[Python] Speech Recognition

https://realpython.com/python-speech-recognition/

[FLIR] 相關連結

校正： * ThermalWrist: Smartphone Thermal Camera Correction Using a Wristband Sensor https://www.mdpi.com/1424-8220/19/18/3826/htm#B14-sensors-19-03826 *

[RPi] NCTU KCWU

https://github.com/coldwufish/RaspPI/tree/master/2019

[Android] 安裝 scrcpy 在 Ubuntu

之前使用 vysor 投影 Android 裝置到 Ubuntu 桌面。但可能是 chromium 沒有更新，所以一直出現 plugin not support 之類的訊息。 爬文建議使用 scrcpy。

官方教學，https://github.com/Genymobile/scrcpy
也參考這篇，https://www.linuxuprising.com/2019/03/control-android-devices-from-your.html

實際安裝步驟有點麻煩。
1. 安裝必要套件
$ sudo apt install adb ffmpeg libsdl2-2.0-0 make gcc pkg-config meson ninja-build libavcodec-dev libavformat-dev libavutil-dev libsdl2-dev

2. 用 pip 安裝 meson 和 ninja
$ sudo pip3 install meson ninja

3. 下載 scrcpy-server，並改名為 scrcpy-server*.jar
https://github.com/Genymobile/scrcpy/releases/download/v1.16/scrcpy-server-v1.16

4. 將 scrcpy 加到某目錄
$ sudo install scrcpy-server-v*.jar /usr/local/bin/scrcpy-server.jar

5. 下載 scrcpy source，目前我是裝 1.16
https://github.com/Genymobile/scrcpy/releases/tag/v1.16

6. 解開目錄後進入 scrcpy 編譯
$ meson build --buildtype release --strip -Db_lto=true -Dprebuilt_server=../scrcpy-server-v1.16.jar
$ cd build $ ninja
$ sudo ninja install

之後應該就可以用 scrcpy 或是 sudo scrcpy 投影啦～

[RPi] 開機順序

這幾篇可以參考 * https://www.vdoo.com/blog/setting-up-u-boot-to-harden-the-boot-process * https://www.slideshare.net/chienhungpan/a-journey-to-boot-linux-on-raspberry-pi

[Python] Pycharm Installation on Ubuntu 16.04

$ sudo snap install pycharm-community --classic

之前辛辛苦苦下載安裝，都裝不起來 QQ
* https://askubuntu.com/questions/1025153/install-pycharm-community-on-ubuntu-16-04
* https://medium.com/@singh.shreya8/how-to-install-pycharm-in-ubuntu-16-04-ubuntu-14-04-ubuntu-18-04-linux-easiest-way-5ae19d052693

[RPi] TFLite

辛辛苦苦安裝，最後一行搞定。全新不用安裝其他東西(也不用先裝 OpenCV)
$ pip3 install https://dl.google.com/coral/python/tflite_runtime-2.1.0.post1-cp37-cp37m-linux_armv7l.whl

再配上範例程式就可以測試了
https://github.com/tensorflow/examples/tree/master/lite/examples/image_classification/raspberry_pi


這幾篇參考
* https://qengineering.eu/install-tensorflow-2-lite-on-raspberry-pi-4.html
* https://qengineering.eu/deep-learning-examples-on-raspberry-32-64-os.html
* https://www.tensorflow.org/lite/examples