Here I am providing a basic script for image classification of Defected and Normal woods based on the CT images (jpg) using an SVM classifier. Please note that this script is intended for understanding the process involved in classification, so accuracy may not be optimal. To achieve better accuracy, additional modifications and optimizations may be necessary.

• numpy: To perform numerical operations
• os: Interacts with the operating system for handling file paths.
• cv2: To perfrom Image processing tasks.
• pandas: Data Analysis.
• train_test_split: For splitting the dataset into training, testing and validation sets.
• StandardScaler: For scaling features.
• SVC: To use Support Vector Classification.
• accuracy_score and classification_report: Evaluating the model's performance.
• matplotlib.pyplot: Data visualization.

• Define the directory path (dire) and categories of images.
• Here reading images from the specified directory (location), after this resize the images into a standard size (SIZE), and store them in the images list.
• After this assigning labels to the loaded images based on their categories and store them in the labels list.

• Define a function feature_extractor to extract features from images. Here we used GLCM (Gray-Level Co-occurrence Matrix).
• Iterate over each image in the dataset and calculate GLCM properties such as contrast, dissimilarity, homogeneity, and energy.
• Store the extracted features in a pandas DataFrame (image_dataset).

• Split the dataset into training and testing sets using train_test_split, with 15% of the data allocated for testing.
• Further split the training set into training and validation sets using train_test_split, with 10% of the training data allocated for validation.

• Scale the features in the training, validation, and test sets using StandardScaler.

• Initialize an SVM classifier (SVC) with a linear kernel and default hyperparameters.
• Train the SVM classifier on the scaled training data.

• Make predictions on the validation and test data using the trained model.
• Calculate the accuracy scores for the training, validation, and test sets.
• Print the training, validation, and test accuracies.
• Generate a classification report to evaluate precision, recall, and F1-score for each class.

• Plot decision boundaries for the validation and test sets to visualize the SVM's classification.

This script provides a comprehensive workflow, including data loading, preprocessing, model training, evaluation, and visualization, to classify images of woods into defected and normal.