OpenCV 图像拼接-Stitcher类-Stitching detailed使用与参数介绍

关于OpenCV图像拼接的方法，如果不熟悉的话，可以先看看我整理的如下四篇博客：

OpenCV常用图像拼接方法(一)：直接拼接(硬拼)
OpenCV常用图像拼接方法(二)：基于模板匹配拼接
OpenCV常用图像拼接方法(三)：基于特征匹配拼接
OpenCV常用图像拼接方法(四)：基于Stitcher类拼接

本篇博客是Stitcher类的扩展介绍，通过例程stitching_detailed.cpp的使用和参数介绍，帮助大家了解Stitcher类拼接的具体步骤和方法，先看看其内部的流程结构图(如下)：

这里写图片描述

stitching_detailed.cpp目录如下，可以在自己安装的OpenCV目录下找到，笔者这里使用的OpenCV4.4版本

stitching_detailed.cpp具体源码如下：

  1 // 05_Image_Stitch_Stitching_Detailed.cpp : 此文件包含 "main" 函数。程序执行将在此处开始并结束。
  2 //
  3 #include "pch.h"
  4 #include <iostream>
  5 #include <fstream>
  6 #include <string>
  7 #include "opencv2/opencv_modules.hpp"
  8 #include <opencv2/core/utility.hpp>
  9 #include "opencv2/imgcodecs.hpp"
 10 #include "opencv2/highgui.hpp"
 11 #include "opencv2/stitching/detail/autocalib.hpp"
 12 #include "opencv2/stitching/detail/blenders.hpp"
 13 #include "opencv2/stitching/detail/timelapsers.hpp"
 14 #include "opencv2/stitching/detail/camera.hpp"
 15 #include "opencv2/stitching/detail/exposure_compensate.hpp"
 16 #include "opencv2/stitching/detail/matchers.hpp"
 17 #include "opencv2/stitching/detail/motion_estimators.hpp"
 18 #include "opencv2/stitching/detail/seam_finders.hpp"
 19 #include "opencv2/stitching/detail/warpers.hpp"
 20 #include "opencv2/stitching/warpers.hpp"
 21  
 22 #ifdef HAVE_OPENCV_XFEATURES2D
 23 #include "opencv2/xfeatures2d.hpp"
 24 #include "opencv2/xfeatures2d/nonfree.hpp"
 25 #endif
 26  
 27 #define ENABLE_LOG 1
 28 #define LOG(msg) std::cout << msg
 29 #define LOGLN(msg) std::cout << msg << std::endl
 30  
 31 using namespace std;
 32 using namespace cv;
 33 using namespace cv::detail;
 34  
 35 static void printUsage(char** argv)
 36 {
 37     cout <<
 38         "Rotation model images stitcher.

"
 39         << argv[0] << " img1 img2 [...imgN] [flags]

