在OpenCV中使用小波變換對傷口進行分割

我們嘗試了用於傷口分割的局部直方圖方法，該方法對於各種圖像都不能很好地工作，然後我們教導使用小波變換進行傷口分割。在OpenCV中使用小波變換對傷口進行分割

哪個小波變換適合傷口分割和一些提示來實現它？

有沒有比小波變換更好的方法來在所有光照條件下分割傷口？

我們也試過圖像集羣哪個沒有那麼好。

下面是我們使用的一些測試用例和聚類程序。

#include "cv.h" 
#include "highgui.h" 

#include <iostream> 
void show_result(const cv::Mat& labels, const cv::Mat& centers, int height, int width); 
int main(int argc, const char * argv[]) 
{  
     cv::Mat image = cv::imread("kmean.jpg"); 
     if (image.empty()) { 
       std::cout << "unable to load an input image\n"; 
       return 1; 
     } 
     //cv::cvtColor(image,image,CV_BGR2HSV); 
     std::cout << "image: " << image.rows << ", " << image.cols << std::endl; 
     assert(image.type() == CV_8UC3); 
     cv::imshow("image", image); 

     cv::Mat reshaped_image = image.reshape(1, image.cols * image.rows); 
     std::cout << "reshaped image: " << reshaped_image.rows << ", " << reshaped_image.cols << std::endl; 
     assert(reshaped_image.type() == CV_8UC1); 
     //check0(image, reshaped_image); 

     cv::Mat reshaped_image32f; 
     reshaped_image.convertTo(reshaped_image32f, CV_32FC1, 1.0/255.0); 
     std::cout << "reshaped image 32f: " << reshaped_image32f.rows << ", " << reshaped_image32f.cols << std::endl; 
     assert(reshaped_image32f.type() == CV_32FC1); 

     cv::Mat labels; 
     int cluster_number = 4; 
     cv::TermCriteria criteria(cv::TermCriteria::COUNT, 100, 1); 
     cv::Mat centers; 
     cv::kmeans(reshaped_image32f, cluster_number, labels, criteria, 1, cv::KMEANS_PP_CENTERS, centers); 

     show_result(labels, centers, image.rows,image.cols); 

     return 0; 
} 

void show_result(const cv::Mat& labels, const cv::Mat& centers, int height, int width) 
{ 
     std::cout << "===\n"; 
     std::cout << "labels: " << labels.rows << " " << labels.cols << std::endl; 
     std::cout << "centers: " << centers.rows << " " << centers.cols << std::endl; 
     assert(labels.type() == CV_32SC1); 
     assert(centers.type() == CV_32FC1); 

     cv::Mat rgb_image(height, width, CV_8UC3); 
     cv::MatIterator_<cv::Vec3b> rgb_first = rgb_image.begin<cv::Vec3b>(); 
     cv::MatIterator_<cv::Vec3b> rgb_last = rgb_image.end<cv::Vec3b>(); 
     cv::MatConstIterator_<int> label_first = labels.begin<int>(); 

     cv::Mat centers_u8; 
     centers.convertTo(centers_u8, CV_8UC1, 255.0); 
     cv::Mat centers_u8c3 = centers_u8.reshape(3); 

     while (rgb_first != rgb_last) { 
       const cv::Vec3b& rgb = centers_u8c3.ptr<cv::Vec3b>(*label_first)[0]; 
       *rgb_first = rgb; 
       ++rgb_first; 
       ++label_first; 
     } 
     cv::imshow("tmp", rgb_image); 


     cv::waitKey(); 
}

將-1背景：（兩個集羣）

Would-1 with Background