# Robot@Home2 Dataset Toolbox #
[](https://pypi.org/project/robotathome/ "R@H2 as PyPI package ready to install")
[](https://doi.org/10.5281/zenodo.7811795 "The downloadable R@H2 dataset in Zenodo")
[](https://doi.org/10.5281/zenodo.7812048 "This repository released on Zenodo")
[](https://drive.google.com/drive/folders/1ENnxbKP5MJdlGl2Q93WTbIlofuy6Icxq)
The Robot-at-Home dataset (Robot@Home, paper
[here](http://mapir.uma.es/papersrepo/2017/2017-raul-IJRR-Robot_at_home_dataset.pdf))
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](https://www.meshlab.net/).
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
```shell
$ 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
```shell
$ 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
```shell
$ source ~/.bashrc
```
For more detailed/updated installation information, go to [Anaconda installation page](https://docs.anaconda.com/anaconda/install/).
### 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](https://docs.anaconda.com/anaconda/install/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
```shell
$ 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
```shell
$ conda activate rh
```
to deactivate run
```shell
$ 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](https://jupyter.org) \*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
```shell
$ 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](https://github.com/goyoambrosio/RobotAtHome_API/blob/master/notebooks/10-Download-and-install.ipynb) 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
```shell
$ conda activate rh
```
2. Enter this command to install `robotathome` with [Jupyter](https://jupyter.org) to run notebooks.
```shell
$ 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:
```shell
$ 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](https://zenodo.org/record/7811795)) 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
```shell
$ 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
```shell
$ 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
```shell
$ 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
```python
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')
```
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"keywords": "toolbox dataset database relational model mobile robotics computer vision",
"author": "G. Ambrosio-Cestero",
"author_email": "gambrosio@uma.es",
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"platform": "Windows",
"description": "# Robot@Home2 Dataset Toolbox #\n\n[](https://pypi.org/project/robotathome/ \"R@H2 as PyPI package ready to install\")\n[](https://doi.org/10.5281/zenodo.7811795 \"The downloadable R@H2 dataset in Zenodo\")\n[](https://doi.org/10.5281/zenodo.7812048 \"This repository released on Zenodo\")\n[](https://drive.google.com/drive/folders/1ENnxbKP5MJdlGl2Q93WTbIlofuy6Icxq)\n\n\n\nThe Robot-at-Home dataset (Robot@Home, paper\n[here](http://mapir.uma.es/papersrepo/2017/2017-raul-IJRR-Robot_at_home_dataset.pdf))\nis a collection of raw and processed data from five domestic settings compiled\nby a mobile robot equipped with 4 RGB-D cameras and a 2D laser scanner. Its main\npurpose is to serve as a testbed for semantic mapping algorithms through the\ncategorization of objects and/or rooms.\n\nNevertheless, the Robot@Home dataset has been updated to **Robot@Home2**. This\nupdate is made up of a relational database file in SQLite format with all the\noriginal data and a size of only 2,2 GB. The image and scene files have been\nreorganized and now takes only 25,9 GB.\n\nThe database, named `rh.db`, is a relational sql database accessible with the\nSQLite engine that usually accompanies the python environment and is popularly\nused in the development of current applications in both fixed (linux and\nwindows) and mobile environments. (android).\n\nThe data files have been organized into two main groups. On the one hand, the\nfiles with RGBD data (RGB images and depth images) and on the other the 3D\nscenes in point cloud files.\n\nThe intensity (RGB) and depth (D) image files have a standard *png* format so\nthey can be opened directly. In addition, the files are linked to the data in\nthe database through tables that relate them. Moreover, the database contains\ntables that relate the files of the new version with those of the old version.\n \nIn the case of 3D scene files, these are plain text files that store the\ncoordinates and colors of the points that make up the 3D cloud. These files can\nbe easily visualized with current software for the visualization of point\nclouds like [MeshLab](https://www.meshlab.net/).\n\nYou no longer need to waste time diving the obscure data formats (despite an API\n-**dataset.py**- for that is provided). Instead, you can simply surf the dataset\nthrough SQL queries or the new toolbox.