| import cv2 |
| import numpy as np |
| import tensorflow as tf |
| import gradio as gr |
|
|
| |
| model = tf.keras.models.load_model('unet_model.h5') |
|
|
| |
| def preprocess_images(old_image, current_image, img_size=128): |
| old_image_resized = cv2.resize(old_image, (img_size, img_size)) / 255.0 |
| current_image_resized = cv2.resize(current_image, (img_size, img_size)) / 255.0 |
| input_combined = np.concatenate([old_image_resized, current_image_resized], axis=-1) |
| input_combined = np.expand_dims(input_combined, axis=0) |
| return input_combined |
|
|
| |
| def predict_mask(old_image, current_image): |
| |
| old_image_np = np.array(old_image) |
| current_image_np = np.array(current_image) |
|
|
| |
| preprocessed_input = preprocess_images(old_image_np, current_image_np) |
|
|
| |
| prediction = model.predict(preprocessed_input) |
| prediction_mask = (prediction[0] > 0.5).astype(np.uint8) * 255 |
|
|
| |
| mask_resized = cv2.resize(prediction_mask, (old_image_np.shape[1], old_image_np.shape[0])) |
|
|
| return mask_resized |
|
|
| |
| interface = gr.Interface( |
| fn=predict_mask, |
| inputs=[ |
| gr.Image(label="Old Image"), |
| gr.Image(label="Current Image") |
| ], |
| outputs=gr.Image(label="Predicted Mask"), |
| title="Change Detection with U-Net", |
| description="Upload two images (old and current) to detect changes using a U-Net model." |
| ) |
|
|
| if __name__ == "__main__": |
| interface.launch() |
|
|
