前几天老大给了个任务,让我帮slam组写一个基于深度摄像头的障碍物检测,捣鼓了两天弄出来了,效果还不错,就在这里记一下了。
代码的核心思路是首先通过二值化,将一米之外的安全距离置零不考虑,然后通过开运算去除掉一些噪点(这个后来发现不一定有必要),在求出所有障碍物的凸包,这个时候要计算面积,当面积小于一定的阈值的时候不予考虑,最终输出障碍物的凸包坐标。
//find_obstacle函数是获取深度图障碍物的函数,返回值是每个障碍物凸包的坐标,参数一depth是realsense返回的深度图(ushort型),
//参数二thresh和参数三max_thresh,是二值化的参数,参数四是凸包的最小有效面积,小于这个面积的障碍物可以视为噪点。//函数首先筛选掉距离大于安全距离的点,然后进行阀值化和开运算减少一下噪点,用findContours得到轮廓图,最后用convexHull得到每个障碍物的凸包,最后返回坐标//mask_depth函数是对深度图二值化,第一个参数image是原图,第二个参数th是目标图,第三个参数throld是最大距离,单位是mm,大于这个距离
//即为安全,不用考虑。#include <iostream>#include <opencv2/core/core.hpp>#include <opencv2/highgui/highgui.hpp>#include "RSWrapper.h"#include "opencv2/imgproc/imgproc.hpp"using namespace std;
using namespace cv;void mask_depth(Mat &image,Mat& th,int throld=1000){ int nr = image.rows; // number of rows int nc = image.cols; // number of columns for (int i = 0; i<nr; i++) { for (int j = 0; j<nc; j++) { if (image.at<ushort>(i, j)>throld) th.at<ushort>(i, j) = 0; } }}vector<vector<Point> > find_obstacle(Mat &depth, int thresh = 20, int max_thresh = 255, int area = 500){ Mat dep; depth.copyTo(dep); mask_depth(depth, dep, 1000); dep.convertTo(dep, CV_8UC1, 1.0 / 16); //imshow("color", color); imshow("depth", dep); Mat element = getStructuringElement(MORPH_RECT, Size(15, 15));//核的大小可适当调整 Mat out; //进行开操作 morphologyEx(dep, out, MORPH_OPEN, element); //dilate(dhc, out, element);//显示效果图
imshow("opencv", out); Mat src_copy = dep.clone(); Mat threshold_output; vector<vector<Point> > contours; vector<Vec4i> hierarchy; RNG rng(12345); /// 对图像进行二值化 threshold(dep, threshold_output, thresh, 255, CV_THRESH_BINARY); //mask_depth(src, threshold_output); /// 寻找轮廓 findContours(threshold_output, contours, hierarchy, CV_RETR_TREE, CV_CHAIN_APPROX_SIMPLE, Point(0, 0));/// 对每个轮廓计算其凸包
vector<vector<Point> >hull(contours.size()); vector<vector<Point> > result; for (int i = 0; i < contours.size(); i++) { convexHull(Mat(contours[i]), hull[i], false); }/// 绘出轮廓及其凸包
Mat drawing = Mat::zeros(threshold_output.size(), CV_8UC3); for (int i = 0; i< contours.size(); i++) { if (contourArea(contours[i]) < area)//面积小于area的凸包,可忽略 continue; result.push_back(hull[i]); Scalar color = Scalar(rng.uniform(0, 255), rng.uniform(0, 255), rng.uniform(0, 255)); drawContours(drawing, contours, i, color, 1, 8, vector<Vec4i>(), 0, Point()); drawContours(drawing, hull, i, color, 1, 8, vector<Vec4i>(), 0, Point()); } imshow("contours", drawing); return result;}int main(int argc, char* argv[]){ Mat dhc; Mat dep; int idxImageRes = 1, idxFrameRate = 30; RSWrapper depthCam(idxImageRes, idxImageRes, idxFrameRate, idxFrameRate); if (!depthCam.init()) { std::cerr << "Init. RealSense Failure!" << std::endl; return -1; }while (true)
{ //Get RGB-D Images cv::Mat color, depth; bool ret = depthCam.capture(color, depth); if (!ret) { std::cerr << "Get realsense camera data failure!" << std::endl; break; } vector<vector<Point> > result; result = find_obstacle(depth, 20, 255, 500);if (cvWaitKey(1) == 27)
break; }depthCam.release();
}