我需要从两个不同的摄像头中检测到一个蓝色物体和一个红色物体，现在所需的任务是在3D空间中找到每个物体位置，这意味着每个物体我们必须有它的x，y，z坐标，我已经看到this video和here女巫正是我正在尝试做的但是在第一个视频的情况下没有示例代码，我的代码看起来像这样现在它让我x ，y是红色/蓝色物体，但没有深度：
#include <iostream>
#include "opencv2/highgui/highgui.hpp"
#include "opencv2/imgproc/imgproc.hpp"
#include <opencv/highgui.h>
using namespace cv;
using namespace std;
int main(int argc, char** argv)
//int func(int argc, char** argv)
{
VideoCapture cap(0); //capture the video from webcam
VideoCapture cap1(1); //capture the video from extrenal camers
if (!cap.isOpened()) // if not success, exit program
{
cout << "Cannot open the web cam" << endl;
return -1;
}
if (!cap1.isOpened()) // if not success, exit program
{
cout << "Cannot open the External camera" << endl;
return -1;
}
namedWindow("Control", CV_WINDOW_AUTOSIZE); //create a window called "Control"
int iLowH = 170;
int iHighH = 179;
int iLowS = 150;
int iHighS = 255;
int iLowV = 60;
int iHighV = 255;
//Create trackbars in "Control" window to control the range of red detection
createTrackbar("LowH", "Control", &iLowH, 179); //Hue (0 - 179)
createTrackbar("HighH", "Control", &iHighH, 179);
createTrackbar("LowS", "Control", &iLowS, 255); //Saturation (0 - 255)
createTrackbar("HighS", "Control", &iHighS, 255);
createTrackbar("LowV", "Control", &iLowV, 255);//Value (0 - 255)
createTrackbar("HighV", "Control", &iHighV, 255);
int iLastX = -1; //last known co-ordinates of red object
int iLastY = -1;
int iLastX1 = -1;
int iLastY1 = -1;
//Capture a temporary image from both cameras to obtain size
Mat imgTmp;
cap.read(imgTmp);
cap1.read(imgTmp);
//Create a black image with the size as the camera output
Mat imgLines = Mat::zeros(imgTmp.size(), CV_8UC3);;
Mat imgLines1 = Mat::zeros(imgTmp.size(), CV_8UC3);;
//loop of continuously capturing frames from video
while (true)
{
Mat imgOriginal;
Mat imgOriginal1;
bool bSuccess = cap.read(imgOriginal); // read a new frame from video webcam
bool bSuccess1 = cap1.read(imgOriginal1); // read a new frame from video external cam
if (!bSuccess || !bSuccess1) //if not success, break loop
{
cout << "Cannot read a frame from video stream" << endl;
break;
}
//WebCam code for image and tracking/detecting
Mat imgHSV;
cvtColor(imgOriginal, imgHSV, COLOR_BGR2HSV); //Convert the captured frame from BGR to HSV to control range of color to obtain and be able to detect it
Mat imgThresholded;
inRange(imgHSV, Scalar(iLowH, iLowS, iLowV), Scalar(iHighH, iHighS, iHighV), imgThresholded); //Threshold the image at the colors within specified range
//morphological opening (removes noise and similar colored objects appearing in thresholded image)
erode(imgThresholded, imgThresholded, getStructuringElement(MORPH_ELLIPSE, Size(5, 5)));
dilate(imgThresholded, imgThresholded, getStructuringElement(MORPH_ELLIPSE, Size(5, 5)));
//morphological closing (removes noise appearing inside our object in the thresholded image)
dilate(imgThresholded, imgThresholded, getStructuringElement(MORPH_ELLIPSE, Size(5, 5)));
erode(imgThresholded, imgThresholded, getStructuringElement(MORPH_ELLIPSE, Size(5, 5)));
//Calculate the moments of the thresholded image to calculate the object position
Moments oMoments = moments(imgThresholded);
double dM01 = oMoments.m01;
double dM10 = oMoments.m10;
double dArea = oMoments.m00;
// if the area <= 10000, I consider that the there are no object in the image and it's because of the noise, the area is not zero
if (dArea > 10000)
{
//calculate the position of the ball
int posX = dM10 / dArea;
int posY = dM01 / dArea;
if (iLastX >= 0 && iLastY >= 0 && posX >= 0 && posY >= 0)
{
//Draw a red line from the previous point to the current point
line(imgLines, Point(posX, posY), Point(iLastX, iLastY), Scalar(0, 0, 255), 2);
}
iLastX = posX; //current point becomes last known point and loop continues
iLastY = posY;
}
imshow("Thresholded Image", imgThresholded); //show the thresholded image
imgOriginal = imgOriginal + imgLines;
imshow("Original", imgOriginal); //show the original image with the tracking lines if exist
//External Cam code track/detect
Mat imgHSV1;
cvtColor(imgOriginal1, imgHSV1, COLOR_BGR2HSV); //Convert the captured frame from BGR to HSV
Mat imgThresholded1;
inRange(imgHSV1, Scalar(iLowH, iLowS, iLowV), Scalar(iHighH, iHighS, iHighV), imgThresholded1); //Threshold the image
//morphological opening (removes noise and similar colored objects appearing in thresholded image)
erode(imgThresholded1, imgThresholded1, getStructuringElement(MORPH_ELLIPSE, Size(5, 5)));
dilate(imgThresholded1, imgThresholded1, getStructuringElement(MORPH_ELLIPSE, Size(5, 5)));
//morphological closing (removes noise appearing inside our object in the thresholded image)
dilate(imgThresholded1, imgThresholded1, getStructuringElement(MORPH_ELLIPSE, Size(5, 5)));
erode(imgThresholded1, imgThresholded1, getStructuringElement(MORPH_ELLIPSE, Size(5, 5)));
//Calculate the moments of the thresholded image to calculate the object position
Moments oMoments1 = moments(imgThresholded1);
double dM011 = oMoments1.m01;
double dM101 = oMoments1.m10;
double dArea1 = oMoments1.m00;
// if the area <= 10000, I consider that the there are no object in the image and it's because of the noise, the area is not zero
if (dArea1 > 10000)
{
//calculate the position of the ball
int posX1 = dM101 / dArea1;
int posY1 = dM011 / dArea1;
if (iLastX1 >= 0 && iLastY1 >= 0 && posX1 >= 0 && posY1 >= 0)
{
//Draw a red line from the previous point to the current point
line(imgLines1, Point(posX1, posY1), Point(iLastX1, iLastY1), Scalar(0, 0, 255), 2);
}
iLastX1 = posX1;
iLastY1 = posY1;
}
imshow("Thresholded Image 2", imgThresholded1); //show the thresholded image
imgOriginal1 = imgOriginal1 + imgLines1;
imshow("Original 2", imgOriginal1); //show the original image
if (waitKey(30) == 27) //wait for 'esc' key press for 30ms. If 'esc' key is pressed, break loop
{
cout << "esc key is pressed by user" << endl;
break;
}
}
return 0;
}
答案 0 :(得分：1)
您的问题的答案将是Stereo Vision。您需要对两个摄像机进行立体校准，以获得允许从2个视图生成场景深度图的变换矩阵。 OpenCV提供some functions来做到这一点。
Here是一个开始的教程。