/PyTextureAnalysis

PyTextureAnalysis is a Python package for analyzing the texture of images. It includes functions for calculating local orientation, degree of coherence, and structure tensor of an image. This package is built using NumPy, SciPy and OpenCV.

Primary LanguagePythonGNU General Public License v3.0GPL-3.0

Streamlit App License: GPL v3 DOI GitHub commit activity GitHub release (latest by date)

Texture Analysis using PyTextureAnalysis

PyTextureAnalysis is a Python package that contains tools to analyze the texture of images. This code contains functions to calculate the local orientation of fibers in an image, as well as the degree of coherence. A web application is also available for demonstrating the PyTextureAnalysis package, which allows users to analyze 2D grayscale images for texture analysis.

Features

  • Upload a 2D grayscale image for texture analysis
  • Adjust image filter sigma, Gaussian local window, and window size for evaluating local density
  • Adjust threshold value for pixel evaluation, spacing between orientation vectors, and scaling for orientation vectors
  • Calculates local density, coherence, and orientation of the image
  • Provides a progress bar for each stage of the analysis

Demo

A web application developed using Streamlit is available at https://textureinformation-package.streamlit.app/. Check out the Example.ipynb file to learn how to use the package to extract and visualize local fiber orientation and organization.

App Overview

Streamlit App Screenshot

Requirements

  • Python 3.8 or higher
  • Streamlit
  • NumPy
  • scikit-image
  • Matplotlib

Installation

  1. Clone the repository.
  2. Install the required packages via pip install -r requirements.txt.
  3. Run the web application via streamlit run PyTextureAnalysis_StreamlitApp.py.

Usage

  1. Open the web application via streamlit run PyTextureAnalysis_StreamlitApp.py.
  2. Upload a 2D grayscale image for analysis.
  3. Adjust the various parameters using the sliders provided.
  4. Click the "Analyze" button to begin the analysis.
  5. View the progress of the analysis via the progress bar.
  6. View the results of the analysis.

Credits

This web application was developed, tested, and maintained by Ajinkya Kulkarni at the Max Planck Institute for Multidisciplinary Sciences, Göttingen.

Contact

For more information or to provide feedback, please visit the project repository or contact the developer directly.