If you need CUDA Tolkit 11 with nvcc
, other tools and libraries you can install it from NVIDIA Ubunutu 20.04 repository.
Add Ubuntu 20.04 repository
def euler_from_quaternion(quaternion): | |
""" | |
Converts quaternion (w in last place) to euler roll, pitch, yaw | |
quaternion = [x, y, z, w] | |
Bellow should be replaced when porting for ROS 2 Python tf_conversions is done. | |
""" | |
x = quaternion.x | |
y = quaternion.y | |
z = quaternion.z | |
w = quaternion.w |
# Script to convert yolo annotations to voc format | |
# Sample format | |
# <annotation> | |
# <folder>_image_fashion</folder> | |
# <filename>brooke-cagle-39574.jpg</filename> | |
# <size> | |
# <width>1200</width> | |
# <height>800</height> | |
# <depth>3</depth> |
### 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 |
The repository for the assignment is public and Github does not allow the creation of private forks for public repositories.
The correct way of creating a private frok by duplicating the repo is documented here.
For this assignment the commands are:
git clone --bare git@github.com:usi-systems/easytrace.git
This is manual way to install necessary packages :
sudo apt-get install ros-kinetic-robot-localization ros-kinetic-controller-manager ros-kinetic-joint-state-controller ros-kinetic-diff-drive-controller ros-kinetic-gazebo-ros ros-kinetic-gazebo-ros-control ros-kinetic-gazebo-plugins ros-kinetic-lms1xx ros-kinetic-pointgrey-camera-description ros-kinetic-roslint ros-kinetic-amcl ros-kinetic-gmapping ros-kinetic-map-server ros-kinetic-move-base ros-kinetic-urdf ros-kinetic-xacro ros-kinetic-message-runtime ros-kinetic-topic-tools ros-kinetic-teleop-twist-joy
If you want to use rosdep
and install all dependencies (for example for doc generation etc), the skip this step.
This is a guide how to install mosquitto on Ubuntu with websockets enabled.
It is more or less the same as explained in the article "Six Steps to install mosquitto 1.4.2 with websockets on debian whezy" http://www.xappsoftware.com/wordpress/2015/05/18/six-steps-to-install-mosquitto-1-4-2-with-websockets-on-debian-wheezy/comment-page-1/
Exceptions:
Sometimes you may want to undo a whole commit with all changes. Instead of going through all the changes manually, you can simply tell git to revert a commit, which does not even have to be the last one. Reverting a commit means to create a new commit that undoes all changes that were made in the bad commit. Just like above, the bad commit remains there, but it no longer affects the the current master and any future commits on top of it.
git revert {commit_id}
Deleting the last commit is the easiest case. Let's say we have a remote origin with branch master that currently points to commit dd61ab32. We want to remove the top commit. Translated to git terminology, we want to force the master branch of the origin remote repository to the parent of dd61ab32:
'''This script goes along the blog post | |
"Building powerful image classification models using very little data" | |
from blog.keras.io. | |
It uses data that can be downloaded at: | |
https://www.kaggle.com/c/dogs-vs-cats/data | |
In our setup, we: | |
- created a data/ folder | |
- created train/ and validation/ subfolders inside data/ | |
- created cats/ and dogs/ subfolders inside train/ and validation/ | |
- put the cat pictures index 0-999 in data/train/cats |
##VGG16 model for Keras
This is the Keras model of the 16-layer network used by the VGG team in the ILSVRC-2014 competition.
It has been obtained by directly converting the Caffe model provived by the authors.
Details about the network architecture can be found in the following arXiv paper:
Very Deep Convolutional Networks for Large-Scale Image Recognition
K. Simonyan, A. Zisserman