您好我正试图用2种方法在C ++中用opencv跟踪一个对象。第一种方法是使用绝对差异算法，第二种方法是使用扩张跟踪颜色并侵蚀它们两者都能完美地工作。当你按下＆ ＃34; T＆＃34;在键盘上它将使用absdiff算法并跟踪对象。当你按下＆＃34; t＆＃34;它会再次停止跟踪但是当我按下＆＃34; p＆＃34;在键盘上使用扩张和侵蚀方法它开始跟踪ROI区域但是当我按下＆＃34; p＆＃34;它时它不会突破方法。再次，我不能回到使用absdiff方法
#include <opencv\cv.h>
#include <opencv\highgui.h>
#include <opencv2/core/core.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <iostream>
#include <opencv2/imgproc/imgproc.hpp>
using namespace cv;
using namespace std;
//our sensitivity value to be used in the absdiff() function
const static int SENSITIVITY_VALUE = 20;
//size of blur used to smooth the intensity image output from absdiff() function
const static int BLUR_SIZE = 10;
//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 H_MIN = 0;
int H_MAX = 256;
int S_MIN = 0;
int S_MAX = 256;
int V_MIN = 0;
int V_MAX = 256;
const string windowName = "Original Image";
const string windowName1 = "HSV Image";
const string windowName2 = "Thresholded Image";
const string windowName3 = "After Morphological Operations";
const string trackbarWindowName = "Trackbars";
void on_trackbar( int, void* )
{//This function gets called whenever a
// trackbar position is changed
}
string intToString(int number){
std::stringstream ss;
ss << number;
return ss.str();
}
void createTrackbars(){
//create window for trackbars
namedWindow(trackbarWindowName,0);
//create memory to store trackbar name on window
char TrackbarName[50];
sprintf( TrackbarName, "H_MIN", H_MIN);
sprintf( TrackbarName, "H_MAX", H_MAX);
sprintf( TrackbarName, "S_MIN", S_MIN);
sprintf( TrackbarName, "S_MAX", S_MAX);
sprintf( TrackbarName, "V_MIN", V_MIN);
sprintf( TrackbarName, "V_MAX", V_MAX);
//create trackbars and insert them into window
//3 parameters are: the address of the variable that is changing when the trackbar is moved(eg.H_LOW),
//the max value the trackbar can move (eg. H_HIGH),
//and the function that is called whenever the trackbar is moved(eg. on_trackbar)
// ----> ----> ---->
createTrackbar( "H_MIN", trackbarWindowName, &H_MIN, H_MAX, on_trackbar );
createTrackbar( "H_MAX", trackbarWindowName, &H_MAX, H_MAX, on_trackbar );
createTrackbar( "S_MIN", trackbarWindowName, &S_MIN, S_MAX, on_trackbar );
createTrackbar( "S_MAX", trackbarWindowName, &S_MAX, S_MAX, on_trackbar );
createTrackbar( "V_MIN", trackbarWindowName, &V_MIN, V_MAX, on_trackbar );
createTrackbar( "V_MAX", trackbarWindowName, &V_MAX, V_MAX, on_trackbar );
}
void morphOps(Mat &thresh) {
//create structuring element that will be used to "dilate" and "erode" image.
//the element chosen here is a 3px by 3px rectangle
Mat erodeElement = getStructuringElement(MORPH_RECT, Size(3, 3));
//dilate with larger element so make sure object is nicely visible
Mat dilateElement = getStructuringElement(MORPH_RECT, Size(8, 8));
erode(thresh, thresh, erodeElement);
erode(thresh, thresh, erodeElement);
dilate(thresh, thresh, dilateElement);
dilate(thresh, thresh, dilateElement);
}
void trackFilteredObject(int &x, int &y, Mat threshold, Mat &cameraFeed) {
Mat temp;
threshold.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);
bool objectFound = false;
if (hierarchy.size() > 0) {
int numObjects = hierarchy.size();
//if number of objects greater than MAX_NUM_OBJECTS we have a noisy filter
}
int maxX = 0, minX = cameraFeed.cols, maxY = 0, minY = cameraFeed.rows;
for (int i = 0; i<contours.size(); i++)
for (int j = 0; j<contours[i].size(); j++)
{
Point p = contours[i][j];
maxX = max(maxX, p.x);
minX = min(minX, p.x);
maxY = max(maxY, p.y);
minY = min(minY, p.y);
}
rectangle(cameraFeed, Point(minX, minY), Point(maxX, maxY), Scalar(0));
//find center of the rectangle
int m = minX + maxX / 4;
int m2 = minY + maxY / 4;
//calculate the arc tangent
double angle;
angle = atan(m / m2);
//draw the circle
circle(cameraFeed, Point(m, m2), 5, Scalar(255, 255, 255), CV_FILLED, 8, 0);
}
void searchForMovement(Mat thresholdImage, Mat &cameraFeed){
//notice how we use the '&' operator for objectDetected and 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 to be 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];
int maxX = 0, minX = cameraFeed.cols, maxY=0, minY = cameraFeed.rows;
for(int i=0; i<contours.size(); i++)
for(int j=0; j<contours[i].size(); j++)
{
Point p = contours[i][j];
maxX = max(maxX, p.x);
minX = min(minX, p.x);
maxY = max(maxY, p.y);
minY = min(minY, p.y);
}
rectangle(cameraFeed, Point(minX, minY), Point(maxX, maxY), Scalar(0));
int m = minX + maxX / 4;
int m2 = minY + maxY / 4;
double angle;
angle = atan(m / m2);
circle(cameraFeed, Point(m, m2), 5, Scalar(255, 255, 255), CV_FILLED, 8, 0);
}
int main(int argc, char* argv[]) {
//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;
bool colordetection=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;
capture.open(0);
if (!capture.isOpened()) {
cout << "ERROR ACQUIRING VIDEO FEED\n";
getchar();
return -1;
}
while (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);
//convert frame2 to gray scale for frame differencing
cv::cvtColor(frame2,grayImage2,COLOR_BGR2GRAY);
//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.
