RajKrishna2123/Rock_Segmentation

Land slide prediction :- the classification of individual rocks into three categories: big, medium, and small. Additionally, the project aims to predict the falling pattern of these rocks by analyzing the provided images of the rock dump.

IIT_Dhanbad

Problem Statement

The objective of this project is to perform the classification of individual rocks into three categories: big, medium, and small. Additionally, the project aims to predict the falling pattern of these rocks by analyzing the provided images of the rock dump.

Environment Setup

This project is developed using Python 3.10. The required libraries and packages are listed below:

Requirements

• tensorflow_gpu==2.10
• opencv
• model hed for opencv: You can find the model at https://github.com/s9xie/hed
• scikit-learn
• matplotlib

To set up the environment, you can use the following commands:

pip install tensorflow_gpu==2.10 opencv-python scikit-learn matplotlib

Folder Structure

IIT_Dhanbad/
├── code/
│   ├── heD_model/
│   ├── segment_data/
│   ├── train/
│   |── model.ipynb
│   │── model2.ipynb
│   │── model3.ipynb
│   └── cook/
├── data/
│   ├── images/
│   ├── masks/
│   └── mask/
├── mask_prep/
├── train/

Here's a brief description of the folders in this project:

• code/: Contains various project-related code files.

  • heD_model/: HED model implementation or files related to it.
  • segment_data/: Semantically segmented individual rock data.
  • train/: Jupyter notebooks for training the models.
  • cook/: Temporary folder for other purposes

• data/: Holds the image and mask data for the project.

  • images/: Image data used for training and analysis.
  • masks/: Mask data with 0 and 255 values.
  • mask/: Mask data with actual mask values of 0 and 1.

• mask_prep/: Contains prepared masks.

• train/: Additional training-related data or files.

Input data

result data