robotathome 1.1.9

This package provides a Python Toolbox with a set of functions to assist in the management of Robot@Home2 Dataset

Robot@Home2 Dataset Toolbox

The Robot-at-Home dataset (Robot@Home, paper here) is a collection of raw and processed data from five domestic settings compiled by a mobile robot equipped with 4 RGB-D cameras and a 2D laser scanner. Its main purpose is to serve as a testbed for semantic mapping algorithms through the categorization of objects and/or rooms.

Nevertheless, the Robot@Home dataset has been updated to Robot@Home2. This update is made up of a relational database file in SQLite format with all the original data and a size of only 2,2 GB. The image and scene files have been reorganized and now takes only 25,9 GB.

The database, named rh.db, is a relational sql database accessible with the SQLite engine that usually accompanies the python environment and is popularly used in the development of current applications in both fixed (linux and windows) and mobile environments. (android).

The data files have been organized into two main groups. On the one hand, the files with RGBD data (RGB images and depth images) and on the other the 3D scenes in point cloud files.

The intensity (RGB) and depth (D) image files have a standard png format so they can be opened directly. In addition, the files are linked to the data in the database through tables that relate them. Moreover, the database contains tables that relate the files of the new version with those of the old version.

In the case of 3D scene files, these are plain text files that store the coordinates and colors of the points that make up the 3D cloud. These files can be easily visualized with current software for the visualization of point clouds like MeshLab.

You no longer need to waste time diving the obscure data formats (despite an API -dataset.py- for that is provided). Instead, you can simply surf the dataset through SQL queries or the new toolbox.

The toolbox (toolbox.py) has been coded for various purposes. The first one consists of encapsulating frequent queries as functions and integrating the results with a data analysis library such as Pandas. Pandas library is widely used in data science and machine learning disciplines in the Python framework. The second one is the integration of the data set with the GluonCV library to apply deep learning algorithms in artificial vision.

Prerequisites: Installing the Python Development Environment

Launched in 1991, Python has achieved enormous popularity in the scientific community in recent years. Python is an interpreted high-level general-purpose programming language with a many useful features. It's platform independent, simple, consistent and with a great code readability. Moreover, it has an extensive set of libraries that help to reduce development time.

Artificial Intelligence (AI) and Machine Learning (ML) projects differ from software projects in other areas due to differences in the technology stack and the skills needed to deal with them.

Python offers AI and ML programmers many features that help to develop and test complex algorithms. Even in Computer Vision (CV), there are solid software libraries that allow developers to focus on their research areas.

There are several different Python distributions, each one created with a different approach and for different audiences.

Robot@Home2 Toolbox is written in Python and works well with Anaconda which is a distribution of the Python and R programming languages for scientific computing. Of course, other distributions can be used to run the toolbox.

Short installation on Linux

To install Anaconda in Linux you must follow these steps.

Download the Anaconda installer

$ cd ~/Downloads
$ wget https://repo.anaconda.com/archive/Anaconda3-2022.10-Linux-x86_64.sh

replace ~/Downloads with the path of your choice

Install the distribution

$ bash ~/Downloads/Anaconda3-2022.10-Linux-x86_64.sh

include the bash command regardless of whether or not you are using Bash shell.

Review and agree the license agreement. Accept the default install location.

When the installer prompts “Do you wish the installer to initialize Anaconda3 by running conda init?”, we recommend “yes”.

Finally, for the installation to take effect

$ source ~/.bashrc

For more detailed/updated installation information, go to Anaconda installation page.

Installation on Windows

Due to the graphic abundance of the installation procedure, we refer you to the specific Anaconda documentation page for installation on Windows.

Verifying your installation on Linux

Enter the command python. This command runs the Python shell. If Anaconda is installed and working, the version information it displays when it starts up will include “Anaconda”. To exit the Python shell, enter the quit() command.

$ python
Python 3.9.16 (main, Jan 11 2023, 16:05:54) 
[GCC 11.2.0] :: Anaconda, Inc. on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> quit()

You can also display a list of installed packages and their versions running conda list

$ conda list
# packages in environment at /home/user/anaconda3:
#
# Name                    Version                   Build  Channel
...

Verifying in Windows

Click Start, search, or select Anaconda Prompt from the menu. After opening Anaconda Prompt on the terminal enter the command python. This command runs the Python shell. If Anaconda is installed and working, the version information it displays when it starts up will include Anaconda . To exit the Python shell, enter the command quit().

As in Linux you can also display a list of installed packages and their versions running conda list

Making a virtual environment

A virtual environment is a Python environment such that the Python interpreter, libraries and scripts installed into it are isolated from those installed in other virtual environments

When a virtual environment is active, the installations tools install Python packages into the virtual environment without needing to be told to do so explicitly and without interfering in other virtual environments.

