LiberiFatali

#! /usr/bin/python

import socket,time,string,base64,hashlib,xml.etree.ElementTree as ET
import sys


RTSP_VERSION        = "RTSP/1.0"

LINE_SPLIT_STR      = "\r\n"
HEADER_END_STR      = LINE_SPLIT_STR*2

LiberiFatali

m3u8 Downloading

1. Open Chrome Developer tools and click the Network tab.
2. Navigate to the page with the video and get it to start playing.
3. Filter the list of files to "m3u8".
4. Find master.m3u8 or index.m3u8 and click on it.
5. Save the file to disk and look inside it.
6. If the file contains a single m3u8 master url, copy that one instead.
7. Run the program m3u8x.
8. Paste the same m3u8 url in both textboxes (URL and Quality URL) and click "Headers" and set the referral url and user-agent from the request as found in Chrome.

LiberiFatali

"""
Create widget with available icons from qt and based on freedesktop.org
https://specifications.freedesktop.org/icon-naming-spec/icon-naming-spec-latest.html
"""
from PyQt5.QtCore import QSize
from PyQt5.QtGui import QIcon
from PyQt5.QtWidgets import QApplication, QMainWindow, QVBoxLayout, QWidget, QGridLayout, QStyle, \
    QFormLayout, QLabel, QScrollArea, QLineEdit, QTabWidget

LiberiFatali

Purposes

1. iGPU will be used for main display and rendering daily-use softwares in default
2. Dedicated GPUs will be used for computing or offload rendering
3. Reduce the used VRAM of Dedicated GPU, which is mostly used for X11 server rendering

---

Tested Hardware Configuration

OS: Ubuntu 18.04.4 LTS
Mainboads
├── iGPU

LiberiFatali

Intel for display, NVIDIA for computing

This guide will show you how to use Intel graphics for rendering display and NVIDIA graphics for CUDA computing on Ubuntu 18.04 / 20.04 desktop.

I made this work on an ordinary gaming PC with two graphics devices, an Intel UHD Graphics 630 plus an NVIDIA GeForce GTX 1080 Ti. Both of them can be shown via lspci | grep VGA.

00:02.0 VGA compatible controller: Intel Corporation Device 3e92
01:00.0 VGA compatible controller: NVIDIA Corporation GP102 [GeForce GTX 1080 Ti] (rev a1)

LiberiFatali

#!/bin/bash

# Purge existign CUDA first
sudo apt --purge remove "cublas*" "cuda*"
sudo apt --purge remove "nvidia*"

# Install CUDA Toolkit 10
wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/cuda-repo-ubuntu1804_10.0.130-1_amd64.deb
sudo apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/7fa2af80.pub && sudo apt update
sudo dpkg -i cuda-repo-ubuntu1804_10.0.130-1_amd64.deb

LiberiFatali

import numpy as np
import faiss



def search_knn(xq, xb, k, distance_type=faiss.METRIC_L2): 
    """ wrapper around the faiss knn functions without index """
    nq, d = xq.shape
    nb, d2 = xb.shape
    assert d == d2

LiberiFatali

"""
author: Peb Ruswono Aryan

Binarization Algorithm by Su et al.

@inproceedings{Su:2010:BHD:1815330.1815351,
 author = {Su, Bolan and Lu, Shijian and Tan, Chew Lim},
 title = {Binarization of Historical Document Images Using the Local Maximum and Minimum},
 booktitle = {Proceedings of the 9th IAPR International Workshop on Document Analysis Systems},
 series = {DAS '10},