Pizzaiolo

This python tool can be used to generate a custom dataset of synthetic pizza images conforming to the pizzas the Pizzaïolo Ontology.

It ha been created and used to generate the Pizzaïolo Dataset [1], containing 4800 samples of synthetic pizza images. (Note that the Pizzaiolo module alone does not generate the 'multilabel' column of the Pizzaïolo Dataset).

Authors

Grégory Bourguin¹ & Arnaud Lewandowski²
SysReIC (Systèmes Réflexifs et Ingenierie de la Connaissance)
LISIC (Laboratoire Informatique Signal et Image de la Côte d'opale)
ULCO (Université du Littoral Côte d'Opale), FRANCE

¹ gregory.bourguin@univ-littoral.fr
² arnaud.lewandowski@univ-littoral.fr

Licence

CC BY-NC 4.0

If you use Pizzaiolo or the Pizzaïolo Dataset, please cite as :

Grégory Bourguin, Arnaud Lewandowski. Pizzaïolo Dataset : Des Images Synthétiques Ontologiquement Explicables. 24ème conférence francophone sur l'Extraction et la Gestion des Connaissances, EGC 2024, Explain'AI, Jan 2024, Dijon, France, https://hal.science/hal-04401953.

Requirements

To use Pizzaiolo, you need to install the following modules:

• Owlready2 [3]
• numpy
• pillow
• pandas
• opencv

Usage

You will find in the repository several notebooks that show usages of the tool and that explore the resulting data.

Basically, here are the steps to generate a dataset:

import owlready2
from libs.pizzaiolo import Pizzaiolo

pizzaiolo = Pizzaiolo()
ontology = pizzaiolo.getOntology()

pizza_types = [
  ontology.American,
  ontology.AmericanHot,
  ontology.Cajun,
  ontology.Capricciosa,
  ontology.Fiorentina,
  ontology.FourSeasons,
  ontology.Giardiniera,
  ontology.LaReine,
  ontology.Mushroom,
  ontology.Napoletana,
  ontology.Parmense,
  ontology.Siciliana,
  ontology.SloppyGiuseppe,
  ontology.Soho,
  ontology.Veneziana,
  None # Random elements
]

pizzaiolo.cook(pizza_types, number_of_each=10, delivery_dir='new_dataset_directory')

The generated 'new_dataset_directory' is constituted of the following sub-directories:

• csv/ :

  • pizzaiolo_dataset.csv : details for all the samples in the dataset
  • NB: the 'type' column contains the type of pizza that the Pizzaiolo was asked to generate : it does not result from an ontological inference.

• images/ :

  • pizza images (224*224) : 1 file / sample -> img_XXXXX.png
    NB: icons used to generate the images are coming from https://www.flaticon.com/.

• ontology/ :

  • pizzaiolo.xml : the Pizzaïolo Ontology (OWL 2) used to generate the samples.
    NB: this ontology was derived from [2], and built/manipulated with [3].

• labels/ :

  • Concepts Encoding : concepts.json
  • Bounding Boxes : 1 file / sample -> img_XXXXX_bboxes.json
  • Contours : 1 file / sample -> img_XXXXX_contours.json
  • Semantic Segmentation : 1 file / sample -> img_XXXXX_segmentation.txt

Generated sample

Concepts

The concepts are the elements constituting pizzas according to the Pizzaïolo Ontology (i.e. the pizza base, the pizza toppings, and the - optional - country of origin).

The concepts.json file contains a Python dict for concepts encoding :

• key(s) : a concept id (uint)
• value(s) : the concept name (string) (i.e. the short name of the concept class in the Pizzaïolo Ontology)

Bouding Boxes

The bounding boxes represent the localization of the concepts instances constituting a pizza sample.

Each img_XXXXX_bboxes.json file contains a Python dict :

• key(s) : the name (string) of each concept class present in the sample
• value(s) : list of all the bouding boxes for the corresponding concept key

NB: each bounding box is encoded as a Python (sub)list : [ x_left, y_top, width, height ]

Contours

The contours represent the localization and shape of the concepts instances constituting a pizza sample.

Each img_XXXXX_contours.json file contains a Python dict :

• key(s) : the name (string) of each concept class present in the sample
• value(s) : a list of all the contours for the corresponding concept key

NB: contours are encoded as OpenCV Contours.

Semantic Segmentation

Each img_XXXXX_segmentation.txt file contains a Python Numpy array (dtype=uint)

• the shape of the array is the (2D) size of the samples (224*224)
• each "pixel" belongs to a concept encoded according to concepts.json.

References

[1] Grégory Bourguin, Arnaud Lewandowski. Pizzaïolo Dataset : Des Images Synthétiques Ontologiquement Explicables. 24ème conférence francophone sur l'Extraction et la Gestion des Connaissances, EGC 2024, Explain'AI, Jan 2024, Dijon, France, https://hal.science/hal-04401953.

[2] Horridge, M. (2011). Protégé OWL Tutorial | OWL research at the University of Manchester..

[3] Lamy, J.-B. (2017). Owlready : Ontology-oriented programming in Python with automatic classification and high level constructs for biomedical ontologies. Artificial Intelligence in Medicine 80:11–28.