Question

我们尝试使用局部直方图方法进行伤口分割，这种方法对各种图像都不起作用，然后我们教会使用小波变换进行伤口分割。

哪种小波变换对伤口分割有好处并且有一些实施技巧？

有没有比小波变换更好的方法来在所有光照条件下分割伤口？

我们还尝试了图像聚类，但并没有那么顺利。

以下是我们使用的一些测试用例和集群程序。

#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();
}

具有背景的意志1：（两个群集）

Would-1 with Background