\n\nThe toolbox (**toolbox.py**) has been coded for various purposes. The first one\nconsists of encapsulating frequent queries as functions and integrating the\nresults with a data analysis library such as Pandas. Pandas library is widely\nused in data science and machine learning disciplines in the Python framework.\nThe second one is the integration of the data set with the GluonCV library to\napply deep learning algorithms in artificial vision.\n\n## Prerequisites: Installing the Python Development Environment\n\nLaunched in 1991, Python has achieved enormous popularity in the scientific\ncommunity in recent years. Python is an interpreted high-level general-purpose\nprogramming language with a many useful features. It's platform independent,\nsimple, consistent and with a great code readability. Moreover, it has an\nextensive set of libraries that help to reduce development time.\n\nArtificial Intelligence (AI) and Machine Learning (ML) projects differ from\nsoftware projects in other areas due to differences in the technology stack and\nthe skills needed to deal with them.\n\nPython offers AI and ML programmers many features that help to develop and test\ncomplex algorithms. Even in Computer Vision (CV), there are solid software\nlibraries that allow developers to focus on their research areas.\n\nThere are several different Python distributions, each one created with a\ndifferent approach and for different audiences.\n\nRobot@Home2 Toolbox is written in Python and works well with Anaconda which is a\ndistribution of the Python and R programming languages for scientific computing.\nOf course, other distributions can be used to run the toolbox.\n\n### Short installation on Linux\n\nTo install Anaconda in Linux you must follow these steps.\n\nDownload the Anaconda installer\n\n```shell\n$ cd ~/Downloads\n$ wget https://repo.anaconda.com/archive/Anaconda3-2022.10-Linux-x86_64.sh\n```\n\n> replace `~/Downloads` with the path of your choice\n\n\nInstall the distribution\n\n```shell\n$ bash ~/Downloads/Anaconda3-2022.10-Linux-x86_64.sh\n```\n\n> include the `bash` command regardless of whether or not you are using Bash shell.\n\nReview and agree the license agreement. Accept the default install location.\n\nWhen the installer prompts *\u201cDo you wish the installer to initialize Anaconda3 by running conda init?\u201d*, we recommend *\u201cyes\u201d*.\n\nFinally, for the installation to take effect\n\n```shell\n$ source ~/.bashrc\n```\n\n\nFor more detailed/updated installation information, go to [Anaconda installation page](https://docs.anaconda.com/anaconda/install/).\n\n### Installation on Windows\n\nDue to the graphic abundance of the installation procedure, we refer you to\nthe specific [Anaconda documentation page for installation on Windows](https://docs.anaconda.com/anaconda/install/windows/).\n\n### Verifying your installation on Linux\n\nEnter the command `python`. This command runs the Python shell. If Anaconda is\ninstalled and working, the version information it displays when it starts up\nwill include `\u201cAnaconda\u201d`. To exit the Python shell, enter the `quit()` command.\n\n $ python\n Python 3.9.16 (main, Jan 11 2023, 16:05:54) \n [GCC 11.2.0] :: Anaconda, Inc. on linux\n Type \"help\", \"copyright\", \"credits\" or \"license\" for more information.\n >>> quit()\n\nYou can also display a list of installed packages and their versions running `conda list`\n\n $ conda list\n # packages in environment at /home/user/anaconda3:\n #\n # Name Version Build Channel\n ...\n\n### Verifying in Windows\n\nClick Start, search, or select **Anaconda Prompt** from the menu. After opening\nAnaconda Prompt on the terminal enter the command `python`. This command runs\nthe Python shell. If Anaconda is installed and working, the version\ninformation it displays when it starts up will include `Anaconda` . To exit\nthe Python shell, enter the command `quit()`.\n\nAs in Linux you can also display a list of installed packages and their\nversions running `conda list`\n\n### Making a virtual environment\n\nA virtual environment is a Python environment such that the Python interpreter,\nlibraries and scripts installed into it are isolated from those installed in\nother virtual environments\n\nWhen a virtual environment is active, the installations tools install Python\npackages into the virtual environment without needing to be told to do so\nexplicitly and without interfering in other virtual environments.