1 #include <iostream> 2 #include <fstream> 3 #include <string> 4 #include "opencv2/opencv_modules.hpp" 5 #include <opencv2/core/utility.hpp> 6 #include "opencv2/imgcodecs.hpp" 7 #include "opencv2/highgui.hpp" 8 #include "opencv2/stitching/detail/autocalib.hpp" 9 #include "opencv2/stitching/detail/blenders.hpp" 10 #include "opencv2/stitching/detail/timelapsers.hpp" 11 #include "opencv2/stitching/detail/camera.hpp" 12 #include "opencv2/stitching/detail/exposure_compensate.hpp" 13 #include "opencv2/stitching/detail/matchers.hpp" 14 #include "opencv2/stitching/detail/motion_estimators.hpp" 15 #include "opencv2/stitching/detail/seam_finders.hpp" 16 #include "opencv2/stitching/detail/warpers.hpp" 17 #include "opencv2/stitching/warpers.hpp" 18 #ifdef HAVE_OPENCV_XFEATURES2D 19 #include "opencv2/xfeatures2d/nonfree.hpp" 20 #endif 21 #define ENABLE_LOG 1 22 #define LOG(msg) std::cout << msg 23 #define LOGLN(msg) std::cout << msg << std::endl 24 using namespace std; 25 using namespace cv; 26 using namespace cv::detail; 27 // Default command line args 28 29 /*#if 1 30 #define DLL_API __declspec(dllexport) 31 #else 32 #define DLL_API __declspec(dllimport) 33 #endif 34 */ 35 36 /*extern "C" { //因为python一般只支持c的接口 37 typedef struct ImageBase { 38 int w; //图像的宽 39 int h; //图像的高 40 int c; //通道数 41 unsigned char *data; //我们要写python和c++交互的数据结构，0-255的单字符指针 42 }ImageMeta; 43 //typedef ImageBase ImageMeta; 44 45 DLL_API int Stitch(ImageMeta *im1, ImageMeta *im2);//函数导出，要改 46 47 }; 48 */ 49 vector<String> img_names; 50 int num_images; 51 bool preview = false; 52 bool try_cuda = false; 53 double work_megapix = 0.6; 54 double seam_megapix = 0.1; 55 double compose_megapix = -1; 56 float conf_thresh = 1.f; 57 #ifdef HAVE_OPENCV_XFEATURES2D 58 string features_type = "surf"; 59 #else 60 string features_type = "orb"; 61 #endif 62 string matcher_type = "homography"; 63 string estimator_type = "homography"; 64 string ba_cost_func = "ray"; 65 string ba_refine_mask = "xxxxx"; 66 bool do_wave_correct = true; 67 WaveCorrectKind wave_correct = detail::WAVE_CORRECT_HORIZ; 68 bool save_graph = false; 69 std::string save_graph_to; 70 string warp_type = "spherical"; 71 int expos_comp_type = ExposureCompensator::GAIN_BLOCKS; 72 int expos_comp_nr_feeds = 1; 73 int expos_comp_nr_filtering = 2; 74 int expos_comp_block_size = 32; 75 float match_conf = 0.3f; 76 string seam_find_type = "gc_color"; 77 int blend_type = Blender::MULTI_BAND; 78 int timelapse_type = Timelapser::AS_IS;//延时摄影 79 float blend_strength = 5; 80 string result_name = "D:/result.jpg"; 81 bool timelapse = false;//首先定义timelapse的默认布尔类型为False 82 int range_width = -1; 83 84 /*static int parseCmdArgs(int argc, char** argv) 85 { 86 if (argc == 1) 87 { 88 printUsage(); 89 return -1; 90 } 91 for (int i = 1; i < argc; ++i) 92 { 93 if (string(argv[i]) == "--help" || string(argv[i]) == "/?") 