OpenCV (计算立体图像的深度)
Eva.Q Lv9
2021-08-04 09:34:18

two camera

要计算立体视觉系统的深度图，就必须计算每个像素的视差。

  1. 得到水平极线
    用鲁棒匹配算法 (robustMatching) ，计算立体视觉系统的基础矩阵，得到水平极线。

    robustMatcher.h

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    #if !defined MATCHER
    #define MATCHER

    #include <iostream>
    #include <vector>
    #include <opencv2/core.hpp>
    #include <opencv2/imgproc.hpp>
    #include <opencv2/highgui.hpp>
    #include <opencv2/features2d.hpp>
    #include <opencv2/calib3d.hpp>
    #include <opencv2/xfeatures2d.hpp>

    #define NOCHECK      0
    #define CROSSCHECK   1
    #define RATIOCHECK   2
    #define BOTHCHECK    3

    class RobustMatcher {

      private:

    	  // pointer to the feature point detector object
    	  cv::Ptr<cv::FeatureDetector> detector;
    	  // pointer to the feature descriptor extractor object
    	  cv::Ptr<cv::DescriptorExtractor> descriptor;
    	  int normType;
    	  float ratio; // max ratio between 1st and 2nd NN
    	  bool refineF; // if true will refine the F matrix
    	  bool refineM; // if true will refine the matches (will refine F also)
    	  double distance; // min distance to epipolar
    	  double confidence; // confidence level (probability)

      public:

    	  RobustMatcher(const cv::Ptr<cv::FeatureDetector> &detector, 
    		            const cv::Ptr<cv::DescriptorExtractor> &descriptor= cv::Ptr<cv::DescriptorExtractor>())
    		  : detector(detector), descriptor(descriptor),normType(cv::NORM_L2), 
    		    ratio(0.8f), refineF(true), refineM(true), confidence(0.98), distance(1.0) {	  

    		// in this case use the associated descriptor
    		if (!this->descriptor) { 
    			this->descriptor = this->detector;
    		} 
    	  }

    	  // Set the feature detector
    	  void setFeatureDetector(const cv::Ptr<cv::FeatureDetector>& detect) {

    		  this->detector= detect;
    	  }

    	  // Set descriptor extractor
    	  void setDescriptorExtractor(const cv::Ptr<cv::DescriptorExtractor>& desc) {

    		  this->descriptor= desc;
    	  }

    	  // Set the norm to be used for matching
    	  void setNormType(int norm) {

    		  normType= norm;
    	  }

    	  // Set the minimum distance to epipolar in RANSAC
    	  void setMinDistanceToEpipolar(double d) {

    		  distance= d;
    	  }

    	  // Set confidence level in RANSAC
    	  void setConfidenceLevel(double c) {

    		  confidence= c;
    	  }

    	  // Set the NN ratio
    	  void setRatio(float r) {

    		  ratio= r;
    	  }

    	  // if you want the F matrix to be recalculated
    	  void refineFundamental(bool flag) {

    		  refineF= flag;
    	  }

    	  // if you want the matches to be refined using F
    	  void refineMatches(bool flag) {

    		  refineM= flag;
    	  }

    	  // Clear matches for which NN ratio is > than threshold
    	  // return the number of removed points 
    	  // (corresponding entries being cleared, i.e. size will be 0)
          int ratioTest(const std::vector<std::vector<cv::DMatch> >& inputMatches,
    		            std::vector<cv::DMatch>& outputMatches) {

    		int removed=0;

            // for all matches
            for (std::vector<std::vector<cv::DMatch> >::const_iterator matchIterator= inputMatches.begin();
    			 matchIterator!= inputMatches.end(); ++matchIterator) {
    				 
    				 //   first best match/second best match
    				 if ((matchIterator->size() > 1) && // if 2 NN has been identified 
    					 (*matchIterator)[0].distance/(*matchIterator)[1].distance < ratio) {
    			
    					 // it is an acceptable match
    					 outputMatches.push_back((*matchIterator)[0]);

    				 } else {

    					 removed++;
    				 }
    		}

    		return removed;
    	  }

    	  // Insert symmetrical matches in symMatches vector
    	  void symmetryTest(const std::vector<cv::DMatch>& matches1,
    		                const std::vector<cv::DMatch>& matches2,
    					    std::vector<cv::DMatch>& symMatches) {
    			
    		// for all matches image 1 -> image 2
    		for (std::vector<cv::DMatch>::const_iterator matchIterator1= matches1.begin();
    			 matchIterator1!= matches1.end(); ++matchIterator1) {

