Considering the impact on the world of artificial intelligence and process automation in recent years, semi-autonomous systems have been developed that act responding to signals from their environment. An example is food delivery robots that establish their trajectory from images of their surroundings. To develop a system like this it is necessary to make use of segmentation algorithms trained to recognize obstacles, and to train these algorithms, databases of segmented images corresponding to the environment in which the robot will operate are required.
To segment images using the models trained in DataTraining, the code available in this repository was developed.
Status: Finished (Waiting for evaluation)
For the installation is necessary to clone the current repository in a local repository. If you don't have GitHub installed yet, you should. If you are on Linux you can run the following command:
sudo apt-get install git
Now you need to configure GitHub. For this run the following commands:
git config --global user.name "user_name"
git config --global user.email "email_id"
Otherwise, if you are not working on Linux, you can install GitHub from the official website.
After installing and configuring GitHub on your computer, you must clone the repository. If you are on Linux, from the terminal you must access the path where you want the repository to be cloned and execute the following command:
git clone https://github.com/Kiwi-PUJ/DataPrediction.git
If you are not working on Linux you must clone the repository from the GitHub application.
Now the application files will be on your device.
The Image Labelling App dependencies, compilation, and configuration are packaged in a Docker Image. Before continuing, make sure you have Docker installed on your device. If it is not installed and you are working on Linux, you can run the following commands in a terminal:
sudo apt-get update
sudo apt-get install docker-ce docker-ce-cli containerd.io
This can take a while. When the installation is done you can test it by running the Docker image hello-world.
sudo docker run hello-world
If the installation was done correctly, you should see some informational text on the screen.
Besides this, there are other post-installation steps for Linux. To complete them, run the following commands:
sudo groupadd docker
sudo usermod -aG docker $USER
newgrp docker
If you are not working on Linux, you can install by visiting the official website.
To run the data prediction Docker image, verify that you are on the DataPrediction path and run the following command:
bash start.sh
If it's the first time probably is going to take a while.
If all goes well, you should be seeing a progress bar for each of the selected images on your screen and the results should be saving to the results folder.
The code documentation was done using PEP8 and Doxygen and is available here
🏎️ Pedro Elí Ruiz Zárate, GitHub, Email: [email protected]
👨🏻🏫 Francisco Carlos Calderón Bocanegra, GitHub
👨🏻💻 John Alberto Betancourt Gonzalez, GitHub