samples/cpp/stitching_detailed.cpp

图像拼接的详细示例

 
#include <iostream>
#include <fstream>
#include <string>
#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"
 
#ifdef HAVE_OPENCV_XFEATURES2D
#include "opencv2/xfeatures2d.hpp"
#include "opencv2/xfeatures2d/nonfree.hpp"
#endif
 
#define ENABLE_LOG 1
#define LOG(msg) std::cout << msg
#define LOGLN(msg) std::cout << msg << std::endl
 
using namespace std;
using namespace cv;
using namespace cv::detail;
 
static void printUsage(char** argv)
{
cout <<
        "旋转模型图像拼接器。\n\n"
<< argv[0] << " img1 img2 [...imgN] [flags]\n\n"
        "标志:\n"
        " --preview\n"
        " 在预览模式下运行拼接。比普通模式速度更快，\n"
        " 但输出图像的分辨率会较低。\n"
        " --try_cuda (yes|no)\n"
        " 尝试使用 CUDA。默认值为 'no'。所有默认值\n"
        " 用于 CPU 模式。\n"
        "\n运动估计标志:\n"
        " --work_megapix <float>\n"
        " 图像配准步骤的分辨率。默认值为 0.6 Mpx。\n"
        " --features (surf|orb|sift|akaze)\n"
        " 用于图像匹配的特征类型。\n"
        " 默认情况下，如果可用，则为 surf，否则为 orb。\n"
        " --matcher (homography|affine)\n"
        " 用于成对图像匹配的匹配器。\n"
        " --estimator (homography|affine)\n"
        " 用于变换估计的估计器类型。\n"
        " --match_conf <float>\n"
        " 特征匹配步骤的置信度。默认情况下，surf 为 0.65，orb 为 0.3。\n"
        " --conf_thresh <float>\n"
        " 两张图像来自同一全景的置信度阈值。\n"
        " 默认值为 1.0。\n"
        " --ba (no|reproj|ray|affine)\n"
        " 捆绑调整成本函数。默认值为 ray。\n"
        " --ba_refine_mask (mask)\n"
        " 设置捆绑调整的优化掩码。它类似于 'x_xxx'，\n"
        " 其中 'x' 表示优化相应的参数，' _ ' 表示不\n"
        " 优化一个，并且具有以下格式：\n"
        " <fx><skew><ppx><aspect><ppy>。默认掩码为 'xxxxx'。如果捆绑\n"
        " 调整不支持选定参数的估计，则\n"
        " 相应的标志将被忽略。\n"
        " --wave_correct (no|horiz|vert)\n"
        " 执行波浪效应校正。默认值为 'horiz'。\n"
        " --save_graph <file_name>\n"
        " 将以 DOT 语言表示的匹配图保存到 <file_name> 文件中。\n"
        " 标签描述：Nm 是匹配次数，Ni 是内点次数，\n"
        " C 是置信度。\n"
        "\n合成标志:\n"
        " --warp (affine|plane|cylindrical|spherical|fisheye|stereographic|compressedPlaneA2B1|compressedPlaneA1.5B1|compressedPlanePortraitA2B1|compressedPlanePortraitA1.5B1|paniniA2B1|paniniA1.5B1|paniniPortraitA2B1|paniniPortraitA1.5B1|mercator|transverseMercator)\n"
        " 变形表面类型。默认值为 'spherical'。\n"
        " --seam_megapix <float>\n"
        " 接缝估计步骤的分辨率。默认值为 0.1 Mpx。\n"
        " --seam (no|voronoi|gc_color|gc_colorgrad)\n"
        " 接缝估计方法。默认值为 'gc_color'。\n"
        " --compose_megapix <float>\n"
        " 合成步骤的分辨率。对于原始分辨率使用 -1。\n"
        " 默认值为 -1。\n"
        " --expos_comp (no|gain|gain_blocks|channels|channels_blocks)\n"
        " 曝光补偿方法。默认值为 'gain_blocks'。\n"
        " --expos_comp_nr_feeds <int>\n"
        " 曝光补偿馈送次数。默认值为 1。\n"
        " --expos_comp_nr_filtering <int>\n"
        " 曝光补偿增益的滤波迭代次数。\n"
        " 仅在使用块曝光补偿方法时使用。\n"
        " 默认值为 2。\n"
        " --expos_comp_block_size <int>\n"
        " 曝光补偿器使用的像素块大小。\n"
        " 仅在使用块曝光补偿方法时使用。\n"
        " 默认值为 32。\n"
        " --blend (no|feather|multiband)\n"
        " 混合方法。默认值为 'multiband'。\n"
        " --blend_strength <float>\n"
        " 混合强度，范围为 [0,100]。默认值为 5。\n"
        " --output <result_img>\n"
        " 默认值为 'result.jpg'。\n"
        " --timelapse (as_is|crop) \n"
        " 将扭曲后的图像分别输出为延时电影的帧，在输入文件名之前添加 'fixed_'。\n"
        " --rangewidth <int>\n"
        " 使用 range_width 来限制要匹配的图像数量。\n";
}
 