    			// for all matches image 2 -> image 1
    			for (std::vector<cv::DMatch>::const_iterator matchIterator2= matches2.begin();
    				matchIterator2!= matches2.end(); ++matchIterator2) {

    				// Match symmetry test
    				if (matchIterator1->queryIdx == matchIterator2->trainIdx  && 
    					matchIterator2->queryIdx == matchIterator1->trainIdx) {

    						// add symmetrical match
    						symMatches.push_back(*matchIterator1);
    						break; // next match in image 1 -> image 2
    				}
    			}
    		}
    	  }

    	  // Apply both ratio and symmetry test
    	  // (often an over-kill)
          void ratioAndSymmetryTest(const std::vector<std::vector<cv::DMatch> >& matches1,
                                    const std::vector<std::vector<cv::DMatch> >& matches2,
    					            std::vector<cv::DMatch>& outputMatches) {

    		// Remove matches for which NN ratio is > than threshold

    		// clean image 1 -> image 2 matches
    		std::vector<cv::DMatch> ratioMatches1;
    		int removed= ratioTest(matches1,ratioMatches1);
    		std::cout << "Number of matched points 1->2 (ratio test) : " << ratioMatches1.size() << std::endl;
    		// clean image 2 -> image 1 matches
    		std::vector<cv::DMatch> ratioMatches2;
    		removed= ratioTest(matches2,ratioMatches2);
    		std::cout << "Number of matched points 1->2 (ratio test) : " << ratioMatches2.size() << std::endl;

    		// Remove non-symmetrical matches
    		symmetryTest(ratioMatches1,ratioMatches2,outputMatches);

    		std::cout << "Number of matched points (symmetry test): " << outputMatches.size() << std::endl;
    	  }

    	  // Identify good matches using RANSAC
    	  // Return fundamental matrix and output matches
    	  cv::Mat ransacTest(const std::vector<cv::DMatch>& matches,
    		                 std::vector<cv::KeyPoint>& keypoints1, 
    						 std::vector<cv::KeyPoint>& keypoints2,
    					     std::vector<cv::DMatch>& outMatches) {

    		// Convert keypoints into Point2f	
    		std::vector<cv::Point2f> points1, points2;	

    		for (std::vector<cv::DMatch>::const_iterator it= matches.begin();
    			 it!= matches.end(); ++it) {

    			 // Get the position of left keypoints
    			 points1.push_back(keypoints1[it->queryIdx].pt);
    			 // Get the position of right keypoints
    			 points2.push_back(keypoints2[it->trainIdx].pt);
    	    }

    		// Compute F matrix using RANSAC
    		std::vector<uchar> inliers(points1.size(),0);
    		cv::Mat fundamental= cv::findFundamentalMat(
    			points1,points2, // matching points
    		    inliers,         // match status (inlier or outlier)  
    		    cv::FM_RANSAC,   // RANSAC method
    		    distance,        // distance to epipolar line
    		    confidence);     // confidence probability
    	
    		// extract the surviving (inliers) matches
    		std::vector<uchar>::const_iterator itIn= inliers.begin();
    		std::vector<cv::DMatch>::const_iterator itM= matches.begin();
    		// for all matches
    		for ( ;itIn!= inliers.end(); ++itIn, ++itM) {

    			if (*itIn) { // it is a valid match

    				outMatches.push_back(*itM);
    			}
    		}

    		if (refineF || refineM) {
    		// The F matrix will be recomputed with all accepted matches

    			// Convert keypoints into Point2f for final F computation	
    			points1.clear();
    			points2.clear();
    	
    			for (std::vector<cv::DMatch>::const_iterator it= outMatches.begin();
    				 it!= outMatches.end(); ++it) {

    				 // Get the position of left keypoints
    				 points1.push_back(keypoints1[it->queryIdx].pt);
    				 // Get the position of right keypoints
    				 points2.push_back(keypoints2[it->trainIdx].pt);
    			}

