243 lines
6.3 KiB
C++
243 lines
6.3 KiB
C++
#include "ros/ros.h"
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#include <image_transport/image_transport.h>
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#include <cv_bridge/cv_bridge.h>
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#include <sensor_msgs/image_encodings.h>
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#include <geometry_msgs/Twist.h>
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#include <typeinfo>
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#include <opencv/cv.h>
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#include <sstream>
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using namespace cv;
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using namespace std;
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class Traite_image {
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public:
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const static int THRESHOLD_DETECT_SENSITIVITY = 10;
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const static int BLUR_SIZE = 5;
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const static int THRESHOLD_MOV = 5;
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constexpr static float MOVEMENT_THRES = 0.05;
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constexpr static float FLOW_MIN_QUAL = 0.01;
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const static int FLOW_MIN_DIST = 20;
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Mat prev;
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// Stabilisation transformation matrices
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Mat T, last_T;
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bool first = true;
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// Features vectors
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vector <Point2f> prev_ftr, cur_ftr;
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// Downsize factor
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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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ros::NodeHandle n;
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image_transport::ImageTransport it;
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image_transport::Publisher pub_img;
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ros::Publisher pub_cmd;
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image_transport::Subscriber sub;
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Traite_image() : n("~"),it(n) {
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pub_img = it.advertise("/image_out", 1);
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pub_cmd = n.advertise<geometry_msgs::Twist>("/vrep/drone/cmd_vel", 1);
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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"));
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}
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// This processes an image and publishes the result.
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void on_image(const sensor_msgs::ImageConstPtr& msg) {
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cv_bridge::CvImageConstPtr bridge_input;
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try {
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bridge_input = cv_bridge::toCvShare(msg,sensor_msgs::image_encodings::RGB8);
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}
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catch (Exception& e) {
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std::ostringstream errstr;
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errstr << "cv_bridge exception caught: " << e.what();
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return;
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}
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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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resize(input, next, Size(input.size().width/resize_f, input.size().height/resize_f));
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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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first = false;
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ROS_INFO("first done");
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}
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Mat next_stab;
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trackFeatures(prev, next);
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stabiliseImg(next, next_stab);
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trackingOptFlow(prev, next_stab, next_stab);
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Mat next_stab2;
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trackFeatures(prev, next);
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stabiliseImg(next, next_stab2);
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trackingOptFlow(prev, next_stab2, output);
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//searchForMovementOptFlow(prev_cropped, next_stab_cropped, output);
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pub_img.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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droneTracking(Rect(Point(0,0), output.size()));
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//ROS_INFO("pub");
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prev = next.clone();
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}
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void trackFeatures(Mat prev, Mat cur) {
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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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// vector from prev to cur
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vector <Point2f> prev_corner, cur_corner;
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vector <uchar> status;
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vector <float> err;
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goodFeaturesToTrack(prev_grey, prev_corner, 200, FLOW_MIN_QUAL, FLOW_MIN_DIST);
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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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prev_ftr.resize(0);
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cur_ftr.resize(0);
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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_ftr.push_back(prev_corner[i]);
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cur_ftr.push_back(cur_corner[i]);
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}
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}
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}
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void stabiliseImg(Mat cur, Mat &output){
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T = estimateRigidTransform(prev_ftr, cur_ftr, true); // false = rigid transform, no scaling/shearing
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if(T.data == NULL)
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last_T.copyTo(T);
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else
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T.copyTo(last_T);
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Mat cur2;
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warpAffine(cur, cur2, T, cur.size(),INTER_NEAREST|WARP_INVERSE_MAP);
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cur2.copyTo(output);
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}
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void warpPoints(vector<Point2f> p, vector<Point2f> &p_warp, Mat T, bool invert=false) {
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Mat H;
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if(invert)
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invertAffineTransform(T, H);
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p_warp.clear();
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for(size_t i=0; i < p.size(); ++i) {
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Mat src(3/*rows*/,1 /* cols */,CV_64F);
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src.at<double>(0,0)=p[i].x;
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src.at<double>(1,0)=p[i].y;
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src.at<double>(2,0)=1.0;
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Mat dst = H*src; //USE MATRIX ALGEBRA
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p_warp.push_back(Point2f(dst.at<double>(0,0),dst.at<double>(1,0)));
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}
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}
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void trackingOptFlow(Mat prev, Mat cur, Mat &output) {
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cur.copyTo(output);
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vector <Point2f> cur_ftr_stab;
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//T = estimateRigidTransform(prev_ftr, cur_ftr, true); // false = rigid transform, no scaling/shearing
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//if(T.data == NULL)
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// last_T.copyTo(T);
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//else
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// T.copyTo(last_T);
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warpPoints(cur_ftr, cur_ftr_stab, T, true);
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vector <Point2f> objects;
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vector <float> flow_norm;
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for(size_t i=0; i < prev_ftr.size(); ++i) {
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flow_norm.push_back(norm(prev_ftr[i] - cur_ftr_stab[i]) / prev.size().height);
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line(output, prev_ftr[i], cur_ftr[i], Scalar(200,0,0),1);
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line(output, prev_ftr[i], cur_ftr_stab[i], Scalar(0,200,0),1);
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}
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for(size_t i=0; i < flow_norm.size(); ++i) {
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if(flow_norm[i] > MOVEMENT_THRES) {
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objects.push_back(cur_ftr_stab[i]);
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prev_ftr.erase(prev_ftr.begin() + i);
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cur_ftr.erase(cur_ftr.begin() + i);
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}
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}
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for(size_t i=0; i < objects.size(); ++i) {
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circle(output, objects[i], 5, Scalar(0, 200, 0), 1);
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}
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}
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inline bool isFlowCorrect(Point2f u)
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{
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return !cvIsNaN(u.x) && !cvIsNaN(u.y) && fabs(u.x) < 1e9 && fabs(u.y) < 1e9;
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}
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void droneTracking(Rect img_size)
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{
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Point2f centre_image = Point2f(img_size.width/2, img_size.height/2);
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Point2f centre_rect = Point2f(objectBoundingRectangle.x + objectBoundingRectangle.width/2, objectBoundingRectangle.y + objectBoundingRectangle.height/2);
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geometry_msgs::Twist twist = geometry_msgs::Twist();
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if(centre_rect.x < centre_image.x-THRESHOLD_MOV)
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{
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twist.angular.z = 0.2;
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}
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else if(centre_rect.x > centre_image.x+THRESHOLD_MOV)
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{
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twist.angular.z = -0.2;
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}
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pub_cmd.publish(twist);
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}
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//int to string helper function
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string intToString(int number){
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//this function has a number input and string output
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std::stringstream ss;
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ss << number;
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return ss.str();
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}
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};
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int main(int argc, char **argv)
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{
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ros::init(argc, argv, "test_opencv");
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Traite_image dataset=Traite_image();
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ros::spin();
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return 0;
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}
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