stitching_backup.cpp

// Stitching.cpp : 定义控制台应用程序的入口点。

#include <QtWidgets/QMainWindow>
#include <QFileDialog>
#include <qprogressdialog.h>
#include "ui_stitchingvsqt.h"
#include <iostream>
#include <fstream>
#include <string>
#include <tchar.h>
#include <Windows.h>
#include "opencv2/opencv_modules.hpp"
#include "opencv2/highgui/highgui.hpp"
#include "opencv2/stitching/detail/autocalib.hpp"
#include "opencv2/stitching/detail/blenders.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/util.hpp"
#include "opencv2/stitching/detail/warpers.hpp"
#include "opencv2/stitching/warpers.hpp"
#include "stitching.h"
#include "qmessagebox.h"
#include <QRunnable>
#include "stitchingvsqt.h"


using namespace std;
using namespace cv;
using namespace cv::detail;

typedef void*(*STITCH)(std::vector<std::vector<std::string>> &img_names,
	const void *config,
	std::string &msg);

typedef void (*PANOIMAGE)(void *img, const std::string &name);

//static void printUsage()
//{
//	cout <<
//		"Rotation model images stitcher.\n\n"
//		"stitching_detailed img1 img2 [...imgN] [flags]\n\n"
//		"Flags:\n"
//		"  --preview\n"
//		"      Run stitching in the preview mode. Works faster than usual mode,\n"
//		"      but output image will have lower resolution.\n"
//		"  --try_gpu (yes|no)\n"
//		"      Try to use GPU. The default value is 'no'. All default values\n"
//		"      are for CPU mode.\n"
//		"\nMotion Estimation Flags:\n"
//		"  --work_megapix <float>\n"
//		"      Resolution for image registration step. The default is 0.6 Mpx.\n"
//		"  --features (surf|orb)\n"
//		"      Type of features used for images matching. The default is surf.\n"
//		"  --match_conf <float>\n"
//		"      Confidence for feature matching step. The default is 0.65 for surf and 0.3 for orb.\n"
//		"  --conf_thresh <float>\n"
//		"      Threshold for two images are from the same panorama confidence.\n"
//		"      The default is 1.0.\n"
//		"  --ba (reproj|ray)\n"
//		"      Bundle adjustment cost function. The default is ray.\n"
//		"  --ba_refine_mask (mask)\n"
//		"      Set refinement mask for bundle adjustment. It looks like 'x_xxx',\n"
//		"      where 'x' means refine respective parameter and '_' means don't\n"
//		"      refine one, and has the following format:\n"
//		"      <fx><skew><ppx><aspect><ppy>. The default mask is 'xxxxx'. If bundle\n"
//		"      adjustment doesn't support estimation of selected parameter then\n"
//		"      the respective flag is ignored.\n"
//		"  --wave_correct (no|horiz|vert)\n"
//		"      Perform wave effect correction. The default is 'horiz'.\n"
//		"  --save_graph <file_name>\n"
//		"      Save matches graph represented in DOT language to <file_name> file.\n"
//		"      Labels description: Nm is number of matches, Ni is number of inliers,\n"
//		"      C is confidence.\n"
//		"\nCompositing Flags:\n"
//		"  --warp (plane|cylindrical|spherical|fisheye|stereographic|compressedPlaneA2B1|compressedPlaneA1.5B1|compressedPlanePortraitA2B1|compressedPlanePortraitA1.5B1|paniniA2B1|paniniA1.5B1|paniniPortraitA2B1|paniniPortraitA1.5B1|mercator|transverseMercator)\n"
//		"      Warp surface type. The default is 'spherical'.\n"
//		"  --seam_megapix <float>\n"
//		"      Resolution for seam estimation step. The default is 0.1 Mpx.\n"
//		"  --seam (no|voronoi|gc_color|gc_colorgrad)\n"
//		"      Seam estimation method. The default is 'gc_color'.\n"
//		"  --compose_megapix <float>\n"
//		"      Resolution for compositing step. Use -1 for original resolution.\n"
//		"      The default is -1.\n"
//		"  --expos_comp (no|gain|gain_blocks)\n"
//		"      Exposure compensation method. The default is 'gain_blocks'.\n"
//		"  --blend (no|feather|multiband)\n"
//		"      Blending method. The default is 'multiband'.\n"
//		"  --blend_strength <float>\n"
//		"      Blending strength from [0,100] range. The default is 5.\n"
//		"  --output <result_img>\n"
//		"      The default is 'result.jpg'.\n";
//}