    			// Compute 8-point F from all accepted matches
    			fundamental= cv::findFundamentalMat(
    				points1,points2, // matching points
    				cv::FM_8POINT); // 8-point method

    			if (refineM) {

    				std::vector<cv::Point2f> newPoints1, newPoints2;	
    				// refine the matches
    				correctMatches(fundamental,             // F matrix
    					           points1, points2,        // original position
    							   newPoints1, newPoints2); // new position
    				for (int i=0; i< points1.size(); i++) {

    					std::cout << "(" << keypoints1[outMatches[i].queryIdx].pt.x 
    						      << "," << keypoints1[outMatches[i].queryIdx].pt.y 
    							  << ") -> ";
    					std::cout << "(" << newPoints1[i].x 
    						      << "," << newPoints1[i].y << std::endl;
    					std::cout << "(" << keypoints2[outMatches[i].trainIdx].pt.x 
    						      << "," << keypoints2[outMatches[i].trainIdx].pt.y 
    							  << ") -> ";
    					std::cout << "(" << newPoints2[i].x 
    						      << "," << newPoints2[i].y << std::endl;

    					keypoints1[outMatches[i].queryIdx].pt.x= newPoints1[i].x;
    					keypoints1[outMatches[i].queryIdx].pt.y= newPoints1[i].y;
    					keypoints2[outMatches[i].trainIdx].pt.x= newPoints2[i].x;
    					keypoints2[outMatches[i].trainIdx].pt.y= newPoints2[i].y;
    				}
    			}
    		}


    		return fundamental;
    	  }

    	  // Match feature points using RANSAC
    	  // returns fundamental matrix and output match set
    	  cv::Mat match(cv::Mat& image1, cv::Mat& image2, // input images 
    		  std::vector<cv::DMatch>& matches, // output matches and keypoints
    		  std::vector<cv::KeyPoint>& keypoints1, std::vector<cv::KeyPoint>& keypoints2,
    		  int check=CROSSCHECK) {  // check type (symmetry or ratio or none or both)

    		// 1. Detection of the feature points
    		detector->detect(image1,keypoints1);
    		detector->detect(image2,keypoints2);

    		std::cout << "Number of feature points (1): " << keypoints1.size() << std::endl;
    		std::cout << "Number of feature points (2): " << keypoints2.size() << std::endl;

    		// 2. Extraction of the feature descriptors
    		cv::Mat descriptors1, descriptors2;
    		descriptor->compute(image1,keypoints1,descriptors1);
    		descriptor->compute(image2,keypoints2,descriptors2);

    		std::cout << "descriptor matrix size: " << descriptors1.rows << " by " << descriptors1.cols << std::endl;

    		// 3. Match the two image descriptors
    		//    (optionaly apply some checking method)
       
    		// Construction of the matcher with crosscheck 
    		cv::BFMatcher matcher(normType,            //distance measure
    	                          check==CROSSCHECK);  // crosscheck flag
                                 
    		// vectors of matches
            std::vector<std::vector<cv::DMatch> > matches1;
            std::vector<std::vector<cv::DMatch> > matches2;
    	    std::vector<cv::DMatch> outputMatches;

    		// call knnMatch if ratio check is required
    		if (check==RATIOCHECK || check==BOTHCHECK) {
    			// from image 1 to image 2
    			// based on k nearest neighbours (with k=2)
    			matcher.knnMatch(descriptors1,descriptors2, 
    				matches1, // vector of matches (up to 2 per entry) 
    				2);		  // return 2 nearest neighbours

    			std::cout << "Number of matched points 1->2: " << matches1.size() << std::endl;

    			if (check==BOTHCHECK) {
    				// from image 2 to image 1
    				// based on k nearest neighbours (with k=2)
    				matcher.knnMatch(descriptors2,descriptors1, 
    					matches2, // vector of matches (up to 2 per entry) 
    					2);		  // return 2 nearest neighbours

    				std::cout << "Number of matched points 2->1: " << matches2.size() << std::endl;
    			}

    		} 
    		
    		// select check method
    		switch (check) {

    			case CROSSCHECK:
    				matcher.match(descriptors1,descriptors2,outputMatches);
    				std::cout << "Number of matched points 1->2 (after cross-check): " << outputMatches.size() << std::endl;
    				break;
    			case RATIOCHECK:
    				ratioTest(matches1,outputMatches);
    				std::cout << "Number of matched points 1->2 (after ratio test): " << outputMatches.size() << std::endl;
    				break;
    			case BOTHCHECK:
    				ratioAndSymmetryTest(matches1,matches2,outputMatches);
    				std::cout << "Number of matched points 1->2 (after ratio and cross-check): " << outputMatches.size() << std::endl;
    				break;
    			case NOCHECK:
    			default:
    				matcher.match(descriptors1,descriptors2,outputMatches);
    				std::cout << "Number of matched points 1->2: " << outputMatches.size() << std::endl;
    				break;
    		}

    		// 4. Validate matches using RANSAC
    		cv::Mat fundamental= ransacTest(outputMatches, keypoints1, keypoints2, matches);
    		std::cout << "Number of matched points (after RANSAC): " << matches.size() << std::endl;

