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How to speed up the creation of responsemap? #21
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有,还可以加速一个量级。 |
想請問meiqua大對於OpenCV4.0 GAPI有什麼看法? G-API pipeline is constructed next. In fact, it is a series of G-API operation calls on cv::GMat data. The important aspect of G-API is that this code block is just a declaration of actions, but not the actions themselves. No processing happens at this point, G-API only tracks which operations form pipeline and how it is connected. G-API Data objects (here it is cv::GMat) are used to connect operations each other. in is an empty cv::GMat signalling that it is a beginning of computation.** 我覺得其中的概念跟Halide很像
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@DennisLiu-elogic 很赞,相当于简化的halide,没有自己安排的接口但用在一般场合完全够了 |
最近简单实现了整个fusion的过程,大家有兴趣可以跑跑看。 |
最近尝试用Halide加速responsemap构造的过程,目前仅仅测试了 quantizedOrientations 函数能不能加速,可是发现一个不理解的现象,就是对于一张 2048x2048的RGB图像, 如果放到整个匹配的过程里面跑,测时间,量化梯度方向 quantizedOrientations 仅仅需要 120ms 左右。但是如果将 quantizedOrientations 单独拿出来跑却需要 450ms。所谓单独跑是这样的,因为要对比 自己写的 Halide 加速的 quantizedOrientations 函数,所以将quantizedOrientations 连带hysteresisGradient 直接从line2Dup.cpp 里复制出来,编译成一个可执行文件 去跑一张图像做对比。请问有谁知道,出现这种情况有可能是什么原因,完全摸不着头脑。opencv 版本是 3.4.5 gcc版本7.5,不知道会不会是编译的问题。 |
一直埋头踩坑,才发现@meiqua大大已经手工做好了fusion版本,太高效了 |
@aemior 优化打开了吗? |
@meiqua 刚按照Halide 的语法写好piplline,只是到量化方向,还没spread,优化只是做了一些parallels和vectors,自动优化也还没尝试, 目前就是卡在前面说的单独测试,Halide写的能把450ms的流程加速到160ms,但是放到整个匹配的流程里面,速度竟然没有opencv的快,有点懵圈,还在检查中 |
另外Halide感觉没有想象中的好用,主要是实现图像操作的语法有些不一样,安装也配LLVM也不容易,踩坑踩得吐血 |
@aemior 哈哈,加油 |
嗨,关于这部分有个疑问: |
hi,meiqua!想请教您一下,对于多个roi图片的识别,有没有一些离线的步骤,可以减少总的匹配时间? |
@zhirui-gao 如果ROI都确定而且重合较少,可以crop成不同的小块匹配 |
Hi Meiqua,
测试中发现整个时间,大部分用于了创建responsemap, 请问这个过程可以有办法加速么?例如我有张130万像素的图片,用了150ms创建的,有办法加快么?谢谢!
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