问题
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
- Stitching. Image Stitching - OpenCV API Documentation, Accessed 2014-02-26, <http://docs.opencv.org/modules/stitching/doc/stitching.html>
- OpenCV Stitching example (Stitcher class, Panorama), Accessed 2014-02-26, <http://feelmare.blogspot.ca/2013/11/opencv-stitching-example-stitcher-class.html>
- Panorama – Image Stitching in OpenCV, Accessed 2014-02-26, <http://ramsrigoutham.com/2012/11/22/panorama-image-stitching-in-opencv/>
回答1:
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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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
回答2:
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:
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!
回答3:
Maybe this could help? https://software.intel.com/en-us/articles/fast-panorama-stitching
Specifically the part about pairwise matching
Ronen
回答4:
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);
回答5:
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.
来源：https://stackoverflow.com/questions/22056737/opencv-stitching-images-from-a-grid-of-images