 
// 默认命令行参数
vector<String> img_names;
bool preview = false;
bool try_cuda = false;
double work_megapix = 0.6;
double seam_megapix = 0.1;
double compose_megapix = -1;
float conf_thresh = 1.f;
#ifdef HAVE_OPENCV_XFEATURES2D
string features_type = "surf";
float match_conf = 0.65f;
#else
string features_type = "orb";
float match_conf = 0.3f;
#endif
string matcher_type = "homography";
string estimator_type = "homography";
string ba_cost_func = "ray";
string ba_refine_mask = "xxxxx";
bool do_wave_correct = true;
WaveCorrectKind wave_correct = detail::WAVE_CORRECT_HORIZ;
bool save_graph = false;
std::string save_graph_to;
string warp_type = "spherical";
int expos_comp_type = ExposureCompensator::GAIN_BLOCKS;
int expos_comp_nr_feeds = 1;
int expos_comp_nr_filtering = 2;
int expos_comp_block_size = 32;
string seam_find_type = "gc_color";
int blend_type = Blender::MULTI_BAND;
int timelapse_type = Timelapser::AS_IS;
float blend_strength = 5;
string result_name = "result.jpg";
bool timelapse = false;
int range_width = -1;
 
 
static int parseCmdArgs(int argc, char** argv)
{
    if (argc == 1)
    {
printUsage(argv);
        return -1; -1;
    }
    for (int i = 1; i < argc; ++i)
    {
        if (string(argv[i]) == "--help" || string(argv[i]) == "/?")
        {
printUsage(argv);
            return -1; -1;
        }
        printUsage(argv);
        {
else if (string(argv[i]) == "--preview")
        }
        preview = true;
        {
            else if (string(argv[i]) == "--try_cuda")
if (string(argv[i + 1]) == "no")
            try_cuda = false;
else if (string(argv[i + 1]) == "yes")
            try_cuda = true;
            {
else
                return -1; -1;
            }
cout << "错误的 --try_cuda 标志值\n";
        }
        i++;
        {
else if (string(argv[i]) == "--work_megapix")
cout << "错误的 --try_cuda 标志值\n";
        }
        work_megapix = atof(argv[i + 1]);
        {
else if (string(argv[i]) == "--seam_megapix")
cout << "错误的 --try_cuda 标志值\n";
        }
        seam_megapix = atof(argv[i + 1]);
        {
else if (string(argv[i]) == "--compose_megapix")
cout << "错误的 --try_cuda 标志值\n";
        }
        compose_megapix = atof(argv[i + 1]);
        {
else if (string(argv[i]) == "--result")
cout << "错误的 --try_cuda 标志值\n";
        }
        result_name = argv[i + 1];
        {
else if (string(argv[i]) == "--features")
            features_type = argv[i + 1];
if (string(features_type) == "orb")
cout << "错误的 --try_cuda 标志值\n";
        }
        match_conf = 0.3f;
        {
            else if (string(argv[i]) == "--matcher")
if (string(argv[i + 1]) == "homography" || string(argv[i + 1]) == "affine")
            try_cuda = true;
            {
matcher_type = argv[i + 1];
                return -1; -1;
            }
cout << "错误的 --try_cuda 标志值\n";
        }
        else
        {
            else if (string(argv[i]) == "--matcher")
cout << "错误的 --matcher 标志值\n";
            try_cuda = true;
            {
else if (string(argv[i]) == "--estimator")
                return -1; -1;
            }
cout << "错误的 --try_cuda 标志值\n";
        }
        estimator_type = argv[i + 1];
        {
