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I have found some basic working examples on stitching via OpenCV for panoramic images. I have also found some useful documentation in the API docs, but I can't find out how to speed up the processing by providing additional information.

In my case, I generate a set of images in a 20x20 grid of individual frames, for a total of 400 images to be stitched into a single large one. This takes an enormous amount of time on a modern PC, so it would likely take hours on a developer board.

Is there any way to tell the OpenCV instance information about the images, such as me knowing in advance the relative positioning of all the images as they would appear on a grid? The only API calls I see so far is to just add all the images indiscriminately to a queue via vImg.push_back().


References

  1. Stitching. Image Stitching - OpenCV API Documentation, Accessed 2014-02-26, <http://docs.opencv.org/modules/stitching/doc/stitching.html>
  2. OpenCV Stitching example (Stitcher class, Panorama), Accessed 2014-02-26, <http://feelmare.blogspot.ca/2013/11/opencv-stitching-example-stitcher-class.html>
  3. Panorama – Image Stitching in OpenCV, Accessed 2014-02-26, <http://ramsrigoutham.com/2012/11/22/panorama-image-stitching-in-opencv/>
Cloud
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    Consider to parallel it, like the merge sort style. – herohuyongtao Mar 11 '14 at 15:33
  • developer board ? you want to port it on a microC/FPGA !? – lucasg Mar 13 '14 at 10:00
  • @georgesl Not something as low powered as that, but a developer board or micro PC for sure. In general, if I can cut down the processing required, regardless of the platform in use, I go for it. – Cloud Mar 13 '14 at 16:32

5 Answers5

10

I did some work with the stitching pipeline and though I do not consider myself an expert on the field, I did get better performance (and better results as well) adjusting each step of the pipeline separately. As you can see in the picture, the Stitching class is nothing but a wrapper of this pipeline: An overview to the Stitching pipeline

Some interesting parts you can adjust are the resizing steps (there comes a point were more resolution just means more computation time and more inaccurate features), the matching process and (though this is just a guess) giving a good camera parameters instead of performing an estimation. This involves getting the camera parameters before doing the stitching, but it is not really hard. Here you have some reference: OpenCV Camera Calibration and 3D Reconstruction.

Again: I am not an expert, this is just based on my experience as an intern doing some experiments with the library!

martinarroyo
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  • Is it possible to extract the parameters the stitching operation uses? I'm revisiting this code almost 3 years later, and was wondering if I could have a bunch of down-sampled (low-res) copies of the files stitched, and then re-use the same parameters with higher res copies to cut down on computation time. – Cloud Dec 21 '17 at 13:35
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    Absolutely. In the end what you get is the relationship between pairs of images in the form of a homography matrix, which essentially explains how to rotate and translate one of them so that you 'reach' the other one. This transformation is not affected by scale, so you can perform all the steps on downsampled images to gain some speedup, and then apply the final transformation on the original ones. Note that accuracy might be affected in some steps of the pipeline if you scale down too much, though. – martinarroyo Dec 23 '17 at 21:42
7

So far as I know, there is no means to provide additional data to the OpenCV engine beyond just giving it a list of images. It does a pretty good job on its own though. I would check out some of the example code, and test how long each stitching operation takes. From my experiments using 4x6, 4x8, ..., 4x20 panoramic reconstructions, the CPU time required seems to increase with the number of overlapping images. I would imagine your case would require at least a minute to compute on a modern machine.

