下可以删除，2代表斜对角方向上可以删除，3代表不能删除
// lut 索引采用8bit 二进制索引方式进行索引
const std::vector<uchar> lut = {0, 0, 0, 1, 0, 0, 1, 3, 0, 0, 3, 1, 1, 0,
1, 3, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 2, 0,
3, 0, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
2, 0, 0, 0, 3, 0, 2, 2, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0,
0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0,
3, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 3, 0,
2, 0, 0, 0, 3, 1, 0, 0, 1, 3, 0, 0, 0, 0,
0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 1, 3, 1, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 3, 1, 3,
0, 0, 1, 3, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
2, 3, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0,
0, 0, 3, 3, 0, 1, 0, 0, 0, 0, 2, 2, 0, 0,
2, 0, 0, 0};
auto mask = src.clone();
mask = mask / 255.0;
int nrows = mask.rows + 2;
int ncols = mask.cols + 2;
cv::Mat skeleton(nrows, ncols, CV_8U, cv::Scalar(0));
mask.copyTo(skeleton(cv::Rect(1, 1, mask.cols, mask.rows)));
cv::Mat cleaned_skeleton = skeleton.clone();
bool pixel_removed = true;
while (pixel_removed) {
pixel_removed = false;
for (int pass_num = 0; pass_num < 2; ++pass_num) {
bool first_pass = (pass_num == 0);
for (int row = 1; row < nrows - 1; ++row) {
uchar* skeleton_ptr = skeleton.ptr<uchar>(row) + 1;
uchar* cleaned_skeleton_ptr = cleaned_skeleton.ptr<uchar>(row) + 1;
for (int col = 1; col < ncols - 1; ++col) {
if (*skeleton_ptr) {
uchar neighbors =
lut[skeleton_ptr[-ncols - 1] +
2 * skeleton_ptr[-ncols] +
4 * skeleton_ptr[-ncols + 1] +
8 * skeleton_ptr[1] +
16 * skeleton_ptr[ncols + 1] +
32 * skeleton_ptr[ncols] +
64 * skeleton_ptr[ncols - 1] +
128 * skeleton_ptr[-1]];
if (neighbors == 0) {
++skeleton_ptr;
++cleaned_skeleton_ptr;
continue;
} else if ((neighbors == 3) ||
(neighbors == 1 && first_pass) ||
(neighbors == 2 && !first_pass)) {
// 删除像素点
*cleaned_skeleton_ptr = 0;
pixel_removed = true;
}
}
++skeleton_ptr;
++cleaned_skeleton_ptr;
}
}
cleaned_skeleton.copyTo(skeleton);
}
}
dst = skeleton(cv::Rect(1, 1, skeleton.cols - 2, skeleton.rows - 2)).clone();
dst = dst * 255;
}
#define TIME_COST(start, end) std::chrono::duration_cast<std::chrono::milliseconds>((end) - (start)).count()
int main() {
std::string filename = "./tmp.png";
auto mask = cv::imread(filename, cv::IMREAD_UNCHANGED);
cv::Mat sk;
auto tic_0 = std::chrono::high_resolution_clock::now();
for(int i=0; i< 100;i++){
faster_thining(mask, sk);
}
std::cout << "faster_thining time: " << TIME_COST(tic_0, std::chrono::system_clock::now()) / 1000.0 / 100.0 << " seconds per time"<< std::endl;
cv::imwrite("sk.png", sk);
cv::Mat sk_orig;
auto tic_1 = std::chrono::high_resolution_clock::now();
for(int i=0; i< 100;i++){
cv::ximgproc::thinning(mask, sk_orig);
}
std::cout << "cv::ximgproc::thinning time: " <<TIME_COST(tic_1, std::chrono::system_clock::now()) / 1000.0 / 100.0 << " seconds per time"<< std::endl;
cv::imwrite("sk_orig.png", sk_orig);
/***
*
* faster_thining time: 0.0292352
cv::ximgproc::thinning time: 0.261634
*/
}