// Default command line args

/*
运行流程：
1.命令行调用程序，输入源图像以及程序的参数

2.特征点检测，判断是使用surf还是orb，默认是surf。

3.对图像的特征点进行匹配，使用最近邻和次近邻方法，将两个最优的匹配的置信度保存下来。

4.对图像进行排序以及将置信度高的图像保存到同一个集合中，删除置信度比较低的图像间的匹配，得到能正确匹配的图像序列。这样将置信度高于门限的所有匹配合并到一个集合中。

5.对所有图像进行相机参数粗略估计，然后求出旋转矩阵

6.使用光束平均法进一步精准的估计出旋转矩阵。

7.波形校正，水平或者垂直

8.拼接

9.融合，多频段融合，光照补偿
*/

//vector<string> img_names;
bool preview = false; //以预览模式运行程序，比正常模式要快，但输出图像分辨率低，拼接的分辨率compose_megapix 设置为0.6
bool try_gpu = false;
double work_megapix = 0.6;//图像匹配的分辨率大小，图像的面积尺寸变为work_megapix*100000，默认为0.6
double seam_megapix = 0.1; //拼接缝像素的大小 默认是0.1
double compose_megapix = -1; //拼接分辨率
float conf_thresh = 1.f; //两幅图来自同一全景图的置信度，默认为1.0
string features_type = "surf";
//string features_type = "orb";
string ba_cost_func = "ray";   //光束平均法的误差函数选择
//string ba_cost_func = "reproj";

string ba_refine_mask = "xxxxx";
bool do_wave_correct = true;
WaveCorrectKind wave_correct = detail::WAVE_CORRECT_HORIZ;//波形校验 水平，垂直或者没有 默认是horiz
bool save_graph = false;
std::string save_graph_to;
string warp_type = "cylindrical";
int expos_comp_type = ExposureCompensator::GAIN_BLOCKS;
float match_conf = 0.65f;//特征点检测置信等级，最近邻匹配距离与次近邻匹配距离的比值，surf默认为0.65，orb默认为0.3
string seam_find_type = "gc_color"; // 拼接缝隙估计方法
int blend_type = Blender::MULTI_BAND;// 融合方法，默认是多频段融合
float blend_strength = 5;//融合强度，0-100
string result_name = "result.jpg";

vector<std::vector<std::string>> imageNames;
vector<int> idx;

class StitchingVSQt;

