Py Image Feature Extractor

Index

1. Overview
2. Requirements
3. Installation
4. Tests
5. Run

Overview" class="reference-link">Overview

This package provides implementations of different methods to perform image feature extraction. These methods are
though a Python package and a command line interface. Available feature extraction methods are:

• Convolutional Neural Networks
  • VGG-19
  • ResNet-50
  • DenseNet-50
  • Custom CNN through .h5 file
• Linear Binary Patterns Histograms (LBPH)
• Bag of Features (bag-of-visual-words)
  • SIFT
  • SURF
  • KAZE

At the notebooks folder, some proofs-of-concept related to feature extraction and image classification may be found.

Requirements" class="reference-link">Requirements

System requirements:

• python >= 3.7.3
• pip >= 19.1.1

All the package requirements are listed on the install_requires property within the setup.py.

Installation" class="reference-link">Installation

This project may be installed as a python package using:

pip install .

Or using the PyPI package.

Tests" class="reference-link">Tests

All the test suite has been developed using the pytest framework.

# All tests
pytest
# Unit tests of extractors module
pytest image_feature_extractor/tests/extractors
# Unit tests of models module
pytest image_feature_extractor/tests/models
# Validation tests
pytest image_feature_extractor/tests/validation

Run" class="reference-link">Run

Model

The package has a command-line entry point configured. This entry point is built using the library
Click. To get all the possible commands, use image_feature_extractor --help.

# Example to perform feature extraction using a pre-trained VGG-19
image_feature_extractor extract --deep --src imgs/train --dst vgg19_train.csv --cnn vgg19 --size 200
# Example to perform feature extraction using LBPs
image_feature_extractor extract --lbp --src imgs/train --dst vgg19_train.csv --detector kaze vgg19 --k 100 --size 200 --export --vocabulary-route vocabulary.npy
# Example to perform feature extraction using bag-of-features with KAZE keypoint detector
image_feature_extractor extract --bow --src imgs/train --dst vgg19_train.csv --points 8 --radius 1 --grid 8 --size 200