OpenCV删除面积小的区域 实现图像二值化分割 标记连通区域
2017年09月30日 14:54:52
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OpenCV删除面积小的区域 实现图像二值化分割 标记连通区域
【尊重原创,转载请注明出处】http://blog.csdn.net/guyuealian/article/details/78142749
之前本博客在Matlab实现了《Matlab形态学图像处理:二值图像分割 标记连通区域和重心位置 删除连通区域》http://blog.csdn.net/guyuealian/article/details/71440949,现在本人使用OpenCV实现这一功能:对图像进行二值化分割,并用“红色矩形”标记连通区域的面积,为了减少噪声的干扰,删除面积小的区域,代码中将连通区域面积(像素个数)不足100的区域认为是噪声点,并将其删除(即置为背景黑色)。本人制作了一个GIF动画图,以便大家观看效果图:
之前本博客在Matlab实现了《Matlab形态学图像处理:二值图像分割 标记连通区域和重心位置 删除连通区域》http://blog.csdn.net/guyuealian/article/details/71440949,现在本人使用OpenCV实现这一功能:对图像进行二值化分割,并用“红色矩形”标记连通区域的面积,为了减少噪声的干扰,删除面积小的区域,代码中将连通区域面积(像素个数)不足100的区域认为是噪声点,并将其删除(即置为背景黑色)。本人制作了一个GIF动画图,以便大家观看效果图:
OpenCV参考代码如下:
- #include "stdafx.h"
- #include <iostream>
- #include<vector>
- #include<algorithm>
- #include <opencv2\opencv.hpp>
- #include <opencv2\highgui\highgui.hpp>
- using namespace std;
- using namespace cv;
- //轮廓按照面积大小升序排序
- bool ascendSort(vector<Point> a, vector<Point> b) {
- return a.size() < b.size();
- }
- //轮廓按照面积大小降序排序
- bool descendSort(vector<Point> a, vector<Point> b) {
- return a.size() > b.size();
- }
- int main() {
- Mat srcImage = imread("D:\\OpencvTest\\123.jpg");
- Mat thresholdImage;
- Mat grayImage;
- cvtColor(srcImage, grayImage, CV_BGR2GRAY);
- threshold(grayImage, thresholdImage, 0, 255, CV_THRESH_OTSU + CV_THRESH_BINARY);
- //Mat resultImage;
- //thresholdImage.copyTo(resultImage);
- vector< vector< Point> > contours; //用于保存所有轮廓信息
- vector< vector< Point> > contours2; //用于保存面积不足100的轮廓
- vector<Point> tempV; //暂存的轮廓
- findContours(thresholdImage, contours, CV_RETR_LIST, CV_CHAIN_APPROX_NONE);
- //cv::Mat labels;
- //int N = connectedComponents(resultImage, labels, 8, CV_16U);
- //findContours(labels, contours2, CV_RETR_EXTERNAL, CV_CHAIN_APPROX_NONE);
- //轮廓按照面积大小进行升序排序
- sort(contours.begin(), contours.end(), ascendSort);//升序排序
- vector<vector<Point> >::iterator itc = contours.begin();
- int i = 0;
- while (itc != contours.end())
- {
- //获得轮廓的矩形边界
- Rect rect = boundingRect(*itc);
- int x = rect.x;
- int y = rect.y;
- int w = rect.width;
- int h = rect.height;
- //绘制轮廓的矩形边界
- cv::rectangle(srcImage, rect, { 0, 0, 255 }, 1);
- //保存图片
- char str[10];
- sprintf(str, "%d.jpg", i++);
- cv::imshow("srcImage", srcImage);
- imwrite(str, srcImage);
- waitKey(1000);
- if (itc->size() < 100)
- {
- //把轮廓面积不足100的区域,放到容器contours2中,
- tempV.push_back(Point(x, y));
- tempV.push_back(Point(x, y+h));
- tempV.push_back(Point(x+w, y+h));
- tempV.push_back(Point(x+w, y));
- contours2.push_back(tempV);
- /*也可以直接用:contours2.push_back(*itc);代替上面的5条语句*/
- //contours2.push_back(*itc);
- //删除轮廓面积不足100的区域,即用黑色填充轮廓面积不足100的区域:
- cv::drawContours(srcImage, contours2, -1, Scalar(0,0,0), CV_FILLED);
- }
- //保存图片
- sprintf(str, "%d.jpg", i++);
- cv::imshow("srcImage", srcImage);
- imwrite(str, srcImage);
- cv::waitKey(100);
- tempV.clear();