////static int parseCmdArgs(int argc, char** argv)
////{
//	/*if (argc == 1)
//	{
//		printUsage();
//		return -1;
//	}
//	for (int i = 1; i < argc; ++i)
//	{
//		if (string(argv[i]) == "--help" || string(argv[i]) == "/?")
//		{
//			printUsage();
//			return -1;
//		}
//		else if (string(argv[i]) == "--preview")
//		{
//			preview = true;
//		}
//		else if (string(argv[i]) == "--try_gpu")
//		{
//			if (string(argv[i + 1]) == "no")
//				try_gpu = false;
//			else if (string(argv[i + 1]) == "yes")
//				try_gpu = true;
//			else
//			{
//				cout << "Bad --try_gpu flag value\n";
//				return -1;
//			}
//			i++;
//		}
//		else if (string(argv[i]) == "--work_megapix")
//		{
//			work_megapix = atof(argv[i + 1]);
//			i++;
//		}
//		else if (string(argv[i]) == "--seam_megapix")
//		{
//			seam_megapix = atof(argv[i + 1]);
//			i++;
//		}
//		else if (string(argv[i]) == "--compose_megapix")
//		{
//			compose_megapix = atof(argv[i + 1]);
//			i++;
//		}
//		else if (string(argv[i]) == "--result")
//		{
//			result_name = argv[i + 1];
//			i++;
//		}
//		else if (string(argv[i]) == "--features")
//		{
//			features_type = argv[i + 1];
//			if (features_type == "orb")
//				match_conf = 0.3f;
//			i++;
//		}
//		else if (string(argv[i]) == "--match_conf")
//		{
//			match_conf = static_cast<float>(atof(argv[i + 1]));
//			i++;
//		}
//		else if (string(argv[i]) == "--conf_thresh")
//		{
//			conf_thresh = static_cast<float>(atof(argv[i + 1]));
//			i++;
//		}
//		else if (string(argv[i]) == "--ba")
//		{
//			ba_cost_func = argv[i + 1];
//			i++;
//		}
//		else if (string(argv[i]) == "--ba_refine_mask")
//		{
//			ba_refine_mask = argv[i + 1];
//			if (ba_refine_mask.size() != 5)
//			{
//				cout << "Incorrect refinement mask length.\n";
//				return -1;
//			}
//			i++;
//		}
//		else if (string(argv[i]) == "--wave_correct")
//		{
//			if (string(argv[i + 1]) == "no")
//				do_wave_correct = false;
//			else if (string(argv[i + 1]) == "horiz")
//			{
//				do_wave_correct = true;
//				wave_correct = detail::WAVE_CORRECT_HORIZ;
//			}
//			else if (string(argv[i + 1]) == "vert")
//			{
//				do_wave_correct = true;
//				wave_correct = detail::WAVE_CORRECT_VERT;
//			}
//			else
//			{
//				cout << "Bad --wave_correct flag value\n";
//				return -1;
//			}
//			i++;
//		}
//		else if (string(argv[i]) == "--save_graph")
//		{
//			save_graph = true;
//			save_graph_to = argv[i + 1];
//			i++;
//		}
//		else if (string(argv[i]) == "--warp")
//		{
//			warp_type = string(argv[i + 1]);
//			i++;
//		}
//		else if (string(argv[i]) == "--expos_comp")
//		{
//			if (string(argv[i + 1]) == "no")
//				expos_comp_type = ExposureCompensator::NO;
//			else if (string(argv[i + 1]) == "gain")
//				expos_comp_type = ExposureCompensator::GAIN;
//			else if (string(argv[i + 1]) == "gain_blocks")
//				expos_comp_type = ExposureCompensator::GAIN_BLOCKS;
//			else
//			{
//				cout << "Bad exposure compensation method\n";
//				return -1;
//			}
//			i++;
//		}
//		else if (string(argv[i]) == "--seam")
//		{
//			if (string(argv[i + 1]) == "no" ||
//				string(argv[i + 1]) == "voronoi" ||
//				string(argv[i + 1]) == "gc_color" ||
//				string(argv[i + 1]) == "gc_colorgrad" ||
//				string(argv[i + 1]) == "dp_color" ||
//				string(argv[i + 1]) == "dp_colorgrad")
//				seam_find_type = argv[i + 1];
//			else
//			{
//				cout << "Bad seam finding method\n";
//				return -1;
//			}
//			i++;
//		}
//		else if (string(argv[i]) == "--blend")
//		{
//			if (string(argv[i + 1]) == "no")
//				blend_type = Blender::NO;
//			else if (string(argv[i + 1]) == "feather")
//				blend_type = Blender::FEATHER;
//			else if (string(argv[i + 1]) == "multiband")
//				blend_type = Blender::MULTI_BAND;
//			else
//			{
//				cout << "Bad blending method\n";
//				return -1;
//			}
//			i++;
//		}
//		else if (string(argv[i]) == "--blend_strength")
//		{
//			blend_strength = static_cast<float>(atof(argv[i + 1]));
//			i++;
//		}
//		else if (string(argv[i]) == "--output")
//		{
//			result_name = argv[i + 1];
//			i++;
//		}
//		else*/
////			img_names.push_back(argv[i]);
////	
////	if (preview)
////	{
////		compose_megapix = 0.6;
////	}
////	return 0;
////}

string& replace_all_distinct(string&   str, const   string&   old_value, const   string&   new_value)
{
	for (string::size_type pos(0); pos != string::npos; pos += new_value.length())   {
		if ((pos = str.find(old_value, pos)) != string::npos)
			str.replace(pos, old_value.length(), new_value);
		else   break;
	}
	return   str;
}

int startCalculation()
{
	
	StitchingVSQt s;
	std::string msg;
	const char* dllName = "stitching.dll";
    const char* funName1 = "stitch";
	const char* funName2 = "pano_image";
	
	HINSTANCE hDll = LoadLibrary(TEXT("stitching.dll"));
	if (hDll != NULL){
		STITCH fp = STITCH(GetProcAddress(hDll, "stitch"));
		PANOIMAGE fPa = PANOIMAGE(GetProcAddress(hDll, "writeImage"));
		if (fp != NULL){
			//s.showMessage(QString::fromLocal8Bit("找到dll中的函数"));
			void* p=fp(imageNames, nullptr, msg);
			//s.showMessage(QString::fromLocal8Bit("第一个函数调用完毕"));

