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| import gradio as gr | |
| import numpy as np | |
| import cv2 | |
| import os | |
| import tensorflow as tf | |
| from tensorflow.keras.models import load_model | |
| # Load the Mask R-CNN model | |
| model_path = os.path.join('toolkit', 'condmodel_100.h5') # Path to your model | |
| mask_rcnn_model = load_model(model_path) | |
| def apply_mask_rcnn(image): | |
| """ | |
| Function to apply the Mask R-CNN model and return the segmented image. | |
| :param image: Input image in numpy array format | |
| :return: Image with segmentation mask overlaid | |
| """ | |
| try: | |
| # Convert image to RGB (in case of RGBA or grayscale) | |
| if image.shape[2] == 4: # Convert RGBA to RGB | |
| image = cv2.cvtColor(image, cv2.COLOR_RGBA2RGB) | |
| # Resize the image to the input size of the model | |
| resized_image = cv2.resize(image, (224, 224)) # Adjust according to model input size | |
| input_image = np.expand_dims(resized_image, axis=0) | |
| # Use Mask R-CNN to predict the mask | |
| prediction = mask_rcnn_model.predict(input_image) | |
| # Assuming the first output is the mask, you may need to adjust based on your model's structure | |
| mask = prediction[0] | |
| mask = np.squeeze(mask) # Remove any unnecessary dimensions | |
| # Resize mask back to the original image size | |
| mask = cv2.resize(mask, (image.shape[1], image.shape[0])) | |
| # Create a segmentation overlay on the original image | |
| mask_overlay = np.zeros_like(image) | |
| mask_overlay[mask > 0.5] = [0, 255, 0] # Green mask | |
| # Combine the original image with the mask | |
| segmented_image = cv2.addWeighted(image, 1, mask_overlay, 0.5, 0) | |
| return segmented_image | |
| except Exception as e: | |
| print(f"Error in segmentation: {e}") | |
| return image # Return original image if segmentation fails | |
| # Update Gradio interface for image input/output | |
| inputs = gr.Image(source="upload", tool="editor", type="numpy", label="Upload an image") | |
| outputs = gr.Image(type="numpy", label="Segmented Image") | |
| # Gradio interface definition | |
| with gr.Blocks() as demo: | |
| gr.Markdown("<h1 style='text-align: center;'>Image Segmentation with Mask R-CNN</h1>") | |
| gr.Markdown("Upload an image to see segmentation results using the Mask R-CNN model.") | |
| # Input and output components | |
| with gr.Row(): | |
| with gr.Column(): | |
| gr.Markdown("### Upload an Image") | |
| inputs.render() # Render the input (image upload) | |
| # Submit button | |
| gr.Button("Submit").click(fn=apply_mask_rcnn, inputs=inputs, outputs=outputs) | |
| gr.Button("Clear").click(fn=lambda: None) | |
| with gr.Column(): | |
| gr.Markdown("### Segmented Image Output") | |
| outputs.render() # Render the output (segmented image) | |
| # Launch the Gradio app | |
| demo.launch() | |