Skip to content

Instantly share code, notes, and snippets.

View jet-c-21's full-sized avatar
🔥

Jet (Ta-Wei Chien) jet-c-21

🔥
View GitHub Profile
@Minionguyjpro
Minionguyjpro / Activate_Windows_8_8.1_10_and_11_Pro_for_Free.md
Last active April 23, 2024 17:21
Activate Windows 8, 8.1, 10 and 11 Pro for Free

Activate Windows 8, 8.1, 10 and 11 Pro for Free

A guide how to get and activate Windows 8, 8.1, 10 and 11 Pro for free!

NOTE

If you see the Windows keyboard button in this guide; and you can't find it on your keyboard, you likely have/had Windows 10 which has the button . If you can't find that one, you likely have a PC that has been upgraded to Windows 8/8.1/10/11 from Windows 8.1/8/7/Vista/XP and other ones. If you have one of those, refer the Windows key button to as yours. A list of them is below:

Windows key buttons

- Windows 11

- Windows 10

Ubuntu 22.04 for Deep Learning

In the name of God

This gist contains steps to setup Ubuntu 22.04 for deep learning.


Install Ubuntu 22.04

@raulqf
raulqf / Install_OpenCV4_CUDA11_CUDNN8.md
Last active April 18, 2024 19:53
How to install OpenCV 4.5 with CUDA 11.2 in Ubuntu 22.04

How to install OpenCV 4.5.2 with CUDA 11.2 and CUDNN 8.2 in Ubuntu 22.04

First of all install update and upgrade your system:

    $ sudo apt update
    $ sudo apt upgrade

Then, install required libraries:

@gachiemchiep
gachiemchiep / OpenCV-3.4.3-Ubuntu-16.04-Cuda-9.2.md
Last active February 25, 2023 06:44 — forked from filitchp/OpenCV-3.1-Ubuntu-16.04-Cuda-8.md
Installing OpenCV 3.4.3 on Ubuntu 16.04 with Cuda 9.2 support

This is a guide for installing OpenCV 3.4.1 on Ubuntu 16.04 with Cuda 9 support. This has been tested using a system with a Quadro K620 .

Nvidia Drivers

Install Nvidia drivers

# Start clean
sudo apt purge nvidia-*
# Add the PPA
sudo add-apt-repository ppa:graphics-drivers/ppa
sudo apt update
@paulserraino
paulserraino / flow.py
Created May 21, 2017 23:16
Predicting Vehicle Motion and Direction using Optical Flow
import cv2
import numpy as np
cap = cv2.VideoCapture('driving_sf.mp4')
# Util functions
def dist(a, b):
return np.sqrt(np.power(b[0] - a[0], 2) + np.power(b[1] - a[1], 2))