    		// return the found fundamental matrix
    		return fundamental;
    	}
    	  
    	 // Match feature points using RANSAC
    	 // returns fundamental matrix and output match set
         // this is the simplified version presented in the book
    	  cv::Mat matchBook(cv::Mat& image1, cv::Mat& image2, // input images 
    		  std::vector<cv::DMatch>& matches, // output matches and keypoints
    		  std::vector<cv::KeyPoint>& keypoints1, std::vector<cv::KeyPoint>& keypoints2) { 
    			  
    		// 1. Detection of the feature points
    		detector->detect(image1,keypoints1);
    		detector->detect(image2,keypoints2);

    		// 2. Extraction of the feature descriptors
    		cv::Mat descriptors1, descriptors2;
    		descriptor->compute(image1,keypoints1,descriptors1);
    		descriptor->compute(image2,keypoints2,descriptors2);

    		// 3. Match the two image descriptors
    		//    (optionnally apply some checking method)
       
    		// Construction of the matcher with crosscheck 
    		cv::BFMatcher matcher(normType,   //distance measure
    	                          true);      // crosscheck flag
                                 
    		// match descriptors
    	    std::vector<cv::DMatch> outputMatches;
    		matcher.match(descriptors1,descriptors2,outputMatches);

    		// 4. Validate matches using RANSAC
    		cv::Mat fundamental= ransacTest(outputMatches, keypoints1, keypoints2, matches);

    		// return the found fundemental matrix
    		return fundamental;
    	}

    };

    #endif

    robustMatcher.cpp

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    #include <iostream>
    #include <vector>
    #include <numeric>
    #include <opencv2/core.hpp>
    #include <opencv2/imgproc.hpp>
    #include <opencv2/highgui.hpp>
    #include <opencv2/features2d.hpp>
    #include <opencv2/calib3d.hpp>
    #include <opencv2/objdetect.hpp>
    #include <opencv2/xfeatures2d.hpp>
    #include <opencv2/viz.hpp>
    #include "robustMatcher.h"

    int main()
    {
    	// Read input images
    	cv::Mat image1= cv::imread("brebeuf1.jpg",0);
    	cv::Mat image2= cv::imread("brebeuf2.jpg",0);
    	if (!image1.data || !image2.data)
    		return 0; 

    	// Prepare the matcher (with default parameters)
    	// here SIFT detector and descriptor
    	RobustMatcher rmatcher(cv::xfeatures2d::SIFT::create(250));

    	// Match the two images
    	std::vector<cv::DMatch> matches;

    	std::vector<cv::KeyPoint> keypoints1, keypoints2;
    	cv::Mat fundamental = rmatcher.match(image1, image2, matches,
    		keypoints1, keypoints2);

    	// draw the matches
    	cv::Mat imageMatches;
    	cv::drawMatches(image1, keypoints1,  // 1st image and its keypoints
    		image2, keypoints2,  // 2nd image and its keypoints
    		matches,			// the matches
    		imageMatches,		// the image produced
    		cv::Scalar(255, 255, 255),  // color of the lines
    		cv::Scalar(255, 255, 255),  // color of the keypoints
    		std::vector<char>(),
    		2);
    	cv::namedWindow("Matches");
    	cv::imshow("Matches", imageMatches);

    	// Convert keypoints into Point2f	
    	std::vector<cv::Point2f> points1, points2;

    	for (std::vector<cv::DMatch>::const_iterator it = matches.begin();
    	it != matches.end(); ++it) {

    		// Get the position of left keypoints
    		float x = keypoints1[it->queryIdx].pt.x;
    		float y = keypoints1[it->queryIdx].pt.y;
    		points1.push_back(keypoints1[it->queryIdx].pt);
    		// Get the position of right keypoints
    		x = keypoints2[it->trainIdx].pt.x;
    		y = keypoints2[it->trainIdx].pt.y;
    		points2.push_back(keypoints2[it->trainIdx].pt);
    	}

    	// Compute homographic rectification
    	cv::Mat h1, h2;
    	cv::stereoRectifyUncalibrated(points1, points2, fundamental, image1.size(), h1, h2);

    	// Rectify the images through warping
    	cv::Mat rectified1;
    	cv::warpPerspective(image1, rectified1, h1, image1.size());
    	cv::Mat rectified2;
    	cv::warpPerspective(image2, rectified2, h2, image1.size());
    	// Display the images
    	cv::namedWindow("Left Rectified Image");
    	cv::imshow("Left Rectified Image", rectified1);
    	cv::namedWindow("Right Rectified Image");
    	cv::imshow("Right Rectified Image", rectified2);

    	points1.clear();
    	points2.clear();
    	for (int i = 20; i < image1.rows - 20; i += 20) {

    		points1.push_back(cv::Point(image1.cols / 2, i));
    		points2.push_back(cv::Point(image2.cols / 2, i));
    	}

