Add background movement compensation
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3830c1e411
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3dbe6d1925
1 changed files with 80 additions and 9 deletions
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@ -16,8 +16,9 @@ class Traite_image {
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const static int BLUR_SIZE = 10;
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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 resize_f = 4;
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int theObject[2] = {0,0};
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Rect objectBoundingRectangle = Rect(0,0,0,0);
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@ -51,9 +52,10 @@ class Traite_image {
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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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Mat next_grey;
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resize(input, next, Size(input.size().width/resize_f, input.size().height/resize_f));
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cvtColor(next, next, CV_BGR2GRAY);
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Mat output = input.clone(); // (input.rows, input.cols, CV_32FC2);
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cvtColor(next, next_grey, CV_BGR2GRAY);
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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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@ -61,14 +63,16 @@ class Traite_image {
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ROS_INFO("first done");
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}
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stabiliseImg(prev, next, output);
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// Subtract the 2 last frames and threshold them
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Mat thres;
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absdiff(prev,next,thres);
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threshold(thres, thres, SENSITIVITY_VALUE, 255, THRESH_BINARY);
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//Mat thres;
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//absdiff(prev,next,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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searchForMovement(thres, output);
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//blur(thres, thres, Size(BLUR_SIZE, BLUR_SIZE));
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//threshold(thres, output, SENSITIVITY_VALUE, 255, THRESH_BINARY);
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//searchForMovement(thres, 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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@ -87,6 +91,73 @@ class Traite_image {
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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 prev_grey, cur_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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Point2f srcTri[3];
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Point2f dstTri[3];
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Mat warp_mat( 2, 3, CV_32FC1 );
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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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// translation + rotation only
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Mat T(2, 3, CV_32FC1);
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T = estimateRigidTransform(prev_corner2, cur_corner2, false); // false = rigid transform, no scaling/shearing
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// cv::Mat H = cv::Mat(3,3,T.type());
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// H.at<double>(0,0) = T.at<double>(0,0);
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// H.at<double>(0,1) = T.at<double>(0,1);
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// H.at<double>(0,2) = T.at<double>(0,2);
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// H.at<double>(1,0) = T.at<double>(1,0);
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// H.at<double>(1,1) = T.at<double>(1,1);
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// H.at<double>(1,2) = T.at<double>(1,2);
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// H.at<double>(2,0) = 0.0;
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// H.at<double>(2,1) = 0.0;
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// H.at<double>(2,2) = 1.0;
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// in rare cases no transform is found. We'll just use the last known good transform.
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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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/// Set your 3 points to calculate the Affine Transform
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srcTri[0] = Point2f( 0,0 );
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srcTri[1] = Point2f( prev.cols, 0 );
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srcTri[2] = Point2f( 0, prev.rows );
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dstTri[0] = Point2f( prev.cols / 2, prev.rows / 2 );
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dstTri[1] = Point2f( prev.cols * 3 / 2, prev.rows / 2);
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dstTri[2] = Point2f( prev.cols / 2, prev.rows * 3 / 2 );
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/// Get the Affine Transform
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warp_mat = getAffineTransform( srcTri, dstTri );
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warpAffine(prev,output,T,cv::Size(prev.cols+cur.cols,prev.rows+cur.rows));
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warpAffine(output,output,warp_mat,cv::Size(prev.cols+cur.cols,prev.rows+cur.rows));
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warpAffine(cur,cur,warp_mat,cv::Size(prev.cols+cur.cols,prev.rows+cur.rows));
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//Mat half(output, cv::Rect(0, 0,cur.cols,cur.rows));
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cur.copyTo(output, cur);
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}
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void searchForMovement(Mat thresholdImage, Mat &cameraFeed){
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//notice how we use the '&' operator for objectDetected and cameraFeed. 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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