94 { 95 printUsage(); 96 return -1; 97 } 98 else if (string(argv[i]) == "--preview") 99 { 100 preview = true; 101 } 102 else if (string(argv[i]) == "--try_cuda") 103 { 104 if (string(argv[i + 1]) == "no") 105 try_cuda = false; 106 else if (string(argv[i + 1]) == "yes") 107 try_cuda = true; 108 else 109 { 110 cout << "Bad --try_cuda flag value "; 111 return -1; 112 } 113 i++; 114 } 115 else if (string(argv[i]) == "--work_megapix") 116 { 117 work_megapix = atof(argv[i + 1]); 118 i++; 119 } 120 else if (string(argv[i]) == "--seam_megapix") 121 { 122 seam_megapix = atof(argv[i + 1]); 123 i++; 124 } 125 else if (string(argv[i]) == "--compose_megapix") 126 { 127 compose_megapix = atof(argv[i + 1]); 128 i++; 129 } 130 else if (string(argv[i]) == "--result") 131 { 132 result_name = argv[i + 1]; 133 i++; 134 } 135 else if (string(argv[i]) == "--features") 136 { 137 features_type = argv[i + 1]; 138 if (features_type == "orb") 139 match_conf = 0.3f; 140 i++; 141 } 142 else if (string(argv[i]) == "--matcher") 143 { 144 if (string(argv[i + 1]) == "homography" || string(argv[i + 1]) == "affine") 145 matcher_type = argv[i + 1]; 146 else 147 { 148 cout << "Bad --matcher flag value "; 149 return -1; 150 } 151 i++; 152 } 153 else if (string(argv[i]) == "--estimator") 154 { 155 if (string(argv[i + 1]) == "homography" || string(argv[i + 1]) == "affine") 156 estimator_type = argv[i + 1]; 157 else 158 { 159 cout << "Bad --estimator flag value "; 160 return -1; 161 } 162 i++; 163 } 164 else if (string(argv[i]) == "--match_conf") 165 { 166 match_conf = static_cast<float>(atof(argv[i + 1])); 167 i++; 168 } 169 else if (string(argv[i]) == "--conf_thresh") 170 { 171 conf_thresh = static_cast<float>(atof(argv[i + 1])); 172 i++; 173 } 174 else if (string(argv[i]) == "--ba") 175 { 176 ba_cost_func = argv[i + 1]; 177 i++; 178 } 179 else if (string(argv[i]) == "--ba_refine_mask") 180 { 181 ba_refine_mask = argv[i + 1]; 182 if (ba_refine_mask.size() != 5) 183 { 184 cout << "Incorrect refinement mask length. "; 185 return -1; 186 } 187 i++; 188 } 189 else if (string(argv[i]) == "--wave_correct") 190 { 191 if (string(argv[i + 1]) == "no") 192 do_wave_correct = false; 193 else if (string(argv[i + 1]) == "horiz") 194 { 195 do_wave_correct = true; 196 wave_correct = detail::WAVE_CORRECT_HORIZ; 197 } 198 else if (string(argv[i + 1]) == "vert") 199 { 200 do_wave_correct = true; 201 wave_correct = detail::WAVE_CORRECT_VERT; 202 } 203 else 204 { 205 cout << "Bad --wave_correct flag value "; 206 return -1; 207 } 208 i++; 209 } 210 else if (string(argv[i]) == "--save_graph") 211 { 212 save_graph = true; 213 save_graph_to = argv[i + 1]; 214 i++; 215 } 216 else if (string(argv[i]) == "--warp") 217 { 218 warp_type = string(argv[i + 1]); 219 i++; 220 } 221 else if (string(argv[i]) == "--expos_comp") 222 { 223 if (string(argv[i + 1]) == "no") 224 expos_comp_type = ExposureCompensator::NO; 225 else if (string(argv[i + 1]) == "gain") 226 expos_comp_type = ExposureCompensator::GAIN; 227 else if (string(argv[i + 1]) == "gain_blocks") 228 expos_comp_type = ExposureCompensator::GAIN_BLOCKS; 229 else if (string(argv[i + 1]) == "channels") 230 expos_comp_type = ExposureCompensator::CHANNELS; 231 else if (string(argv[i + 1]) == "channels_blocks") 232 expos_comp_type = ExposureCompensator::CHANNELS_BLOCKS; 233 else 234 { 235 cout << "Bad exposure compensation method "; 236 return -1; 237 } 238 i++; 239 } 240 else if (string(argv[i]) == "--expos_comp_nr_feeds") 241 { 242 expos_comp_nr_feeds = atoi(argv[i + 1]); 243 i++; 244 } 245 else if (string(argv[i]) == "--expos_comp_nr_filtering") 246 { 247 expos_comp_nr_filtering = atoi(argv[i + 1]); 248 i++; 249 } 250 else if (string(argv[i]) == "--expos_comp_block_size") 