对Opencv 的stitching 的使用串联匹配

前面已经对串联匹配有了一定的了解，现在用它来改进 Opencv 的stitching ，

先找来三个博文为模板，分别是：

1。《任意n张图像拼接_效果很好_计算机视觉大作业1终版》

2。《 Opencv2.4.9源码分析——Stitching（八）》

3。《图像拼接（十）：OPenCV stitching和stitching_detailed》中的“stitching_detailed使用示例”

把他们中的一些Mat 转化为opencv 3.0 用到的 UMat 。

为什么不直接用3.0的示例呢？主要是示例不太友好方便，修改地方太多，自己的e文也太差。

通过测试：

1文只有一种长宽比，改变长宽比就出错。

2文速度较慢，注解不错。

3文没有中文注解，但速度较快，所以就以3文为模板修改匹配。

针对3.0修改后为：

//stitching_detailed使用 3.0
//用串联匹配代替原匹配

//
#define ENABLE_LOG 1

#include <iostream>
#include <fstream>
#include <string>
#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"

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


//

// 默认命令行参数
vector<string> img_names;
bool preview = false;
bool try_gpu = true;
double work_megapix = 0.6;
double seam_megapix = 0.1;
double compose_megapix = -1;
float conf_thresh = 1.f;
string features_type = "orb";//"surf";
string ba_cost_func = "reproj";//重映射误差方法   "ray";//射线发散误差方法  
string ba_refine_mask = "xxxxx";
bool do_wave_correct = true;
WaveCorrectKind wave_correct = detail::WAVE_CORRECT_HORIZ;// 波形校验，水平 // 波校正垂直 WAVE_CORRECT_VERT
bool save_graph = false;//是否保存匹配图
std::string save_graph_to;
string warp_type = "spherical";//球面投影
int expos_comp_type = ExposureCompensator::GAIN_BLOCKS;
float match_conf = 0.3f;
string seam_find_type = "gc_color";
int blend_type = Blender::MULTI_BAND;
float blend_strength = 5;
string result_name = "result.jpg";

void LoadImageNamesFromFile(char* name,vector<string>& image_names);//从列表中载入图像名
void i_matcher(vector<ImageFeatures> &features, vector<MatchesInfo> &pairwise_matches);

int main(int argc, char* argv[])
{
    //读入图像
    double ttt = getTickCount();

    cout<<"读出文件名..."<<endl;  
    LoadImageNamesFromFile("list.txt",img_names);//从list.txt文件装载图像文件名

#if ENABLE_LOG
    int64 app_start_time = getTickCount();
#endif

    cv::setBreakOnError(true);

    /*int retval = parseCmdArgs(argc, argv);
    if (retval)
        return retval;*/

    // Check if have enough images
    int num_images = static_cast<int>(img_names.size());
	cout<<"有 "<<num_images<<" 个图"<<endl;  
    if (num_images < 2)
    {
        LOGLN("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;

    //LOGLN("Finding features...");
	cout<<"正在寻找图像特征..."<<endl; 
#if ENABLE_LOG
    int64 t = getTickCount();
#endif

    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";
        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]);
        full_img_sizes[i] = full_img.size();

        if (full_img.empty())
        {
            LOGLN("Can't open image " << img_names[i]);
            return -1;
        }
        if (work_megapix < 0)
        {
            img = full_img;
            work_scale = 1;
            is_work_scale_set = true;
        }
        else
        {
            if (!is_work_scale_set)
            {
                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)
        {
            seam_scale = min(1.0, sqrt(seam_megapix * 1e6 / full_img.size().