"
 40         "Flags:
"
 41         "  --preview
"
 42         "      Run stitching in the preview mode. Works faster than usual mode,
"
 43         "      but output image will have lower resolution.
"
 44         "  --try_cuda (yes|no)
"
 45         "      Try to use CUDA. The default value is 'no'. All default values
"
 46         "      are for CPU mode.
"
 47         "
Motion Estimation Flags:
"
 48         "  --work_megapix <float>
"
 49         "      Resolution for image registration step. The default is 0.6 Mpx.
"
 50         "  --features (surf|orb|sift|akaze)
"
 51         "      Type of features used for images matching.
"
 52         "      The default is surf if available, orb otherwise.
"
 53         "  --matcher (homography|affine)
"
 54         "      Matcher used for pairwise image matching.
"
 55         "  --estimator (homography|affine)
"
 56         "      Type of estimator used for transformation estimation.
"
 57         "  --match_conf <float>
"
 58         "      Confidence for feature matching step. The default is 0.65 for surf and 0.3 for orb.
"
 59         "  --conf_thresh <float>
"
 60         "      Threshold for two images are from the same panorama confidence.
"
 61         "      The default is 1.0.
"
 62         "  --ba (no|reproj|ray|affine)
"
 63         "      Bundle adjustment cost function. The default is ray.
"
 64         "  --ba_refine_mask (mask)
"
 65         "      Set refinement mask for bundle adjustment. It looks like 'x_xxx',
"
 66         "      where 'x' means refine respective parameter and '_' means don't
"
 67         "      refine one, and has the following format:
"
 68         "      <fx><skew><ppx><aspect><ppy>. The default mask is 'xxxxx'. If bundle
"
 69         "      adjustment doesn't support estimation of selected parameter then
"
 70         "      the respective flag is ignored.
"
 71         "  --wave_correct (no|horiz|vert)
"
 72         "      Perform wave effect correction. The default is 'horiz'.
"
 73         "  --save_graph <file_name>
"
 74         "      Save matches graph represented in DOT language to <file_name> file.
"
 75         "      Labels description: Nm is number of matches, Ni is number of inliers,
"
 76         "      C is confidence.
"
 77         "
Compositing Flags:
"
 78         "  --warp (affine|plane|cylindrical|spherical|fisheye|stereographic|compressedPlaneA2B1|compressedPlaneA1.5B1|compressedPlanePortraitA2B1|compressedPlanePortraitA1.5B1|paniniA2B1|paniniA1.5B1|paniniPortraitA2B1|paniniPortraitA1.5B1|mercator|transverseMercator)
"
 79         "      Warp surface type. The default is 'spherical'.
"
 80         "  --seam_megapix <float>
"
 81         "      Resolution for seam estimation step. The default is 0.1 Mpx.
"
 82         "  --seam (no|voronoi|gc_color|gc_colorgrad)
"
 83         "      Seam estimation method. The default is 'gc_color'.
"
 84         "  --compose_megapix <float>
"
 85         "      Resolution for compositing step. Use -1 for original resolution.
"
 86         "      The default is -1.
"
 87         "  --expos_comp (no|gain|gain_blocks|channels|channels_blocks)
"
 88         "      Exposure compensation method. The default is 'gain_blocks'.
"
 89         "  --expos_comp_nr_feeds <int>
"
 90         "      Number of exposure compensation feed. The default is 1.
"
 91         "  --expos_comp_nr_filtering <int>
"
 92         "      Number of filtering iterations of the exposure compensation gains.
"
 93         "      Only used when using a block exposure compensation method.
"
 94         "      The default is 2.
"
 95         "  --expos_comp_block_size <int>
"
 96         "      BLock size in pixels used by the exposure compensator.
"
 97         "      Only used when using a block exposure compensation method.
"
 98         "      The default is 32.
"
 99         "  --blend (no|feather|multiband)
"
100         "      Blending method. The default is 'multiband'.
"
101         "  --blend_strength <float>
"
102         "      Blending strength from [0,100] range. The default is 5.
"
103         "  --output <result_img>
"
104         "      The default is 'result.jpg'.
"
105         "  --timelapse (as_is|crop) 
"
106         "      Output warped images separately as frames of a time lapse movie, with 'fixed_' prepended to input file names.
"
107         "  --rangewidth <int>
"
108         "      uses range_width to limit number of images to match with.
";
109 }
110  
111  
112 // Default command line args
113 vector<String> img_names;
114 bool preview = false;
115 bool try_cuda = false;
116 double work_megapix = 0.6;
117 double seam_megapix = 0.1;
118 double compose_megapix = -1;
119 float conf_thresh = 1.f;
120 #ifdef HAVE_OPENCV_XFEATURES2D
121 string features_type = "surf";
122 float match_conf = 0.65f;
123 #else
124 string features_type = "orb";
125 float match_conf = 0.3f;
126 #endif
127 string matcher_type = "homography";
128 string estimator_type = "homography";
129 string ba_cost_func = "ray";
130 string ba_refine_mask = "xxxxx";
131 bool do_wave_correct = true;
132 WaveCorrectKind wave_correct = detail::WAVE_CORRECT_HORIZ;
133 bool save_graph = false;
134 std::string save_graph_to;
135 string warp_type = "spherical";
136 int expos_comp_type = ExposureCompensator::GAIN_BLOCKS;
137 int expos_comp_nr_feeds = 1;
138 int expos_comp_nr_filtering = 2;
139 int expos_comp_block_size = 32;
140 string seam_find_type = "gc_color";
141 int blend_type = Blender::MULTI_BAND;
142 int timelapse_type = Timelapser::AS_IS;
143 float blend_strength = 5;
144 string result_name = "result.jpg";
145 bool timelapse = false;
146 int range_width = -1;
147  
148  
149 static int parseCmdArgs(int argc, char** argv)
150 {
151     if (argc == 1)
152     {
153         printUsage(argv);
154         return -1;
155     }
156     for (int i = 1; i < argc; ++i)
157     {
158         if (string(argv[i]) == "--help" || string(argv[i]) == "/?")
159         {
160             printUsage(argv);
161             return -1;
162         }
163         else if (string(argv[i]) == "--preview")
164         {
165             preview = true;
166         }
167         else if (string(argv[i]) == "--try_cuda")
168         {
169             if (string(argv[i + 1]) == "no")
170                 try_cuda = false;
171             else if (string(argv[i + 1]) == "yes")
172                 try_cuda = true;
173             else
174             {
175                 cout << "Bad --try_cuda flag value
";
176                 return -1;
177             }
178             i++;
179         }
180         else if (string(argv[i]) == "--work_megapix")
181         {
182             work_megapix = atof(argv[i + 1]);
183             i++;
184         }
185         else if (string(argv[i]) == "--seam_megapix")
186         {
187             seam_megapix = atof(argv[i + 1]);
188             i++;
189         }
190         else if (string(argv[i]) == "--compose_megapix")
191         {
192             compose_megapix = atof(argv[i + 1]);
193             i++;
194         }
195         else if (string(argv[i]) == "--result")
