| #Code for checking 1 sample image | |
| import os | |
| import numpy as np | |
| from tensorflow.keras.preprocessing import image | |
| from tensorflow.keras.models import load_model | |
| # Set correct image size based on training | |
| IMG_SIZE = (128, 128) | |
| # Load your model | |
| model = load_model("best_model.h5") | |
| # Get class names from folders | |
| DATA_DIR = r"C:\Users\arsul\Desktop\RESISC45\NWPU-RESISC45" | |
| CLASS_NAMES = sorted(os.listdir(DATA_DIR)) | |
| # Load and preprocess the image | |
| img_path = r"C:\Users\arsul\Desktop\RESISC45\ball-courts.jpg" | |
| img = image.load_img(img_path, target_size=IMG_SIZE) | |
| img_array = image.img_to_array(img) | |
| img_array = np.expand_dims(img_array, axis=0) # Add batch dimension | |
| # Make prediction | |
| preds = model.predict(img_array) | |
| predicted_class = CLASS_NAMES[np.argmax(preds)] | |
| print(f"Predicted class: {predicted_class}") | |