			QString fileName(QString::fromStdString(result_name));
			QString dir = QFileDialog::getExistingDirectory(NULL, QString::fromLocal8Bit("选择生成图片保存目录"),
				"/home",
				QFileDialog::ShowDirsOnly
				| QFileDialog::DontResolveSymlinks);
			s.changePicture(dir + "/" + fileName);

			fPa(p, (dir + "/" + fileName).toStdString());
			//s.showMessage(QString::fromLocal8Bit("第二个函数调用完毕"));
			/*img_names.clear();*/
			imageNames.clear();
			idx.clear();
			delete p;
		}
		else{
			s.showMessage(QString::fromLocal8Bit("未找到dll中的函数"));
		}
		FreeLibrary(hDll);
	}
	else {
		
		s.showMessage(QString::fromLocal8Bit("未找到dll"));
		return -1;
	}

	
//	QMessageBox::StandardButton rb = QMessageBox::question(NULL, QString::fromLocal8Bit("相机标定参数选择"), QString::fromLocal8Bit("是否导入已有的相机标定参数文件？"), QMessageBox::Yes | QMessageBox::No, QMessageBox::Yes);
//	if (rb == QMessageBox::Yes)
//	{
//		QStringList fileNames = QFileDialog::getOpenFileNames(NULL,
//			QString::fromLocal8Bit("选择此次拼接的相机标定参数"),
//			"",
//			"TXT Files(*.txt)",
//			0);
//		if (fileNames.length() == 0)
//		{
//			return -1;
//		}
//	}
//	else{
//		s.showMessage(QString::fromLocal8Bit("若拼接所用的相机参数尚未标定或保存，请先在标定模块标定，再进行计算"));
//		return -1;
//	}
//
//	QProgressDialog progress(QString::fromLocal8Bit("正在导入图片数据，请稍候..."),
//		QString::fromLocal8Bit("取消"),
//		0, 12, // Range
//		NULL);
//	progress.show();
//	qApp->processEvents();
//	progress.setWindowModality(Qt::WindowModal);
//	progress.setWindowTitle(QString::fromLocal8Bit("正在导入图片数据，请稍候..."));
//
//	progress.setValue(1);
//	progress.setModal(true);
//	progress.setLabelText(QString::fromLocal8Bit("读取图片，请稍候..."));
//
//	// Check if have enough images
//	int num_images = static_cast<int>(img_names.size());
//	if (num_images < 2)
//	{
//		//LOGLN("Need more images");
//		s.showMessage("Need more images");
//		
//		return -1;
//	}
//
//	double work_scale = 1, seam_scale = 1, compose_scale = 1;
//	bool is_work_scale_set = false, is_seam_scale_set = false, is_compose_scale_set = false;
//
//	/**
//	*特征点提取
//	*/
//	progress.setValue(2);
//	progress.setLabelText(QString::fromLocal8Bit("特征点提取...."));
//
//	Ptr<FeaturesFinder> finder;
//	if (features_type == "surf")
//	{
//#if defined(HAVE_OPENCV_NONFREE) && defined(HAVE_OPENCV_GPU)
//		if (try_gpu && gpu::getCudaEnabledDeviceCount() > 0)
//			finder = new SurfFeaturesFinderGpu();
//		else
//#endif
//			finder = new SurfFeaturesFinder();
//	}
//	else if (features_type == "orb")
//	{
//		    finder = new OrbFeaturesFinder();
//	}
//	else
//	{
//	//	cout << "Unknown 2D features type: '" << features_type << "'.\n";
//		s.showMessage("Unknown 2D features type: '" +QString::fromStdString(features_type));
//		return -1;
//	}
//
//	Mat full_img, img;
//	vector<ImageFeatures> features(num_images);
//	vector<Mat> images(num_images);
//	vector<Size> full_img_sizes(num_images);
//	double seam_work_aspect = 1;
//
//	for (int i = 0; i < num_images; ++i)
//	{
//		/*full_img = imread(img_names[i].replace()));*/
//
//		full_img = imread(img_names[i]);
//		full_img_sizes[i] = full_img.size();
//
//		if (full_img.empty())
//		{
//			//LOGLN("Can't open image " << img_names[i]);
//			s.showMessage("Can't open image " + QString::fromStdString(img_names[i]));
//			return -1;
//		}
//
//		if (work_megapix < 0)//图像匹配的分辨率大小
//		{
//			img = full_img;
//			work_scale = 1;
//			is_work_scale_set = true;
//		}
//		else
//		{
//			if (!is_work_scale_set)
//			{
//				//work_scale参数设定图像大小（当图像尺寸大于1e6时，缩小图像，提高特征点检测速度），来寻找特征点。
//				work_scale = min(1.0, sqrt(work_megapix * 1e6 / full_img.size().area()));
//				is_work_scale_set = true;
