OpenCV图像拼接终章--Stitching detailed使用与参数介绍

Color Space

发布于 2020-10-29 14:42:45

5.4K1

发布于 2020-10-29 14:42:45

文章被收录于专栏：OpenCV与AI深度学习

关于OpenCV图像拼接的方法，如果不熟悉的话，可以先看看公众号整理的如下四篇文章介绍：

本篇文章是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)：

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具体源码如下目录，由于代码较多，这里不贴出来，大家找到位置自行查阅。

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：


E:\Practice\OpenCV\Algorithm_Summary\Image_Stitching\x64\Debug>05_Image_Stitch_Stitching_Detailed.exe ./imgs/boat1.jpg ./imgs/boat2.jpg ./imgs/boat3.jpg ./imgs/boat4.jpg ./imgs/boat5.jpg ./imgs/boat6.jpg
Finding features...
[ INFO:0] global C:\build\master_winpack-build-win64-vc15\opencv\modules\core\src\ocl.cpp (891) cv::ocl::haveOpenCL Initialize OpenCL runtime...
Features in image #1: 500
[ INFO:0] global C:\build\master_winpack-build-win64-vc15\opencv\modules\core\src\ocl.cpp (433) cv::ocl::OpenCLBinaryCacheConfigurator::OpenCLBinaryCacheConfigurator Successfully initialized OpenCL cache directory: C:\Users\A4080599\AppData\Local\Temp\opencv\4.4\opencl_cache\
[ INFO:0] global C:\build\master_winpack-build-win64-vc15\opencv\modules\core\src\ocl.cpp (457) cv::ocl::OpenCLBinaryCacheConfigurator::prepareCacheDirectoryForContext Preparing OpenCL cache configuration for context: NVIDIA_Corporation--GeForce_GTX_1070--411_31
Features in image #2: 500
Features in image #3: 500
Features in image #4: 500
Features in image #5: 500
Features in image #6: 500
Finding features, time: 5.46377 sec
Pairwise matchingPairwise matching, time: 3.24159 sec
Initial camera intrinsics #1:
K:
[534.6674906996568, 0, 474.5;
 0, 534.6674906996568, 316;
 0, 0, 1]
R:
[0.91843718, -0.09762425, -1.1678253;
 0.0034433089, 1.0835428, -0.025021957;
 0.28152198, 0.16100603, 0.91920781]
Initial camera intrinsics #2:
K:
[534.6674906996568, 0, 474.5;
 0, 534.6674906996568, 316;
 0, 0, 1]
R:
[1.001171, -0.085758291, -0.64530683;
 0.010103324, 1.0520245, -0.030576767;
 0.15743911, 0.12035993, 1]
Initial camera intrinsics #3:
K:
[534.6674906996568, 0, 474.5;
 0, 534.6674906996568, 316;
 0, 0, 1]
R:
[1, 0, 0;
 0, 1, 0;
 0, 0, 1]
Initial camera intrinsics #4:
K:
[534.6674906996568, 0, 474.5;
 0, 534.6674906996568, 316;
 0, 0, 1]
R:
[0.8474561, 0.028589081, 0.75133896;
 -0.0014587968, 0.92028928, 0.033205934;
 -0.17483309, 0.018777205, 0.84592116]
Initial camera intrinsics #5:
K:
[534.6674906996568, 0, 474.5;
 0, 534.6674906996568, 316;
 0, 0, 1]
R:
[0.60283858, 0.069275051, 1.2121853;
 -0.014153662, 0.85474133, 0.014057174;
 -0.29529575, 0.053770453, 0.61932623]
Initial camera intrinsics #6:
K:
[534.6674906996568, 0, 474.5;
 0, 534.6674906996568, 316;
 0, 0, 1]
R:
[0.41477469, 0.075901195, 1.4396564;
 -0.015423983, 0.82344943, 0.0061162044;
 -0.35168326, 0.055747174, 0.42653102]
Camera #1:
K:
[1068.953598931666, 0, 474.5;
 0, 1068.953598931666, 316;
 0, 0, 1]
R:
[0.84266716, -0.010490002, -0.53833258;
 0.004485324, 0.99991232, -0.01246338;
 0.53841609, 0.0080878884, 0.84264034]
Camera #2:
K:
[1064.878323247434, 0, 474.5;
 0, 1064.878323247434, 316;
 0, 0, 1]
R:
[0.95117813, -0.015436338, -0.3082563;
 0.01137107, 0.99982315, -0.014980057;
 0.308433, 0.010743499, 0.95118535]
Camera #3:
K:
[1065.382193682081, 0, 474.5;
 0, 1065.382193682081, 316;
 0, 0, 1]
R:
[1, -1.6298145e-09, 0;
 -1.5716068e-09, 1, 0;
 0, 0, 1]
Camera #4:
K:
[1067.611537959627, 0, 474.5;
 0, 1067.611537959627, 316;
 0, 0, 1]
R:
[0.91316396, -7.9067249e-06, 0.40759254;
 -0.0075879274, 0.99982637, 0.017019274;
 -0.4075219, -0.018634165, 0.91300529]
Camera #5:
K:
[1080.708135180496, 0, 474.5;
 0, 1080.708135180496, 316;
 0, 0, 1]
R:
[0.70923853, 0.0025724203, 0.70496398;
 -0.0098195076, 0.99993235, 0.0062302947;
 -0.70490021, -0.01134116, 0.70921582]
Camera #6:
K:
[1080.90412660159, 0, 474.5;
 0, 1080.90412660159, 316;
 0, 0, 1]
R:
[0.49985889, 3.5938341e-05, 0.86610687;
 -0.00682831, 0.99996907, 0.0038993564;
 -0.86607999, -0.0078631733, 0.49984369]
Warping images (auxiliary)...
Warping images, time: 0.0791121 sec
Compensating exposure...
Compensating exposure, time: 0.72288 sec
Finding seams...
Finding seams, time: 3.09237 sec
Compositing...
Compositing image #1
Multi-band blender, number of bands: 8
Compositing image #2
Compositing image #3
Compositing image #4
Compositing image #5
Compositing image #6
Compositing, time: 13.7766 sec
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:\OpenCV4.4\opencv_extra-master\testdata\stitching)