Data-Gouv-ML/catalogue-des-donnees-Sql-Lite huggingface dataset:
PetroSeg
Sigle : PS

PetroSeg is a user-friendly web application for unsupervised image segmentation, particularly optimized for thin section images and microscopic analysis. Developed with Streamlit and PyTorch, this application enables users to upload images, adjust segment colors, and download segmented outputs. The latest version incorporates TensorFlow and Tkinter for an enhanced, intuitive user interface.

Please cite the app as follow: 
Azzam, F., Blaise, T., & Brigaud, B. (2024). Automated petrographic image analysis by supervised and unsupervised machine learning methods. Sedimentologika, 2(2). https://doi.org/10.57035/journals/sdk.2024.e22.1594


Mots-clés : image-analysis,image-segmentation,microscopy,petrology,thin-sections,unsupervised-segmentation
Fréquence de mise à jour : punctual
GrainSight
Sigle : GS

GrainSight is a user-friendly application designed for petrographers and microscopists, allowing real-time segmentation and analysis of grains in microscopic thin section images. It utilizes the FastSAM segmentation model to extract quantitative data such as grain area, perimeter, roundness, and aspect ratio, aiding in various petrographic studies including mineral identification, texture analysis, and understanding the depositional history of rocks.

Please cite the app as follows:
Azzam, F., Blaise, T., & Brigaud, B. (2024). Automated petrographic image analysis by supervised and unsupervised machine learning methods. Sedimentologika, 2(2). https://doi.org/10.57035/journals/sdk.2024.e22.1594.


Mots-clés : fastsam,grain-segmentation,image-analysis,microscopy,mineral-identification,petrography,quantitative-analysis,textural-analysis,thin-sections
Fréquence de mise à jour : punctual