#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/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;
// Default command line args
/*#if 1
#define DLL_API __declspec(dllexport)
#else
#define DLL_API __declspec(dllimport)
#endif
*/
/*extern "C" { //因爲python一般只支持c的接口
typedef struct ImageBase {
int w; //圖像的寬
int h; //圖像的高
int c; //通道數
unsigned char *data; //我們要寫python和c++交互的數據結構，0-255的單字符指針
}ImageMeta;
//typedef ImageBase ImageMeta;
DLL_API int Stitch(ImageMeta *im1, ImageMeta *im2);//函數導出，要改
};
*/
vector<String> img_names;
int num_images;
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";
#else
string features_type = "orb";
#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;
float match_conf = 0.3f;
string seam_find_type = "gc_color";
int blend_type = Blender::MULTI_BAND;
int timelapse_type = Timelapser::AS_IS;//延時攝影
float blend_strength = 5;
string result_name = "D:/result.jpg";
bool timelapse = false;//首先定義timelapse的默認布爾類型爲False
int range_width = -1;
/*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_cuda")
{
if (string(argv[i + 1]) == "no")
try_cuda = false;
else if (string(argv[i + 1]) == "yes")
try_cuda = true;
else
{
cout << "Bad --try_cuda 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]) == "--matcher")
{
if (string(argv[i + 1]) == "homography" || string(argv[i + 1]) == "affine")
matcher_type = argv[i + 1];
else
{
cout << "Bad --matcher flag value\n";
return -1;
}
i++;
}
else if (string(argv[i]) == "--estimator")
{
if (string(argv[i + 1]) == "homography" || string(argv[i + 1]) == "affine")
estimator_type = argv[i + 1];
else
{
cout << "Bad --estimator flag value\n";
return -1;
}
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 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;
else
{
cout << "Bad exposure compensation method\n";
return -1;
}
i++;
}
else if (string(argv[i]) == "--expos_comp_nr_feeds")
{
expos_comp_nr_feeds = atoi(argv[i + 1]);
i++;
}
else if (string(argv[i]) == "--expos_comp_nr_filtering")
{
expos_comp_nr_filtering = atoi(argv[i + 1]);
i++;
}
else if (string(argv[i]) == "--expos_comp_block_size")
{
expos_comp_block_size = atoi(argv[i + 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]) == "--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;
else
{
cout << "Bad timelapse method\n";
return -1;
}
i++;
}
else if (string(argv[i]) == "--rangewidth")
{
range_width = atoi(argv[i + 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;
}*/
int main()
//vector<Mat> img_list
{//一個int數；一個圖片類型的列表
//predict先判斷長度 然後長度作爲一個參數傳給
//preview = true;
//try_cuda = true;
//preview = true;
//result = 'D:/result.jpg';
//work_megapix = -1;
//features_type = "orb";
Mat img1, img2;
img1 = imread("D:/1Hill.jpg");
img2 = imread("D:/2Hill.jpg");
vector<Mat> ALLimages(2);
ALLimages[0] = img1.clone();
ALLimages[1] = img2.clone();
//img_names.push_back("D:/1Hill.jpg");
//img_names.push_back("D:/2Hill.jpg");//??
//img_names.push_back("D:/3Hill.jpg");//??
num_images = 2;
#if ENABLE_LOG
int64 app_start_time = getTickCount();
#endif
#if 0
cv::setBreakOnError(true);
#endif
//int retval = parseCmdArgs(argc, argv);
//if (retval)
//return retval;
// Check if have enough images
//int num_images = static_cast<int>(img_names.size());
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...");
#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();
}
else if (features_type == "sift") {
finder = xfeatures2d::SIFT::create();
}
#endif
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 = ALLimages[i];
//獲取一張圖。imread_img -------->Mat
//先把一邊調通了再去組合調試，分治
//full_img = img_list;
//python傳進來n張圖片的base64,可以轉成讀取後的圖片。
//先在c中定義圖像HWC結構數組數組轉一次 Mat, dll返回Mat結果，Mat轉一次結構體
//main輸入 Mat1,Mat2
//dll返回數組，python轉化成cv2image,然後輸出image2base64
//full_image裏面是讀取的imread_img類型
//base64的size容易確定
//先在predict前提取到圖片的整個Mat傳給DLL
full_img_sizes[i] = full_img.size();//結果：full_img_sizes = [(500,300),(200,100)]
if (full_img.empty())
{
//LOGLN("Can't open image " << img_names[i]);//訪問了空指針，和img_names有關
return -2;
}
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, 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("Features in image #" << i + 1 << ": " << features[i].keypoints.size());
resize(full_img, img, Size(), seam_scale, seam_scale, INTER_LINEAR_EXACT);
images[i] = img.clone();
//循環是爲了找到每張圖的特徵，然後把圖片copy到images裏
}
full_img.release();
img.release();