196         {
197             result_name = argv[i + 1];
198             i++;
199         }
200         else if (string(argv[i]) == "--features")
201         {
202             features_type = argv[i + 1];
203             if (string(features_type) == "orb")
204                 match_conf = 0.3f;
205             i++;
206         }
207         else if (string(argv[i]) == "--matcher")
208         {
209             if (string(argv[i + 1]) == "homography" || string(argv[i + 1]) == "affine")
210                 matcher_type = argv[i + 1];
211             else
212             {
213                 cout << "Bad --matcher flag value
";
214                 return -1;
215             }
216             i++;
217         }
218         else if (string(argv[i]) == "--estimator")
219         {
220             if (string(argv[i + 1]) == "homography" || string(argv[i + 1]) == "affine")
221                 estimator_type = argv[i + 1];
222             else
223             {
224                 cout << "Bad --estimator flag value
";
225                 return -1;
226             }
227             i++;
228         }
229         else if (string(argv[i]) == "--match_conf")
230         {
231             match_conf = static_cast<float>(atof(argv[i + 1]));
232             i++;
233         }
234         else if (string(argv[i]) == "--conf_thresh")
235         {
236             conf_thresh = static_cast<float>(atof(argv[i + 1]));
237             i++;
238         }
239         else if (string(argv[i]) == "--ba")
240         {
241             ba_cost_func = argv[i + 1];
242             i++;
243         }
244         else if (string(argv[i]) == "--ba_refine_mask")
245         {
246             ba_refine_mask = argv[i + 1];
247             if (ba_refine_mask.size() != 5)
248             {
249                 cout << "Incorrect refinement mask length.
";
250                 return -1;
251             }
252             i++;
253         }
254         else if (string(argv[i]) == "--wave_correct")
255         {
256             if (string(argv[i + 1]) == "no")
257                 do_wave_correct = false;
258             else if (string(argv[i + 1]) == "horiz")
259             {
260                 do_wave_correct = true;
261                 wave_correct = detail::WAVE_CORRECT_HORIZ;
262             }
263             else if (string(argv[i + 1]) == "vert")
264             {
265                 do_wave_correct = true;
266                 wave_correct = detail::WAVE_CORRECT_VERT;
267             }
268             else
269             {
270                 cout << "Bad --wave_correct flag value
";
271                 return -1;
272             }
273             i++;
274         }
275         else if (string(argv[i]) == "--save_graph")
276         {
277             save_graph = true;
278             save_graph_to = argv[i + 1];
279             i++;
280         }
281         else if (string(argv[i]) == "--warp")
282         {
283             warp_type = string(argv[i + 1]);
284             i++;
285         }
286         else if (string(argv[i]) == "--expos_comp")
287         {
288             if (string(argv[i + 1]) == "no")
289                 expos_comp_type = ExposureCompensator::NO;
290             else if (string(argv[i + 1]) == "gain")
291                 expos_comp_type = ExposureCompensator::GAIN;
292             else if (string(argv[i + 1]) == "gain_blocks")
293                 expos_comp_type = ExposureCompensator::GAIN_BLOCKS;
294             else if (string(argv[i + 1]) == "channels")
295                 expos_comp_type = ExposureCompensator::CHANNELS;
296             else if (string(argv[i + 1]) == "channels_blocks")
297                 expos_comp_type = ExposureCompensator::CHANNELS_BLOCKS;
298             else
299             {
300                 cout << "Bad exposure compensation method
";
301                 return -1;
302             }
303             i++;
304         }
305         else if (string(argv[i]) == "--expos_comp_nr_feeds")
306         {
307             expos_comp_nr_feeds = atoi(argv[i + 1]);
308             i++;
309         }
310         else if (string(argv[i]) == "--expos_comp_nr_filtering")
311         {
312             expos_comp_nr_filtering = atoi(argv[i + 1]);
313             i++;
314         }
315         else if (string(argv[i]) == "--expos_comp_block_size")
316         {
317             expos_comp_block_size = atoi(argv[i + 1]);
318             i++;
319         }
320         else if (string(argv[i]) == "--seam")
321         {
322             if (string(argv[i + 1]) == "no" ||
323                 string(argv[i + 1]) == "voronoi" ||
324                 string(argv[i + 1]) == "gc_color" ||
325                 string(argv[i + 1]) == "gc_colorgrad" ||
326                 string(argv[i + 1]) == "dp_color" ||
327                 string(argv[i + 1]) == "dp_colorgrad")
328                 seam_find_type = argv[i + 1];
329             else
330             {
331                 cout << "Bad seam finding method
";
332                 return -1;
333             }
334             i++;
335         }
336         else if (string(argv[i]) == "--blend")
337         {
338             if (string(argv[i + 1]) == "no")
339                 blend_type = Blender::NO;
340             else if (string(argv[i + 1]) == "feather")
341                 blend_type = Blender::FEATHER;
342             else if (string(argv[i + 1]) == "multiband")
343                 blend_type = Blender::MULTI_BAND;
344             else
345             {
346                 cout << "Bad blending method
";
347                 return -1;
348             }
349             i++;
350         }
351         else if (string(argv[i]) == "--timelapse")
352         {
353             timelapse = true;
354  
355             if (string(argv[i + 1]) == "as_is")
356                 timelapse_type = Timelapser::AS_IS;
357             else if (string(argv[i + 1]) == "crop")
358                 timelapse_type = Timelapser::CROP;
359             else
360             {
361                 cout << "Bad timelapse method
";
362                 return -1;
363             }
364             i++;
365         }
366         else if (string(argv[i]) == "--rangewidth")
367         {
368             range_width = atoi(argv[i + 1]);
369             i++;
370         }
371         else if (string(argv[i]) == "--blend_strength")
372         {
373             blend_strength = static_cast<float>(atof(argv[i + 1]));
374             i++;
375         }
376         else if (string(argv[i]) == "--output")
377         {
378             result_name = argv[i + 1];
379             i++;
380         }
381         else
382             img_names.push_back(argv[i]);
383     }
384     if (preview)
385     {
386         compose_megapix = 0.6;
387     }
388     return 0;
389 }
390  
391  
392 int main(int argc, char* argv[])
393 {
394 #if ENABLE_LOG
395     int64 app_start_time = getTickCount();
396 #endif
397  
398 #if 0
399     cv::setBreakOnError(true);
400 #endif
401  
402     int retval = parseCmdArgs(argc, argv);
403     if (retval)
404         return retval;