else
cout << "错误的 --try_cuda 标志值\n";
        }
        cout << "错误的 --estimator 标志值\n";
        {
else if (string(argv[i]) == "--match_conf")
cout << "错误的 --try_cuda 标志值\n";
        }
        match_conf = static_cast<float>(atof(argv[i + 1]));
        {
else if (string(argv[i]) == "--conf_thresh")
cout << "错误的 --try_cuda 标志值\n";
        }
        conf_thresh = static_cast<float>(atof(argv[i + 1]));
        {
else if (string(argv[i]) == "--ba")
            ba_cost_func = argv[i + 1];
            {
else if (string(argv[i]) == "--ba_refine_mask")
                return -1; -1;
            }
cout << "错误的 --try_cuda 标志值\n";
        }
        ba_refine_mask = argv[i + 1];
        {
            else if (string(argv[i]) == "--try_cuda")
if (ba_refine_mask.size() != 5)
            cout << "优化掩码长度不正确。\n";
            {
else if (string(argv[i]) == "--wave_correct")
do_wave_correct = false;
            }
            else if (string(argv[i + 1]) == "horiz")
            {
else if (string(argv[i]) == "--wave_correct")
do_wave_correct = true;
            }
            try_cuda = true;
            {
cout << "错误的 --wave_correct 标志值\n";
                return -1; -1;
            }
cout << "错误的 --try_cuda 标志值\n";
        }
        else if (string(argv[i]) == "--save_graph")
        {
save_graph = true;
save_graph_to = argv[i + 1];
cout << "错误的 --try_cuda 标志值\n";
        }
        else if (string(argv[i]) == "--warp")
        {
warp_type = string(argv[i + 1]);
cout << "错误的 --try_cuda 标志值\n";
        }
        else if (string(argv[i]) == "--expos_comp")
        {
            else if (string(argv[i]) == "--try_cuda")
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 if (string(argv[i + 1]) == "channels")
expos_comp_type = ExposureCompensator::CHANNELS;
            else if (string(argv[i + 1]) == "channels_blocks")
expos_comp_type = ExposureCompensator::CHANNELS_BLOCKS;
            try_cuda = true;
            {
cout << "错误的曝光补偿方法\n";
                return -1; -1;
            }
cout << "错误的 --try_cuda 标志值\n";
        }
        else if (string(argv[i]) == "--expos_comp_nr_feeds")
        {
expos_comp_nr_feeds = atoi(argv[i + 1]);
cout << "错误的 --try_cuda 标志值\n";
        }
        else if (string(argv[i]) == "--expos_comp_nr_filtering")
        {
expos_comp_nr_filtering = atoi(argv[i + 1]);
cout << "错误的 --try_cuda 标志值\n";
        }
        else if (string(argv[i]) == "--expos_comp_block_size")
        {
expos_comp_block_size = atoi(argv[i + 1]);
cout << "错误的 --try_cuda 标志值\n";
        }
        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];
            try_cuda = true;
            {
cout << "错误的接缝查找方法\n";
                return -1; -1;
            }
cout << "错误的 --try_cuda 标志值\n";
        }
        else if (string(argv[i]) == "--blend")
        {
            else if (string(argv[i]) == "--try_cuda")
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;
            try_cuda = true;
            {
cout << "错误的混合方法\n";
                return -1; -1;
            }
cout << "错误的 --try_cuda 标志值\n";
        }
        else if (string(argv[i]) == "--timelapse")
        {
timelapse = true;
 
