viasegura 2.0.0

A python package to interact with Inter-American Development Bank machine learning models to automatic label elements for iRAP certification

VIAsegura

Automatic labeling of road safety attributes

Users manual

Content Table:

---

• Project Description
• Main Features
• Quick Start
  • Installation
  • Using the Models
• User's guides
• Authors
• License

Project Description

---

VIAsegura is a library that helps to use artificial intelligence models developed by the Inter-American Development Bank to automatically tag items on the streets. The tags it places are some of those needed to implement the iRAP road safety methodology.

These models require images with the specifications of the iRAP projects. This means that they have been taken every 20 meters along the entire path to be analyzed. In addition, some of the models require images to be taken from the front and others from the side of the car. The models yield 1 result for each model for groups of 5 images or fewer.

So far, 15 models compatible with the iRAP labeling specifications have been developed and are specified in the table below.

Model Name	Description	Type of Image	Classes
delineation	Adequacy of road lines	Frontal	2
street_lighting	Presence of street lighting	Frontal	2
carriageway	Carriageway label for section	Frontal	2
service_road	Presence of a service road	Frontal	2
road_condition	Condition of the road surface	Frontal	3
skid_resistance	Skidding resistance	Frontal	3
upgrade_cost	Influence surroundings on cost of major works	Frontal	3
speed_management	Presence of features to reduce operating speed	Frontal	3
bicycle_facility	Presence of facilities for bicyclists	Frontal	2
quality_of_curve	How adequate is the curve	Frontal	2
vehicle_parking	Presence of parking on the road	Frontal	2
property_access_points	Detects access to properties	Frontal	2
area_type	Detects if there is an urban or rural area	Lateral	2
land_use	Describes the use of the land surrounding the road	Lateral	4
number_of_lanes	The number of lanes detected	Frontal	5

Some of the models can identify all the classes or categories, others can help you sort through the available options.

Main Features

---

Some of the features now available are as follows:

• Scoring using the models already developed
• Grouping by groups of 5 images from an image list
• Download models directly into the root of the package

Quick Start

---

Environment Setup

It is advisable to install the library in a Python virtual environment. If you use conda, you can do so with the following command:

conda create -n viasegura python=3.10
conda activate viasegura

Supported versions of Python are 3.9, 3.10, 3.11 and 3.12. For more information on how to install conda, you can visit the official documentation

Installation

To install the package you can use the following commands in the terminal

pip install viasegura

To download the models use this link from the repostitory. Put the downloaded file on a models folder and decompress it with the following command:

tar -xzvf models.tar.gz

The path where the models are must be models/models_artifacts Remember to put that path every time you instantiate a model so that you can find the artifacts you need to run them.

Using the Models

In order to make the instance of a model you can use the following commands

from viasegura import ModelLabeler

frontal_labeler = ModelLabeler('frontal')  # or 'lateral' 

You can use either "frontal" or "lateral" tag in order to use the group of models desired (see table above)

Also, you can specify which models to load using the parameter model filter and the name of the models to use, (see the table above):

from viasegura import ModelLabeler

frontal_labeler = ModelLabeler('frontal', model_filter=['delineation', 'street_lighting', 'carriageway']) 

In addition, you can make it work using the GPU specifying the device where the models are going to run, for example

from viasegura import ModelLabeler

frontal_labeler = ModelLabeler('frontal', device='/device:GPU:0') 

You can modify the devices used according to the TensorFlow documentation regarding GPU usage.

For a full example of use on this package, you can see this notebook. In the notebooks folder you can also find example images to test the execution of the models.

Users Guide

I invite you to visit the manual to understand the scope of the project and how to make a project from scratch using the viasegura models.

Authors

This package has been developed by:

Jose Maria Marquez Blanco
Joan Alberto Cerretani
Victor Durand

License

The distribution of this software is according to the following license