227 lines
7.6 KiB
C++
227 lines
7.6 KiB
C++
#include "ros/ros.h"
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#include <image_transport/image_transport.h>
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#include <cv_bridge/cv_bridge.h>
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#include <sensor_msgs/image_encodings.h>
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#include <papillon/BoundingBox.h>
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#include <opencv/cv.h>
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#include <sstream>
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using namespace std;
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class Traite_image {
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public:
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const static int SENSITIVITY_VALUE = 40;
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const static int BLUR_Size = 15;
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cv::Mat prev;
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cv::Mat last_T;
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bool first = true;
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int resize_f = 1;
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int theObject[2] = {0,0};
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cv::Rect objectBoundingRectangle = cv::Rect(0,0,0,0);
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ros::NodeHandle n;
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image_transport::ImageTransport it;
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image_transport::Publisher pub_img;
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ros::Publisher pub_cmd;
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image_transport::Subscriber sub;
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Traite_image() : n("~"),it(n) {
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pub_img = it.advertise("/image_out", 1);
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pub_cmd = n.advertise<papillon::BoundingBox>("/bbox", 1);
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sub = it.subscribe("/usb_cam/image_raw", 1, [this](const sensor_msgs::ImageConstPtr& img) -> void { this->on_image(img);},ros::VoidPtr(),image_transport::TransportHints("compressed"));
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}
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// This processes an image and publishes the result.
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void on_image(const sensor_msgs::ImageConstPtr& msg) {
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cv_bridge::CvImageConstPtr bridge_input;
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try {
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bridge_input = cv_bridge::toCvShare(msg,sensor_msgs::image_encodings::RGB8);
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}
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catch (cv::Exception& e) {
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std::ostringstream errstr;
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errstr << "cv_bridge exception caught: " << e.what();
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return;
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}
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//cv::Mat& input = const_cast<cv::Mat&>(bridge_input->image);
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const cv::Mat& input = bridge_input->image;
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cv::Mat next;
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resize(input, next, cv::Size(input.size().width/resize_f, input.size().height/resize_f));
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cv::Mat output;// = input.clone(); // (input.rows, input.cols, CV_32FC2);
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//ROS_INFO("got input");
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if (first) {
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prev = next.clone();
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first = false;
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ROS_INFO("first done");
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}
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cv::Mat next_stab;
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stabiliseImg(prev, next, next_stab);
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int crop_ratio = 6;
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float crop_x = next_stab.size().width/crop_ratio;
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float crop_y = next_stab.size().height/crop_ratio;
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float crop_w = next_stab.size().width*(1-2.0/crop_ratio);
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float crop_h = next_stab.size().height*(1-2.0/crop_ratio);
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cv::Rect myROI(crop_x, crop_y, crop_w, crop_h);
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cv::Mat next_stab_cropped = next_stab(myROI);
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cv::Mat prev_cropped = prev(myROI);
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searchForMovement(prev_cropped, next_stab_cropped, output);
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pub_img.publish(cv_bridge::CvImage(msg->header, "rgb8", output).toImageMsg());
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// bridge_input is handled by a smart-pointer. No explicit delete needed.
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//droneTracking(Rect(Point(0,0), output.size()));
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//ROS_INFO("pub");
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prev = next.clone();
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}
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//int to string helper function
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string intToString(int number){
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//this function has a number input and string output
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std::stringstream ss;
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ss << number;
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return ss.str();
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}
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void stabiliseImg(cv::Mat prev, cv::Mat cur, cv::Mat &output){
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cv::Mat cur_grey, prev_grey;
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cv::cvtColor(cur, cur_grey, cv::COLOR_BGR2GRAY);
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cv::cvtColor(prev, prev_grey, cv::COLOR_BGR2GRAY);
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// vector from prev to cur
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vector <cv::Point2f> prev_corner, cur_corner;
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vector <cv::Point2f> prev_corner2, cur_corner2;
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vector <uchar> status;
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vector <float> err;
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cv::goodFeaturesToTrack(prev_grey, prev_corner, 200, 0.01, 30);
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cv::calcOpticalFlowPyrLK(prev_grey, cur_grey, prev_corner, cur_corner, status, err);
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// weed out bad cv::Matches
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for(size_t i=0; i < status.size(); i++) {
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if(status[i]) {
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prev_corner2.push_back(prev_corner[i]);
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cur_corner2.push_back(cur_corner[i]);
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}
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}
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cv::Mat T = estimateRigidTransform(prev_corner2, cur_corner2, true); // false = rigid transform, no scaling/shearing
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if(T.data == NULL) {
