vs2019版本未经处理的异常0x00007FFA1BFA4F69
用的是一位大佬的图像拼接代码，已经测试过环境，但是运行是会出现错误：0x00007FFC9C06AFEC (ucrtbased.dll) (Project1.exe 中)处有未经处理的异常: 将一个无效参数传递给了将无效参数视为严重错误的函数。
代码如下
#include <fstream>
#include <string>
#include<iostream>
#include "opencv2/opencv_modules.hpp"
#include <opencv2/core/utility.hpp>
#include "opencv2/imgcodecs.hpp"
#include "opencv2/highgui.hpp"
#include "opencv2/stitching/detail/autocalib.hpp"
#include "opencv2/stitching/detail/blenders.hpp"
#include "opencv2/stitching/detail/timelapsers.hpp"
#include "opencv2/stitching/detail/camera.hpp"
#include "opencv2/stitching/detail/exposure_compensate.hpp"
#include "opencv2/stitching/detail/matchers.hpp"
#include "opencv2/stitching/detail/motion_estimators.hpp"
#include "opencv2/stitching/detail/seam_finders.hpp"
#include "opencv2/stitching/detail/warpers.hpp"
#include "opencv2/stitching/warpers.hpp"
// #include <opencv2/nofree/nofree.hpp>
#include<opencv2/xfeatures2d.hpp>
using namespace std;
using namespace cv;
using namespace cv::detail;
bool readCamera(const string& filename, Mat& cameraMatrix, Mat& distCoeffs, float& ratio);
int main(int argc, char** argv)
{
vector<Mat> imgs;
ifstream fin("../img.txt");
string img_name;
while (getline(fin, img_name))
{
Mat img = imread(img_name);
//resize(img, img, Size(), 0.25, 0.25);
imgs.push_back(img);
}
int num_images = imgs.size(); //图像数量
cout << "图像数量为" << num_images << endl;
cout << "图像读取完毕" << endl;
Ptr<FeaturesFinder> finder; //定义特征寻找器
finder = new SurfFeaturesFinder(); //应用SURF方法寻找特征
//finder = new OrbFeaturesFinder(); //应用ORB方法寻找特征
vector<ImageFeatures> features(num_images); //表示图像特征
for (int i = 0; i < num_images; i++)
(*finder)(imgs[i], features[i]); //特征检测
cout << "特征提取完毕" << endl;
vector<MatchesInfo> pairwise_matches; //表示特征匹配信息变量
BestOf2NearestMatcher matcher(false, 0.3f, 6, 6); //定义特征匹配器，2NN方法
matcher(features, pairwise_matches); //进行特征匹配
/*打印图像之间的匹配关系匹配*/
for (size_t i = 0; i < num_images; i++)
for (size_t j = 0; j < num_images; j++)
{
if (pairwise_matches[i * num_images + j].H.empty())
continue;
cout << "第" << i << "匹配" << j << "幅图片置信度为"
<< pairwise_matches[i * num_images + j].confidence << endl;
}
cout << "特征匹配完毕" << endl;
HomographyBasedEstimator estimator; //定义参数评估器
vector<CameraParams> cameras; //表示相机参数，内参加外参
estimator(features, pairwise_matches, cameras); //进行相机参数评估
for (size_t i = 0; i < cameras.size(); ++i) //转换相机旋转参数的数据类型
{
Mat R;
cameras[i].R.convertTo(R, CV_32F);
cameras[i].R = R;
}
cout << "相机参数预测完毕" << endl;
for (size_t i = 0; i < cameras.size(); ++i)
{
cout << "第" << i << "焦距为" << cameras[i].focal << endl;
}
// 在一部可以计算重映射误差，想办法让他可以输出出来
Ptr<detail::BundleAdjusterBase> adjuster; //光束平差法，精确相机参数
//adjuster->setRefinementMask();
adjuster = new detail::BundleAdjusterReproj(); //重映射误差方法
//adjuster = new detail::BundleAdjusterRay(); //射线发散误差方法
adjuster->setConfThresh(0.3f); //设置匹配置信度，该值设为1
(*adjuster)(features, pairwise_matches, cameras); //精确评估相机参数
/*查看进行光束平差法之后的树节点数量和核心节点位置*/