LOGLN("Finding features, time: " << ((getTickCount() - t) / getTickFrequency()) << " sec");
LOG("Pairwise matching");
#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);
else
matcher = makePtr<BestOf2NearestRangeMatcher>(range_width, try_cuda, match_conf);
(*matcher)(features, pairwise_matches);
matcher->collectGarbage();
LOGLN("Pairwise matching, time: " << ((getTickCount() - t) / getTickFrequency()) << " sec");
// Check if we should save matches graph
//if (save_graph)
//{
// LOGLN("Saving matches 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);
if (indices.size() != 2)//判斷兩個圖片的相關性
return -1;
if (num_images < 2)
{
LOGLN("Need more images");
return -1;
}
Ptr<Estimator> estimator;
if (estimator_type == "affine")
estimator = makePtr<AffineBasedEstimator>();
else
estimator = makePtr<HomographyBasedEstimator>();
vector<CameraParams> cameras;
if (!(*estimator)(features, pairwise_matches, cameras))
{
cout << "Homography estimation failed.\n";
return -1;
}
for (size_t i = 0; i < cameras.size(); ++i)
{
Mat R;
cameras[i].R.convertTo(R, CV_32F);
cameras[i].R = R;
//LOGLN("Initial camera intrinsics #" << indices[i] + 1 << ":\nK:\n" << cameras[i].K() << "\nR:\n" << cameras[i].R);
}
Ptr<detail::BundleAdjusterBase> adjuster;
if (ba_cost_func == "reproj") adjuster = makePtr<detail::BundleAdjusterReproj>();
else if (ba_cost_func == "ray") adjuster = makePtr<detail::BundleAdjusterRay>();
else if (ba_cost_func == "affine") adjuster = makePtr<detail::BundleAdjusterAffinePartial>();
else if (ba_cost_func == "no") adjuster = makePtr<NoBundleAdjuster>();
else
{
cout << "Unknown bundle adjustment cost function: '" << ba_cost_func << "'.\n";
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);
if (!(*adjuster)(features, pairwise_matches, cameras))
{
cout << "Camera parameters adjusting failed.\n";
return -1;
}
// Find median focal length
vector<double> focals;
for (size_t i = 0; i < cameras.size(); ++i)
{
//LOGLN("Camera #" << 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;
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;
if (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("Warping images (auxiliary)... ");
#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);
// Prepare images masks
for (int i = 0; i < num_images; ++i)
{
masks[i].create(images[i].size(), CV_8U);
masks[i].setTo(Scalar::all(255));
}
// Warp images and their masks
Ptr<WarperCreator> warper_creator;
#ifdef HAVE_OPENCV_CUDAWARPING
if (try_cuda && cuda::getCudaEnabledDeviceCount() > 0)
{
if (warp_type == "plane")
warper_creator = makePtr<cv::PlaneWarperGpu>();
else if (warp_type == "cylindrical")
warper_creator = makePtr<cv::CylindricalWarperGpu>();
else if (warp_type == "spherical")
warper_creator = makePtr<cv::SphericalWarperGpu>();
}
else
#endif
{
if (warp_type == "plane")
warper_creator = makePtr<cv::PlaneWarper>();
else if (warp_type == "affine")
warper_creator = makePtr<cv::AffineWarper>();
else if (warp_type == "cylindrical")
warper_creator = makePtr<cv::CylindricalWarper>();
else if (warp_type == "spherical")
warper_creator = makePtr<cv::SphericalWarper>();
else if (warp_type == "fisheye")
warper_creator = makePtr<cv::FisheyeWarper>();
else if (warp_type == "stereographic")
warper_creator = makePtr<cv::StereographicWarper>();
else if (warp_type == "compressedPlaneA2B1")
warper_creator = makePtr<cv::CompressedRectilinearWarper>(2.0f, 1.0f);
else if (warp_type == "compressedPlaneA1.5B1")
warper_creator = makePtr<cv::CompressedRectilinearWarper>(1.5f, 1.0f);
else if (warp_type == "compressedPlanePortraitA2B1")
warper_creator = makePtr<cv::CompressedRectilinearPortraitWarper>(2.0f, 1.0f);
else if (warp_type == "compressedPlanePortraitA1.5B1")
warper_creator = makePtr<cv::CompressedRectilinearPortraitWarper>(1.5f, 1.0f);
else if (warp_type == "paniniA2B1")
warper_creator = makePtr<cv::PaniniWarper>(2.0f, 1.0f);
else if (warp_type == "paniniA1.5B1")
warper_creator = makePtr<cv::PaniniWarper>(1.5f, 1.0f);
else if (warp_type == "paniniPortraitA2B1")
warper_creator = makePtr<cv::PaniniPortraitWarper>(2.0f, 1.0f);
else if (warp_type == "paniniPortraitA1.5B1")
warper_creator = makePtr<cv::PaniniPortraitWarper>(1.5f, 1.0f);
else if (warp_type == "mercator")
warper_creator = makePtr<cv::MercatorWarper>();
else if (warp_type == "transverseMercator")
warper_creator = makePtr<cv::TransverseMercatorWarper>();
}