251 { 252 expos_comp_block_size = atoi(argv[i + 1]); 253 i++; 254 } 255 else if (string(argv[i]) == "--seam") 256 { 257 if (string(argv[i + 1]) == "no" || 258 string(argv[i + 1]) == "voronoi" || 259 string(argv[i + 1]) == "gc_color" || 260 string(argv[i + 1]) == "gc_colorgrad" || 261 string(argv[i + 1]) == "dp_color" || 262 string(argv[i + 1]) == "dp_colorgrad") 263 seam_find_type = argv[i + 1]; 264 else 265 { 266 cout << "Bad seam finding method "; 267 return -1; 268 } 269 i++; 270 } 271 else if (string(argv[i]) == "--blend") 272 { 273 if (string(argv[i + 1]) == "no") 274 blend_type = Blender::NO; 275 else if (string(argv[i + 1]) == "feather") 276 blend_type = Blender::FEATHER; 277 else if (string(argv[i + 1]) == "multiband") 278 blend_type = Blender::MULTI_BAND; 279 else 280 { 281 cout << "Bad blending method "; 282 return -1; 283 } 284 i++; 285 } 286 else if (string(argv[i]) == "--timelapse") 287 { 288 timelapse = true; 289 if (string(argv[i + 1]) == "as_is") 290 timelapse_type = Timelapser::AS_IS; 291 else if (string(argv[i + 1]) == "crop") 292 timelapse_type = Timelapser::CROP; 293 else 294 { 295 cout << "Bad timelapse method "; 296 return -1; 297 } 298 i++; 299 } 300 else if (string(argv[i]) == "--rangewidth") 301 { 302 range_width = atoi(argv[i + 1]); 303 i++; 304 } 305 else if (string(argv[i]) == "--blend_strength") 306 { 307 blend_strength = static_cast<float>(atof(argv[i + 1])); 308 i++; 309 } 310 else if (string(argv[i]) == "--output") 311 { 312 result_name = argv[i + 1]; 313 i++; 314 } 315 else 316 img_names.push_back(argv[i]); 317 } 318 if (preview) 319 { 320 compose_megapix = 0.6; 321 } 322 return 0; 323 }*/ 324 325 int main() 326 //vector<Mat> img_list 327 {//一个int数；一个图片类型的列表 328 //predict先判断长度 然后长度作为一个参数传给 329 //preview = true; 330 //try_cuda = true; 331 //preview = true; 332 //result = 'D:/result.jpg'; 333 //work_megapix = -1; 334 //features_type = "orb"; 335 Mat img1, img2; 336 img1 = imread("D:/1Hill.jpg"); 337 img2 = imread("D:/2Hill.jpg"); 338 vector<Mat> ALLimages(2); 339 ALLimages[0] = img1.clone(); 340 ALLimages[1] = img2.clone(); 341 //img_names.push_back("D:/1Hill.jpg"); 342 //img_names.push_back("D:/2Hill.jpg");//?? 343 //img_names.push_back("D:/3Hill.jpg");//?? 344 num_images = 2; 345 #if ENABLE_LOG 346 int64 app_start_time = getTickCount(); 347 #endif 348 #if 0 349 cv::setBreakOnError(true); 350 #endif 351 //int retval = parseCmdArgs(argc, argv); 352 //if (retval) 353 //return retval; 354 // Check if have enough images 355 //int num_images = static_cast<int>(img_names.size()); 356 if (num_images < 2) 357 { 358 LOGLN("Need more images"); 359 return -1; 360 } 361 double work_scale = 1, seam_scale = 1, compose_scale = 1; 362 bool is_work_scale_set = false, is_seam_scale_set = false, is_compose_scale_set = false; 363 LOGLN("Finding features..."); 364 #if ENABLE_LOG 365 int64 t = getTickCount(); 366 #endif 367 Ptr<Feature2D> finder; 368 if (features_type == "orb") 369 { 370 finder = ORB::create(); 371 } 372 else if (features_type == "akaze") 373 { 374 finder = AKAZE::create(); 375 } 376 #ifdef HAVE_OPENCV_XFEATURES2D 377 else if (features_type == "surf") 378 { 379 finder = xfeatures2d::SURF::create(); 380 } 381 else if (features_type == "sift") { 382 finder = xfeatures2d::SIFT::create(); 383 } 384 #endif 385 else 386 { 387 cout << "Unknown 2D features type: '" << features_type << "'. "; 388 return -1; 389 } 390 Mat full_img, img; 391 vector<ImageFeatures> features(num_images); 392 vector<Mat> images(num_images); 393 vector<Size> full_img_sizes(num_images); 394 double seam_work_aspect = 1; 395 for (int i = 0; i < num_images; ++i) 396 { 397 full_img = ALLimages[i]; 398 399 //获取一张图。