// const int node_number = static_cast<int>(features.size());
// cout<<"树节点数量"<<node_number<<endl;
// //Graph span_tree;
// //std::vector<int> span_tree_centers;
// findMaxSpanningTree(node_number, pairwise_matches, span_tree, span_tree_centers);
// for(size_t i=0; i<span_tree_centers.size(); i++)
// cout<<"核心节点包括"<<span_tree_centers[i]<<endl;
vector<Mat> rmats;
for (size_t i = 0; i < cameras.size(); ++i) //复制相机的旋转参数
rmats.push_back(cameras[i].R.clone());
waveCorrect(rmats, WAVE_CORRECT_HORIZ); //进行波形校正
for (size_t i = 0; i < cameras.size(); ++i) //相机参数赋值
cameras[i].R = rmats[i];
rmats.clear(); //清变量
cout << "利用光束平差法进行相机矩阵更新" << endl;
vector<Point> corners(num_images); //表示映射变换后图像的左上角坐标
vector<UMat> masks_warped(num_images); //表示映射变换后的图像掩码
vector<UMat> images_warped(num_images); //表示映射变换后的图像
vector<Size> sizes(num_images); //表示映射变换后的图像尺寸
vector<UMat> masks(num_images); //表示源图的掩码
for (int i = 0; i < num_images; ++i) //初始化源图的掩码
{
masks[i].create(imgs[i].size(), CV_8U); //定义尺寸大小
masks[i].setTo(Scalar::all(255)); //全部赋值为255，表示源图的所有区域都使用
}
Ptr<WarperCreator> warper_creator; //定义图像映射变换创造器
warper_creator = new cv::SphericalWarper();
//warper_creator = makePtr<cv::PlaneWarper>(); //平面投影
//warper_creator = new cv::CylindricalWarper(); //柱面投影
//warper_creator = new cv::SphericalWarper(); //球面投影
//warper_creator = new cv::FisheyeWarper(); //鱼眼投影
//warper_creator = new cv::StereographicWarper(); //立方体投影
//定义图像映射变换器，设置映射的尺度为相机的焦距，所有相机的焦距都相同
vector<double> focals;
for (size_t i = 0; i < cameras.size(); ++i)
{
cout << "第" << i << "焦距为" << cameras[i].focal << endl;
focals.push_back(cameras[i].focal);
}
sort(focals.begin(), focals.end());
float warped_image_scale;
if (focals.size() % 2 == 1)
warped_image_scale = static_cast<float>(focals[focals.size() / 2]);
else
warped_image_scale = static_cast<float>(focals[focals.size() / 2 - 1] + focals[focals.size() / 2]) * 0.5f;
cout << "最终选择的图像的焦距为" << warped_image_scale << endl;
Ptr<RotationWarper> warper = warper_creator->create(static_cast<float>(warped_image_scale));
for (int i = 0; i < num_images; ++i)
{
Mat_<float> K;
cameras[i].K().convertTo(K, CV_32F); //转换相机内参数的数据类型
//对当前图像镜像投影变换，得到变换后的图像以及该图像的左上角坐标
corners[i] = warper->warp(imgs[i], K, cameras[i].R, INTER_LINEAR, BORDER_REFLECT, images_warped[i]);
sizes[i] = images_warped[i].size(); //得到尺寸
cout << "width: " << sizes[i].width << "height: " << sizes[i].height << endl;
//得到变换后的图像掩码
warper->warp(masks[i], K, cameras[i].R, INTER_NEAREST, BORDER_CONSTANT, masks_warped[i]);
}
imgs.clear(); //清变量
masks.clear();
cout << "图像映射完毕" << endl;
//创建曝光补偿器，应用增益补偿方法
Ptr<ExposureCompensator> compensator =
ExposureCompensator::createDefault(ExposureCompensator::GAIN);
compensator->feed(corners, images_warped, masks_warped); //得到曝光补偿器
for (int i = 0; i < num_images; ++i) //应用曝光补偿器，对图像进行曝光补偿
{
compensator->apply(i, corners[i], images_warped[i], masks_warped[i]);
}
cout << "图像曝光完毕" << endl;
//在后面，我们还需要用到映射变换图的掩码masks_warped，因此这里为该变量添加一个副本masks_seam