//			}
//			resize(full_img, img, Size(), work_scale, work_scale);
//		}
//
//		if (!is_seam_scale_set)////默认值seam_megapix=0.1
//		{
//			seam_scale = min(1.0, sqrt(seam_megapix * 1e6 / full_img.size().area()));
//			seam_work_aspect = seam_scale / work_scale;
//			is_seam_scale_set = true;
//		}
//
//		(*finder)(img, features[i]);
//		features[i].img_idx = i;
//	//	LOGLN("Features in image #" << i + 1 << ": " << features[i].keypoints.size());
//	//	s.showMessage("Features in image # " + QString::number(i + 1) + ":" + QString::number(features[i].keypoints.size()));
//		//按照seam_scale调整图像大小，作为后面缝合缝寻找（seam_finder）的输入图像
//		resize(full_img, img, Size(), seam_scale, seam_scale);
//		images[i] = img.clone();
//	}
//
//	finder->collectGarbage();
//	full_img.release();
//	img.release();
//
////	LOGLN("Finding features, time: " << ((getTickCount() - t) / getTickFrequency()) << " sec");
//
//	progress.setValue(3);
//	/*
//	特征点匹配
//	*/
//	vector<MatchesInfo> pairwise_matches;
//	BestOf2NearestMatcher matcher=new BestOf2NearestMatcher(try_gpu,match_conf);
//	
//	try{
//		progress.setLabelText(QString::fromLocal8Bit("特征点匹配...."));
//		matcher(features, pairwise_matches);
//		matcher.collectGarbage();
//	}
//	catch(Exception){
//		s.showMessage(QString::fromLocal8Bit("计算失败，请重试..."));
//			return -1;
//	}
//	
//
//// Check if we should save matches graph
//	if (save_graph)
//	{
//		ofstream f(save_graph_to.c_str());
//		f << matchesGraphAsString(img_names, pairwise_matches, conf_thresh);
//	}
//
//	// Leave only images we are sure are from the same panorama
//	vector<int> indices = leaveBiggestComponent(features, pairwise_matches, conf_thresh);
//	vector<Mat> img_subset;
//	vector<string> img_names_subset;
//	vector<Size> full_img_sizes_subset;
//
//	for (size_t i = 0; i < indices.size(); ++i)
//	{
//		img_names_subset.push_back(img_names[indices[i]]);
//		img_subset.push_back(images[indices[i]]);
//		full_img_sizes_subset.push_back(full_img_sizes[indices[i]]);
//	}
//
//	images = img_subset;
//	img_names = img_names_subset;
//	full_img_sizes = full_img_sizes_subset;
//
//	// Check if we still have enough images
//	num_images = static_cast<int>(img_names.size());
//	if (num_images < 2)
//	{
//		//LOGLN("Need more images");
//		s.showMessage("Need more images");
//		return -1;
//	}
//
//	progress.setValue(4);
//	progress.setLabelText(QString::fromLocal8Bit("估计相机参数，计算变换矩阵...."));
//	//CameraParams 是结构体参数为 Focal length（焦距），double aspect（ Aspect ratio纵横比），double ppx 主点x; double ppy 主点y; Mat R// Rotation，Mat t//Translation
//	HomographyBasedEstimator estimator;
//	vector<CameraParams> cameras;
//
//	//投影矩阵k,旋转矩阵R
//	estimator(features, pairwise_matches, cameras);
//	
//
//	for (size_t i = 0; i < cameras.size(); ++i)
//	{
//		Mat R;
//		cameras[i].R.convertTo(R, CV_32F);
//		//cout << R << std::endl;
//		cameras[i].R = R;
//		//LOGLN("Initial intrinsics #" << indices[i] + 1 << ":\n" << cameras[i].K());
//	}
//
//	Ptr<detail::BundleAdjusterBase> adjuster;
//	if (ba_cost_func == "reproj") adjuster = new detail::BundleAdjusterReproj();
//	else if (ba_cost_func == "ray") adjuster = new detail::BundleAdjusterRay();
//	else
//	{
//	//	cout << "Unknown bundle adjustment cost function: '" << ba_cost_func << "'.\n";
//		s.showMessage("Unknown bundle adjustment cost function: '" + QString::fromStdString(ba_cost_func));
//		
//		return -1;
//	}
//	adjuster->setConfThresh(conf_thresh);
//	Mat_<uchar> refine_mask = Mat::zeros(3, 3, CV_8U);
//	if (ba_refine_mask[0] == 'x') refine_mask(0, 0) = 1;
//	if (ba_refine_mask[1] == 'x') refine_mask(0, 1) = 1;