405  
406     // Check if have enough images
407     int num_images = static_cast<int>(img_names.size());
408     if (num_images < 2)
409     {
410         LOGLN("Need more images");
411         return -1;
412     }
413  
414     double work_scale = 1, seam_scale = 1, compose_scale = 1;
415     bool is_work_scale_set = false, is_seam_scale_set = false, is_compose_scale_set = false;
416  
417     LOGLN("Finding features...");
418 #if ENABLE_LOG
419     int64 t = getTickCount();
420 #endif
421  
422     Ptr<Feature2D> finder;
423     if (features_type == "orb")
424     {
425         finder = ORB::create();
426     }
427     else if (features_type == "akaze")
428     {
429         finder = AKAZE::create();
430     }
431 #ifdef HAVE_OPENCV_XFEATURES2D
432     else if (features_type == "surf")
433     {
434         finder = xfeatures2d::SURF::create();
435     }
436 #endif
437     else if (features_type == "sift")
438     {
439         finder = SIFT::create();
440     }
441     else
442     {
443         cout << "Unknown 2D features type: '" << features_type << "'.
";
444         return -1;
445     }
446  
447     Mat full_img, img;
448     vector<ImageFeatures> features(num_images);
449     vector<Mat> images(num_images);
450     vector<Size> full_img_sizes(num_images);
451     double seam_work_aspect = 1;
452  
453     for (int i = 0; i < num_images; ++i)
454     {
455         full_img = imread(samples::findFile(img_names[i]));
456         full_img_sizes[i] = full_img.size();
457  
458         if (full_img.empty())
459         {
460             LOGLN("Can't open image " << img_names[i]);
461             return -1;
462         }
463         if (work_megapix < 0)
464         {
465             img = full_img;
466             work_scale = 1;
467             is_work_scale_set = true;
468         }
469         else
470         {
471             if (!is_work_scale_set)
472             {
473                 work_scale = min(1.0, sqrt(work_megapix * 1e6 / full_img.size().area()));
474                 is_work_scale_set = true;
475             }
476             resize(full_img, img, Size(), work_scale, work_scale, INTER_LINEAR_EXACT);
477         }
478         if (!is_seam_scale_set)
479         {
480             seam_scale = min(1.0, sqrt(seam_megapix * 1e6 / full_img.size().area()));
481             seam_work_aspect = seam_scale / work_scale;
482             is_seam_scale_set = true;
483         }
484  
485         computeImageFeatures(finder, img, features[i]);
486         features[i].img_idx = i;
487         LOGLN("Features in image #" << i + 1 << ": " << features[i].keypoints.size());
488  
489         resize(full_img, img, Size(), seam_scale, seam_scale, INTER_LINEAR_EXACT);
490         images[i] = img.clone();
491     }
492  
493     full_img.release();
494     img.release();
495  
496     LOGLN("Finding features, time: " << ((getTickCount() - t) / getTickFrequency()) << " sec");
497  
498     LOG("Pairwise matching");
499 #if ENABLE_LOG
500     t = getTickCount();
501 #endif
502     vector<MatchesInfo> pairwise_matches;
503     Ptr<FeaturesMatcher> matcher;
504     if (matcher_type == "affine")
505         matcher = makePtr<AffineBestOf2NearestMatcher>(false, try_cuda, match_conf);
506     else if (range_width == -1)
507         matcher = makePtr<BestOf2NearestMatcher>(try_cuda, match_conf);
508     else
509         matcher = makePtr<BestOf2NearestRangeMatcher>(range_width, try_cuda, match_conf);
510  
511     (*matcher)(features, pairwise_matches);
512     matcher->collectGarbage();
513  
514     LOGLN("Pairwise matching, time: " << ((getTickCount() - t) / getTickFrequency()) << " sec");
515  
516     // Check if we should save matches graph
517     if (save_graph)
518     {
519         LOGLN("Saving matches graph...");
520         ofstream f(save_graph_to.c_str());
521         f << matchesGraphAsString(img_names, pairwise_matches, conf_thresh);
522     }
523  
524     // Leave only images we are sure are from the same panorama
525     vector<int> indices = leaveBiggestComponent(features, pairwise_matches, conf_thresh);
526     vector<Mat> img_subset;
527     vector<String> img_names_subset;
528     vector<Size> full_img_sizes_subset;
529     for (size_t i = 0; i < indices.size(); ++i)
530     {
531         img_names_subset.push_back(img_names[indices[i]]);
532         img_subset.push_back(images[indices[i]]);
533         full_img_sizes_subset.push_back(full_img_sizes[indices[i]]);
534     }
535  
536     images = img_subset;
537     img_names = img_names_subset;
538     full_img_sizes = full_img_sizes_subset;
539  
540     // Check if we still have enough images
541     num_images = static_cast<int>(img_names.size());
542     if (num_images < 2)
543     {
544         LOGLN("Need more images");
545         return -1;
546     }
547  
548     Ptr<Estimator> estimator;
549     if (estimator_type == "affine")
550         estimator = makePtr<AffineBasedEstimator>();
551     else
552         estimator = makePtr<HomographyBasedEstimator>();
553  
554     vector<CameraParams> cameras;
555     if (!(*estimator)(features, pairwise_matches, cameras))
556     {
557         cout << "Homography estimation failed.
";
558         return -1;
559     }
560  
561     for (size_t i = 0; i < cameras.size(); ++i)
562     {
563         Mat R;
564         cameras[i].R.convertTo(R, CV_32F);
565         cameras[i].R = R;
566         LOGLN("Initial camera intrinsics #" << indices[i] + 1 << ":
K:
" << cameras[i].K() << "
R:
" << cameras[i].R);
567     }
568  
569     Ptr<detail::BundleAdjusterBase> adjuster;
570     if (ba_cost_func == "reproj") adjuster = makePtr<detail::BundleAdjusterReproj>();
571     else if (ba_cost_func == "ray") adjuster = makePtr<detail::BundleAdjusterRay>();
572     else if (ba_cost_func == "affine") adjuster = makePtr<detail::BundleAdjusterAffinePartial>();
573     else if (ba_cost_func == "no") adjuster = makePtr<NoBundleAdjuster>();
574     else
575     {
576         cout << "Unknown bundle adjustment cost function: '" << ba_cost_func << "'.
";
577         return -1;
578     }
579     adjuster->setConfThresh(conf_thresh);
580     Mat_<uchar> refine_mask = Mat::zeros(3, 3, CV_8U);
581     if (ba_refine_mask[0] == 'x') refine_mask(0, 0) = 1;
582     if (ba_refine_mask[1] == 'x') refine_mask(0, 1) = 1;
583     if (ba_refine_mask[2] == 'x') refine_mask(0, 2) = 1;
584     if (ba_refine_mask[3] == 'x') refine_mask(1, 1) = 1;
585     if (ba_refine_mask[4] == 'x') refine_mask(1, 2) = 1;
586     adjuster->setRefinementMask(refine_mask);
587     if (!(*adjuster)(features, pairwise_matches, cameras))
588     {
589         cout << "Camera parameters adjusting failed.
";
590         return -1;
591     }
592  
593     // Find median focal length
594  
595     vector<double> focals;
596     for (size_t i = 0; i < cameras.size(); ++i)
597     {
598         LOGLN("Camera #" << indices[i] + 1 << ":
K:
" << cameras[i].K() << "
R:
" << cameras[i].R);
599         focals.push_back(cameras[i].focal);
600     }
601  
602     sort(focals.begin(), focals.end());
603     float warped_image_scale;
