Lumber Defect Classifier

This project classifies lumber defects using texture features extracted from grayscale image sections. It implements a BayesianDefectClassifier for defect classification based on statistical and co-occurrence matrix-based texture features. The program is available in both Python and C++.

Identifying and Locating Surface Defects in Wood: An Automated System

Features

• Extracts statistical features: Mean, Variance, Skewness, Kurtosis.
• Extracts texture features from the co-occurrence matrix: Inertia, Cluster Shade, Cluster Prominence, Local Homogeneity, Energy, and Entropy.
• Implements a Bayesian Classifier for defect classification.
• Available in Python and C++ for flexibility and ease of use.

Requirements

C++ Version

• CMake (version 3.10 or higher)
• OpenCV (version 4.x recommended)
• C++17 compatible compiler

Python Version

• Python 3.8 or higher
• NumPy
• OpenCV-Python

Build Instructions

C++ Version

1. Clone the Repository

   git clone https://github.com/MohammadrezaMC2/LumberDefectClassifier.git
   cd LumberDefectClassifier

2. Create a Build Directory

   mkdir build
   cd build

3. Run CMake and Build

   cmake ..
   make