//	if (ba_refine_mask[2] == 'x') refine_mask(0, 2) = 1;
//	if (ba_refine_mask[3] == 'x') refine_mask(1, 1) = 1;
//	if (ba_refine_mask[4] == 'x') refine_mask(1, 2) = 1;
//	adjuster->setRefinementMask(refine_mask);
//	(*adjuster)(features, pairwise_matches, cameras);
//
//	// Find median focal length
//
//	vector<double> focals;
//	for (size_t i = 0; i < cameras.size(); ++i)
//	{
//	//	LOGLN("Camera #" << indices[i] + 1 << ":\n" << cameras[i].K());
//		focals.push_back(cameras[i].focal);
//	}
//
//	sort(focals.begin(), focals.end());
//	float warped_image_scale;   //赋值为所有焦距的中值
//	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;
//
//	progress.setValue(5);
//
//	if (do_wave_correct)
//	{
//		vector<Mat> rmats;
//		for (size_t i = 0; i < cameras.size(); ++i)
//			rmats.push_back(cameras[i].R);
//		waveCorrect(rmats, wave_correct);
//		for (size_t i = 0; i < cameras.size(); ++i)
//			cameras[i].R = rmats[i];
//	}
//
//	vector<Point> corners(num_images);
//	vector<Mat> masks_warped(num_images);
//	vector<Mat> images_warped(num_images);
//	vector<Size> sizes(num_images);
//	vector<Mat> masks(num_images);
//
//	// Preapre images masks
//	for (int i = 0; i < num_images; ++i)
//	{
//		masks[i].create(images[i].size(), CV_8U);
//		masks[i].setTo(Scalar::all(255));
//	}
//
//	progress.setValue(6);
//	// Warp images and their masks
//	progress.setLabelText(QString::fromLocal8Bit("柱面投影...."));
//
//	Ptr<WarperCreator> warper_creator;
//#if defined(HAVE_OPENCV_GPU)
//	if (try_gpu && gpu::getCudaEnabledDeviceCount() > 0)
//	{
//		if (warp_type == "plane") warper_creator = new cv::PlaneWarperGpu();
//		else if (warp_type == "cylindrical") warper_creator = new cv::CylindricalWarperGpu();
//		else if (warp_type == "spherical") warper_creator = new cv::SphericalWarperGpu();
//	}
//	else
//#endif
//	{
//		if (warp_type == "plane") warper_creator = new cv::PlaneWarper();
//		else if (warp_type == "cylindrical") warper_creator = new cv::CylindricalWarper();
//		else if (warp_type == "spherical") warper_creator = new cv::SphericalWarper();
//		else if (warp_type == "fisheye") warper_creator = new cv::FisheyeWarper();
//		else if (warp_type == "stereographic") warper_creator = new cv::StereographicWarper();
//		else if (warp_type == "compressedPlaneA2B1") warper_creator = new cv::CompressedRectilinearWarper(2, 1);
//		else if (warp_type == "compressedPlaneA1.5B1") warper_creator = new cv::CompressedRectilinearWarper(1.5, 1);
//		else if (warp_type == "compressedPlanePortraitA2B1") warper_creator = new cv::CompressedRectilinearPortraitWarper(2, 1);
//		else if (warp_type == "compressedPlanePortraitA1.5B1") warper_creator = new cv::CompressedRectilinearPortraitWarper(1.5, 1);
//		else if (warp_type == "paniniA2B1") warper_creator = new cv::PaniniWarper(2, 1);
//		else if (warp_type == "paniniA1.5B1") warper_creator = new cv::PaniniWarper(1.5, 1);
//		else if (warp_type == "paniniPortraitA2B1") warper_creator = new cv::PaniniPortraitWarper(2, 1);
//		else if (warp_type == "paniniPortraitA1.5B1") warper_creator = new cv::PaniniPortraitWarper(1.5, 1);
//		else if (warp_type == "mercator") warper_creator = new cv::MercatorWarper();
//		else if (warp_type == "transverseMercator") warper_creator = new cv::TransverseMercatorWarper();
//	}
//
//	if (warper_creator.empty())
//	{
//		cout << "Can't create the following warper '" << warp_type << "'\n";
//		return 1;
//	}
//
//	// 投影变换
//	Ptr<RotationWarper> warper = warper_creator->create(static_cast<float>(warped_image_scale * seam_work_aspect));
//
//	for (int i = 0; i < num_images; ++i)
//	{
//		Mat_<float> K;
//		cameras[i].K().convertTo(K, CV_32F);