604     if (focals.size() % 2 == 1)
605         warped_image_scale = static_cast<float>(focals[focals.size() / 2]);
606     else
607         warped_image_scale = static_cast<float>(focals[focals.size() / 2 - 1] + focals[focals.size() / 2]) * 0.5f;
608  
609     if (do_wave_correct)
610     {
611         vector<Mat> rmats;
612         for (size_t i = 0; i < cameras.size(); ++i)
613             rmats.push_back(cameras[i].R.clone());
614         waveCorrect(rmats, wave_correct);
615         for (size_t i = 0; i < cameras.size(); ++i)
616             cameras[i].R = rmats[i];
617     }
618  
619     LOGLN("Warping images (auxiliary)... ");
620 #if ENABLE_LOG
621     t = getTickCount();
622 #endif
623  
624     vector<Point> corners(num_images);
625     vector<UMat> masks_warped(num_images);
626     vector<UMat> images_warped(num_images);
627     vector<Size> sizes(num_images);
628     vector<UMat> masks(num_images);
629  
630     // Prepare images masks
631     for (int i = 0; i < num_images; ++i)
632     {
633         masks[i].create(images[i].size(), CV_8U);
634         masks[i].setTo(Scalar::all(255));
635     }
636  
637     // Warp images and their masks
638  
639     Ptr<WarperCreator> warper_creator;
640 #ifdef HAVE_OPENCV_CUDAWARPING
641     if (try_cuda && cuda::getCudaEnabledDeviceCount() > 0)
642     {
643         if (warp_type == "plane")
644             warper_creator = makePtr<cv::PlaneWarperGpu>();
645         else if (warp_type == "cylindrical")
646             warper_creator = makePtr<cv::CylindricalWarperGpu>();
647         else if (warp_type == "spherical")
648             warper_creator = makePtr<cv::SphericalWarperGpu>();
649     }
650     else
651 #endif
652     {
653         if (warp_type == "plane")
654             warper_creator = makePtr<cv::PlaneWarper>();
655         else if (warp_type == "affine")
656             warper_creator = makePtr<cv::AffineWarper>();
657         else if (warp_type == "cylindrical")
658             warper_creator = makePtr<cv::CylindricalWarper>();
659         else if (warp_type == "spherical")
660             warper_creator = makePtr<cv::SphericalWarper>();
661         else if (warp_type == "fisheye")
662             warper_creator = makePtr<cv::FisheyeWarper>();
663         else if (warp_type == "stereographic")
664             warper_creator = makePtr<cv::StereographicWarper>();
665         else if (warp_type == "compressedPlaneA2B1")
666             warper_creator = makePtr<cv::CompressedRectilinearWarper>(2.0f, 1.0f);
667         else if (warp_type == "compressedPlaneA1.5B1")
668             warper_creator = makePtr<cv::CompressedRectilinearWarper>(1.5f, 1.0f);
669         else if (warp_type == "compressedPlanePortraitA2B1")
670             warper_creator = makePtr<cv::CompressedRectilinearPortraitWarper>(2.0f, 1.0f);
671         else if (warp_type == "compressedPlanePortraitA1.5B1")
672             warper_creator = makePtr<cv::CompressedRectilinearPortraitWarper>(1.5f, 1.0f);
673         else if (warp_type == "paniniA2B1")
674             warper_creator = makePtr<cv::PaniniWarper>(2.0f, 1.0f);
675         else if (warp_type == "paniniA1.5B1")
676             warper_creator = makePtr<cv::PaniniWarper>(1.5f, 1.0f);
677         else if (warp_type == "paniniPortraitA2B1")
678             warper_creator = makePtr<cv::PaniniPortraitWarper>(2.0f, 1.0f);
679         else if (warp_type == "paniniPortraitA1.5B1")
680             warper_creator = makePtr<cv::PaniniPortraitWarper>(1.5f, 1.0f);
681         else if (warp_type == "mercator")
682             warper_creator = makePtr<cv::MercatorWarper>();
683         else if (warp_type == "transverseMercator")
684             warper_creator = makePtr<cv::TransverseMercatorWarper>();
685     }
686  
687     if (!warper_creator)
688     {
689         cout << "Can't create the following warper '" << warp_type << "'
";
690         return 1;
691     }
692  
693     Ptr<RotationWarper> warper = warper_creator->create(static_cast<float>(warped_image_scale * seam_work_aspect));
694  
695     for (int i = 0; i < num_images; ++i)
696     {
697         Mat_<float> K;
698         cameras[i].K().convertTo(K, CV_32F);
699         float swa = (float)seam_work_aspect;
700         K(0, 0) *= swa; K(0, 2) *= swa;
701         K(1, 1) *= swa; K(1, 2) *= swa;
702  
703         corners[i] = warper->warp(images[i], K, cameras[i].R, INTER_LINEAR, BORDER_REFLECT, images_warped[i]);
704         sizes[i] = images_warped[i].size();
705  
706         warper->warp(masks[i], K, cameras[i].R, INTER_NEAREST, BORDER_CONSTANT, masks_warped[i]);
707     }
708  
709     vector<UMat> images_warped_f(num_images);
710     for (int i = 0; i < num_images; ++i)
711         images_warped[i].convertTo(images_warped_f[i], CV_32F);
712  
713     LOGLN("Warping images, time: " << ((getTickCount() - t) / getTickFrequency()) << " sec");
714  
715     LOGLN("Compensating exposure...");
716 #if ENABLE_LOG
717     t = getTickCount();
718 #endif
719  
720     Ptr<ExposureCompensator> compensator = ExposureCompensator::createDefault(expos_comp_type);
721     if (dynamic_cast<GainCompensator*>(compensator.get()))
722     {
723         GainCompensator* gcompensator = dynamic_cast<GainCompensator*>(compensator.get());
724         gcompensator->setNrFeeds(expos_comp_nr_feeds);
725     }
726  
727     if (dynamic_cast<ChannelsCompensator*>(compensator.get()))
728     {
729         ChannelsCompensator* ccompensator = dynamic_cast<ChannelsCompensator*>(compensator.get());
730         ccompensator->setNrFeeds(expos_comp_nr_feeds);
731     }
732  
733     if (dynamic_cast<BlocksCompensator*>(compensator.get()))
734     {
735         BlocksCompensator* bcompensator = dynamic_cast<BlocksCompensator*>(compensator.get());
736         bcompensator->setNrFeeds(expos_comp_nr_feeds);
737         bcompensator->setNrGainsFilteringIterations(expos_comp_nr_filtering);
738         bcompensator->setBlockSize(expos_comp_block_size, expos_comp_block_size);
739     }
740  
741     compensator->feed(corners, images_warped, masks_warped);
742  
743     LOGLN("Compensating exposure, time: " << ((getTickCount() - t) / getTickFrequency()) << " sec");
744  
745     LOGLN("Finding seams...");
746 #if ENABLE_LOG
747     t = getTickCount();
748 #endif
749  
750     Ptr<SeamFinder> seam_finder;
751     if (seam_find_type == "no")
752         seam_finder = makePtr<detail::NoSeamFinder>();
753     else if (seam_find_type == "voronoi")
754         seam_finder = makePtr<detail::VoronoiSeamFinder>();
755     else if (seam_find_type == "gc_color")
756     {
757 #ifdef HAVE_OPENCV_CUDALEGACY
758         if (try_cuda && cuda::getCudaEnabledDeviceCount() > 0)
759             seam_finder = makePtr<detail::GraphCutSeamFinderGpu>(GraphCutSeamFinderBase::COST_COLOR);
760         else
761 #endif
762             seam_finder = makePtr<detail::GraphCutSeamFinder>(GraphCutSeamFinderBase::COST_COLOR);
763     }
764     else if (seam_find_type == "gc_colorgrad")
765     {
766 #ifdef HAVE_OPENCV_CUDALEGACY
767         if (try_cuda && cuda::getCudaEnabledDeviceCount() > 0)