- ++itc;
- }
- return 0;
- }
【2】findContours的用法:
- using namespace std;
- using namespace cv;
- using namespace cv::xphoto;
- #include "stdafx.h"
- #include <iostream>
- #include<vector>
- #include<algorithm>
- #include <opencv2\opencv.hpp>
- #include <opencv2\highgui\highgui.hpp>
- using namespace std;
- using namespace cv;
- //轮廓按照面积大小升序排序
- bool ascendSort(vector<Point> a, vector<Point> b) {
- return a.size() < b.size();
- }
- //轮廓按照面积大小降序排序
- bool descendSort(vector<Point> a, vector<Point> b) {
- return a.size() > b.size();
- }
- //自己实现的将灰度图像转为三通道的BGR图像
- cv::Mat gray2BGR(cv::Mat grayImg) {
- if (grayImg.channels() == 3)
- return grayImg;
- cv::Mat bgrImg = cv::Mat::zeros(grayImg.size(), CV_8UC3);
- std::vector<cv::Mat> bgr_channels;
- cv::split(bgrImg, bgr_channels);
- bgr_channels.at(0) = grayImg;
- bgr_channels.at(1) = grayImg;
- bgr_channels.at(2) = grayImg;
- cv::merge(bgr_channels, bgrImg);
- return bgrImg;
- }
- //自定义的drawImage函数的功能类似于OpenCV的drawContours函数
- cv::Mat drawImage(cv::Mat image, vector< vector< Point> > pointV) {
- cv::Mat destImage=image.clone();
- if (destImage.channels()==1)
- {
- destImage = gray2BGR(destImage);
- }
- for (size_t i=0;i<pointV.size();i++)
- {
- for (size_t j = 0; j<pointV.at(i).size(); j++)
- {
- cv::Point point = pointV.at(i).at(j);
- destImage.at<Vec3b>(point) = cv::Vec3b(0, 0, saturate_cast<uchar>(255-i*5));
- }
- }
- return destImage;
- }
- int main() {
- Mat srcImage = imread("D:\\OpencvTest\\mask5.jpg");
- cv::imshow("srcImage", srcImage);
- Mat thresholdImage;
- Mat grayImage;
- cvtColor(srcImage, grayImage, CV_BGR2GRAY);
- threshold(grayImage, thresholdImage, 0, 255, CV_THRESH_OTSU + CV_THRESH_BINARY);
- cv::Mat mask = thresholdImage.clone();
- //(1)CV_RETR_EXTERNAL, CV_CHAIN_APPROX_SIMPLE
- vector< vector< Point> > contours1;
- findContours(mask, contours1, CV_RETR_EXTERNAL, CV_CHAIN_APPROX_SIMPLE);
- cv::Mat destImage = drawImage(mask, contours1);
- imshow("destImage", destImage);
- //(2)CV_RETR_EXTERNAL, CV_CHAIN_APPROX_NONE
- vector< vector< Point> > contours2;
- findContours(mask, contours2, CV_RETR_EXTERNAL, CV_CHAIN_APPROX_NONE);
- cv::Mat destImage2 = drawImage(mask, contours2);
- imshow("destImage2", destImage2);
- //(3)CV_RETR_LIST, CV_CHAIN_APPROX_NONE
- vector< vector< Point> > contours3;
- findContours(mask, contours3, CV_RETR_LIST, CV_CHAIN_APPROX_NONE);
- cv::Mat destImage3 = drawImage(mask, contours3);
- imshow("destImage3", destImage3);
- //(4)CV_RETR_CCOMP, CV_CHAIN_APPROX_NONE
- vector< vector< Point> > contours4;
- findContours(mask, contours4, CV_RETR_CCOMP, CV_CHAIN_APPROX_NONE);
- cv::Mat destImage4 = drawImage(mask, contours4);
- imshow("destImage4", destImage4);
- //cv::Mat imge2 = gray2BGR(mask);
- //drawContours(imge2, contours4, -1, cv::Scalar(0, 0, 255), 1);
- //cv::fillConvexPoly(mask, contours1.at(0), cv::Scalar(255, 255, 255));
- cv::waitKey(0);