imread_img -------->Mat 400 //先把一边调通了再去组合调试，分治 401 //full_img = img_list; 402 //python传进来n张图片的base64,可以转成读取后的图片。 403 404 //先在c中定义图像HWC结构数组数组转一次 Mat, dll返回Mat结果，Mat转一次结构体 405 //main输入 Mat1,Mat2 406 //dll返回数组，python转化成cv2image,然后输出image2base64 407 408 //full_image里面是读取的imread_img类型 409 //base64的size容易确定 410 //先在predict前提取到图片的整个Mat传给DLL 411 full_img_sizes[i] = full_img.size();//结果：full_img_sizes = [(500,300),(200,100)] 412 if (full_img.empty()) 413 { 414 //LOGLN("Can't open image " << img_names[i]);//访问了空指针，和img_names有关 415 return -2; 416 } 417 if (work_megapix < 0) 418 { 419 img = full_img; 420 work_scale = 1; 421 is_work_scale_set = true; 422 } 423 else 424 { 425 if (!is_work_scale_set) 426 { 427 work_scale = min(1.0, sqrt(work_megapix * 1e6 / full_img.size().area())); 428 is_work_scale_set = true; 429 } 430 resize(full_img, img, Size(), work_scale, work_scale, INTER_LINEAR_EXACT); 431 } 432 if (!is_seam_scale_set) 433 { 434 seam_scale = min(1.0, sqrt(seam_megapix * 1e6 / full_img.size().area())); 435 seam_work_aspect = seam_scale / work_scale; 436 is_seam_scale_set = true; 437 } 438 computeImageFeatures(finder, img, features[i]); 439 features[i].img_idx = i; 440 LOGLN("Features in image #" << i + 1 << ": " << features[i].keypoints.size()); 441 resize(full_img, img, Size(), seam_scale, seam_scale, INTER_LINEAR_EXACT); 442 images[i] = img.clone(); 443 //循环是为了找到每张图的特征，然后把图片copy到images里 444 } 445 full_img.release(); 446 img.release(); 447 LOGLN("Finding features, time: " << ((getTickCount() - t) / getTickFrequency()) << " sec"); 448 LOG("Pairwise matching"); 449 #if ENABLE_LOG 450 t = getTickCount(); 451 #endif 452 vector<MatchesInfo> pairwise_matches; 453 Ptr<FeaturesMatcher> matcher; 454 if (matcher_type == "affine") 455 matcher = makePtr<AffineBestOf2NearestMatcher>(false, try_cuda, match_conf); 456 else if (range_width == -1) 457 matcher = makePtr<BestOf2NearestMatcher>(try_cuda, match_conf); 458 else 459 matcher = makePtr<BestOf2NearestRangeMatcher>(range_width, try_cuda, match_conf); 460 (*matcher)(features, pairwise_matches); 461 matcher->collectGarbage(); 462 LOGLN("Pairwise matching, time: " << ((getTickCount() - t) / getTickFrequency()) << " sec"); 463 // Check if we should save matches graph 464 //if (save_graph) 465 //{ 466 // LOGLN("Saving matches graph..."); 467 // ofstream f(save_graph_to.c_str()); 468 // f << matchesGraphAsString(img_names, pairwise_matches, conf_thresh); 469 //} 470 // Leave only images we are sure are from the same panorama 471 vector<int> indices = leaveBiggestComponent(features, pairwise_matches, conf_thresh); 472 if (indices.size() != 2)//判断两个图片的相关性 473 return -1; 474 475 if (num_images < 2) 476 { 477 LOGLN("Need more images"); 478 return -1; 479 } 480 Ptr<Estimator> estimator; 481 if (estimator_type == "affine") 482 estimator = makePtr<AffineBasedEstimator>(); 483 else 484 estimator = makePtr<HomographyBasedEstimator>(); 485 vector<CameraParams> cameras; 486 if (!