//		float swa = (float)seam_work_aspect;
//		K(0, 0) *= swa; K(0, 2) *= swa;
//		K(1, 1) *= swa; K(1, 2) *= swa;
//		
//		corners[i] = warper->warp(images[i], K, cameras[i].R, INTER_LINEAR, BORDER_REFLECT, images_warped[i]);
//		//QMessageBox::warning(this, QString::fromLocal8Bit("提示"), content, QMessageBox::Yes | QMessageBox::No);
//
//		sizes[i] = images_warped[i].size();
//
//		warper->warp(masks[i], K, cameras[i].R, INTER_NEAREST, BORDER_CONSTANT, masks_warped[i]);
//	}
//
//	vector<Mat> images_warped_f(num_images);
//	for (int i = 0; i < num_images; ++i)
//		images_warped[i].convertTo(images_warped_f[i], CV_32F);
//
//	//LOGLN("Warping images, time: " << ((getTickCount() - t) / getTickFrequency()) << " sec");
//	
//	progress.setValue(7);
//	Ptr<ExposureCompensator> compensator = ExposureCompensator::createDefault(expos_comp_type);
//	compensator->feed(corners, images_warped, masks_warped);
//
//	progress.setValue(8);
//	Ptr<SeamFinder> seam_finder;
//	if (seam_find_type == "no")
//		seam_finder = new detail::NoSeamFinder();
//	else if (seam_find_type == "voronoi")
//		seam_finder = new detail::VoronoiSeamFinder();
//	else if (seam_find_type == "gc_color")
//	{
//#if defined(HAVE_OPENCV_GPU)
//		if (try_gpu && gpu::getCudaEnabledDeviceCount() > 0)
//			seam_finder = new detail::GraphCutSeamFinderGpu(GraphCutSeamFinderBase::COST_COLOR);
//		else
//#endif
//			seam_finder = new detail::GraphCutSeamFinder(GraphCutSeamFinderBase::COST_COLOR);
//	}
//	else if (seam_find_type == "gc_colorgrad")
//	{
//#if defined(HAVE_OPENCV_GPU)
//		if (try_gpu && gpu::getCudaEnabledDeviceCount() > 0)
//			seam_finder = new detail::GraphCutSeamFinderGpu(GraphCutSeamFinderBase::COST_COLOR_GRAD);
//		else
//#endif
//			seam_finder = new detail::GraphCutSeamFinder(GraphCutSeamFinderBase::COST_COLOR_GRAD);
//	}
//	else if (seam_find_type == "dp_color")
//		seam_finder = new detail::DpSeamFinder(DpSeamFinder::COLOR);
//	else if (seam_find_type == "dp_colorgrad")
//		seam_finder = new detail::DpSeamFinder(DpSeamFinder::COLOR_GRAD);
//	if (seam_finder.empty())
//	{
//		cout << "Can't create the following seam finder '" << seam_find_type << "'\n";
//		return 1;
//	}
//
//	seam_finder->find(images_warped_f, corners, masks_warped);
//
//	// Release unused memory
//	images.clear();
//	images_warped.clear();
//	images_warped_f.clear();
//	masks.clear();
//
//	//LOGLN("Compositing...");
////#if ENABLE_LOG
////	t = getTickCount();
////#endif
//
//	progress.setValue(9);
//	progress.setLabelText(QString::fromLocal8Bit("图像融合...."));
//	Mat img_warped, img_warped_s;
//	Mat dilated_mask, seam_mask, mask, mask_warped;
//
//	//图像融合
//	Ptr<Blender> blender;
//	//double compose_seam_aspect = 1;
//	double compose_work_aspect = 1;
//
//	for (int img_idx = 0; img_idx < num_images; ++img_idx)
//	{
//		//LOGLN("Compositing image #" << indices[img_idx] + 1);
//		/*
//		由于以前进行处理的图片都是以work_scale（3.1节有介绍）进行缩放的，
//		所以图像的内参，corner（同一坐标后的顶点），mask（融合的掩码）都需要重新计算。
//		以及将之前计算的光照增强的gain也要计算进去
//		*/
//		// Read image and resize it if necessary
//		full_img = imread(img_names[img_idx]);
//		if (!is_compose_scale_set)
//		{
//			if (compose_megapix > 0)
//				compose_scale = min(1.0, sqrt(compose_megapix * 1e6 / full_img.size().area()));
//			is_compose_scale_set = true;
//
//			// Compute relative scales
//		   //compose_seam_aspect = compose_scale / seam_scale;
//			compose_work_aspect = compose_scale / work_scale;
//
//			// Update warped image scale
//			warped_image_scale *= static_cast<float>(compose_work_aspect);
//			warper = warper_creator->create(warped_image_scale);
//
