THIS GIST WAS MOVED TO TERMSTANDARD/COLORS
REPOSITORY.
PLEASE ASK YOUR QUESTIONS OR ADD ANY SUGGESTIONS AS A REPOSITORY ISSUES OR PULL REQUESTS INSTEAD!
"""Genetic Algorithmn Implementation | |
see: | |
http://www.obitko.com/tutorials/genetic-algorithms/ga-basic-description.php | |
""" | |
import random | |
class GeneticAlgorithm(object): | |
def __init__(self, genetics): | |
self.genetics = genetics | |
pass |
THIS GIST WAS MOVED TO TERMSTANDARD/COLORS
REPOSITORY.
PLEASE ASK YOUR QUESTIONS OR ADD ANY SUGGESTIONS AS A REPOSITORY ISSUES OR PULL REQUESTS INSTEAD!
MIT License | |
Copyright (c) 2018 Jason Sperske | |
Permission is hereby granted, free of charge, to any person obtaining a copy | |
of this software and associated documentation files (the "Software"), to deal | |
in the Software without restriction, including without limitation the rights | |
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell | |
copies of the Software, and to permit persons to whom the Software is | |
furnished to do so, subject to the following conditions: |
ffmpeg -ss <start_time> -i video.mp4 -t <duration> -q:v 2 -vf select="eq(pict_type\,PICT_TYPE_I)" -vsync 0 frame%03d.jpg |
/* open up chrome dev tools (Menu > More tools > Developer tools) | |
* go to network tab, refresh the page, wait for images to load (on some sites you may have to scroll down to the images for them to start loading) | |
* right click/ctrl click on any entry in the network log, select Copy > Copy All as HAR | |
* open up JS console and enter: var har = [paste] | |
* (pasting could take a while if there's a lot of requests) | |
* paste the following JS code into the console | |
* copy the output, paste into a text file | |
* open up a terminal in same directory as text file, then: wget -i [that file] | |
*/ |
import requests as req | |
import sys | |
from dateutil.parser import parse | |
from PIL import Image | |
from StringIO import StringIO | |
# hi8-fetch.py <date> <zoom level> <output> | |
# E.g.: hi8-fetch.py 2016-01-13T22:10:00 8 2016-01-13T221000-z8.png | |
# Fetch Himawari-8 full disks at a given zoom level. | |
# Valid zoom levels seem to be powers of 2, 1..16, and 20. |
Hello guys,
Continuing from this guide to building ffmpeg and libav with NVENC and VAAPI enabled, this snippet will cover advanced options that you can use with ffmpeg and libav on both NVENC and VAAPI hardware-based encoders.
For ffmpeg:
Quickly check for supported NVENC and NPP hardware acceleration capabilities in FFmpeg on your platform:
Depending on how you built ffmpeg, you may want to check the supported NVENC-based hardware acceleration capabilities in ffmpeg by running:
$ for i in encoders decoders filters; do
echo $i:; ffmpeg -hide_banner -${i} | egrep -i "npp|cuvid|nvenc|cuda|nvdec"
done
Sample output (as on my testbed):
import torch | |
import torch.nn.functional as F | |
from torch.nn.parameter import Parameter | |
import numpy as np | |
class SpatialSoftmax(torch.nn.Module): | |
def __init__(self, height, width, channel, temperature=None, data_format='NCHW'): | |
super(SpatialSoftmax, self).__init__() | |
self.data_format = data_format |
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)