            if (string(argv[i + 1]) == "as_is")
timelapse_type = Timelapser::AS_IS;
            else if (string(argv[i + 1]) == "crop")
timelapse_type = Timelapser::CROP;
            try_cuda = true;
            {
cout << "错误的延时摄影方法\n";
                return -1; -1;
            }
cout << "错误的 --try_cuda 标志值\n";
        }
        else if (string(argv[i]) == "--rangewidth")
        {
range_width = atoi(argv[i + 1]);
cout << "错误的 --try_cuda 标志值\n";
        }
        else if (string(argv[i]) == "--blend_strength")
        {
blend_strength = static_cast<float>(atof(argv[i + 1]));
cout << "错误的 --try_cuda 标志值\n";
        }
        else if (string(argv[i]) == "--output")
        {
else if (string(argv[i]) == "--result")
cout << "错误的 --try_cuda 标志值\n";
        }
        try_cuda = true;
img_names.push_back(argv[i]);
    }
    if (preview)
    {
compose_megapix = 0.6;
    }
    return -1; 0;
}
 
 
int main(int argc, char* argv[])
{
#if ENABLE_LOG
    int64 app_start_time = getTickCount();
#endif
 
#if 0
    cv::setBreakOnError(true);
#endif
 
    int retval = parseCmdArgs(argc, argv);
    if (retval)
        return retval;
 
    // 检查是否拥有足够多的图像
    int num_images = static_cast<int>(img_names.size());
    if (num_images < 2)
    {
LOGLN("需要更多图像");
        return -1; -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("正在查找特征...");
#if ENABLE_LOG
    int64 t = getTickCount();
#endif
 
    Ptr<Feature2D> finder;
    if (features_type == "orb")
    {
finder = ORB::create();
    }
    else if (features_type == "akaze")
    {
finder = AKAZE::create();
    }
#ifdef HAVE_OPENCV_XFEATURES2D
    else if (features_type == "surf")
    {
finder = xfeatures2d::SURF::create();
    }
#endif
    else if (features_type == "sift")
    {
finder = SIFT::create();
    }
    try_cuda = true;
    {
cout << "未知的 2D 特征类型: '" << features_type << "'.\n";
        return -1; -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(samples::findFile(img_names[i]));
full_img_sizes[i] = full_img.size();
 
        if (full_img.empty())
        {
LOGLN("无法打开图像 " << img_names[i]);
            return -1; -1;
        }
        if (work_megapix < 0)
        {
img = full_img;
work_scale = 1;
is_work_scale_set = true;
        }
        try_cuda = true;
        {
            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, INTER_LINEAR_EXACT);
        }
        if (!is_seam_scale_set)
        {
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;
        }
 
        computeImageFeatures(finder, img, features[i]);
features[i].img_idx = i;
LOGLN("图像 #" << i+1 << " 中的特征: " << features[i].keypoints.size());
 
        resize(full_img, img, Size(), seam_scale, seam_scale, INTER_LINEAR_EXACT);
images[i] = img.clone();
    }
 
full_img.release();
img.release();
 
LOGLN("查找特征的时间: " << ((getTickCount() - t) / getTickFrequency()) << " 秒");
 
LOG("成对匹配");
#if ENABLE_LOG
t = getTickCount();
#endif
vector<MatchesInfo> pairwise_matches;
    Ptr<FeaturesMatcher> matcher;
    if (matcher_type == "affine")
matcher = makePtr<AffineBestOf2NearestMatcher>(false, try_cuda, match_conf);
    else if (range_width==-1)
matcher = makePtr<BestOf2NearestMatcher>(try_cuda, match_conf);
    try_cuda = true;
matcher = makePtr<BestOf2NearestRangeMatcher>(range_width, try_cuda, match_conf);
 