//			// Update corners and sizes
//			for (int i = 0; i < num_images; ++i)
//			{
//				// Update intrinsics
//				cameras[i].focal *= compose_work_aspect;
//				cameras[i].ppx *= compose_work_aspect;
//				cameras[i].ppy *= compose_work_aspect;
//
//				// Update corner and size
//				Size sz = full_img_sizes[i];
//				if (std::abs(compose_scale - 1) > 1e-1)
//				{
//					sz.width = cvRound(full_img_sizes[i].width * compose_scale);
//					sz.height = cvRound(full_img_sizes[i].height * compose_scale);
//				}
//
//				Mat K;
//				cameras[i].K().convertTo(K, CV_32F);
//				Rect roi = warper->warpRoi(sz, K, cameras[i].R);
//				corners[i] = roi.tl();
//				sizes[i] = roi.size();
//			}
//		}
//
//		if (abs(compose_scale - 1) > 1e-1)
//			resize(full_img, img, Size(), compose_scale, compose_scale);
//		else
//			img = full_img;
//		full_img.release();
//		Size img_size = img.size();
//
//		Mat K;
//		cameras[img_idx].K().convertTo(K, CV_32F);
//
//		progress.setValue(10);
//		// Warp the current image
//		warper->warp(img, K, cameras[img_idx].R, INTER_LINEAR, BORDER_REFLECT, img_warped);
//
//		// Warp the current image mask
//		mask.create(img_size, CV_8U);
//		mask.setTo(Scalar::all(255));
//		warper->warp(mask, K, cameras[img_idx].R, INTER_NEAREST, BORDER_CONSTANT, mask_warped);
//
//		// Compensate exposure,光照补偿
//		progress.setLabelText(QString::fromLocal8Bit("光照补偿...."));
//		compensator->apply(img_idx, corners[img_idx], img_warped, mask_warped);
//
//		img_warped.convertTo(img_warped_s, CV_16S);
//		img_warped.release();
//		img.release();
//		mask.release();
//
//		dilate(masks_warped[img_idx], dilated_mask, Mat());
//		resize(dilated_mask, seam_mask, mask_warped.size());
//		mask_warped = seam_mask & mask_warped;
//
//		/*
//		进行多波段融合，首先初始化blend，确定blender的融合的方式，默认是多波段融合MULTI_BAND，
//		以及根据corners顶点和图像的大小确定最终全景图的尺寸
//		*/		
//		
//		if (blender.empty())
//		{
//			blender = Blender::createDefault(blend_type, try_gpu);
//			Size dst_sz = resultRoi(corners, sizes).size();//计算最后图像的大小
//			float blend_width = sqrt(static_cast<float>(dst_sz.area())) * blend_strength / 100.f;
//			if (blend_width < 1.f)
//				blender = Blender::createDefault(Blender::NO, try_gpu);
//			else if (blend_type == Blender::MULTI_BAND)
//			{
//				MultiBandBlender* mb = dynamic_cast<MultiBandBlender*>(static_cast<Blender*>(blender));
//				mb->setNumBands(static_cast<int>(ceil(log(blend_width) / log(2.)) - 1.));
//				//LOGLN("Multi-band blender, number of bands: " << mb->numBands());
//			}
//			else if (blend_type == Blender::FEATHER)
//			{
//				FeatherBlender* fb = dynamic_cast<FeatherBlender*>(static_cast<Blender*>(blender));
//				fb->setSharpness(1.f / blend_width);
//				//LOGLN("Feather blender, sharpness: " << fb->sharpness());
//			}
//			blender->prepare(corners, sizes);////根据corners顶点和图像的大小确定最终全景图的尺寸 
//		}
//
//		/*
//		*然后对每幅图图形构建金字塔，层数可以由输入的系数确定，默认是5层。
//		先对顶点以及图像的宽和高做处理，使其能被2^num_bands除尽，
//		这样才能将进行向下采样num_bands次，首先从源图像pyrDown向下采样，
//		在由最底部的图像pyrUp向上采样，把对应层得上采样和下采样的相减，
//		就得到了图像的高频分量，存储到每一个金字塔中。然后根据mask，
//		将每幅图像的各层金字塔分别写入最终的金字塔层dst_pyr_laplace中。
//		*
//		*/
//
//		// Blend the current image
//		//将图像写入金字塔中
//		blender->feed(img_warped_s, mask_warped, corners[img_idx]);
//	}
//	progress.setLabelText(QString::fromLocal8Bit("根据corners顶点和图像大小确定最终全景图尺寸...."));
//
//	progress.setValue(11);
//	Mat result, result_mask;
//	blender->blend(result, result_mask);////将多层金字塔图形叠加  
//
//	progress.setValue(12);
//	//s.showMessage("Finished!");
//	s.showMessage(QString::fromLocal8Bit("计算结束！"));
    
	
	return 0;
}

void controlProgress(int status){

}