if (!warper_creator)
{
cout << "Can't create the following warper '" << warp_type << "'\n";
return 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("Warping images, time: " << ((getTickCount() - t) / getTickFrequency()) << " sec");
LOGLN("Compensating exposure...");
#if ENABLE_LOG
t = getTickCount();
#endif
Ptr<ExposureCompensator> compensator = ExposureCompensator::createDefault(expos_comp_type);
if (dynamic_cast<GainCompensator*>(compensator.get()))
{
GainCompensator* gcompensator = dynamic_cast<GainCompensator*>(compensator.get());
gcompensator->setNrFeeds(expos_comp_nr_feeds);
}
if (dynamic_cast<ChannelsCompensator*>(compensator.get()))
{
ChannelsCompensator* ccompensator = dynamic_cast<ChannelsCompensator*>(compensator.get());
ccompensator->setNrFeeds(expos_comp_nr_feeds);
}
if (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("Compensating exposure, time: " << ((getTickCount() - t) / getTickFrequency()) << " sec");
LOGLN("Finding seams...");
#if ENABLE_LOG
t = getTickCount();
#endif
Ptr<SeamFinder> seam_finder;
if (seam_find_type == "no")
seam_finder = makePtr<detail::NoSeamFinder>();
else if (seam_find_type == "voronoi")
seam_finder = makePtr<detail::VoronoiSeamFinder>();
else if (seam_find_type == "gc_color")
{
#ifdef HAVE_OPENCV_CUDALEGACY
if (try_cuda && cuda::getCudaEnabledDeviceCount() > 0)
seam_finder = makePtr<detail::GraphCutSeamFinderGpu>(GraphCutSeamFinderBase::COST_COLOR);
else
#endif
seam_finder = makePtr<detail::GraphCutSeamFinder>(GraphCutSeamFinderBase::COST_COLOR);
}
else if (seam_find_type == "gc_colorgrad")
{
#ifdef HAVE_OPENCV_CUDALEGACY
if (try_cuda && cuda::getCudaEnabledDeviceCount() > 0)
seam_finder = makePtr<detail::GraphCutSeamFinderGpu>(GraphCutSeamFinderBase::COST_COLOR_GRAD);
else
#endif
seam_finder = makePtr<detail::GraphCutSeamFinder>(GraphCutSeamFinderBase::COST_COLOR_GRAD);
}
else if (seam_find_type == "dp_color")
seam_finder = makePtr<detail::DpSeamFinder>(DpSeamFinder::COLOR);
else if (seam_find_type == "dp_colorgrad")
seam_finder = makePtr<detail::DpSeamFinder>(DpSeamFinder::COLOR_GRAD);
if (!seam_finder)
{
cout << "Can't create the following seam finder '" << seam_find_type << "'\n";
return 1;
}
seam_finder->find(images_warped_f, corners, masks_warped);
LOGLN("Finding seams, time: " << ((getTickCount() - t) / getTickFrequency()) << " sec");
// Release unused memory
images.clear();
images_warped.clear();
images_warped_f.clear();
masks.clear();
LOGLN("Compositing...");
#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("Compositing image #" << indices[img_idx] + 1);
// Read image and resize it if necessary
full_img = ALLimages[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];
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)//沒用
resize(full_img, img, Size(), compose_scale, compose_scale, INTER_LINEAR_EXACT);
else
img = full_img;
full_img.release();
Size img_size = img.size();
Mat K;
cameras[img_idx].K().convertTo(K, CV_32F);
// 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
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是False,timelapse也是False,這裏運行了！
{//做multiband
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("Multi-band blender, number of bands: " << mb->numBands());
}
else if (blend_type == Blender::FEATHER)//未運行
{
FeatherBlender* fb = dynamic_cast<FeatherBlender*>(blender.get());
fb->setSharpness(1.f / blend_width);
LOGLN("Feather blender, sharpness: " << fb->sharpness());
}
blender->prepare(corners, sizes);
}
else if (!timelapser && timelapse)//timelapse是假，timelapser是什麼？？沒運行
{
timelapser = Timelapser::createDefault(timelapse_type);
timelapser->initialize(corners, sizes);
cout << "----------------------------運行---------------------------------" << endl;
}
// Blend the current image
if (timelapse)//默認是假
{
cout << "----------------------------運行2---------------------------------" << endl;
}
else
{//這裏運行了兩次，因爲在循環體中，圖片有兩張
blender->feed(img_warped_s, mask_warped, corners[img_idx]);
cout << "----------------------------運行3---------------------------------" << endl;
}
}
if (!timelapse)//運行了
{
Mat result, result_mask;
blender->blend(result, result_mask);
LOGLN("Compositing, time: " << ((getTickCount() - t) / getTickFrequency()) << " sec");
imwrite(result_name, result);
}
LOGLN("Finished, total time: " << ((getTickCount() - app_start_time) / getTickFrequency()) << " sec");
return 0;
}
