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lhark 2016-06-02 16:47:55 +02:00
commit d8a97dbbdd
1 changed files with 65 additions and 7 deletions
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src/papillon.cpp
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@ -3,6 +3,7 @@
#include <cv_bridge/cv_bridge.h> #include <cv_bridge/cv_bridge.h>
#include <sensor_msgs/image_encodings.h> #include <sensor_msgs/image_encodings.h>
#include <geometry_msgs/Twist.h> #include <geometry_msgs/Twist.h>
#include <typeinfo>
#include <opencv/cv.h> #include <opencv/cv.h>
@ -13,14 +14,15 @@ using namespace std;
class Traite_image { class Traite_image {
public: public:
const static int SENSITIVITY_VALUE = 40; const static int THRESHOLD_DETECT_SENSITIVITY = 10;
const static int BLUR_SIZE = 10; const static int BLUR_SIZE = 5;
const static int THRESHOLD_MOV = 5;
Mat prev; Mat prev;
Mat last_T; Mat last_T;
bool first = true; bool first = true;
int resize_f = 1; int resize_f = 2;
int theObject[2] = {0,0}; int theObject[2] = {0,0};
Rect objectBoundingRectangle = Rect(0,0,0,0); Rect objectBoundingRectangle = Rect(0,0,0,0);
@ -142,10 +144,10 @@ class Traite_image {
// Subtract the 2 last frames and threshold them // Subtract the 2 last frames and threshold them
Mat thres; Mat thres;
absdiff(prev_grey,cur_grey,thres); absdiff(prev_grey,cur_grey,thres);
threshold(thres, thres, SENSITIVITY_VALUE, 255, THRESH_BINARY); threshold(thres, thres, THRESHOLD_DETECT_SENSITIVITY, 255, THRESH_BINARY);
// Blur to eliminate noise // Blur to eliminate noise
blur(thres, thres, Size(BLUR_SIZE, BLUR_SIZE)); blur(thres, thres, Size(BLUR_SIZE, BLUR_SIZE));
threshold(thres, thres, SENSITIVITY_VALUE, 255, THRESH_BINARY); threshold(thres, thres, THRESHOLD_DETECT_SENSITIVITY, 255, THRESH_BINARY);
//notice how we use the '&' operator for objectDetected and output. This is because we wish //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. //to take the values passed into the function and manipulate them, rather than just working with a copy.
@ -186,6 +188,62 @@ class Traite_image {
putText(output,"Tracking object at (" + intToString(x)+","+intToString(y)+")",Point(x,y),1,1,Scalar(255,0,0),2); putText(output,"Tracking object at (" + intToString(x)+","+intToString(y)+")",Point(x,y),1,1,Scalar(255,0,0),2);
} }
void searchForMovementOptFlow(Mat prev, Mat cur, Mat &output){
Mat cur_grey, prev_grey;
cur.copyTo(output);
cvtColor(prev, prev_grey, COLOR_BGR2GRAY);
cvtColor(cur, cur_grey, COLOR_BGR2GRAY);
Mat flow;
calcOpticalFlowFarneback(prev_grey, cur_grey, flow, 0.5, 3, 15, 3, 5, 1.2, 0);
vector<Mat> flow_coord(2);
Mat flow_norm, angle;
split(flow, flow_coord);
cartToPolar(flow_coord[0], flow_coord[1], flow_norm, angle);
//threshold(flow_norm, flow_norm, THRESHOLD_DETECT_SENSITIVITY, 255, THRESH_BINARY);
// Blur to eliminate noise
blur(flow_norm, flow_norm, Size(BLUR_SIZE, BLUR_SIZE));
threshold(flow_norm, flow_norm, THRESHOLD_DETECT_SENSITIVITY, 255, THRESH_BINARY);
flow_norm.convertTo(flow_norm, CV_8U);
bool objectDetected = false;
Mat temp;
flow_norm.copyTo(temp);
//these two vectors needed for output of findContours
vector< vector<Point> > contours;
vector<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
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 estimated position.
objectBoundingRectangle = boundingRect(largestContourVec.at(0));
}
//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), 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,Scalar(255,0,0),2);
}
inline bool isFlowCorrect(Point2f u) inline bool isFlowCorrect(Point2f u)
{ {
return !cvIsNaN(u.x) && !cvIsNaN(u.y) && fabs(u.x) < 1e9 && fabs(u.y) < 1e9; return !cvIsNaN(u.x) && !cvIsNaN(u.y) && fabs(u.x) < 1e9 && fabs(u.y) < 1e9;
@ -199,11 +257,11 @@ class Traite_image {
geometry_msgs::Twist twist = geometry_msgs::Twist(); geometry_msgs::Twist twist = geometry_msgs::Twist();
if(centre_rect.x < centre_image.x) if(centre_rect.x < centre_image.x-THRESHOLD_MOV)
{ {
twist.angular.z = 0.2; twist.angular.z = 0.2;
} }
else if(centre_rect.x > centre_image.x) else if(centre_rect.x > centre_image.x+THRESHOLD_MOV)
{ {
twist.angular.z = -0.2; twist.angular.z = -0.2;
} }