Source: https://code.ros.org/trac/opencv/browser/trunk/opencv/samples/cpp/stitching.cpp?rev=6682

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40  //
41  //M*/
42  
43  // We follow to these papers:
44  // 1) Construction of panoramic mosaics with global and local alignment.
45  //    Heung-Yeung Shum and Richard Szeliski. 2000.
46  // 2) Eliminating Ghosting and Exposure Artifacts in Image Mosaics.
47  //    Matthew Uyttendaele, Ashley Eden and Richard Szeliski. 2001.
48  // 3) Automatic Panoramic Image Stitching using Invariant Features.
49  //    Matthew Brown and David G. Lowe. 2007.
50  
51  #include <iostream>
52  #include <fstream>
53  #include "opencv2/highgui/highgui.hpp"
54  #include "opencv2/stitching/stitcher.hpp"
55  
56  using namespace std;
57  using namespace cv;
58  
59  void printUsage()
60  {
61      cout <<
62          "Rotation model images stitcher.\n\n"
63          "stitching img1 img2 [...imgN]\n\n"
64          "Flags:\n"
65          "  --try_use_gpu (yes|no)\n"
66          "      Try to use GPU. The default value is 'no'. All default values\n"
67          "      are for CPU mode.\n"
68          "  --output <result_img>\n"
69          "      The default is 'result.jpg'.\n";
70  }
71  
72  bool try_use_gpu = false;
73  vector<Mat> imgs;
74  string result_name = "result.jpg";
75  
76  int parseCmdArgs(int argc, char** argv)
77  {
78      if (argc == 1)
79      {
80          printUsage();
81          return -1;
82      }
83      for (int i = 1; i < argc; ++i)
84      {
85          if (string(argv[i]) == "--help" || string(argv[i]) == "/?")
86          {
87              printUsage();
88              return -1;
89          }
90          else if (string(argv[i]) == "--try_gpu")
91          {
92              if (string(argv[i + 1]) == "no")
93                  try_use_gpu = false;
94              else if (string(argv[i + 1]) == "yes")
95                  try_use_gpu = true;
96              else
97              {
98                  cout << "Bad --try_use_gpu flag value\n";
99                  return -1;
100             }
101             i++;
102         }
103         else if (string(argv[i]) == "--output")
104         {
105             result_name = argv[i + 1];
106             i++;
107         }
108         else
109         {
110             Mat img = imread(argv[i]);
111             if (img.empty())
112             {
113                 cout << "Can't read image '" << argv[i] << "'\n";
114                 return -1;
115             }
116             imgs.push_back(img);
117         }
118     }
119     return 0;
120 }
121 
122 
123 int main(int argc, char* argv[])
124 {
125     int retval = parseCmdArgs(argc, argv);
126     if (retval) return -1;
127 
128     Mat pano;
129     Stitcher stitcher = Stitcher::createDefault(try_use_gpu);
130     Stitcher::Status status = stitcher.stitch(imgs, pano);
131 
132     if (status != Stitcher::OK)
133     {
134         cout << "Can't stitch images, error code = " << status << endl;
135         return -1;
136     }
137 
138     imwrite(result_name, pano);
139     return 0;
140 }
141 
142 
5

Maybe this could help? https://software.intel.com/en-us/articles/fast-panorama-stitching

Specifically the part about pairwise matching

Ronen

RonenKi
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    This isn't an answer, it is just a link. You should explain the answer here, and use the link as reference. If the link ever went bad, your answer (as written) would be useless. – Matthew Bakaitis Jul 13 '14 at 14:29
3

Consider enabling the use of GPU in the Opencv Stitcher:

bool try_use_gpu = true;
Stitcher myStitcher = Stitcher::createDefault(try_use_gpu); 
Stitcher::Status status = myStitcher.stitch(Imgs, pano);
Samer
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2

If you know the relative positions of the images, it seems that you could break down the problem into sub-problems and possibly reduce the computational load by approaching it with knowledge of the substructure of the problem. Basically break the set of images into groups of 4 adjacent images, process the frames, then proceed to process the resulting images using the same idea until you have arrived at your panorama. That being said, I've only recently began toying with this toolset of opencv. I know it's a pretty simple idea, but it might be useful to someone.

EnergyElk
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  • I ended up creating progressively larger sub-images by stitching together 4x4 grids, and then stitching those up as well, etc, etc. I suffer a minor loss in quality, but since many images look similar, it saves me from catastrophic artefacts like an image being in the wrong location entirely. – Cloud Dec 21 '15 at 17:38