问题描述
我正在从事学校项目，其中一部分应该是关于Android的计算机视觉库的当前状况.我非常热衷于此，因为计算机视觉似乎是一个有趣的主题，但是我一直在寻找一个多星期，但没有发现太多.我希望能够提供有关库本身以及它们之间的比较的信息.
I am working on school project and part of it should be about current situation about computer vision libraries for Android. I went to it with large enthusiasm because computer vision seems like fascinating subject but I have been searching for more then a week and I did not find much. I would like to be able to provide information about libraries themselves and about comparison between them.
我将分享到目前为止的发现.
I will share what I found so far.
OpenCV
-
似乎是最先进的和最受欢迎的.
seems like the most advanced one and the most popular.
提供最多的功能
存在向后兼容性问题
很快(至少我听说过，但是我对此零信息)
is fast(at least so I heard but I have zero information about it)
JavaCV
- 是包括opencv在内的其他一些库的包装器
FastCv
- 新产品，后面是高通公司.
Wikitude
- 这更多地用于增强现实，但其核心仍然是计算机视野.
- this is more for augmentedreality but in its core is still computervision.
如您所见，我对此有一点了解，并且对每个库进行自己的测试远远超出了我目前的计算机视觉技能.
As you can see I have a little information about it and doing my own tests for every library is far beyond my current computer vision skills.
亲切的问候，彼得.
推荐答案
基本上有两个选项:OpenCV和FastCV
Basically there are two options: OpenCV and FastCV
OpenCV是一个更成熟的库，具有比FastCV更多的功能.对于许多计算机视觉任务，您只能在FastCV中找到最基本的功能，而在OpenCV中可以找到几乎所有流行的替代方法.例如，检查可用的特征检测器. OpenCV具有Harris，SURF，SIFT，FAST等.另一方面，FastCV仅具有Harris和FAST. OpenCV包含针对不同硬件的硬件优化，包括台式计算机和移动计算设备.在桌面中使用OpenCV的能力为您提供了更灵活的开发选项，因为您可以在开始进行移动开发之前在快速的台式计算机上调整和测试代码.另外，OpenCV也被视为 Khronos计算机视觉小组提案.因此，如果获得批准，OpenCV可以成为计算机视觉的标准API.据我所知，FastCV为Snapdragon CPU提供了出色的优化.在短期内，这可能会在决策中发挥重要作用，但我相信OpenCV会尽快缩小差距.
OpenCV is a more mature library with a lot more functionality than FastCV. For many computer vision tasks you can find only the most basic functionality in FastCV, whereas you can have almost all popular alternatives available in OpenCV. Check the available feature detectors for instance. OpenCV has Harris, SURF, SIFT, FAST, etc. FastCV, on the other hand, has only Harris and FAST. OpenCV contains hardware optimizations for different HW including desktop computers and mobile computing devices. The ability to use OpenCV in desktop provides you a more flexible development option, as you can tweak and test the code on a fast desktop computer before you start working the mobile development. Also OpenCV is considered as part of a Khronos Computer Vision Group Proposal. So, if this gets approved OpenCV can become the standard API for computer vision. As far as I can see, FastCV provides superior optimizations for Snapdragon CPUs. This might play an important role in the decision in the short term, but I'm sure OpenCV will close the gap very fast, if any.
如果选择OpenCV路径，则有两个子路径:带有Android NDK的OpenCV与带有Android SDK的JavaCV. JavaCV是基于JavaCpp的OpenCV的包装.尽管OpenCV还提供了面向对象的C ++ API，但JavaCV大多包装了C API.例如，C ++ API自动处理未使用的内存的释放.但是，C API(因此为JavaCV)要求您手动处理未使用的图像的释放.同样，当您在JavaCV中遇到问题时，也很难解决该问题，因为有太多的间接检查要检查.直接使用OpenCV时，问题更容易定位.但是，在Android的情况下，不应忘记NDK的额外困难.
If you choose the OpenCV path, then there are two sub-paths: OpenCV with Android NDK vs. JavaCV with Android SDK. JavaCV is a wrapper around OpenCV based on JavaCpp. JavaCV mostly wraps C API, although OpenCV provides an Object Oriented C++ API as well. The C++ API handles releasing of unused memory automatically, for instance. However, C API (hence JavaCV) requires you to handle releasing of unused imagery manually. Also when you face a problem in JavaCV, it is difficult to tackle the problem, because there are too many indirections to check. Problems are easier to locate when you use OpenCV directly. However, in case of Android, the added difficulty of NDK should not be forgotten.
如果特定应用程序需要可用的OpenCV功能，并且不需要自定义像素级处理代码，那么JavaCV是必经之路.但是，如果需要大量的自定义图像处理代码，则Java代码会使您变慢，并且无论如何您都需要切换到NDK.在后一种情况下，可以选择OpenCV.
If available OpenCV functionality is necesary for the specific application, and no custom pixel level processing code is needed, JavaCV is the way to go. However, if a considerable amount of custom image processing code is required, Java code will slow you down, and you will need to switch to NDK, anyway. In the latter case, OpenCV is the alternative to choose.
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