(*estimator)(features, pairwise_matches, cameras)) 487 { 488 cout << "Homography estimation failed. "; 489 return -1; 490 } 491 for (size_t i = 0; i < cameras.size(); ++i) 492 { 493 Mat R; 494 cameras[i].R.convertTo(R, CV_32F); 495 cameras[i].R = R; 496 //LOGLN("Initial camera intrinsics #" << indices[i] + 1 << ": K: " << cameras[i].K() << " R: " << cameras[i].R); 497 } 498 Ptr<detail::BundleAdjusterBase> adjuster; 499 if (ba_cost_func == "reproj") adjuster = makePtr<detail::BundleAdjusterReproj>(); 500 else if (ba_cost_func == "ray") adjuster = makePtr<detail::BundleAdjusterRay>(); 501 else if (ba_cost_func == "affine") adjuster = makePtr<detail::BundleAdjusterAffinePartial>(); 502 else if (ba_cost_func == "no") adjuster = makePtr<NoBundleAdjuster>(); 503 else 504 { 505 cout << "Unknown bundle adjustment cost function: '" << ba_cost_func << "'. "; 506 return -1; 507 } 508 adjuster->setConfThresh(conf_thresh); 509 Mat_<uchar> refine_mask = Mat::zeros(3, 3, CV_8U); 510 if (ba_refine_mask[0] == 'x') refine_mask(0, 0) = 1; 511 if (ba_refine_mask[1] == 'x') refine_mask(0, 1) = 1; 512 if (ba_refine_mask[2] == 'x') refine_mask(0, 2) = 1; 513 if (ba_refine_mask[3] == 'x') refine_mask(1, 1) = 1; 514 if (ba_refine_mask[4] == 'x') refine_mask(1, 2) = 1; 515 adjuster->setRefinementMask(refine_mask); 516 if (!(*adjuster)(features, pairwise_matches, cameras)) 517 { 518 cout << "Camera parameters adjusting failed. "; 519 return -1; 520 } 521 // Find median focal length 522 vector<double> focals; 523 for (size_t i = 0; i < cameras.size(); ++i) 524 { 525 //LOGLN("Camera #" << indices[i] + 1 << ": K: " << cameras[i].K() << " R: " << cameras[i].R); 526 focals.push_back(cameras[i].focal); 527 } 528 sort(focals.begin(), focals.end()); 529 float warped_image_scale; 530 if (focals.size() % 2 == 1) 531 warped_image_scale = static_cast<float>(focals[focals.size() / 2]); 532 else 533 warped_image_scale = static_cast<float>(focals[focals.size() / 2 - 1] + focals[focals.size() / 2]) * 0.5f; 534 if (do_wave_correct) 535 { 536 vector<Mat> rmats; 537 for (size_t i = 0; i < cameras.size(); ++i) 538 rmats.push_back(cameras[i].R.clone()); 539 waveCorrect(rmats, wave_correct); 540 for (size_t i = 0; i < cameras.size(); ++i) 541 cameras[i].R = rmats[i]; 542 } 543 LOGLN("Warping images (auxiliary)... "); 544 #if ENABLE_LOG 545 t = getTickCount(); 546 #endif 547 vector<Point> corners(num_images); 548 vector<UMat> masks_warped(num_images); 549 vector<UMat> images_warped(num_images); 550 vector<Size> sizes(num_images); 551 vector<UMat> masks(num_images); 552 // Prepare images masks 553 for (int i = 0; i < num_images; ++i) 554 { 555 masks[i].create(images[i].size(), CV_8U); 556 masks[i].setTo(Scalar::all(255)); 557 } 558 // Warp images and their masks 559 Ptr<WarperCreator> warper_creator; 560 #ifdef HAVE_OPENCV_CUDAWARPING 561 if (try_cuda && cuda::getCudaEnabledDeviceCount() > 0) 562 { 563 if (warp_type == "plane") 564 warper_creator = makePtr<cv::PlaneWarperGpu>(); 565 else if (warp_type == "cylindrical") 566 warper_creator = makePtr<cv::CylindricalWarperGpu>(); 567 else if (warp_type == "spherical") 568 warper_creator = makePtr<cv::SphericalWarperGpu>(); 569 } 570 else 571 #endif 572 { 573 