视频运动物体跟踪与标记

//motionTracking.cpp //Written by Kyle Hounslow, January 2014 //Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software") //, to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, //and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: //The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. //THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, //FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER //LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS //IN THE SOFTWARE. #include <opencv\cv.h> #include <opencv\highgui.h> #include <opencv2/opencv.hpp> #include "opencv2/imgproc/imgproc.hpp" #include "opencv2/highgui/highgui.hpp" using namespace std; using namespace cv; //our sensitivity value to be used in the threshold() function const static int SENSITIVITY_VALUE = 20; //size of blur used to smooth the image to remove possible noise and //increase the size of the object we are trying to track. (Much like dilate and erode) const static int BLUR_SIZE = 50; //we'll have just one object to search for //and keep track of its position. int theObject[2] = { 0, 0 }; //bounding rectangle of the object, we will use the center of this as its position. Rect objectBoundingRectangle = Rect(0, 0, 0, 0); //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 searchForMovement(Mat thresholdImage, Mat &cameraFeed){ //notice how we use the '&' operator for the cameraFeed. 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 cameraFeed in this function which is then displayed in the main() function. bool objectDetected = false; Mat temp; thresholdImage.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)); int xpos = objectBoundingRectangle.x + objectBoundingRectangle.width / 2; int ypos = objectBoundingRectangle.y + objectBoundingRectangle.height / 2; //update the objects positions by changing the 'theObject' array values theObject[0] = xpos, theObject[1] = ypos; } //make some temp x and y variables so we dont have to type out so much int x = theObject[0]; int y = theObject[1]; //draw some crosshairs on the object circle(cameraFeed, Point(x, y), 20, Scalar(0, 255, 0), 2); line(cameraFeed, Point(x, y), Point(x, y - 25), Scalar(0, 255, 0), 2); line(cameraFeed, Point(x, y), Point(x, y + 25), Scalar(0, 255, 0), 2); line(cameraFeed, Point(x, y), Point(x - 25, y), Scalar(0, 255, 0), 2); line(cameraFeed, Point(x, y), Point(x + 25, y), Scalar(0, 255, 0), 2); putText(cameraFeed, "Tracking object at (" + intToString(x) + "," + intToString(y) + ")", Point(x, y), 1, 1, Scalar(255, 0, 0), 2); } int main(){ //some boolean variables for added functionality bool objectDetected = false; //these two can be toggled by pressing 'd' or 't' bool debugMode = false; bool trackingEnabled = false; //pause and resume code bool pause = false; //set up the matrices that we will need //the two frames we will be comparing Mat frame1, frame2; //their grayscale images (needed for absdiff() function) Mat grayImage1, grayImage2; //resulting difference image Mat differenceImage; //thresholded difference image (for use in findContours() function) Mat thresholdImage; //video capture object. VideoCapture capture; while (1){ //we can loop the video by re-opening the capture every time the video reaches its last frame capture.open("bouncingBall.avi"); if (!capture.isOpened()){ cout << "ERROR ACQUIRING VIDEO FEED\n"; getchar(); return -1; } //check if the video has reach its last frame. //we add '-1' because we are reading two frames from the video at a time. //if this is not included, we get a memory error! while (capture.get(CV_CAP_PROP_POS_FRAMES)<capture.get(CV_CAP_PROP_FRAME_COUNT) - 1){ //read first frame capture.read(frame1); //convert frame1 to gray scale for frame differencing cv::cvtColor(frame1, grayImage1, COLOR_BGR2GRAY); //copy second frame capture.read(frame2); cv::cvtColor(frame2, grayImage2, COLOR_BGR2GRAY); //convert frame2 to gray scale for frame differencing cv::absdiff(grayImage1, grayImage2, differenceImage); cv::threshold(differenceImage, thresholdImage, SENSITIVITY_VALUE, 255, THRESH_BINARY); //perform frame differencing with the sequential images. This will output an "intensity image" //do not confuse this with a threshold image, we will need to perform thresholding afterwards. //threshold intensity image at a given sensitivity value if (debugMode == true){ //show the difference image and threshold image cv::imshow("Difference Image", differenceImage); cv::imshow("Threshold Image", thresholdImage); } else{ //if not in debug mode, destroy the windows so we don't see them anymore cv::destroyWindow("Difference Image"); cv::destroyWindow("Threshold Image"); } //use blur() to smooth the image, remove possible noise and //increase the size of the object we are trying to track. (Much like dilate and erode) cv::blur(thresholdImage, thresholdImage, cv::Size(BLUR_SIZE, BLUR_SIZE)); cv::threshold(thresholdImage, thresholdImage, SENSITIVITY_VALUE, 255, THRESH_BINARY); //threshold again to obtain binary image from blur output if (debugMode == true){ //show the threshold image after it's been "blurred" imshow("final Threshold Image",thresholdImage); } else { //if not in debug mode, destroy the windows so we don't see them anymore cv::destroyWindow("final Threshold Image"); } //if tracking enabled, search for contours in our thresholded image if (trackingEnabled) { searchForMovement(thresholdImage, frame1); } //show our captured frame imshow("Frame1", frame1); //check to see if a button has been pressed. //this 10ms delay is necessary for proper operation of this program //if removed, frames will not have enough time to referesh and a blank //image will appear. switch (waitKey(100)){ case 27: //'esc' key has been pressed, exit program. ret