Vuurwerkverkenner

This repository contains code for the Vuurwerkverkenner ("Fireworks Explorer") web application, which can be used for linking snippets of exploded (heavy) fireworks to the type of firework that they originate from. It is built on top of the Flask framework.

Vuurwerkverkenner is intended for aiding the investigation at a crime scene where snippets are found, and where determining the type of the exploded fireworks is of interest. The intended use looks as follows:

1. The user provides input, which can consist of either or both of the following:
   • a photo of all retrieved snippets that originate from a wrapper (ideally of high quality, with a plain background and good lighting conditions)
   • any text that is present on the snippets
2. The AI model contained in the application compares the uploaded photo and text to its reference database
3. The application shows the search results in descending order of similarity (according to the AI model)
4. The user compares the snippets and the shown wrappers to establish a match

The application is developed and maintained by the Netherlands Forensic Institute (NFI). It is deployed on www.vuurwerkverkenner.nl.

Model and database

The application relies on a trained AI model and a background database of fireworks, both maintained by the NFI. They are both stored on the HuggingFace organization page of the NFI (see here).

• The model, as well as a description of how it was trained, can be found here.
• The background database can be found here.

If you want to run the application locally, download a copy of the model and database to your machine.

Getting started

The following describes how to run the application on your own machine. It can be run with a dummy model and database (which are contained within this repository), or with a copy of the real model and database which are stored on HuggingFace.

Prerequisites

• Python 3.8
• Any web browser
• Optionally, a local copy of the model and data (see above)

Requirements

All package requirements for running the application are specified in requirements.txt. Packages can be installed from PyPi, e.g. through pip install -r requirements.txt.

Configuration

Make sure parameters in app/setup.cfg (in particular MODEL_CONFIG_FILE and META_DATA_DIR) match the desired configuration.

• For running the application with a dummy model and database, uncomment the indicated lines.
• For running the application with the real model and database, make sure that the paths point to the correct locations on your machine. Additionally, make sure that the filepath for the weights file (in the model configuration file) matches the filepath on your machine.

Running the application

Simply run flask run inside a terminal. Now access the application by navigating to http://localhost:5000 in your web browser.