if (warp_type == "plane") 574 warper_creator = makePtr<cv::PlaneWarper>(); 575 else if (warp_type == "affine") 576 warper_creator = makePtr<cv::AffineWarper>(); 577 else if (warp_type == "cylindrical") 578 warper_creator = makePtr<cv::CylindricalWarper>(); 579 else if (warp_type == "spherical") 580 warper_creator = makePtr<cv::SphericalWarper>(); 581 else if (warp_type == "fisheye") 582 warper_creator = makePtr<cv::FisheyeWarper>(); 583 else if (warp_type == "stereographic") 584 warper_creator = makePtr<cv::StereographicWarper>(); 585 else if (warp_type == "compressedPlaneA2B1") 586 warper_creator = makePtr<cv::CompressedRectilinearWarper>(2.0f, 1.0f); 587 else if (warp_type == "compressedPlaneA1.5B1") 588 warper_creator = makePtr<cv::CompressedRectilinearWarper>(1.5f, 1.0f); 589 else if (warp_type == "compressedPlanePortraitA2B1") 590 warper_creator = makePtr<cv::CompressedRectilinearPortraitWarper>(2.0f, 1.0f); 591 else if (warp_type == "compressedPlanePortraitA1.5B1") 592 warper_creator = makePtr<cv::CompressedRectilinearPortraitWarper>(1.5f, 1.0f); 593 else if (warp_type == "paniniA2B1") 594 warper_creator = makePtr<cv::PaniniWarper>(2.0f, 1.0f); 595 else if (warp_type == "paniniA1.5B1") 596 warper_creator = makePtr<cv::PaniniWarper>(1.5f, 1.0f); 597 else if (warp_type == "paniniPortraitA2B1") 598 warper_creator = makePtr<cv::PaniniPortraitWarper>(2.0f, 1.0f); 599 else if (warp_type == "paniniPortraitA1.5B1") 600 warper_creator = makePtr<cv::PaniniPortraitWarper>(1.5f, 1.0f); 601 else if (warp_type == "mercator") 602 warper_creator = makePtr<cv::MercatorWarper>(); 603 else if (warp_type == "transverseMercator") 604 warper_creator = makePtr<cv::TransverseMercatorWarper>(); 605 } 606 if (!warper_creator) 607 { 608 cout << "Can't create the following warper '" << warp_type << "' "; 609 return 1; 610 } 611 Ptr<RotationWarper> warper = warper_creator->create(static_cast<float>(warped_image_scale * seam_work_aspect)); 612 for (int i = 0; i < num_images; ++i) 613 { 614 Mat_<float> K; 615 cameras[i].K().convertTo(K, CV_32F); 616 float swa = (float)seam_work_aspect; 617 K(0, 0) *= swa; K(0, 2) *= swa; 618 K(1, 1) *= swa; K(1, 2) *= swa; 619 corners[i] = warper->warp(images[i], K, cameras[i].R, INTER_LINEAR, BORDER_REFLECT, images_warped[i]); 620 sizes[i] = images_warped[i].size(); 621 warper->warp(masks[i], K, cameras[i].R, INTER_NEAREST, BORDER_CONSTANT, masks_warped[i]); 622 } 623 vector<UMat> images_warped_f(num_images); 624 for (int i = 0; i < num_images; ++i) 625 images_warped[i].convertTo(images_warped_f[i], CV_32F); 626 LOGLN("Warping images, time: " << ((getTickCount() - t) / getTickFrequency()) << " sec"); 627 LOGLN("Compensating exposure..."); 628 #if ENABLE_LOG 629 t = getTickCount(); 630 #endif 631 Ptr<ExposureCompensator> compensator = ExposureCompensator::createDefault(expos_comp_type); 632 if (dynamic_cast<GainCompensator*>(compensator.get())) 633 { 634 GainCompensator* gcompensator = dynamic_cast<GainCompensator*>(compensator.get()); 635 gcompensator->setNrFeeds(expos_comp_nr_feeds); 636 } 637 if (dynamic_cast<ChannelsCompensator*>(compensator.get())) 638 { 639 ChannelsCompensator* ccompensator = dynamic_cast<ChannelsCompensator*>(compensator.get()); 640 ccompensator->setNrFeeds(expos_comp_nr_feeds); 641 } 642 