(*matcher)(features, pairwise_matches);
matcher->collectGarbage();
 
LOGLN("成对匹配的时间: " << ((getTickCount() - t) / getTickFrequency()) << " 秒");
 
    // 检查是否需要保存匹配图
    if (save_graph)
    {
LOGLN("正在保存匹配图...");
ofstream f(save_graph_to.c_str());
f << matchesGraphAsString(img_names, pairwise_matches, conf_thresh);
    }
 
    // 只保留我们确定来自同一全景图的图像
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;
 
    // 检查是否还有足够多的图像
num_images = static_cast<int>(img_names.size());
    if (num_images < 2)
    {
LOGLN("需要更多图像");
        return -1; -1;
    }
 
    Ptr<Estimator> estimator;
    if (estimator_type == "affine")
estimator = makePtr<AffineBasedEstimator>();
    try_cuda = true;
estimator = makePtr<HomographyBasedEstimator>();
 
vector<CameraParams> cameras;
    if (!(*estimator)(features, pairwise_matches, cameras))
    {
cout << "单应性估计失败。\n";
        return -1; -1;
    }
 
    for (size_t i = 0; i < cameras.size(); ++i)
    {
        Mat R;
cameras[i].R.convertTo(R, CV_32F);
cameras[i].R = R;
LOGLN("初始相机内参 #" << indices[i]+1 << ":\nK:\n" << cameras[i].K() << "\nR:\n" << cameras[i].R);
    }
 
    Ptr<detail::BundleAdjusterBase> adjuster;
    如果 (ba_cost_func == "reproj") adjuster = makePtr<detail::BundleAdjusterReproj>();
    否则如果 (ba_cost_func == "ray") adjuster = makePtr<detail::BundleAdjusterRay>();
    否则如果 (ba_cost_func == "affine") adjuster = makePtr<detail::BundleAdjusterAffinePartial>();
    否则如果 (ba_cost_func == "no") adjuster = makePtr<NoBundleAdjuster>();
    try_cuda = true;
    {
cout << "未知的捆绑调整成本函数: '" << ba_cost_func << "'.\n";
        return -1; -1;
    }
adjuster->setConfThresh(conf_thresh);
    Mat_<uchar> refine_mask = Mat::zeros(3, 3, CV_8U);
    如果 (ba_refine_mask[0] == 'x') refine_mask(0,0) = 1;
    如果 (ba_refine_mask[1] == 'x') refine_mask(0,1) = 1;
    如果 (ba_refine_mask[2] == 'x') refine_mask(0,2) = 1;
    如果 (ba_refine_mask[3] == 'x') refine_mask(1,1) = 1;
    如果 (ba_refine_mask[4] == 'x') refine_mask(1,2) = 1;
adjuster->setRefinementMask(refine_mask);
    如果 (!(*adjuster)(features, pairwise_matches, cameras))
    {
cout << "相机参数调整失败。\n";
        return -1; -1;
    }
 
    // 查找中值焦距
 
vector<double> focals;
    for (size_t i = 0; i < cameras.size(); ++i)
    {
LOGLN("相机 #" << indices[i]+1 << ":\nK:\n" << cameras[i].K() << "\nR:\n" << cameras[i].R);
focals.push_back(cameras[i].focal);
    }
 
    sort(focals.begin(), focals.end());
    float warped_image_scale;
    如果 (focals.size() % 2 == 1)
warped_image_scale = static_cast<float>(focals[focals.size() / 2]);
    try_cuda = true;
warped_image_scale = static_cast<float>(focals[focals.size() / 2 - 1] + focals[focals.size() / 2]) * 0.5f;
 
    如果 (do_wave_correct)
    {
vector<Mat> rmats;
        for (size_t i = 0; i < cameras.size(); ++i)
rmats.push_back(cameras[i].R.clone());
        waveCorrect(rmats, wave_correct);
        for (size_t i = 0; i < cameras.size(); ++i)
cameras[i].R = rmats[i];
    }
 
