Baskara agtbaskara

Using Docker

Before building the docker image go to the cloned whill-robot directory

cd ~/catkin_ws/src/whill-robot

Build Docker images

Place the python scripts on ~/.custom_scripts/msi-mystic-1562-rgb-off.py then add this to Ubuntu Startup to turnoff keyboard light at boot (change agtbaskara to your username)

Name:

msi-mystic-1562-rgb-off

Command:

Here is the best setup (I think so :D) for K-series Keychron keyboards on Linux.

Most of these commands have been tested on Ubuntu 20.04 and should also work on most Debian-based distributions. If a command happens not to work for you, take a look in the comment section.

Make Fn + F-keys work

Keychron Keyboards on Linux use the hid_apple driver (even in Windows/Android mode), both in Bluetooth and Wired modes. By default, this driver uses the F-keys as multimedia shortcuts and you have to press Fn + the key to get the usual F1 through F12 keys.

	My recommendation is to start with RTL basics in verilog using one of the online platforms.

	https://hdlbits.01xz.net/wiki/Main_Page

	https://chipdev.io/question-list

	https://www.edaplayground.com/

	And verilog docs:

	import os

	import cv2
	from enlighten_inference import EnlightenOnnxModel
	from tqdm import tqdm

	# Set input and output path
	input_path = 'image_rgb'
	output_path = 'image_enlighten'

	import os
	import shutil

	from tqdm import tqdm
	from natsort import natsorted

	if __name__ == '__main__':
	dirlist = ['tut-sync/tut-ringroad/day/image_depth', 'tut-sync/tut-ringroad/night/image_depth',
	'tut-sync/tut-backyard/day/image_depth', 'tut-sync/tut-backyard/night/image_depth']

	import os
	import argparse

	import cv2


	# Setup Argument Parser
	parser = argparse.ArgumentParser()

	parser.add_argument('-i', '--image_dir', type=str, default='', help='input directory')

	import os

	import cv2
	import skvideo.io
	import numpy as np
	import pandas as pd
	from tqdm import tqdm

	if __name__ == '__main__':
	reference_file_path = os.path.join('town07.csv')

	# Just adding example if noob like me came here to find how to calculate the Cosine similarity from scratch
	# Source https://github.com/facebookresearch/faiss/issues/95

	import numpy as np
	import faiss

	dataSetI = [.1, .2, .3]
	dataSetII = [.4, .5, .6]
	#dataSetII = [.1, .2, .3]