if (dynamic_cast<BlocksCompensator*>(compensator.get())) 643 { 644 BlocksCompensator* bcompensator = dynamic_cast<BlocksCompensator*>(compensator.get()); 645 bcompensator->setNrFeeds(expos_comp_nr_feeds); 646 bcompensator->setNrGainsFilteringIterations(expos_comp_nr_filtering); 647 bcompensator->setBlockSize(expos_comp_block_size, expos_comp_block_size); 648 } 649 compensator->feed(corners, images_warped, masks_warped); 650 LOGLN("Compensating exposure, time: " << ((getTickCount() - t) / getTickFrequency()) << " sec"); 651 LOGLN("Finding seams..."); 652 #if ENABLE_LOG 653 t = getTickCount(); 654 #endif 655 Ptr<SeamFinder> seam_finder; 656 if (seam_find_type == "no") 657 seam_finder = makePtr<detail::NoSeamFinder>(); 658 else if (seam_find_type == "voronoi") 659 seam_finder = makePtr<detail::VoronoiSeamFinder>(); 660 else if (seam_find_type == "gc_color") 661 { 662 #ifdef HAVE_OPENCV_CUDALEGACY 663 if (try_cuda && cuda::getCudaEnabledDeviceCount() > 0) 664 seam_finder = makePtr<detail::GraphCutSeamFinderGpu>(GraphCutSeamFinderBase::COST_COLOR); 665 else 666 #endif 667 seam_finder = makePtr<detail::GraphCutSeamFinder>(GraphCutSeamFinderBase::COST_COLOR); 668 } 669 else if (seam_find_type == "gc_colorgrad") 670 { 671 #ifdef HAVE_OPENCV_CUDALEGACY 672 if (try_cuda && cuda::getCudaEnabledDeviceCount() > 0) 673 seam_finder = makePtr<detail::GraphCutSeamFinderGpu>(GraphCutSeamFinderBase::COST_COLOR_GRAD); 674 else 675 #endif 676 seam_finder = makePtr<detail::GraphCutSeamFinder>(GraphCutSeamFinderBase::COST_COLOR_GRAD); 677 } 678 else if (seam_find_type == "dp_color") 679 seam_finder = makePtr<detail::DpSeamFinder>(DpSeamFinder::COLOR); 680 else if (seam_find_type == "dp_colorgrad") 681 seam_finder = makePtr<detail::DpSeamFinder>(DpSeamFinder::COLOR_GRAD); 682 if (!seam_finder) 683 { 684 cout << "Can't create the following seam finder '" << seam_find_type << "' "; 685 return 1; 686 } 687 seam_finder->find(images_warped_f, corners, masks_warped); 688 LOGLN("Finding seams, time: " << ((getTickCount() - t) / getTickFrequency()) << " sec"); 689 // Release unused memory 690 images.clear(); 691 images_warped.clear(); 692 images_warped_f.clear(); 693 masks.clear(); 694 LOGLN("Compositing..."); 695 #if ENABLE_LOG 696 t = getTickCount(); 697 #endif 698 Mat img_warped, img_warped_s; 699 Mat dilated_mask, seam_mask, mask, mask_warped; 700 Ptr<Blender> blender; 701 Ptr<Timelapser> timelapser; 702 //double compose_seam_aspect = 1; 703 double compose_work_aspect = 1; 704 for (int img_idx = 0; img_idx < num_images; ++img_idx) 705 { 706 //LOGLN("Compositing image #" << indices[img_idx] + 1); 707 // Read image and resize it if necessary 708 full_img = ALLimages[img_idx]; 709 if (!is_compose_scale_set) 710 { 711 if (compose_megapix > 0) 712 compose_scale = min(1.0, sqrt(compose_megapix * 1e6 / full_img.size().area())); 713 is_compose_scale_set = true; 714 // Compute relative scales 715 //compose_seam_aspect = compose_scale / seam_scale; 716 compose_work_aspect = compose_scale / work_scale; 717 // Update warped image scale 718 warped_image_scale *= static_cast<float>(compose_work_aspect); 719 warper = warper_creator->create(warped_image_scale); 720 // Update corners and sizes 721 for (int i = 0; i < num_images; ++i) 722 { 723 // Update intrinsics 