768             seam_finder = makePtr<detail::GraphCutSeamFinderGpu>(GraphCutSeamFinderBase::COST_COLOR_GRAD);
769         else
770 #endif
771             seam_finder = makePtr<detail::GraphCutSeamFinder>(GraphCutSeamFinderBase::COST_COLOR_GRAD);
772     }
773     else if (seam_find_type == "dp_color")
774         seam_finder = makePtr<detail::DpSeamFinder>(DpSeamFinder::COLOR);
775     else if (seam_find_type == "dp_colorgrad")
776         seam_finder = makePtr<detail::DpSeamFinder>(DpSeamFinder::COLOR_GRAD);
777     if (!seam_finder)
778     {
779         cout << "Can't create the following seam finder '" << seam_find_type << "'
";
780         return 1;
781     }
782  
783     seam_finder->find(images_warped_f, corners, masks_warped);
784  
785     LOGLN("Finding seams, time: " << ((getTickCount() - t) / getTickFrequency()) << " sec");
786  
787     // Release unused memory
788     images.clear();
789     images_warped.clear();
790     images_warped_f.clear();
791     masks.clear();
792  
793     LOGLN("Compositing...");
794 #if ENABLE_LOG
795     t = getTickCount();
796 #endif
797  
798     Mat img_warped, img_warped_s;
799     Mat dilated_mask, seam_mask, mask, mask_warped;
800     Ptr<Blender> blender;
801     Ptr<Timelapser> timelapser;
802     //double compose_seam_aspect = 1;
803     double compose_work_aspect = 1;
804  
805     for (int img_idx = 0; img_idx < num_images; ++img_idx)
806     {
807         LOGLN("Compositing image #" << indices[img_idx] + 1);
808  
809         // Read image and resize it if necessary
810         full_img = imread(samples::findFile(img_names[img_idx]));
811         if (!is_compose_scale_set)
812         {
813             if (compose_megapix > 0)
814                 compose_scale = min(1.0, sqrt(compose_megapix * 1e6 / full_img.size().area()));
815             is_compose_scale_set = true;
816  
817             // Compute relative scales
818             //compose_seam_aspect = compose_scale / seam_scale;
819             compose_work_aspect = compose_scale / work_scale;
820  
821             // Update warped image scale
822             warped_image_scale *= static_cast<float>(compose_work_aspect);
823             warper = warper_creator->create(warped_image_scale);
824  
825             // Update corners and sizes
826             for (int i = 0; i < num_images; ++i)
827             {
828                 // Update intrinsics
829                 cameras[i].focal *= compose_work_aspect;
830                 cameras[i].ppx *= compose_work_aspect;
831                 cameras[i].ppy *= compose_work_aspect;
832  
833                 // Update corner and size
834                 Size sz = full_img_sizes[i];
835                 if (std::abs(compose_scale - 1) > 1e-1)
836                 {
837                     sz.width = cvRound(full_img_sizes[i].width * compose_scale);
838                     sz.height = cvRound(full_img_sizes[i].height * compose_scale);
839                 }
840  
841                 Mat K;
842                 cameras[i].K().convertTo(K, CV_32F);
843                 Rect roi = warper->warpRoi(sz, K, cameras[i].R);
844                 corners[i] = roi.tl();
845                 sizes[i] = roi.size();
846             }
847         }
848         if (abs(compose_scale - 1) > 1e-1)
849             resize(full_img, img, Size(), compose_scale, compose_scale, INTER_LINEAR_EXACT);
850         else
851             img = full_img;
852         full_img.release();
853         Size img_size = img.size();
854  
855         Mat K;
856         cameras[img_idx].K().convertTo(K, CV_32F);
857  
858         // Warp the current image
859         warper->warp(img, K, cameras[img_idx].R, INTER_LINEAR, BORDER_REFLECT, img_warped);
860  
861         // Warp the current image mask
862         mask.create(img_size, CV_8U);
863         mask.setTo(Scalar::all(255));
864         warper->warp(mask, K, cameras[img_idx].R, INTER_NEAREST, BORDER_CONSTANT, mask_warped);
865  
866         // Compensate exposure
867         compensator->apply(img_idx, corners[img_idx], img_warped, mask_warped);
868  
869         img_warped.convertTo(img_warped_s, CV_16S);
870         img_warped.release();
871         img.release();
872         mask.release();
873  
874         dilate(masks_warped[img_idx], dilated_mask, Mat());
875         resize(dilated_mask, seam_mask, mask_warped.size(), 0, 0, INTER_LINEAR_EXACT);
876         mask_warped = seam_mask & mask_warped;
877  
878         if (!blender && !timelapse)
879         {
880             blender = Blender::createDefault(blend_type, try_cuda);
881             Size dst_sz = resultRoi(corners, sizes).size();
882             float blend_width = sqrt(static_cast<float>(dst_sz.area())) * blend_strength / 100.f;
883             if (blend_width < 1.f)
884                 blender = Blender::createDefault(Blender::NO, try_cuda);
885             else if (blend_type == Blender::MULTI_BAND)
886             {
887                 MultiBandBlender* mb = dynamic_cast<MultiBandBlender*>(blender.get());
888                 mb->setNumBands(static_cast<int>(ceil(log(blend_width) / log(2.)) - 1.));
889                 LOGLN("Multi-band blender, number of bands: " << mb->numBands());
890             }
891             else if (blend_type == Blender::FEATHER)
892             {
893                 FeatherBlender* fb = dynamic_cast<FeatherBlender*>(blender.get());
894                 fb->setSharpness(1.f / blend_width);
895                 LOGLN("Feather blender, sharpness: " << fb->sharpness());
896             }
897             blender->prepare(corners, sizes);
898         }
899         else if (!timelapser && timelapse)
900         {
901             timelapser = Timelapser::createDefault(timelapse_type);
902             timelapser->initialize(corners, sizes);
903         }
904  
905         // Blend the current image
906         if (timelapse)
907         {
908             timelapser->process(img_warped_s, Mat::ones(img_warped_s.size(), CV_8UC1), corners[img_idx]);
909             String fixedFileName;
910             size_t pos_s = String(img_names[img_idx]).find_last_of("/\");
911             if (pos_s == String::npos)
912             {
913                 fixedFileName = "fixed_" + img_names[img_idx];
914             }
915             else
916             {
917                 fixedFileName = "fixed_" + String(img_names[img_idx]).substr(pos_s + 1, String(img_names[img_idx]).length() - pos_s);
918             }
919             imwrite(fixedFileName, timelapser->getDst());
920         }
921         else
922         {
923             blender->feed(img_warped_s, mask_warped, corners[img_idx]);
924         }
925     }
926  
927     if (!timelapse)
928     {
929         Mat result, result_mask;
930         blender->blend(result, result_mask);
931  
932         LOGLN("Compositing, time: " << ((getTickCount() - t) / getTickFrequency()) << " sec");
933  
934         imwrite(result_name, result);
935     }
936  
937     LOGLN("Finished, total time: " << ((getTickCount() - app_start_time) / getTickFrequency()) << " sec");
938     return 0;
939 }