- return 0;
- }
如果你觉得该帖子帮到你,还望贵人多多支持,鄙人会再接再厉,继续努力的~
OpenCV删除面积小的区域 实现图像二值化分割 标记连通区域
【尊重原创,转载请注明出处】http://blog.csdn.net/guyuealian/article/details/78142749
之前本博客在Matlab实现了《Matlab形态学图像处理:二值图像分割 标记连通区域和重心位置 删除连通区域》http://blog.csdn.net/guyuealian/article/details/71440949,现在本人使用OpenCV实现这一功能:对图像进行二值化分割,并用“红色矩形”标记连通区域的面积,为了减少噪声的干扰,删除面积小的区域,代码中将连通区域面积(像素个数)不足100的区域认为是噪声点,并将其删除(即置为背景黑色)。本人制作了一个GIF动画图,以便大家观看效果图:
之前本博客在Matlab实现了《Matlab形态学图像处理:二值图像分割 标记连通区域和重心位置 删除连通区域》http://blog.csdn.net/guyuealian/article/details/71440949,现在本人使用OpenCV实现这一功能:对图像进行二值化分割,并用“红色矩形”标记连通区域的面积,为了减少噪声的干扰,删除面积小的区域,代码中将连通区域面积(像素个数)不足100的区域认为是噪声点,并将其删除(即置为背景黑色)。本人制作了一个GIF动画图,以便大家观看效果图:
OpenCV参考代码如下:
- #include "stdafx.h"
- #include <iostream>
- #include<vector>
- #include<algorithm>
- #include <opencv2\opencv.hpp>
- #include <opencv2\highgui\highgui.hpp>
- using namespace std;
- using namespace cv;
- //轮廓按照面积大小升序排序
- bool ascendSort(vector<Point> a, vector<Point> b) {
- return a.size() < b.size();
- }
- //轮廓按照面积大小降序排序
- bool descendSort(vector<Point> a, vector<Point> b) {
- return a.size() > b.size();
- }
- int main() {
- Mat srcImage = imread("D:\\OpencvTest\\123.jpg");
- Mat thresholdImage;
- Mat grayImage;
- cvtColor(srcImage, grayImage, CV_BGR2GRAY);
- threshold(grayImage, thresholdImage, 0, 255, CV_THRESH_OTSU + CV_THRESH_BINARY);
- //Mat resultImage;
- //thresholdImage.copyTo(resultImage);
- vector< vector< Point> > contours; //用于保存所有轮廓信息
- vector< vector< Point> > contours2; //用于保存面积不足100的轮廓
- vector<Point> tempV; //暂存的轮廓
- findContours(thresholdImage, contours, CV_RETR_LIST, CV_CHAIN_APPROX_NONE);
- //cv::Mat labels;
- //int N = connectedComponents(resultImage, labels, 8, CV_16U);
- //findContours(labels, contours2, CV_RETR_EXTERNAL, CV_CHAIN_APPROX_NONE);
- //轮廓按照面积大小进行升序排序
- sort(contours.begin(), contours.end(), ascendSort);//升序排序
- vector<vector<Point> >::iterator itc = contours.begin();
- int i = 0;
- while (itc != contours.end())
- {
- //获得轮廓的矩形边界
- Rect rect = boundingRect(*itc);
- int x = rect.x;
- int y = rect.y;
- int w = rect.width;
- int h = rect.height;
- //绘制轮廓的矩形边界
- cv::rectangle(srcImage, rect, { 0, 0, 255 }, 1);
- //保存图片
- char str[10];
- sprintf(str, "%d.jpg", i++);
- cv::imshow("srcImage", srcImage);
- imwrite(str, srcImage);
- waitKey(1000);
- if (itc->size() < 100)
- {
- //把轮廓面积不足100的区域,放到容器contours2中,
- tempV.push_back(Point(x, y));
- tempV.push_back(Point(x, y+h));
- tempV.push_back(Point(x+w, y+h));
- tempV.push_back(Point(x+w, y));
- contours2.push_back(tempV);
- /*也可以直接用:contours2.push_back(*itc);代替上面的5条语句*/
- //contours2.push_back(*itc);
- //删除轮廓面积不足100的区域,即用黑色填充轮廓面积不足100的区域:
- cv::drawContours(srcImage, contours2, -1, Scalar(0,0,0), CV_FILLED);
- }
- //保存图片
- sprintf(str, "%d.jpg", i++);
- cv::imshow("srcImage", srcImage);
- imwrite(str, srcImage);
- cv::waitKey(100);
- tempV.clear();
- ++itc;
- }
- return 0;
- }
【2】findContours的用法:
