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A bit of cleaning, simul arg

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lhark 2016-06-02 18:06:24 +02:00
parent d8a97dbbdd
commit c608313530
1 changed files with 23 additions and 15 deletions
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38
src/papillon.cpp
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@ -17,12 +17,13 @@ class Traite_image {
const static int THRESHOLD_DETECT_SENSITIVITY = 10; const static int THRESHOLD_DETECT_SENSITIVITY = 10;
const static int BLUR_SIZE = 5; const static int BLUR_SIZE = 5;
const static int THRESHOLD_MOV = 5; const static int THRESHOLD_MOV = 5;
const static int crop_ratio = 8;
Mat prev; Mat prev;
Mat last_T; Mat last_T;
bool first = true; bool first = true;
int resize_f = 2; int resize_f = 1;
int theObject[2] = {0,0}; int theObject[2] = {0,0};
Rect objectBoundingRectangle = Rect(0,0,0,0); Rect objectBoundingRectangle = Rect(0,0,0,0);
@ -35,10 +36,22 @@ class Traite_image {
image_transport::Subscriber sub; image_transport::Subscriber sub;
Traite_image() : n("~"),it(n) { Traite_image(bool sim) : n("~"),it(n) {
pub_img = it.advertise("/image_out", 1); String img_out, cmd_out, img_in;
pub_cmd = n.advertise<geometry_msgs::Twist>("/vrep/drone/cmd_vel", 1); if (!sim) {
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")); img_out = "/image_out";
cmd_out = "/bebop/cmd_vel";
img_in = "/bebop/image_raw";
}
else
{
img_out = "/image_out";
cmd_out = "/vrep/drone/cmd_vel";
img_in = "/usb_cam/image_raw";
}
pub_img = it.advertise(img_out, 1);
pub_cmd = n.advertise<geometry_msgs::Twist>(cmd_out, 1);
sub = it.subscribe(img_in, 1, [this](const sensor_msgs::ImageConstPtr& img) -> void { this->on_image(img);},ros::VoidPtr(),image_transport::TransportHints("compressed"));
} }
@ -69,7 +82,6 @@ class Traite_image {
Mat next_stab; Mat next_stab;
stabiliseImg(prev, next, next_stab); stabiliseImg(prev, next, next_stab);
int crop_ratio = 6;
float crop_x = next_stab.size().width/crop_ratio; float crop_x = next_stab.size().width/crop_ratio;
float crop_y = next_stab.size().height/crop_ratio; float crop_y = next_stab.size().height/crop_ratio;
float crop_w = next_stab.size().width*(1-2.0/crop_ratio); float crop_w = next_stab.size().width*(1-2.0/crop_ratio);
@ -77,7 +89,7 @@ class Traite_image {
Rect myROI(crop_x, crop_y, crop_w, crop_h); Rect myROI(crop_x, crop_y, crop_w, crop_h);
Mat next_stab_cropped = next_stab(myROI); Mat next_stab_cropped = next_stab(myROI);
Mat prev_cropped = prev(myROI); Mat prev_cropped = prev(myROI);
searchForMovement(prev_cropped, next_stab_cropped, output); searchForMovementOptFlow(prev_cropped, next_stab_cropped, output);
pub_img.publish(cv_bridge::CvImage(msg->header, "rgb8", output).toImageMsg()); pub_img.publish(cv_bridge::CvImage(msg->header, "rgb8", output).toImageMsg());
@ -207,7 +219,6 @@ class Traite_image {
threshold(flow_norm, flow_norm, THRESHOLD_DETECT_SENSITIVITY, 255, THRESH_BINARY); threshold(flow_norm, flow_norm, THRESHOLD_DETECT_SENSITIVITY, 255, THRESH_BINARY);
flow_norm.convertTo(flow_norm, CV_8U); flow_norm.convertTo(flow_norm, CV_8U);
bool objectDetected = false;
Mat temp; Mat temp;
flow_norm.copyTo(temp); flow_norm.copyTo(temp);
//these two vectors needed for output of findContours //these two vectors needed for output of findContours
@ -217,11 +228,7 @@ class Traite_image {
//findContours(temp,contours,hierarchy,CV_RETR_CCOMP,CV_CHAIN_APPROX_SIMPLE );// retrieves all contours //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 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){ //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 //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. //we will simply assume that the biggest contour is the object we are looking for.
vector< vector<Point> > largestContourVec; vector< vector<Point> > largestContourVec;
@ -273,8 +280,9 @@ class Traite_image {
int main(int argc, char **argv) int main(int argc, char **argv)
{ {
ros::init(argc, argv, "test_opencv"); ros::init(argc, argv, "Papillon");
Traite_image dataset=Traite_image(); bool sim = false;
Traite_image dataset=Traite_image(sim);
ros::spin(); ros::spin();
return 0; return 0;