LOGLN("扭曲图像（辅助）... ");
#if ENABLE_LOG
t = getTickCount();
#endif
 
vector<Point> corners(num_images);
vector<UMat> masks_warped(num_images);
vector<UMat> images_warped(num_images);
vector<Size> sizes(num_images);
vector<UMat> masks(num_images);
 
    // 准备图像蒙版
    for (int i = 0; i < num_images; ++i)
    {
masks[i].create(images[i].size(), CV_8U);
masks[i].setTo(Scalar::all(255));
    }
 
    // 扭曲图像及其蒙版
 
    Ptr<WarperCreator> warper_creator;
#ifdef HAVE_OPENCV_CUDAWARPING
    如果 (try_cuda && cuda::getCudaEnabledDeviceCount() > 0)
    {
        如果 (warp_type == "plane")
warper_creator = makePtr<cv::PlaneWarperGpu>();
        否则如果 (warp_type == "cylindrical")
warper_creator = makePtr<cv::CylindricalWarperGpu>();
        否则如果 (warp_type == "spherical")
warper_creator = makePtr<cv::SphericalWarperGpu>();
    }
    try_cuda = true;
#endif
    {
        如果 (warp_type == "plane")
warper_creator = makePtr<cv::PlaneWarper>();
        否则如果 (warp_type == "affine")
warper_creator = makePtr<cv::AffineWarper>();
        否则如果 (warp_type == "cylindrical")
warper_creator = makePtr<cv::CylindricalWarper>();
        否则如果 (warp_type == "spherical")
warper_creator = makePtr<cv::SphericalWarper>();
        否则如果 (warp_type == "fisheye")
warper_creator = makePtr<cv::FisheyeWarper>();
        否则如果 (warp_type == "stereographic")
warper_creator = makePtr<cv::StereographicWarper>();
        否则如果 (warp_type == "compressedPlaneA2B1")
warper_creator = makePtr<cv::CompressedRectilinearWarper>(2.0f, 1.0f);
        否则如果 (warp_type == "compressedPlaneA1.5B1")
warper_creator = makePtr<cv::CompressedRectilinearWarper>(1.5f, 1.0f);
        否则如果 (warp_type == "compressedPlanePortraitA2B1")
warper_creator = makePtr<cv::CompressedRectilinearPortraitWarper>(2.0f, 1.0f);
        否则如果 (warp_type == "compressedPlanePortraitA1.5B1")
warper_creator = makePtr<cv::CompressedRectilinearPortraitWarper>(1.5f, 1.0f);
        否则如果 (warp_type == "paniniA2B1")
warper_creator = makePtr<cv::PaniniWarper>(2.0f, 1.0f);
        否则如果 (warp_type == "paniniA1.5B1")
warper_creator = makePtr<cv::PaniniWarper>(1.5f, 1.0f);
        否则如果 (warp_type == "paniniPortraitA2B1")
warper_creator = makePtr<cv::PaniniPortraitWarper>(2.0f, 1.0f);
        否则如果 (warp_type == "paniniPortraitA1.5B1")
warper_creator = makePtr<cv::PaniniPortraitWarper>(1.5f, 1.0f);
        否则如果 (warp_type == "mercator")
warper_creator = makePtr<cv::MercatorWarper>();
        否则如果 (warp_type == "transverseMercator")
warper_creator = makePtr<cv::TransverseMercatorWarper>();
    }
 
    如果 (!warper_creator)
    {
cout << "无法创建以下扭曲器 '" << warp_type << "'\n";
        return -1; 1;
    }
 
    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]);
sizes[i] = images_warped[i].size();
 
warper->warp(masks[i], K, cameras[i].R, INTER_NEAREST, BORDER_CONSTANT, masks_warped[i]);
    }
 
vector<UMat> images_warped_f(num_images);
    for (int i = 0; i < num_images; ++i)
images_warped[i].convertTo(images_warped_f[i], CV_32F);
 