vector<UMat> masks_seam(num_images);
for (int i = 0; i < num_images; i++)
masks_warped[i].copyTo(masks_seam[i]);
Ptr<SeamFinder> seam_finder; //定义接缝线寻找器
//seam_finder = new NoSeamFinder(); //无需寻找接缝线
//seam_finder = new VoronoiSeamFinder(); //逐点法
seam_finder = new DpSeamFinder(DpSeamFinder::COLOR); //动态规范法
//seam_finder = new DpSeamFinder(DpSeamFinder::COLOR_GRAD);
//图割法
//seam_finder = new GraphCutSeamFinder(GraphCutSeamFinder::COST_COLOR);
//seam_finder = new GraphCutSeamFinder(GraphCutSeamFinder::COST_COLOR_GRAD);
vector<UMat> images_warped_f(num_images);
for (int i = 0; i < num_images; ++i) //图像数据类型转换
images_warped[i].convertTo(images_warped_f[i], CV_32F);
images_warped.clear(); //清内存
//得到接缝线的掩码图像masks_seam
seam_finder->find(images_warped_f, corners, masks_seam);
// Mat tmpImg;
// for (int i=0; i<masks_seam.size(); i++)
// {
// masks_seam[i].copyTo(tmpImg);
// imwrite(to_string(i) + "masks_seam.jpg", tmpImg);
// }
cout << "拼缝优化完毕" << endl;
vector<Mat> images_warped_s(num_images);
Ptr<Blender> blender; //定义图像融合器
//blender = Blender::createDefault(Blender::NO, false); //简单融合方法
//羽化融合方法
blender = Blender::createDefault(Blender::FEATHER, false);
//dynamic_cast多态强制类型转换时候使用
FeatherBlender* fb = dynamic_cast<FeatherBlender*>(static_cast<Blender*>(blender));
fb->setSharpness(0.005); //设置羽化锐度
// blender = Blender::createDefault(Blender::MULTI_BAND, false); //多频段融合
// MultiBandBlender* mb = dynamic_cast<MultiBandBlender*>(static_cast<Blender*>(blender));
// mb->setNumBands(8); //设置频段数，即金字塔层数
blender->prepare(corners, sizes); //生成全景图像区域
cout << "生成全景图像区域" << endl;
//在融合的时候，最重要的是在接缝线两侧进行处理，而上一步在寻找接缝线后得到的掩码的边界就是接缝线处，因此我们还需要在接缝线两侧开辟一块区域用于融合处理，这一处理过程对羽化方法尤为关键
//应用膨胀算法缩小掩码面积
vector<Mat> dilate_img(num_images);
vector<Mat> masks_seam_new(num_images);
Mat tem;
Mat element = getStructuringElement(MORPH_RECT, Size(20, 20)); //定义结构元素
for (int k = 0; k < num_images; k++)
{
images_warped_f[k].convertTo(images_warped_s[k], CV_16S); //改变数据类型
dilate(masks_seam[k], masks_seam_new[k], element); //膨胀运算
//映射变换图的掩码和膨胀后的掩码相“与”，从而使扩展的区域仅仅限于接缝线两侧，其他边界处不受影响
//resize(dilated_mask, tem, mask_warped.size(), 0, 0, INTER_LINEAR_EXACT);
masks_warped[k].copyTo(tem);
masks_seam_new[k] = masks_seam_new[k] & tem;
// masks_seam_new[k].copyTo(tmpImg);
// imwrite(to_string(k) + "masks_seam_new.jpg", masks_seam_new[k]);
// namedWindow("mask_seam_new", WINDOW_NORMAL);
// imshow("mask_seam_new", masks_seam_new[k]);
// waitKey(0);
blender->feed(images_warped_s[k], masks_seam_new[k], corners[k]); //初始化数据
cout << "处理完成" << k << "图片" << endl;
}
masks_seam.clear(); //清内存
images_warped_s.clear();
masks_warped.clear();
images_warped_f.clear();
Mat result, result_mask;
//完成融合操作，得到全景图像result和它的掩码result_mask
blender->blend(result, result_mask);
imwrite("result.jpg", result); //存储全景图像
return 0;
}
bool readCamera(const string& filename, Mat& cameraMatrix, Mat& distCoeffs, float& ratio)
{
return false;
}
运行结果及报错内容
找了许多人说的很多方法，有人知道吗
数组越界了，你打个断点看下是哪句代码开始出错