VS2013+OpevCV3.4.1运行时出现_BLOCK_TYPE_IS_VALID(pHead->nBLOCKUse)错误

最近利用VS2013+OpevCV3.4.1演示如何使用2D-2D的特征匹配估计相机运动过程中出错，如图：

经调试，发现代码在执行find_feature_matches()函数的最后一行代码，然后返回时报了上述错误。初步看来是内存泄漏的问题，多次检查无果后，换了个思路。因为之前吃过环境变量设置的亏，然后又检查了一下OpenCV3.4.1环境变量设置，仍然没有问题。直到我看到这篇文章才突然想到会不会是因为VS2013和OpenCV3.4.1的版本不匹配的缘故。如图：

最后我直接改用VS2015来跑改程序，就没有再出现这个问题了。

具体程序如下：

#include <iostream>
#include <opencv2/core/core.hpp>
#include <opencv2/features2d/features2d.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/calib3d/calib3d.hpp>
#include<crtdbg.h>
// #include "extra.h" // use this if in OpenCV2

using namespace std;
using namespace cv;

/****************************************************
* 本程序演示了如何使用2D-2D的特征匹配估计相机运动
* **************************************************/

void find_feature_matches(
	const Mat &img_1, const Mat &img_2,
	std::vector<KeyPoint> &keypoints_1,
	std::vector<KeyPoint> &keypoints_2,
	std::vector<DMatch> &matches);

void pose_estimation_2d2d(
	std::vector<KeyPoint> keypoints_1,
	std::vector<KeyPoint> keypoints_2,
	std::vector<DMatch> matches,
	Mat &R, Mat &t);

// 像素坐标转相机归一化坐标
Point2d pixel2cam(const Point2d &p, const Mat &K);

int main(int argc, char **argv) {
	if (argc != 3) {
		cout << "usage: pose_estimation_2d2d img1 img2" << endl;
		return 1;
	}
	//-- 读取图像
	Mat img_1 = imread(argv[1], CV_LOAD_IMAGE_COLOR);
	Mat img_2 = imread(argv[2], CV_LOAD_IMAGE_COLOR);
	assert(img_1.data && img_2.data && "Can not load images!");

//	imshow("原图1", img_1);//wxh

	vector<KeyPoint> keypoints_1, keypoints_2;
	vector<DMatch> matches;
	
	find_feature_matches(img_1, img_2, keypoints_1, keypoints_2, matches);
	

	cout << "一共找到了" << matches.size() << "组匹配点" << endl;

	//-- 估计两张图像间运动
	Mat R, t;
	pose_estimation_2d2d(keypoints_1, keypoints_2, matches, R, t);

	//-- 验证E=t^R*scale
	Mat t_x =
		(Mat_<double>(3, 3) << 0, -t.at<double>(2, 0), t.at<double>(1, 0),
		t.at<double>(2, 0), 0, -t.at<double>(0, 0),
		-t.at<double>(1, 0), t.at<double>(0, 0), 0);

	cout << "t^R=" << endl << t_x * R << endl;

	//-- 验证对极约束
	/*Mat K = (Mat_<double>(3, 3) << 520.9, 0, 325.1, 0, 521.0, 249.7, 0, 0, 1);*/
	Mat K = (Mat_<double>(3, 3) << 517.306408, 0, 318.643040, 0, 516.469215, 255.313989, 0, 0, 1);
	for (DMatch m : matches) {
		Point2d pt1 = pixel2cam(keypoints_1[m.queryIdx].pt, K);
		Mat y1 = (Mat_<double>(3, 1) << pt1.x, pt1.y, 1);
		Point2d pt2 = pixel2cam(keypoints_2[m.trainIdx].pt, K);
		Mat y2 = (Mat_<double>(3, 1) << pt2.x, pt2.y, 1);
		Mat d = y2.t() * t_x * R * y1;
		cout << "epipolar constraint = " << d << endl;
	}
	
	return 0;
}

void find_feature_matches(const Mat &img_1, const Mat &img_2,
	std::vector<KeyPoint> &keypoints_1,
	std::vector<KeyPoint> &keypoints_2,
	std::vector<DMatch> &matches) 
{
	//-- 初始化
	Mat descriptors_1, descriptors_2;
	// used in OpenCV3
	Ptr<FeatureDetector> detector = ORB::create();
	Ptr<DescriptorExtractor> descriptor = ORB::create();
	// use this if you are in OpenCV2
	// Ptr<FeatureDetector> detector = FeatureDetector::create ( "ORB" );
	// Ptr<DescriptorExtractor> descriptor = DescriptorExtractor::create ( "ORB" );
	Ptr<DescriptorMatcher> matcher = DescriptorMatcher::create("BruteForce-Hamming");
	//-- 第一步:检测 Oriented FAST 角点位置
	detector->detect(img_1, keypoints_1);
	detector->detect(img_2, keypoints_2);