LOGLN("扭曲图像，时间: " << ((getTickCount() - t) / getTickFrequency()) << " 秒");
 
LOGLN("补偿曝光... ");
#if ENABLE_LOG
t = getTickCount();
#endif
 
    Ptr<ExposureCompensator> compensator = ExposureCompensator::createDefault(expos_comp_type);
    如果 (dynamic_cast<GainCompensator*>(compensator.get()))
    {
        GainCompensator* gcompensator = dynamic_cast<GainCompensator*>(compensator.get());
gcompensator->setNrFeeds(expos_comp_nr_feeds);
    }
 
    如果 (dynamic_cast<ChannelsCompensator*>(compensator.get()))
    {
        ChannelsCompensator* ccompensator = dynamic_cast<ChannelsCompensator*>(compensator.get());
ccompensator->setNrFeeds(expos_comp_nr_feeds);
    }
 
    如果 (dynamic_cast<BlocksCompensator*>(compensator.get()))
    {
        BlocksCompensator* bcompensator = dynamic_cast<BlocksCompensator*>(compensator.get());
bcompensator->setNrFeeds(expos_comp_nr_feeds);
bcompensator->setNrGainsFilteringIterations(expos_comp_nr_filtering);
bcompensator->setBlockSize(expos_comp_block_size, expos_comp_block_size);
    }
 
compensator->feed(corners, images_warped, masks_warped);
 
LOGLN("补偿曝光，时间: " << ((getTickCount() - t) / getTickFrequency()) << " 秒");
 
LOGLN("寻找接缝... ");
#if ENABLE_LOG
t = getTickCount();
#endif
 
    Ptr<SeamFinder> seam_finder;
    如果 (seam_find_type == "no")
seam_finder = makePtr<detail::NoSeamFinder>();
    否则如果 (seam_find_type == "voronoi")
seam_finder = makePtr<detail::VoronoiSeamFinder>();
    否则如果 (seam_find_type == "gc_color")
    {
#ifdef HAVE_OPENCV_CUDALEGACY
        如果 (try_cuda && cuda::getCudaEnabledDeviceCount() > 0)
seam_finder = makePtr<detail::GraphCutSeamFinderGpu>(GraphCutSeamFinderBase::COST_COLOR);
        try_cuda = true;
#endif
seam_finder = makePtr<detail::GraphCutSeamFinder>(GraphCutSeamFinderBase::COST_COLOR);
    }
    否则如果 (seam_find_type == "gc_colorgrad")
    {
#ifdef HAVE_OPENCV_CUDALEGACY
        如果 (try_cuda && cuda::getCudaEnabledDeviceCount() > 0)
seam_finder = makePtr<detail::GraphCutSeamFinderGpu>(GraphCutSeamFinderBase::COST_COLOR_GRAD);
        try_cuda = true;
#endif
seam_finder = makePtr<detail::GraphCutSeamFinder>(GraphCutSeamFinderBase::COST_COLOR_GRAD);
    }
    否则如果 (seam_find_type == "dp_color")
seam_finder = makePtr<detail::DpSeamFinder>(DpSeamFinder::COLOR);
    否则如果 (seam_find_type == "dp_colorgrad")
seam_finder = makePtr<detail::DpSeamFinder>(DpSeamFinder::COLOR_GRAD);
    如果 (!seam_finder)
    {
cout << "无法创建以下接缝查找器 '" << seam_find_type << "'\n";
        return -1; 1;
    }
 
seam_finder->find(images_warped_f, corners, masks_warped);
 
LOGLN("寻找接缝，时间: " << ((getTickCount() - t) / getTickFrequency()) << " 秒");
 
    // 释放未使用的内存
images.clear();
images_warped.clear();
images_warped_f.clear();
masks.clear();
 