- using namespace std;
- using namespace cv;
- using namespace cv::xphoto;
- #include "stdafx.h"
- #include <iostream>
- #include<vector>
- #include<algorithm>
- #include <opencv2\opencv.hpp>
- #include <opencv2\highgui\highgui.hpp>
- using namespace std;
- using namespace cv;
- //轮廓按照面积大小升序排序
- bool ascendSort(vector<Point> a, vector<Point> b) {
- return a.size() < b.size();
- }
- //轮廓按照面积大小降序排序
- bool descendSort(vector<Point> a, vector<Point> b) {
- return a.size() > b.size();
- }
- //自己实现的将灰度图像转为三通道的BGR图像
- cv::Mat gray2BGR(cv::Mat grayImg) {
- if (grayImg.channels() == 3)
- return grayImg;
- cv::Mat bgrImg = cv::Mat::zeros(grayImg.size(), CV_8UC3);
- std::vector<cv::Mat> bgr_channels;
- cv::split(bgrImg, bgr_channels);
- bgr_channels.at(0) = grayImg;
- bgr_channels.at(1) = grayImg;
- bgr_channels.at(2) = grayImg;
- cv::merge(bgr_channels, bgrImg);
- return bgrImg;
- }
- //自定义的drawImage函数的功能类似于OpenCV的drawContours函数
- cv::Mat drawImage(cv::Mat image, vector< vector< Point> > pointV) {
- cv::Mat destImage=image.clone();
- if (destImage.channels()==1)
- {
- destImage = gray2BGR(destImage);
- }
- for (size_t i=0;i<pointV.size();i++)
- {
- for (size_t j = 0; j<pointV.at(i).size(); j++)
- {
- cv::Point point = pointV.at(i).at(j);
- destImage.at<Vec3b>(point) = cv::Vec3b(0, 0, saturate_cast<uchar>(255-i*5));
- }
- }
- return destImage;
- }
- int main() {
- Mat srcImage = imread("D:\\OpencvTest\\mask5.jpg");
- cv::imshow("srcImage", srcImage);
- Mat thresholdImage;
- Mat grayImage;
- cvtColor(srcImage, grayImage, CV_BGR2GRAY);
- threshold(grayImage, thresholdImage, 0, 255, CV_THRESH_OTSU + CV_THRESH_BINARY);
- cv::Mat mask = thresholdImage.clone();
- //(1)CV_RETR_EXTERNAL, CV_CHAIN_APPROX_SIMPLE
- vector< vector< Point> > contours1;
- findContours(mask, contours1, CV_RETR_EXTERNAL, CV_CHAIN_APPROX_SIMPLE);
- cv::Mat destImage = drawImage(mask, contours1);
- imshow("destImage", destImage);
- //(2)CV_RETR_EXTERNAL, CV_CHAIN_APPROX_NONE
- vector< vector< Point> > contours2;
- findContours(mask, contours2, CV_RETR_EXTERNAL, CV_CHAIN_APPROX_NONE);
- cv::Mat destImage2 = drawImage(mask, contours2);
- imshow("destImage2", destImage2);
- //(3)CV_RETR_LIST, CV_CHAIN_APPROX_NONE
- vector< vector< Point> > contours3;
- findContours(mask, contours3, CV_RETR_LIST, CV_CHAIN_APPROX_NONE);
- cv::Mat destImage3 = drawImage(mask, contours3);
- imshow("destImage3", destImage3);
- //(4)CV_RETR_CCOMP, CV_CHAIN_APPROX_NONE
- vector< vector< Point> > contours4;
- findContours(mask, contours4, CV_RETR_CCOMP, CV_CHAIN_APPROX_NONE);
- cv::Mat destImage4 = drawImage(mask, contours4);
- imshow("destImage4", destImage4);
- //cv::Mat imge2 = gray2BGR(mask);
- //drawContours(imge2, contours4, -1, cv::Scalar(0, 0, 255), 1);
- //cv::fillConvexPoly(mask, contours1.at(0), cv::Scalar(255, 255, 255));
- cv::waitKey(0);
- return 0;
- }
如果你觉得该帖子帮到你,还望贵人多多支持,鄙人会再接再厉,继续努力的~