stitching_detail 程序运行流程

命令行调用程序，输入源图像以及程序的参数
特征点检测，判断是使用 surf 还是 orb，默认是 surf
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
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
对所有图像进行相机参数粗略估计，然后求出旋转矩阵
使用光束平均法进一步精准的估计出旋转矩阵
波形校正，水平或者垂直
拼接
融合，多频段融合，光照补偿

stitching_detail 程序接口介绍

img1 img2 img3 输入图像
--preview 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
--try_gpu (yes|no) 是否使用 CUDA加速，默认为 no，使用CPU模式
/* 运动估计参数 */
--work_megapix <--work_megapix <float>> 图像匹配时的分辨率大小，默认为 0.6
--features (surf | orb | sift | akaze) 选择 surf 或者 orb 算法进行特征点匹配，默认为 surf
--matcher (homography | affine) 用于成对图像匹配的匹配器
--estimator (homography | affine) 用于转换估计的估计器类型
--match_conf <float> 特征点匹配步骤的匹配置信度，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
--conf_thresh <float> 两幅图来自同一全景图的置信度，默认为 1.0
--ba (no | reproj | ray | affine) 光束平均法的误差函数选择，默认是 ray 方法
--ba_refine_mask (mask) 光束平均法设置优化掩码
--wave_correct (no|horiz|vert) 波形校验水平，垂直或者没有默认是 horiz(水平)
--save_graph <file_name> 将匹配的图形以点的形式保存到文件中， Nm 代表匹配的数量，NI代表正确匹配的数量，C 表示置信度
/*图像融合参数:*/
--warp (plane|cylindrical|spherical|fisheye|stereographic|compressedPlaneA2B1|compressedPla neA1.5B1|compressedPlanePortraitA2B1|compressedPlanePortraitA1.5B1|paniniA2B1|paniniA1.5B1|paniniPortraitA2B1|paniniPor traitA1.5B1|mercator|transverseMercator) 选择融合的平面，默认是球形
--seam_megapix <float> 拼接缝像素的大小默认是 0.1
--seam (no|voronoi|gc_color|gc_colorgrad) 拼接缝隙估计方法默认是 gc_color
--compose_megapix <float> 拼接分辨率，默认为-1
--expos_comp (no|gain|gain_blocks) 光照补偿方法，默认是 gain_blocks
--blend (no|feather|multiband) 融合方法，默认是多频段融合
--blend_strength <float> 融合强度，0-100.默认是 5.
--output <result_img> 输出图像的文件名，默认是 result,jpg 命令使用实例，以及程序运行时的提示：

