OpenCV实现图像搜索引擎

简单介绍一下OpenCV

OpenCV was designed for computational efficiency and with a strong focus on real-time applications. Written in optimized C/C++, the library can take advantage of multi-core processing. Enabled with OpenCL, it can take advantage of the hardware acceleration of the underlying heterogeneous compute platform. Adopted all around the world, OpenCV has more than 47 thousand people of user community and estimated number of downloads exceeding 9 million. Usage ranges from interactive art, to mines inspection, stitching maps on the web or through advanced robotics.

OpenCV(Open Source Computer Vision Library)的计算效率很高且能够完成实时任务。OpenCV库由优化的C/C++代码编写而成,能够充分发挥多核处理和硬件加速的优势。OpenCV有大量技术社区和超过900万的下载量,它的使用范围极为广泛,如人机互动、资源检查、拼接地图等。

0.Python+OpenCV实现图像搜索引擎

之前看到谷歌和百度出了图像搜索引擎,查阅了相关资料深入了解了图像搜索引擎的算法原理。一部分参考了用Python和OpenCV创建一个图片搜索引擎的完整指南。决定自己实现一个简单的图像搜索引擎,也可以让自己更快地查找mac中的图片。为什么使用OpenCV+Python实现图像搜索引擎呢?

  • 首先,OpenCV是一个开源的计算机视觉处理库,在计算机视觉图像处理模式识别中有广泛的应用。接口安全易用,而且跨平台做的相当不错,是一个不可多得的计算机图像及视觉处理库。

  • 其次,Python的语法更加易用,贴近自然语言,极为灵活。虽然计算效率并不高,但快速开发上它远胜于C++或其他语言,引入pysco能够优化python代码中的循环,一定程度上缩小与C/C++在计算上的差距。而且图像处理中需要大量的矩阵计算,引入numpy做矩阵运算能够降低编程的冗杂度,更多地把精力放在匹配的逻辑上,而非计算的细枝末节。

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