cv::absdiff(grayImage1,grayImage2,differenceImage);
//threshold intensity image at a given sensitivity value
cv::threshold(differenceImage,thresholdImage,SENSITIVITY_VALUE,255,THRESH_BINARY);
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");
}
//blur the image to get rid of the noise. This will output an intensity image
cv::blur(thresholdImage,thresholdImage,cv::Size(BLUR_SIZE,BLUR_SIZE));
//threshold again to obtain binary image from blur output
cv::threshold(thresholdImage,thresholdImage,SENSITIVITY_VALUE,255,THRESH_BINARY);
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 (colordetection == true) {
//some boolean variables for different functionality within this
//program
bool trackObjects = true;
bool useMorphOps = true;
//Matrix to store each frame of the webcam feed
Mat cameraFeed;
//matrix storage for HSV image
Mat HSV;
//matrix storage for binary threshold image
//x and y values for the location of the object
int x = 0, y = 0;
//create slider bars for HSV filtering
createTrackbars();
//start an infinite loop where webcam feed is copied to cameraFeed matrix
//all of our operations will be performed within this loop
while (1) {
//store image to matrix
capture.read(cameraFeed);
//convert frame from BGR to HSV colorspace
cvtColor(cameraFeed, HSV, COLOR_BGR2HSV);
//filter HSV image between values and store filtered image to
//threshold matrix
inRange(HSV, Scalar(H_MIN, S_MIN, V_MIN), Scalar(H_MAX, S_MAX, V_MAX), thresholdImage);
//perform morphological operations on thresholded image to eliminate noise
//and emphasize the filtered object(s)
if (useMorphOps)
morphOps(thresholdImage);
//pass in thresholded frame to our object tracking function
//this function will return the x and y coordinates of the
//filtered object
if (trackObjects)
trackFilteredObject(x, y, thresholdImage, cameraFeed);
//show frames
cv::imshow("Thresholded Image", thresholdImage);
cv::imshow("HSV Image", HSV);
cv::imshow("Original Image", cameraFeed);
//delay 30ms so that screen can refresh.
//image will not appear without this waitKey() command
waitKey(30);
}
}
else {
cv::destroyWindow("Original Image");
cv::destroyWindow("HSV Image");
cv::destroyWindow("Thresholded Image");
cv::destroyWindow("Trackbars");
}
if (trackingEnabled) {
searchForMovement(thresholdImage, frame1);
}
imshow("Frame1", frame1);
switch (waitKey(10)) {
case 27: //'esc' key has been pressed, exit program.
return 0;
case 116: //'t' has been pressed. this will toggle tracking
trackingEnabled = !trackingEnabled;
if (trackingEnabled == false) cout << "Tracking disabled." << endl;
else cout << "Tracking enabled." << endl;
break;
case 100: //'d' has been pressed. this will debug mode
debugMode = !debugMode;
if (debugMode == false) cout << "Debug mode disabled." << endl;
else cout << "Debug mode enabled." << endl;
break;
case 112: //'t' has been pressed. this will toggle tracking to color tracking using dilate and erode
colordetection = !colordetection;
if (colordetection == false) cout << "Tracking not switched" << endl;
else {
cout << "Tracking switched" << endl;
break;
}
}
}
return 0;
}
答案 0 :(得分：1)
当 colordetection 变量为 true 时，您输入了无限的 while（1）循环，因此程序永远不会到达<第一次按“ p ”并切换 colordetection 变量的值后，强>切换语句。在循环结束时放置 waitKey（30）; 不会突破它 - 它只是等待键输入，接收或不接收，然后忽略它并返回到开头循环。
您可以将开关语句放到一个单独的函数中，并在上述循环结束时调用此函数，而不是现在调用它。