上面使用默认参数，详细输出信息如下：

  1 E:PracticeOpenCVAlgorithm_SummaryImage_Stitchingx64Debug>05_Image_Stitch_Stitching_Detailed.exe ./imgs/boat1.jpg ./imgs/boat2.jpg ./imgs/boat3.jpg ./imgs/boat4.jpg ./imgs/boat5.jpg ./imgs/boat6.jpg
  2 Finding features...
  3 [ INFO:0] global C:uildmaster_winpack-build-win64-vc15opencvmodulescoresrcocl.cpp (891) cv::ocl::haveOpenCL Initialize OpenCL runtime...
  4 Features in image #1: 500
  5 [ INFO:0] global C:uildmaster_winpack-build-win64-vc15opencvmodulescoresrcocl.cpp (433) cv::ocl::OpenCLBinaryCacheConfigurator::OpenCLBinaryCacheConfigurator Successfully initialized OpenCL cache directory: C:UsersA4080599AppDataLocalTempopencv4.4opencl_cache
  6 [ INFO:0] global C:uildmaster_winpack-build-win64-vc15opencvmodulescoresrcocl.cpp (457) cv::ocl::OpenCLBinaryCacheConfigurator::prepareCacheDirectoryForContext Preparing OpenCL cache configuration for context: NVIDIA_Corporation--GeForce_GTX_1070--411_31
  7 Features in image #2: 500
  8 Features in image #3: 500
  9 Features in image #4: 500
 10 Features in image #5: 500
 11 Features in image #6: 500
 12 Finding features, time: 5.46377 sec
 13 Pairwise matchingPairwise matching, time: 3.24159 sec
 14 Initial camera intrinsics #1:
 15 K:
 16 [534.6674906996568, 0, 474.5;
 17  0, 534.6674906996568, 316;
 18  0, 0, 1]
 19 R:
 20 [0.91843718, -0.09762425, -1.1678253;
 21  0.0034433089, 1.0835428, -0.025021957;
 22  0.28152198, 0.16100603, 0.91920781]
 23 Initial camera intrinsics #2:
 24 K:
 25 [534.6674906996568, 0, 474.5;
 26  0, 534.6674906996568, 316;
 27  0, 0, 1]
 28 R:
 29 [1.001171, -0.085758291, -0.64530683;
 30  0.010103324, 1.0520245, -0.030576767;
 31  0.15743911, 0.12035993, 1]
 32 Initial camera intrinsics #3:
 33 K:
 34 [534.6674906996568, 0, 474.5;
 35  0, 534.6674906996568, 316;
 36  0, 0, 1]
 37 R:
 38 [1, 0, 0;
 39  0, 1, 0;
 40  0, 0, 1]
 41 Initial camera intrinsics #4:
 42 K:
 43 [534.6674906996568, 0, 474.5;
 44  0, 534.6674906996568, 316;
 45  0, 0, 1]
 46 R:
 47 [0.8474561, 0.028589081, 0.75133896;
 48  -0.0014587968, 0.92028928, 0.033205934;
 49  -0.17483309, 0.018777205, 0.84592116]
 50 Initial camera intrinsics #5:
 51 K:
 52 [534.6674906996568, 0, 474.5;
 53  0, 534.6674906996568, 316;
 54  0, 0, 1]
 55 R:
 56 [0.60283858, 0.069275051, 1.2121853;
 57  -0.014153662, 0.85474133, 0.014057174;
 58  -0.29529575, 0.053770453, 0.61932623]
 59 Initial camera intrinsics #6:
 60 K:
 61 [534.6674906996568, 0, 474.5;
 62  0, 534.6674906996568, 316;
 63  0, 0, 1]
 64 R:
 65 [0.41477469, 0.075901195, 1.4396564;
 66  -0.015423983, 0.82344943, 0.0061162044;
 67  -0.35168326, 0.055747174, 0.42653102]
 68 Camera #1:
 69 K:
 70 [1068.953598931666, 0, 474.5;
 71  0, 1068.953598931666, 316;
 72  0, 0, 1]
 73 R:
 74 [0.84266716, -0.010490002, -0.53833258;
 75  0.004485324, 0.99991232, -0.01246338;
 76  0.53841609, 0.0080878884, 0.84264034]
 77 Camera #2:
 78 K:
 79 [1064.878323247434, 0, 474.5;
 80  0, 1064.878323247434, 316;
 81  0, 0, 1]
 82 R:
 83 [0.95117813, -0.015436338, -0.3082563;
 84  0.01137107, 0.99982315, -0.014980057;
 85  0.308433, 0.010743499, 0.95118535]
 86 Camera #3:
 87 K:
 88 [1065.382193682081, 0, 474.5;
 89  0, 1065.382193682081, 316;
 90  0, 0, 1]
 91 R:
 92 [1, -1.6298145e-09, 0;
 93  -1.5716068e-09, 1, 0;
 94  0, 0, 1]
 95 Camera #4:
 96 K:
 97 [1067.611537959627, 0, 474.5;
 98  0, 1067.611537959627, 316;
 99  0, 0, 1]
100 R:
101 [0.91316396, -7.9067249e-06, 0.40759254;
102  -0.0075879274, 0.99982637, 0.017019274;
103  -0.4075219, -0.018634165, 0.91300529]
104 Camera #5:
105 K:
106 [1080.708135180496, 0, 474.5;
107  0, 1080.708135180496, 316;
108  0, 0, 1]
109 R:
110 [0.70923853, 0.0025724203, 0.70496398;
111  -0.0098195076, 0.99993235, 0.0062302947;
112  -0.70490021, -0.01134116, 0.70921582]
113 Camera #6:
114 K:
115 [1080.90412660159, 0, 474.5;
116  0, 1080.90412660159, 316;
117  0, 0, 1]
118 R:
119 [0.49985889, 3.5938341e-05, 0.86610687;
120  -0.00682831, 0.99996907, 0.0038993564;
121  -0.86607999, -0.0078631733, 0.49984369]
122 Warping images (auxiliary)...
123 Warping images, time: 0.0791121 sec
124 Compensating exposure...
125 Compensating exposure, time: 0.72288 sec
126 Finding seams...
127 Finding seams, time: 3.09237 sec
128 Compositing...
129 Compositing image #1
130 Multi-band blender, number of bands: 8
131 Compositing image #2
132 Compositing image #3
133 Compositing image #4
134 Compositing image #5
135 Compositing image #6
136 Compositing, time: 13.7766 sec
137 Finished, total time: 29.4535 sec

输入图像boat1.jpg、boat2.jpg、boat3.jpg、boat4.jpg、boat5.jpg、boat6.jpg如下(可以在OpenCV安装目录下找到D:OpenCV4.4opencv_extra-master estdatastitching)

结果图：

参数warp_type 设置为"plane"，效果图如下：

参数warp_type 设置为"fisheye"，效果图如下(旋转90°后)：

其他的参数可以根据自己需要修改，如果要自己完成还需要详细了解拼接步骤再优化。

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原文地址：https://www.cnblogs.com/ybqjymy/p/14182716.html