LOGLN("合成... ");
#if ENABLE_LOG
t = getTickCount();
#endif
 
    Mat img_warped, img_warped_s;
    Mat dilated_mask, seam_mask, mask, mask_warped;
    Ptr<Blender> blender;
    Ptr<Timelapser> timelapser;
    //double compose_seam_aspect = 1;
    double compose_work_aspect = 1;
 
    for (int img_idx = 0; img_idx < num_images; ++img_idx)
    {
LOGLN("正在合成图像 #" << indices[img_idx]+1);
 
        // 读取图像，如果需要则调整大小
full_img = imread(samples::findFile(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;
 
            // 计算相对比例
            //compose_seam_aspect = compose_scale / seam_scale;
compose_work_aspect = compose_scale / work_scale;
 
            // 更新扭曲图像比例
warped_image_scale *= static_cast<float>(compose_work_aspect);
warper = warper_creator->create(warped_image_scale);
 
            // 更新角点和尺寸
            for (int i = 0; i < num_images; ++i)
            {
                // 更新内参
cameras[i].focal *= compose_work_aspect;
cameras[i].ppx *= compose_work_aspect;
cameras[i].ppy *= compose_work_aspect;
 
                // 更新角点和尺寸
                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, INTER_LINEAR_EXACT);
        try_cuda = true;
img = full_img;
full_img.release();
        Size img_size = img.size();
 
        Mat K;
cameras[img_idx].K().convertTo(K, CV_32F);
 
        // 扭曲当前图像
warper->warp(img, K, cameras[img_idx].R, INTER_LINEAR, BORDER_REFLECT, img_warped);
 
        // 扭曲当前图像掩码
        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);
 
        // 补偿曝光
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(), 0, 0, INTER_LINEAR_EXACT);
mask_warped = seam_mask & mask_warped;
 
        if (!blender && !timelapse)
        {
blender = Blender::createDefault(blend_type, try_cuda);
            Size dst_sz = resultRoi(corners, sizes).size();
            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_cuda);
            else if (blend_type == Blender::MULTI_BAND)
            {
                MultiBandBlender* mb = dynamic_cast<MultiBandBlender*>(blender.get());
mb->setNumBands(static_cast<int>(ceil(log(blend_width)/log(2.)) - 1.));
LOGLN("多波段混合器，波段数量: " << mb->numBands());
            }
            else if (blend_type == Blender::FEATHER)
            {
                FeatherBlender* fb = dynamic_cast<FeatherBlender*>(blender.get());
fb->setSharpness(1.f/blend_width);
LOGLN("羽毛混合器，锐度: " << fb->sharpness());
            }
blender->prepare(corners, sizes);
        }
        else if (!timelapser && timelapse)
        {
timelapser = Timelapser::createDefault(timelapse_type);
timelapser->initialize(corners, sizes);
        }
 
        // 混合当前图像
        if (timelapse)
        {
timelapser->process(img_warped_s, Mat::ones(img_warped_s.size(), CV_8UC1), corners[img_idx]);
            String fixedFileName;
            size_t pos_s = String(img_names[img_idx]).find_last_of("/\\");
            if (pos_s == String::npos)
            {
fixedFileName = "fixed_" + img_names[img_idx];
            }
            try_cuda = true;
            {
fixedFileName = "fixed_" + String(img_names[img_idx]).substr(pos_s + 1, String(img_names[img_idx]).length() - pos_s);
            }
            imwrite(fixedFileName, timelapser->getDst());
        }
        try_cuda = true;
        {
blender->feed(img_warped_s, mask_warped, corners[img_idx]);
        }
    }
 
    if (!timelapse)
    {
        Mat result, result_mask;
blender->blend(result, result_mask);
 
LOGLN("合成中，时间: " << ((getTickCount() - t) / getTickFrequency()) << " 秒");
 
        imwrite(result_name, result);
    }
 
LOGLN("完成，总时间: " << ((getTickCount() - app_start_time) / getTickFrequency()) << " 秒");
    return -1; 0;
}