724 cameras[i].focal *= compose_work_aspect; 725 cameras[i].ppx *= compose_work_aspect; 726 cameras[i].ppy *= compose_work_aspect; 727 // Update corner and size 728 Size sz = full_img_sizes[i]; 729 Mat K; 730 cameras[i].K().convertTo(K, CV_32F); 731 Rect roi = warper->warpRoi(sz, K, cameras[i].R); 732 corners[i] = roi.tl(); 733 sizes[i] = roi.size(); 734 } 735 } 736 if (abs(compose_scale - 1) > 1e-1)//没用 737 resize(full_img, img, Size(), compose_scale, compose_scale, INTER_LINEAR_EXACT); 738 else 739 img = full_img; 740 full_img.release(); 741 Size img_size = img.size(); 742 Mat K; 743 cameras[img_idx].K().convertTo(K, CV_32F); 744 // Warp the current image 745 warper->warp(img, K, cameras[img_idx].R, INTER_LINEAR, BORDER_REFLECT, img_warped); 746 // Warp the current image mask 747 mask.create(img_size, CV_8U); 748 mask.setTo(Scalar::all(255)); 749 warper->warp(mask, K, cameras[img_idx].R, INTER_NEAREST, BORDER_CONSTANT, mask_warped); 750 // Compensate exposure 751 compensator->apply(img_idx, corners[img_idx], img_warped, mask_warped); 752 img_warped.convertTo(img_warped_s, CV_16S); 753 img_warped.release(); 754 img.release(); 755 mask.release(); 756 dilate(masks_warped[img_idx], dilated_mask, Mat()); 757 resize(dilated_mask, seam_mask, mask_warped.size(), 0, 0, INTER_LINEAR_EXACT); 758 mask_warped = seam_mask & mask_warped; 759 if (!blender && !timelapse)//blender是False,timelapse也是False,这里运行了！ 760 {//做multiband 761 blender = Blender::createDefault(blend_type, try_cuda); 762 Size dst_sz = resultRoi(corners, sizes).size(); 763 float blend_width = sqrt(static_cast<float>(dst_sz.area())) * blend_strength / 100.f; 764 if (blend_width < 1.f) 765 blender = Blender::createDefault(Blender::NO, try_cuda); 766 else if (blend_type == Blender::MULTI_BAND) 767 { 768 MultiBandBlender* mb = dynamic_cast<MultiBandBlender*>(blender.get()); 769 mb->setNumBands(static_cast<int>(ceil(log(blend_width) / log(2.)) - 1.)); 770 LOGLN("Multi-band blender, number of bands: " << mb->numBands()); 771 } 772 else if (blend_type == Blender::FEATHER)//未运行 773 { 774 FeatherBlender* fb = dynamic_cast<FeatherBlender*>(blender.get()); 775 fb->setSharpness(1.f / blend_width); 776 LOGLN("Feather blender, sharpness: " << fb->sharpness()); 777 } 778 blender->prepare(corners, sizes); 779 } 780 else if (!timelapser && timelapse)//timelapse是假，timelapser是什么？？没运行 781 { 782 timelapser = Timelapser::createDefault(timelapse_type); 783 timelapser->initialize(corners, sizes); 784 cout << "----------------------------运行---------------------------------" << endl; 785 } 786 // Blend the current image 787 if (timelapse)//默认是假 788 { 789 cout << "----------------------------运行2---------------------------------" << endl; 790 } 791 else 792 {//这里运行了两次，因为在循环体中，图片有两张 793 blender->feed(img_warped_s, mask_warped, corners[img_idx]); 794 cout << "----------------------------运行3---------------------------------" << endl; 795 } 796 } 797 if (!timelapse)//运行了 798 { 799 Mat result, result_mask; 800 blender->blend(result, result_mask); 801 LOGLN("Compositing, time: " << ((getTickCount() - t) / getTickFrequency()) << " sec"); 802 imwrite(result_name, result); 803 } 804 LOGLN("Finished, total time: " << ((getTickCount() - app_start_time) / getTickFrequency()) << " sec"); 805 return 0; 806 } 807