\n\nThat's the reason why it's recommended to work with a virtual environment\nspecifically for Robot@Home2. To do that with conda\n\n```shell\n$ conda create --name rh python=3.9\n```\n\n> change `rh` to a name of your choice\n\n> Robot@Home2 runs with python 3.6 or higher. Also, version 3.6 is recommended for\n> Windows\n\nonce it has been created, it can already be activated\n\n```shell\n$ conda activate rh\n```\nto deactivate run\n\n```shell\n$ conda deactivate\n```\n\n### Literate programming with Jupyter\n\nLiterate programming is a programming paradigm introduced by Donald Knuth in\nwhich a computer program is given an explanation of its logic in a natural\nlanguage, such as English, interspersed with snippets of macros and traditional\nsource code. The approach is typically used in scientific computing and in data\nscience routinely for reproducible research and open access purposes.\n\nOn the other hand, the **[Jupyter](https://jupyter.org) \\*Notebook** is an open-source web application that\nallows you to create and share documents that contain live code, equations,\nvisualizations and narrative text. Additionally **JupyterLab** is a web-based\ninteractive development environment for Jupyter notebooks, code, and data.\n\nJupyter is an application of literate programming and Robot@Home2 includes\nJupyter notebooks for introductions, easy learning, and technical explanations.\n\nInstalling Jupyter in Anaconda distribution is an easy task\n\n```shell\n$ conda install -c conda-forge jupyterlab\n```\n\n> remember to previously activate your virtual environment with \\`conda activate\\` command\n\nIf you have followed the previous sections you have the right working\nenvironment to open [this notebook](https://github.com/goyoambrosio/RobotAtHome_API/blob/master/notebooks/10-Download-and-install.ipynb) with Jupyter to download and install both the toolbox and the dataset.\n\nHowever, if jupyter notebook is not your choice right now you can try the\nfollowing instructions.\n\n\n## Time to install Robot@Home2\n\n### Installing the toolbox\n\nRobot@Home2 Toolbox can be installed through the Python package manager.\n\n1. Confirm you are in the right virtual environment\n\n ```shell\n $ conda activate rh\n ```\n\n2. Enter this command to install `robotathome` with [Jupyter](https://jupyter.org) to run notebooks.\n\n ```shell\n $ pip install robotathome\n ```\n\n > `pip` is a common Python package manager that is included in Anaconda and many other distributions\n\n If you have not previously installed `jupyterlab` you can do it right now adding\n the `interactive` option to the `pip` command as follows:\n \n ```shell\n $ pip install robotathome[interactive]\n ```\n > `interactive` will include jupyter and needed libraries.\n\n3. Run `python` and import the `robotathome` library\n\n ```\n $ python\n Python 3.10.9 (main, Jan 11 2023, 15:21:40) [GCC 11.2.0] on linux\n Type \"help\", \"copyright\", \"credits\" or \"license\" for more information.\n >>> import robotathome as rh\n >>> print (rh.__version__)\n 1.1.1\n ```\n\n4. Congratulations ! the `robotathome` package has been installed successfully.\n\n\n### Downloading the dataset\n\nRobot@Home resides in Zenodo site where all data versions can be downloaded.\nLatest version ([v2.0.3](https://zenodo.org/record/7811795)) is composed of two\nfiles: `Robot@Home2_db.tgz` and `Robot@Home2_files.tgz`. The first one contains the\ndatabase, and the second one contains the bunch of RGBD images and 3D scenes\n\nYou can choose to download it on your own or through the new brand toolbox.\n\nIn case you are considering Linux\n\n```shell\n$ wget https://zenodo.org/record/7811795/files/Robot@Home2_db.tgz\n$ wget https://zenodo.org/record/7811795/files/Robot@Home2_files.tgz\n```\n\ncheck the files integrity\n\n```shell\n$ md5sum Robot@Home2_db.tgz \nd34fb44c01f31c87be8ab14e5ecd0767 Robot@Home2_db.tgz\n\n$ md5sum Robot@Home2_files.tgz \nc55465536738ec3470c75e1671bab5f2 Robot@Home2_files.tgz\n```\n\nand to finish unzip files\n\n```shell\n$ pv /home/user/Downloads/Robot@Home2_db.tgz | tar -xzf - -C /home/user/WORKSPACE/\n$ pv /home/user/Downloads/Robot@Home2_files.tgz | tar -xzf - -C /home/user/WORKSPACE/files\n```\n\nor even better, now you can do the same programmatically using the toolbox\n\n```python\nimport robotathome as rh\n\n# Download files\nrh.download('https://zenodo.org/record/7811795/files/Robot@Home2_db.tgz', '~/Downloads')\nrh.download('https://zenodo.org/record/7811795/files/Robot@Home2_files.tgz', '~/Downloads')\n\n# Compute md5 checksums\nmd5_checksum_db = rh.get_md5('~/Downloads/Robot@Home2_db.tgz')\nmd5_checksum_files = rh.get_md5('~/Downloads/Robot@Home2_files.tgz')\n\n# Check the files integrity and download\nif md5_checksum_db == 'd34fb44c01f31c87be8ab14e5ecd0767':\n rh.uncompress('~/Downloads/Robot@Home2_db.tgz', '~/WORKSPACE')\nelse:\n print('Integrity of Robot@Home2_db.tgz is compromised, please download again')\n \nif md5_checksum_files == 'c55465536738ec3470c75e1671bab5f2':\n rh.uncompress('~/Downloads/Robot@Home2_files.tgz', '~/WORKSPACE/files')\nelse:\n print('Integrity of Robot@Home2_files.tgz is compromised, please download again')\n```\n\n\n\n",
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