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# 2020 Fall Update | ||
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课程安排还是前八周上课+后八周project,但今年和去年都不存在选择project这种事情,只给了一个project做。两年都是在树莓派上,都是使用摄像头,去年好像是识别人的石头剪刀布的动作,今年是用摄像头正对屏幕上的图像的一部分进行拍摄,计算拍摄到的部分位于原图像的实时位置。感觉今年这个简单不少,去年怎么也得用点AI的技术,今年这个调调OpenCV就完事了。 | ||
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主要的难点应该是用[Buildroot](https://buildroot.org/)裁剪出一个能运行识别程序且尽量小的树莓派Linux系统,用到一些OS课相关的技术,至少我是这么觉得的。但也没说非得这样裁剪,助教提供了一个Python写的识别程序,我猜测(不负任何责任)即使只是把它在完整的Linux系统上跑起来也能及格,这应该不需要任何工作量。 | ||
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我的代码见:[https://github.com/MashPlant/rpi-image-locating](https://github.com/MashPlant/rpi-image-locating)。识别部分确实只调了OpenCV,但把整个系统裁剪到了8.1MiB。 | ||
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—— by MashPlant | ||
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# 原内容 | ||
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嵌入式系统是前八周上课,最后有一个project,后八周写project,课程压力较小,作业耗时在八个小时左右,作业有 (1)在树莓派上最小化一个可以跑起来监控程序的系统 (2)太阳能供电,用wifi控制开关的树莓派 (3)神经网络框架的加速(4)NBlot (5)测试树莓派的实时性能 (6)用树莓派做人机交互 | ||
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我选的是太阳能供电,最后要自己手动连线,烧板子,剪USB线,比较有趣 | ||
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——by吴昊哲学长 | ||
——by吴昊哲学长 |