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ZIPlinemod算法的C++原始代码

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资源文件列表:

opencv-linemod.zip 大约有25个文件
  1. opencv-linemod/
  2. opencv-linemod/Gradient Response Maps for Real-TimeDetection of Textureless Objects.pdf 2.73MB
  3. opencv-linemod/MIPP/
  4. opencv-linemod/MIPP/math/
  5. opencv-linemod/MIPP/math/avx512_mathfun.h 600B
  6. opencv-linemod/MIPP/math/avx512_mathfun.hxx 20.2KB
  7. opencv-linemod/MIPP/math/avx_mathfun.h 1.56KB
  8. opencv-linemod/MIPP/math/avx_mathfun.hxx 22.86KB
  9. opencv-linemod/MIPP/math/neon_mathfun.h 1.44KB
  10. opencv-linemod/MIPP/math/neon_mathfun.hxx 9.14KB
  11. opencv-linemod/MIPP/math/sse_mathfun.h 1.63KB
  12. opencv-linemod/MIPP/math/sse_mathfun.hxx 21.26KB
  13. opencv-linemod/MIPP/mipp.h 43.54KB
  14. opencv-linemod/MIPP/mipp_impl_AVX.hxx 121.98KB
  15. opencv-linemod/MIPP/mipp_impl_AVX512.hxx 149.37KB
  16. opencv-linemod/MIPP/mipp_impl_NEON.hxx 93.69KB
  17. opencv-linemod/MIPP/mipp_impl_SSE.hxx 113.59KB
  18. opencv-linemod/MIPP/mipp_object.hxx 52.04KB
  19. opencv-linemod/MIPP/mipp_scalar_op.h 748B
  20. opencv-linemod/MIPP/mipp_scalar_op.hxx 6.04KB
  21. opencv-linemod/README.md 4.8KB
  22. opencv-linemod/Transforms in shape-based matching.pdf 115.06KB
  23. opencv-linemod/line2Dup.cpp 52.92KB
  24. opencv-linemod/line2Dup.h 10.01KB
  25. opencv-linemod/test.cpp 18.64KB

资源介绍:

opencv中linemod算法置于contribute库中，官方预编译的opencv库是不带这个库的。可实现实时的多角度多尺度模板匹配，同时带有原始论文。
# shape_based_matching update: **[fusion implementation to run faster!](https://github.com/meiqua/shape_based_matching/issues/77)** **[icp is also refined to be faster and easier to use](https://github.com/meiqua/shape_based_matching/issues/100)** [Transforms in shape-based matching](./Transforms%20in%20shape-based%20matching.pdf) [pose refine with icp branch](https://github.com/meiqua/shape_based_matching/tree/icp2D), 0.1-0.5 degree accuracy [icp + subpixel branch](https://github.com/meiqua/shape_based_matching/tree/subpixel), < 0.1 degree accuracy [icp + subpixel + sim3(previous is so3) branch](https://github.com/meiqua/shape_based_matching/tree/sim3), deal with scale error try to implement halcon shape based matching, refer to machine vision algorithms and applications, page 317 3.11.5, written by halcon engineers We find that shape based matching is the same as linemod. [linemod pdf](Gradient%20Response%20Maps%20for%20Real-TimeDetection%20of%20Textureless%20Objects.pdf) halcon match solution guide for how to select matching methods([halcon documentation](https://www.mvtec.com/products/halcon/documentation/#reference_manual)): ![match](./match.png) ## steps 1. change test.cpp line 9 prefix to top level folder 2. in cmakeList line 23, change /opt/ros/kinetic to somewhere opencv3 can be found(if opencv3 is installed in default env then don't need to) 3. cmake make & run. To learn usage, see different tests in test.cpp. Particularly, scale_test are fully commented. NOTE: On windows, it's confirmed that visual studio 17 works fine, but there are some problems with MIPP in vs13. You may want old codes without [MIPP](https://github.com/aff3ct/MIPP): [old commit](https://github.com/meiqua/shape_based_matching/tree/fc3560a1a3bc7c6371eacecdb6822244baac17ba) ## thoughts about the method The key of shape based matching, or linemod, is using gradient orientation only. Though both edge and orientation are resistant to disturbance, edge have only 1bit info(there is an edge or not), so it's hard to dig wanted shapes out if there are too many edges, but we have to have as many edges as possible if we want to find all the target shapes. It's quite a dilemma. However, gradient orientation has much more info than edge, so we can easily match shape orientation in the overwhelming img orientation by template matching across the img. Speed is also important. Thanks to the speeding up magic in linemod, we can handle 1000 templates in 20ms or so. [Chinese blog about the thoughts](https://www.zhihu.com/question/39513724/answer/441677905) ## improvment Comparing to opencv linemod src, we improve from 6 aspects: 1. delete depth modality so we don't need virtual func, this may speed up 2. opencv linemod can't use more than 63 features. Now wo can have up to 8191 3. simple codes for rotating and scaling img for training. see test.cpp for examples 4. nms for accurate edge selection 5. one channel orientation extraction to save time, slightly faster for gray img 6. use [MIPP](https://github.com/aff3ct/MIPP) for multiple platforms SIMD, for example, x86 SSE AVX, arm neon. To have better performance, we have extended MIPP to uint8_t for some instructions.(Otherwise we can only use half feature points to avoid int8_t overflow) 7. rotate features directly to speed up template extractions; selectScatteredFeatures more evenly; exautive select all features if not enough rather than abort templates(but features <= 4 will abort) ## some test ### Example for circle shape #### You can imagine how many circles we will find if use edges ![circle1](test/case0/1.jpg) ![circle1](test/case0/result/1.png) #### Not that circular ![circle2](test/case0/2.jpg) ![circle2](test/case0/result/2.png) #### Blur ![circle3](test/case0/3.png) ![circle3](test/case0/result/3.png) ### circle template before and after nms #### before nms ![before](test/case0/features/no_nms_templ.png) #### after nms ![after](test/case0/features/nms_templ.png) ### Simple example for arbitary shape Well, the example is too simple to show the robustness running time: 1024x1024, 60ms to construct response map, 7ms for 360 templates test img & templ features ![test](./test/case1/result.png) ![templ](test/case1/templ.png) ### noise test ![test2](test/case2/result/together.png) ## some issues you may want to know Well, issues are not clearly classified and many questions are discussed in one issue sometimes. For better reference, some typical discussions are pasted here. [object too small?](https://github.com/meiqua/shape_based_matching/issues/13#issuecomment-474780205) [failure case?](https://github.com/meiqua/shape_based_matching/issues/19#issuecomment-481153907) [how to run even faster?](https://github.com/meiqua/shape_based_matching/issues/21#issuecomment-489664586)
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