303 lines
8.6 KiB
C++
303 lines
8.6 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 <opencv/cv.h>
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#include <sstream>
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using namespace cv;
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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 = 30;
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const static int BLUR_SIZE = 10;
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const int HORIZONTAL_BORDER_CROP = 20; // In pixels. Crops the border to reduce the black borders from stabilisation being too noticeable.
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Mat prev;
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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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Rect objectBoundingRectangle = 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;
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image_transport::Subscriber sub;
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Traite_image() : n("~"),it(n) {
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pub = it.advertise("/image_out", 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 (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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//Mat& input = const_cast<Mat&>(bridge_input->image);
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const Mat& input = bridge_input->image;
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Mat next;
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resize(input, next, Size(input.size().width/resize_f, input.size().height/resize_f));
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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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Mat next_stab;
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stabiliseImg(prev, next, next_stab);
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Rect myROI(next_stab.size().width/8, next_stab.size().height/8, next_stab.size().width*3/4, next_stab.size().height*3/4);
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Mat next_stab_cropped = next_stab(myROI);
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Mat prev_cropped = prev(myROI);
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searchForMovement(prev_cropped, next_stab_cropped, output);
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pub.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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//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(Mat prev, Mat cur, Mat &output){
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Mat cur_grey, prev_grey;
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cvtColor(cur, cur_grey, COLOR_BGR2GRAY);
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cvtColor(prev, prev_grey, COLOR_BGR2GRAY);
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// vector from prev to cur
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vector <Point2f> prev_corner, cur_corner;
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vector <Point2f> prev_corner2, cur_corner2;
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vector <uchar> status;
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vector <float> err;
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goodFeaturesToTrack(prev_grey, prev_corner, 200, 0.01, 30);
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calcOpticalFlowPyrLK(prev_grey, cur_grey, prev_corner, cur_corner, status, err);
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// weed out bad 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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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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Mat cur2;
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warpAffine(cur, cur2, T, cur.size(),INTER_NEAREST|WARP_INVERSE_MAP);
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cur2.copyTo(output);
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}
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void searchForMovement(Mat prev, Mat cur, Mat &output){
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Mat cur_grey, prev_grey;
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cur.copyTo(output);
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cvtColor(prev, prev_grey, COLOR_BGR2GRAY);
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cvtColor(cur, cur_grey, COLOR_BGR2GRAY);
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// Subtract the 2 last frames and threshold them
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Mat thres;
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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, Size(BLUR_SIZE, BLUR_SIZE));
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threshold(thres, thres, SENSITIVITY_VALUE, 255, THRESH_BINARY);
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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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Mat temp;
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thres.copyTo(temp);
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//these two vectors needed for output of findContours
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vector< vector<Point> > contours;
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vector<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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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 estimated position.
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objectBoundingRectangle = boundingRect(largestContourVec.at(0));
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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), 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,Scalar(255,0,0),2);
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}
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inline bool isFlowCorrect(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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Vec3b computeColor(float fx, float fy)
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{
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static bool first = true;
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// relative lengths of color transitions:
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// these are chosen based on perceptual similarity
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// (e.g. one can distinguish more shades between red and yellow
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// than between yellow and green)
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const int RY = 15;
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const int YG = 6;
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const int GC = 4;
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const int CB = 11;
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const int BM = 13;
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const int MR = 6;
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const int NCOLS = RY + YG + GC + CB + BM + MR;
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static Vec3i colorWheel[NCOLS];
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if (first)
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{
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int k = 0;
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for (int i = 0; i < RY; ++i, ++k)
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colorWheel[k] = Vec3i(255, 255 * i / RY, 0);
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for (int i = 0; i < YG; ++i, ++k)
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colorWheel[k] = Vec3i(255 - 255 * i / YG, 255, 0);
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for (int i = 0; i < GC; ++i, ++k)
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colorWheel[k] = Vec3i(0, 255, 255 * i / GC);
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for (int i = 0; i < CB; ++i, ++k)
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colorWheel[k] = Vec3i(0, 255 - 255 * i / CB, 255);
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for (int i = 0; i < BM; ++i, ++k)
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colorWheel[k] = Vec3i(255 * i / BM, 0, 255);
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for (int i = 0; i < MR; ++i, ++k)
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colorWheel[k] = Vec3i(255, 0, 255 - 255 * i / MR);
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first = false;
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}
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const float rad = sqrt(fx * fx + fy * fy);
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const float a = atan2(-fy, -fx) / (float)CV_PI;
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const float fk = (a + 1.0f) / 2.0f * (NCOLS - 1);
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const int k0 = static_cast<int>(fk);
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const int k1 = (k0 + 1) % NCOLS;
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const float f = fk - k0;
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Vec3b pix;
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for (int b = 0; b < 3; b++)
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{
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const float col0 = colorWheel[k0][b] / 255.f;
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const float col1 = colorWheel[k1][b] / 255.f;
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float col = (1 - f) * col0 + f * col1;
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if (rad <= 1)
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col = 1 - rad * (1 - col); // increase saturation with radius
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else
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col *= .75; // out of range
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pix[2 - b] = static_cast<uchar>(255.f * col);
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}
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return pix;
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}
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void drawOpticalFlow(const Mat_<Point2f>& flow, Mat& dst, float maxmotion = -1)
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{
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dst.create(flow.size(), CV_8UC3);
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dst.setTo(Scalar::all(0));
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// determine motion range:
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float maxrad = maxmotion;
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if (maxmotion <= 0)
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{
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maxrad = 1;
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for (int y = 0; y < flow.rows; ++y)
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{
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for (int x = 0; x < flow.cols; ++x)
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{
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Point2f u = flow(y, x);
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if (!isFlowCorrect(u))
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continue;
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maxrad = max(maxrad, sqrt(u.x * u.x + u.y * u.y));
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}
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}
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}
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for (int y = 0; y < flow.rows; ++y)
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{
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for (int x = 0; x < flow.cols; ++x)
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{
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Point2f u = flow(y, x);
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if (isFlowCorrect(u))
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dst.at<Vec3b>(y, x) = computeColor(u.x / maxrad, u.y / maxrad);
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}
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}
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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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