    	// Draw the epipolar lines
    	std::vector<cv::Vec3f> lines1;
    	cv::computeCorrespondEpilines(points1, 1, fundamental, lines1);

    	for (std::vector<cv::Vec3f>::const_iterator it = lines1.begin();
    	it != lines1.end(); ++it) {

    		cv::line(image2, cv::Point(0, -(*it)[2] / (*it)[1]),
    			cv::Point(image2.cols, -((*it)[2] + (*it)[0] * image2.cols) / (*it)[1]),
    			cv::Scalar(255, 255, 255));
    	}

    	std::vector<cv::Vec3f> lines2;
    	cv::computeCorrespondEpilines(points2, 2, fundamental, lines2);

    	for (std::vector<cv::Vec3f>::const_iterator it = lines2.begin();
    	it != lines2.end(); ++it) {

    		cv::line(image1, cv::Point(0, -(*it)[2] / (*it)[1]),
    			cv::Point(image1.cols, -((*it)[2] + (*it)[0] * image1.cols) / (*it)[1]),
    			cv::Scalar(255, 255, 255));
    	}

    	// Display the images with epipolar lines
    	cv::namedWindow("Left Epilines");
    	cv::imshow("Left Epilines", image1);
    	cv::namedWindow("Right Epilines");
    	cv::imshow("Right Epilines", image2);

    	// draw the pair
    	cv::drawMatches(image1, keypoints1,  // 1st image 
    		image2, keypoints2,              // 2nd image 
    		std::vector<cv::DMatch>(),			
    		imageMatches,		             // the image produced
    		cv::Scalar(255, 255, 255),  
    		cv::Scalar(255, 255, 255),  
    		std::vector<char>(),
    		2);
    	cv::namedWindow("A Stereo pair");
    	cv::imshow("A Stereo pair", imageMatches);

    	// Compute disparity
    	cv::Mat disparity;
    	cv::Ptr<cv::StereoMatcher> pStereo = cv::StereoSGBM::create(0,   // minimum disparity
    		                                                        32,  // maximum disparity
    		                                                        5);  // block size
    	pStereo->compute(rectified1, rectified2, disparity);

    	// draw the rectified pair
    	/*
    	cv::warpPerspective(image1, rectified1, h1, image1.size());
    	cv::warpPerspective(image2, rectified2, h2, image1.size());
    	cv::drawMatches(rectified1, keypoints1,  // 1st image 
    		rectified2, keypoints2,              // 2nd image
    		std::vector<cv::DMatch>(),		
    		imageMatches,		                // the image produced
    		cv::Scalar(255, 255, 255),  
    		cv::Scalar(255, 255, 255),  
    		std::vector<char>(),
    		2);
    	cv::namedWindow("Rectified Stereo pair");
    	cv::imshow("Rectified Stereo pair", imageMatches);
    	*/

    	double minv, maxv;
    	disparity = disparity * 64;
    	cv::minMaxLoc(disparity, &minv, &maxv);
    	std::cout << minv << "+" << maxv << std::endl;
    	// Display the disparity map
    	cv::namedWindow("Disparity Map");
    	cv::imshow("Disparity Map", disparity);

    	cv::waitKey();
    	return 0;
    }

  2. 利用单应变换将每个相机的图像平面投影到完全对齐的虚拟平面上。

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    // 计算单应变换矫正量
    Mat h1, h2;
    stereoRectifyUncalibrated(points1, points2, fundamental, image1.size(), h1, h2);
    // 用变换实现图像校正
    Mat rectified1;
    warpPerspective(image1, rectified1, h1, image1.size());
    Mat rectified2;
    warpPerspective(image2, rectified2, h2, image1.size()); // ??? image1 or image2
    // 计算视差
    Mat disparity;
    Ptr<StereoMatcher> pStereo = StereoSGBM::create(0, 32, 5); // 最小视差，最大视差，块的大小
    pStereo -> compute(rectified1, rectified2, disparity);

    部分对极线

    经矫正的图像对

    视差图：亮的地方视差大，离物体近

  • Post title：OpenCV (计算立体图像的深度)
  • Post author：Eva.Q
  • Create time：2021-08-04 09:34:18
  • Post link：https://qyy/2021/08/04/OPENCV/OPENCV1-6/
  • Copyright Notice：All articles in this blog are licensed under BY-NC-SA unless stating additionally.