lhark/papillon
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JCC 2016-06-16 22:51:17 +02:00
parent 658cd286ba
commit 1d4447e29a
1 changed files with 10 additions and 59 deletions
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65
src/papillon.cpp
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@ -56,17 +56,16 @@ class Traite_image {
return;
}
//cv::Mat& input = const_cast<cv::Mat&>(bridge_input->image);
const cv::Mat& input = bridge_input->image;
cv::Mat next;
resize(input, next, cv::Size(input.size().width/resize_f, input.size().height/resize_f));
cv::Mat output;// = input.clone(); // (input.rows, input.cols, CV_32FC2);
cv::Mat output;
if (first) {
for (int i = 0; i < NB_FRAME_DROP; ++i) {
prevs.push_back(next.clone());
}
first = false;
ROS_INFO("first done");
ROS_INFO("Ready");
}
cv::Mat next_stab;
@ -85,22 +84,11 @@ class Traite_image {
pub_img.publish(cv_bridge::CvImage(msg->header, "rgb8", output).toImageMsg());
pub_thres.publish(cv_bridge::CvImage(msg->header, "mono8", closed_thres).toImageMsg());
// bridge_input is handled by a smart-pointer. No explicit delete needed.
prevs.pop_back();
prevs.insert(prevs.begin(), next.clone());
}
//int to string helper function
string intToString(int number){
//this function has a number input and string output
std::stringstream ss;
ss << number;
return ss.str();
}
void stabiliseImg(cv::Mat prev, cv::Mat cur, cv::Mat &output){
cv::Mat cur_grey, prev_grey;
cv::cvtColor(cur, cur_grey, cv::COLOR_BGR2GRAY);
@ -115,7 +103,7 @@ class Traite_image {
cv::goodFeaturesToTrack(prev_grey, prev_corner, 200, 0.01, 30);
cv::calcOpticalFlowPyrLK(prev_grey, cur_grey, prev_corner, cur_corner, status, err);
// weed out bad cv::Matches
// weed out bad matches
for(size_t i=0; i < status.size(); i++) {
if(status[i]) {
prev_corner2.push_back(prev_corner[i]);
@ -144,64 +132,40 @@ class Traite_image {
cv::cvtColor(cur, cur_grey, cv::COLOR_BGR2GRAY);
cv::GaussianBlur(prev_grey, prev_grey, cv::Size(BLUR_Size,BLUR_Size), 3.0);
cv::GaussianBlur(cur_grey, cur_grey, cv::Size(BLUR_Size,BLUR_Size), 3.0);
//blur(prev_grey, prev_grey, cv::Size(BLUR_Size, BLUR_Size));
//blur(cur_grey, cur_grey, cv::Size(BLUR_Size, BLUR_Size));
// Subtract the 2 last frames and threshold them
cv::Mat thres;
cv::absdiff(prev_grey,cur_grey,thres);
// threshold(thres, thres, SENSITIVITY_VALUE, 255, THRESH_BINARY);
// // Blur to eliminate noise
// blur(thres, thres, cv::Size(BLUR_Size, BLUR_Size));
cv::Mat element = getStructuringElement( cv::MORPH_ELLIPSE,
cv::Size( 2*ERODE_SIZE + 1, 2*ERODE_SIZE+1 ),
cv::Point( ERODE_SIZE, ERODE_SIZE ) );
// Apply the dilation operation
// Apply the erode operation
cv::erode(thres, thres, element );
thres.copyTo(out2);
cv::threshold(thres, thres, SENSITIVITY_VALUE, 255, cv::THRESH_BINARY);
// Intermediate output
thres.copyTo(out2);
cv::Mat closed_thres;
cv::Mat structuringElement = getStructuringElement(cv::MORPH_ELLIPSE, cv::Size(CLOSE_SIZE, CLOSE_SIZE));
cv::morphologyEx( thres, closed_thres, cv::MORPH_CLOSE, structuringElement );
// dilated_thres.copyTo(output);
//closed_thres.copyTo(output);
//notice how we use the '&' operator for objectDetected and output. This is because we wish
//to take the values passed into the function and manipulate them, rather than just working with a copy.
//eg. we draw to the output to be displayed in the main() function.
bool objectDetected = false;
cv::Mat temp;
closed_thres.copyTo(temp);
//these two vectors needed for output of findContours
vector< vector<cv::Point> > contours;
vector<cv::Vec4i> hierarchy;
//find contours of filtered image using openCV findContours function
//findContours(temp,contours,hierarchy,CV_RETR_CCOMP,CV_CHAIN_APPROX_SIMPLE );// retrieves all contours
cv::findContours(temp,contours,hierarchy,CV_RETR_EXTERNAL,CV_CHAIN_APPROX_SIMPLE );// retrieves external contours
//if contours vector is not empty, we have found some objects
if(contours.size()>0)objectDetected=true;
else objectDetected = false;
if(objectDetected){
//the largest contour is found at the end of the contours vector
//we will simply assume that the biggest contour is the object we are looking for.
//vector< vector<Point> > largestContourVec;
//largestContourVec.push_back(contours.at(contours.size()-1));
//make a bounding rectangle around the largest contour then find its centroid
//this will be the object's final esticv::Mated position.
if(contours.size()>0){
vector<cv::Rect> nc_rects; // Non connected rectangles
for(size_t i=0; i<contours.size();i++)
{
nc_rects.push_back(cv::boundingRect(contours[i]));
//cv::rectangle(output, objectBoundingRectangle, cv::Scalar(0, 255, 0), 2);
}
vector<cv::Rect> c_rects; // Connected rectangles
@ -211,7 +175,6 @@ class Traite_image {
cv::rectangle(output, rect, cv::Scalar(0, 255, 0), 2);
cv::Rect objBRect = c_rects.front();
//cv::rectangle(output, objBRect, cv::Scalar(0, 255, 0), 2);
papillon::BoundingBox bbox = papillon::BoundingBox();
bbox.x = objBRect.x / (float)cur.size().width;
bbox.y = objBRect.y / (float)cur.size().height;
@ -220,20 +183,8 @@ class Traite_image {
pub_cmd.publish(bbox);
}
}
//make some temp x and y variables so we dont have to type out so much
//~ int x = objectBoundingRectangle.x;
//~ int y = objectBoundingRectangle.y;
//~ int width = objectBoundingRectangle.width;
//~ int height = objectBoundingRectangle.height;
//draw a rectangle around the object
//rectangle(output, Point(x,y), Point(x+width, y+height), cv::Scalar(0, 255, 0), 2);
//write the position of the object to the screen
//putText(output,"Tracking object at (" + intToString(x)+","+intToString(y)+")",Point(x,y),1,1,cv::Scalar(255,0,0),2);
}
void cleanBBoxes(vector<cv::Rect> nc_rects, cv::Size img, vector<cv::Rect> &c_rects) {
int max = 0;
for (auto r : nc_rects) {