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embedded
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MeshNormalSmooth() takes a vertex and iterates all the others, looking for vertices with a distance < epsilon.
Then it calculates their normals and sums it.
That's not a good idea, IMO:
- Two close vertices can be not physically linked, I don't think summing normals makes sense.
- It's quadratic.
A simpler and faster way to smooth a model could be:
- Iterating through each triangle
I was talking with Gabor about contributing with LVGL, and one of the points he was interested in was in writing unit and functional tests. I'm interested on contributing, as I use LVGL in some of my projects. If you'd like, we could discuss the following steps here.
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Is your enhancement proposal related to a problem? Please describe.
Refer to: zephyrproject-rtos/zephyr#28932
Describe the solution you'd like
Refer to: zephyrproject-rtos/zephyr#28932 (comment)
Describe alternatives you've considered
NA
Additional context
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Python 3 bindings
Implement Python 3 bindings, so wasm3 can be easily used from Python.
Resurrecting the second bullet from #1334
- Figure out how to fix favicon / logo more generally (here's a start https://github.com/tock/tock/tree/doc-updates)
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On the FeatherS2, if code.py has wifi.radio.stop_scanning_networks() with no scan first, it causes the device to hang, stops showing up as CIRCUITPY, and there is no way to get the REPL. This is with MacOS and CircuitPython 6.0.0-rc.1. The board was purchased from Adafruit Nov 6, 2020.
See attached code:
[code-crash.py.zip](https://github.com/adafruit/circuitpython/files/5513427/code-crash.p
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On Linux, packets that stay local to the machine (moving from the host to a container for example) don't always have their checksum computed, see for example https://patchwork.ozlabs.org/patch/261822/.
The proper fix would be to check for TP_STATUS_CSUMNOTREADY, but an easy workaround is to have a way to change the ChecksumCapabilities or DeviceCapabilities.
As a rust noob here my solution for
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paddle-mobile将开发一个新的版本,为了:
因此,paddle-mobile将会进行一次完全的重构,从框架设计到代码开发。在新版本开发完成后,将会删除原有mobile-deep-learning的代码。新版本开发期间,代码将放置在paddle-mobile repo的一个子目录下。对于子目录的命名,目前有3个候选,希望大家投票:
有更好的建议,请回复issue。相关issue:#121
另外,待新版本代码开发完成,移除老版本mobile-deep-learning代码后,新版本代码将移出子目录,直接放置在paddle-mobile repo下面。