	//-- 第二步:根据角点位置计算 BRIEF 描述子
	descriptor->compute(img_1, keypoints_1, descriptors_1);
	descriptor->compute(img_2, keypoints_2, descriptors_2);

	//-- 第三步:对两幅图像中的BRIEF描述子进行匹配，使用 Hamming 距离
	vector<DMatch> match;
	//BFMatcher matcher ( NORM_HAMMING );
	matcher->match(descriptors_1, descriptors_2, match);

	//-- 第四步:匹配点对筛选
	double min_dist = 10000, max_dist = 0;

	//找出所有匹配之间的最小距离和最大距离, 即是最相似的和最不相似的两组点之间的距离
	for (int i = 0; i < descriptors_1.rows; i++) {
		double dist = match[i].distance;
		printf("match[i].distance:%fn", match[i].distance);
		if (dist < min_dist) min_dist = dist;
		if (dist > max_dist) max_dist = dist;
	}

	printf("-- Max dist : %f n", max_dist);
	printf("-- Min dist : %f n", min_dist);

	//当描述子之间的距离大于两倍的最小距离时,即认为匹配有误.但有时候最小距离会非常小,设置一个经验值30作为下限.
	for (int i = 0; i < descriptors_1.rows; i++) {
		if (match[i].distance <= max(2 * min_dist, 30.0)) {
			matches.push_back(match[i]);
		}
	}

	
}

Point2d pixel2cam(const Point2d &p, const Mat &K) {
	return Point2d
		(
		   (p.x - K.at<double>(0, 2)) / K.at<double>(0, 0),
		   (p.y - K.at<double>(1, 2)) / K.at<double>(1, 1)
		);
}

void pose_estimation_2d2d(std::vector<KeyPoint> keypoints_1,
	std::vector<KeyPoint> keypoints_2,
	std::vector<DMatch> matches,
	Mat &R, Mat &t) {
	// 相机内参,TUM Freiburg2
	Mat K = (Mat_<double>(3, 3) << 517.306408, 0, 318.643040, 0, 516.469215, 255.313989, 0, 0, 1/*520.9, 0, 325.1, 0, 521.0, 249.7, 0, 0, 1*/);

	//-- 把匹配点转换为vector<Point2f>的形式
	vector<Point2f> points1;
	vector<Point2f> points2;

	for (int i = 0; i < (int)matches.size(); i++) {
		points1.push_back(keypoints_1[matches[i].queryIdx].pt);
		points2.push_back(keypoints_2[matches[i].trainIdx].pt);
	}

	//-- 计算基础矩阵
	Mat fundamental_matrix;
	fundamental_matrix = findFundamentalMat(points1, points2, CV_FM_8POINT);
	cout << "fundamental_matrix is " << endl << fundamental_matrix << endl;

	//-- 计算本质矩阵
	Point2d principal_point(/*325.1, 249.7*/318.643040, 255.313989);  //相机光心, TUM dataset标定值
	double focal_length = 521;      //相机焦距, TUM dataset标定值
	Mat essential_matrix;
	essential_matrix = findEssentialMat(points1, points2, focal_length, principal_point);
	cout << "essential_matrix is " << endl << essential_matrix << endl;

	//-- 计算单应矩阵
	//-- 但是本例中场景不是平面，单应矩阵意义不大
	Mat homography_matrix;
	homography_matrix = findHomography(points1, points2, RANSAC, 3);
	cout << "homography_matrix is " << endl << homography_matrix << endl;

	//-- 从本质矩阵中恢复旋转和平移信息.
	// 此函数仅在Opencv3中提供
	recoverPose(essential_matrix, points1, points2, R, t, focal_length, principal_point);
	cout << "R is " << endl << R << endl;
	cout << "t is " << endl << t << endl;

}

最后

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