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last_T.copyTo(T);
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}
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T.copyTo(last_T);
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cv::Mat cur2;
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cv::warpAffine(cur, cur2, T, cur.size(),cv::INTER_CUBIC|cv::WARP_INVERSE_MAP);
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cur2.copyTo(output);
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}
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void searchForMovement(cv::Mat prev, cv::Mat cur, cv::Mat &output){
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cv::Mat cur_grey, prev_grey;
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cur.copyTo(output);
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cv::cvtColor(prev, prev_grey, cv::COLOR_BGR2GRAY);
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cv::cvtColor(cur, cur_grey, cv::COLOR_BGR2GRAY);
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cv::GaussianBlur(prev_grey, prev_grey, cv::Size(BLUR_Size,BLUR_Size), 3.0);
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cv::GaussianBlur(cur_grey, cur_grey, cv::Size(BLUR_Size,BLUR_Size), 3.0);
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//blur(prev_grey, prev_grey, cv::Size(BLUR_Size, BLUR_Size));
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//blur(cur_grey, cur_grey, cv::Size(BLUR_Size, BLUR_Size));
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// Subtract the 2 last frames and threshold them
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cv::Mat thres;
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cv::absdiff(prev_grey,cur_grey,thres);
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// threshold(thres, thres, SENSITIVITY_VALUE, 255, THRESH_BINARY);
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// // Blur to eliminate noise
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// blur(thres, thres, cv::Size(BLUR_Size, BLUR_Size));
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cv::threshold(thres, thres, SENSITIVITY_VALUE, 255, cv::THRESH_BINARY);
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//~ int dilation_Size = 2;
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//~ cv::Mat element = getStructuringElement( MORPH_ELLIPSE,
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//~ cv::Size( 2*dilation_Size + 1, 2*dilation_Size+1 ),
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//~ Point( dilation_Size, dilation_Size ) );
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//~ // Apply the dilation operation
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//~ cv::Mat dilated_thres;
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//~ dilate(thres, dilated_thres, element );
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//~
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//~ dilated_thres.copyTo(output);
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cv::Mat closed_thres;
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cv::Mat structuringElement = getStructuringElement(cv::MORPH_ELLIPSE, cv::Size(40, 40));
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cv::morphologyEx( thres, closed_thres, cv::MORPH_CLOSE, structuringElement );
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//closed_thres.copyTo(output);
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//notice how we use the '&' operator for objectDetected and output. This is because we wish
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//to take the values passed into the function and manipulate them, rather than just working with a copy.
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//eg. we draw to the output to be displayed in the main() function.
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bool objectDetected = false;
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cv::Mat temp;
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closed_thres.copyTo(temp);
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//these two vectors needed for output of findContours
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vector< vector<cv::Point> > contours;
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vector<cv::Vec4i> hierarchy;
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//find contours of filtered image using openCV findContours function
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//findContours(temp,contours,hierarchy,CV_RETR_CCOMP,CV_CHAIN_APPROX_SIMPLE );// retrieves all contours
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cv::findContours(temp,contours,hierarchy,CV_RETR_EXTERNAL,CV_CHAIN_APPROX_SIMPLE );// retrieves external contours
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//if contours vector is not empty, we have found some objects
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if(contours.size()>0)objectDetected=true;
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else objectDetected = false;
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if(objectDetected){
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//the largest contour is found at the end of the contours vector
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//we will simply assume that the biggest contour is the object we are looking for.
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//vector< vector<Point> > largestContourVec;
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//largestContourVec.push_back(contours.at(contours.size()-1));
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//make a bounding rectangle around the largest contour then find its centroid
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//this will be the object's final esticv::Mated position.
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for(int i=0; i<contours.size();i++)
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{
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objectBoundingRectangle = cv::boundingRect(contours[i]);
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cv::rectangle(output, objectBoundingRectangle, cv::Scalar(0, 255, 0), 2);
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}
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}
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//make some temp x and y variables so we dont have to type out so much
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//~ int x = objectBoundingRectangle.x;
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//~ int y = objectBoundingRectangle.y;
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//~ int width = objectBoundingRectangle.width;
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//~ int height = objectBoundingRectangle.height;
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//draw a rectangle around the object
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//rectangle(output, Point(x,y), Point(x+width, y+height), cv::Scalar(0, 255, 0), 2);
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//write the position of the object to the screen
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//putText(output,"Tracking object at (" + intToString(x)+","+intToString(y)+")",Point(x,y),1,1,cv::Scalar(255,0,0),2);
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}
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inline bool isFlowCorrect(cv::Point2f u)
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{
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return !cvIsNaN(u.x) && !cvIsNaN(u.y) && fabs(u.x) < 1e9 && fabs(u.y) < 1e9;
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}
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};
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int main(int argc, char **argv)
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{
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ros::init(argc, argv, "test_opencv");
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Traite_image dataset=Traite_image();
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ros::spin();
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return 0;
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}
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