Threshold motion detection not working well
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lhark
parent
8bd67f3203
commit
cb55c6dcd3
129
src/papillon.cpp
129
src/papillon.cpp
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@ -20,7 +20,7 @@ class Traite_image {
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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 = 4;
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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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@ -63,16 +63,10 @@ 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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Mat next_stab;
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stabiliseImg(prev, next, next_stab);
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searchForMovement(prev, next_stab, 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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// Blur to eliminate noise
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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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@ -92,12 +86,9 @@ class Traite_image {
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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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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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//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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@ -116,88 +107,40 @@ class Traite_image {
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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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Mat T = estimateRigidTransform(prev_corner2, cur_corner2, false); // false = rigid transform, no scaling/shearing
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Mat T = estimateRigidTransform(prev_corner2, cur_corner2, true); // false = rigid transform, no scaling/shearing
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ROS_INFO("coucou1");
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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 = T.clone();
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}
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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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T = last_T.clone();*/
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ROS_INFO("coucou2");
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// decompose T
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double dx = T.at<double>(0,2);
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double dy = T.at<double>(1,2);
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double da = atan2(T.at<double>(1,0), T.at<double>(0,0));
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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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Mat cur2;
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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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warpAffine(cur, cur2, T, cur.size(),INTER_NEAREST|WARP_INVERSE_MAP);
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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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Mat T1(2, 3, CV_32FC1);
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T1.at<double>(0,0) = cos(da);
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T1.at<double>(0,1) = -sin(da);
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T1.at<double>(1,0) = sin(da);
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T1.at<double>(1,1) = cos(da);
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T1.at<double>(0,2) = dx;
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T1.at<double>(1,2) = dy;
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Mat cur2;
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warpAffine(cur, cur2, T1, cur.size());
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int vert_border = HORIZONTAL_BORDER_CROP * prev.rows / prev.cols; // get the aspect ratio correct
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cur2 = cur2(Range(vert_border, cur2.rows-vert_border), Range(HORIZONTAL_BORDER_CROP, cur2.cols-HORIZONTAL_BORDER_CROP));
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// Resize cur2 back to cur size, for better side by side comparison
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resize(cur2, cur2, cur.size());
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output = cur2.clone();
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/*
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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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*/
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cur2.copyTo(output);
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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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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 cameraFeed to be displayed in the main() function.
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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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thresholdImage.copyTo(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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@ -228,14 +171,14 @@ class Traite_image {
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int y = theObject[1];
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//draw some crosshairs around the object
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circle(cameraFeed,Point(x,y),20,Scalar(0,255,0),2);
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line(cameraFeed,Point(x,y),Point(x,y-25),Scalar(0,255,0),2);
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line(cameraFeed,Point(x,y),Point(x,y+25),Scalar(0,255,0),2);
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line(cameraFeed,Point(x,y),Point(x-25,y),Scalar(0,255,0),2);
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line(cameraFeed,Point(x,y),Point(x+25,y),Scalar(0,255,0),2);
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circle(output,Point(x,y),20,Scalar(0,255,0),2);
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line(output,Point(x,y),Point(x,y-25),Scalar(0,255,0),2);
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line(output,Point(x,y),Point(x,y+25),Scalar(0,255,0),2);
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line(output,Point(x,y),Point(x-25,y),Scalar(0,255,0),2);
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line(output,Point(x,y),Point(x+25,y),Scalar(0,255,0),2);
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//write the position of the object to the screen
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putText(cameraFeed,"Tracking object at (" + intToString(x)+","+intToString(y)+")",Point(x,y),1,1,Scalar(255,0,0),2);
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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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