That's the reason why it's recommended to work with a virtual environment specifically for Robot@Home2. To do that with conda

$ conda create --name rh python=3.9

change rh to a name of your choice

Robot@Home2 runs with python 3.6 or higher. Also, version 3.6 is recommended for Windows

once it has been created, it can already be activated

$ conda activate rh

to deactivate run

$ conda deactivate

Literate programming with Jupyter

Literate programming is a programming paradigm introduced by Donald Knuth in which a computer program is given an explanation of its logic in a natural language, such as English, interspersed with snippets of macros and traditional source code. The approach is typically used in scientific computing and in data science routinely for reproducible research and open access purposes.

On the other hand, the Jupyter *Notebook is an open-source web application that allows you to create and share documents that contain live code, equations, visualizations and narrative text. Additionally JupyterLab is a web-based interactive development environment for Jupyter notebooks, code, and data.

Jupyter is an application of literate programming and Robot@Home2 includes Jupyter notebooks for introductions, easy learning, and technical explanations.

Installing Jupyter in Anaconda distribution is an easy task

$ conda install -c conda-forge jupyterlab

remember to previously activate your virtual environment with `conda activate` command

If you have followed the previous sections you have the right working environment to open this notebook with Jupyter to download and install both the toolbox and the dataset.

However, if jupyter notebook is not your choice right now you can try the following instructions.

Time to install Robot@Home2

Installing the toolbox

Robot@Home2 Toolbox can be installed through the Python package manager.

1. Confirm you are in the right virtual environment

   $ conda activate rh
   

2. Enter this command to install robotathome with Jupyter to run notebooks.

   $ pip install robotathome
   

   pip is a common Python package manager that is included in Anaconda and many other distributions

   If you have not previously installed jupyterlab you can do it right now adding the interactive option to the pip command as follows:

   $ pip install robotathome[interactive]
   

   interactive will include jupyter and needed libraries.

3. Run python and import the robotathome library

   $ python
   Python 3.10.9 (main, Jan 11 2023, 15:21:40) [GCC 11.2.0] on linux
   Type "help", "copyright", "credits" or "license" for more information.
   >>> import robotathome as rh
   >>> print (rh.__version__)
   1.1.1
   

4. Congratulations ! the robotathome package has been installed successfully.

Downloading the dataset

Robot@Home resides in Zenodo site where all data versions can be downloaded. Latest version (v2.0.3) is composed of two files: Robot@Home2_db.tgz and Robot@Home2_files.tgz. The first one contains the database, and the second one contains the bunch of RGBD images and 3D scenes

You can choose to download it on your own or through the new brand toolbox.

In case you are considering Linux

$ wget https://zenodo.org/record/7811795/files/Robot@Home2_db.tgz
$ wget https://zenodo.org/record/7811795/files/Robot@Home2_files.tgz

check the files integrity

$ md5sum Robot@Home2_db.tgz 
d34fb44c01f31c87be8ab14e5ecd0767  Robot@Home2_db.tgz

$ md5sum Robot@Home2_files.tgz 
c55465536738ec3470c75e1671bab5f2  Robot@Home2_files.tgz

and to finish unzip files

$ pv /home/user/Downloads/Robot@Home2_db.tgz | tar -xzf - -C /home/user/WORKSPACE/
$ pv /home/user/Downloads/Robot@Home2_files.tgz | tar -xzf - -C /home/user/WORKSPACE/files

or even better, now you can do the same programmatically using the toolbox

import robotathome as rh

# Download files
rh.download('https://zenodo.org/record/7811795/files/Robot@Home2_db.tgz', '~/Downloads')
rh.download('https://zenodo.org/record/7811795/files/Robot@Home2_files.tgz', '~/Downloads')

# Compute md5 checksums
md5_checksum_db = rh.get_md5('~/Downloads/Robot@Home2_db.tgz')
md5_checksum_files = rh.get_md5('~/Downloads/Robot@Home2_files.tgz')

# Check the files integrity and download
if md5_checksum_db == 'd34fb44c01f31c87be8ab14e5ecd0767':
    rh.uncompress('~/Downloads/Robot@Home2_db.tgz', '~/WORKSPACE')
else:
    print('Integrity of Robot@Home2_db.tgz is compromised, please download again')
    
if md5_checksum_files == 'c55465536738ec3470c75e1671bab5f2':
    rh.uncompress('~/Downloads/Robot@Home2_files.tgz', '~/WORKSPACE/files')
else:
    print('Integrity of Robot@Home2_files.tgz is compromised, please download again')