| Byobu Commands | |
| ============== | |
| byobu Screen manager | |
| Level 0 Commands (Quick Start) | |
| ------------------------------ | |
| <F2> Create a new window |
People
 :bowtie: |
😄 :smile: |
😆 :laughing: |
|---|---|---|
😊 :blush: |
😃 :smiley: |
:relaxed: |
😏 :smirk: |
😍 :heart_eyes: |
😘 :kissing_heart: |
😚 :kissing_closed_eyes: |
😳 :flushed: |
😌 :relieved: |
😆 :satisfied: |
😁 :grin: |
😉 :wink: |
😜 :stuck_out_tongue_winking_eye: |
😝 :stuck_out_tongue_closed_eyes: |
😀 :grinning: |
😗 :kissing: |
😙 :kissing_smiling_eyes: |
😛 :stuck_out_tongue: |
Saya support Internet Positif untuk memblokir porn, situs judi, dan hal-hal ilegal lainnya. Tapi pemerintah dan ISP sangat konyol karena tidak mengizinkan akses ke Vimeo, Reddit, Imgur, Netflix--yang mana bukanlah situs dengan konten utama ilegal.
Tambahkan list di bawah ke /etc/hosts.
| import cv2 | |
| import sys | |
| class Camera(object): | |
| def __init__(self, index=0): | |
| self.cap = cv2.VideoCapture(index) | |
| self.openni = index in (cv2.CAP_OPENNI, cv2.CAP_OPENNI2) | |
| self.fps = 0 |
Updated 4/11/2018
Here's my experience of installing the NVIDIA CUDA kit 9.0 on a fresh install of Ubuntu Desktop 16.04.4 LTS.
OpenCV does a reasonable job of reading videos from file or webcams. It's simple and mostly works. When it comes to writing videos, it however leaves a lot to be desired. There is little control over the codecs and it is almost impossible to know which codecs are installed. It also wants to know things like the frame size at intailisation. This isn't always a problem, but if you don't know it yet it means you have to set up the video writer inside your main processing loop.
To make something as cross-platform compatible as possible it would be nice to use FFmpeg. There are a few python wrappers around, but as far as I can tell they are mainly used for transcoding type applications. One solution is run FFmpeg as a subprocess and set its input to accept a pipe. Then every video frame is passed through the pipe. You write this yourself, in fact it's only a few lines of code. However, the scikit-video package will do this for us, with some nice boilerplate to ma
| ### This script wraps all executables in the anaconda bin folder so that they can be used without adding Anaconda | |
| ### to the path which would break some functionality of ROS (Robot Operating System) | |
| ### | |
| ### The commands e.g. jupyter notebook will cause the script to add anaconda to the path, start jupyter notebook | |
| ### and after jupyter notebook terminated remove anaconda from the path again | |
| ### | |
| ### Notable commands: | |
| ### * release-the-snake Adds conda to the path and removes all aliases defined by this script | |
| ### Conda will stay in the PATH until the end of the session (terminal is closed) or | |
| ### until "cage-the-snake" is called |
Most of the instructions from this section originate from this aricle. Feel free to follow along with this gist or use the article.
Let's start by making sure our machine is up to date.
sudo apt-get update
sudo apt-get upgrade
| def soft_dice_loss(y_true, y_pred, epsilon=1e-6): | |
| ''' | |
| Soft dice loss calculation for arbitrary batch size, number of classes, and number of spatial dimensions. | |
| Assumes the `channels_last` format. | |
| # Arguments | |
| y_true: b x X x Y( x Z...) x c One hot encoding of ground truth | |
| y_pred: b x X x Y( x Z...) x c Network output, must sum to 1 over c channel (such as after softmax) | |
| epsilon: Used for numerical stability to avoid divide by zero errors | |