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| import gradio as gr | |
| import numpy as np | |
| from PIL import Image, ImageDraw | |
| # Define your labels | |
| part_labels = ["front-bumper", "fender", "hood", "door", "trunk"] | |
| damage_labels = ["dent", "scratch", "misalignment", "crack"] | |
| def mock_inference(image): | |
| # This function mocks the segmentation model output | |
| # It randomly assigns labels to different parts of the image | |
| height, width = image.shape[:2] | |
| part_mask = np.random.randint(0, len(part_labels), (height, width)) | |
| damage_mask = np.random.randint(0, len(damage_labels), (height, width)) | |
| return part_mask, damage_mask | |
| def combine_masks(part_mask, damage_mask): | |
| part_damage_pairs = [] | |
| for part_id, part_name in enumerate(part_labels): | |
| for damage_id, damage_name in enumerate(damage_labels): | |
| part_binary = (part_mask == part_id) | |
| damage_binary = (damage_mask == damage_id) | |
| intersection = np.logical_and(part_binary, damage_binary) | |
| if np.any(intersection): | |
| part_damage_pairs.append((part_name, damage_name)) | |
| return part_damage_pairs | |
| def create_one_hot_vector(part_damage_pairs): | |
| vector = np.zeros(len(part_labels) * len(damage_labels)) | |
| for part, damage in part_damage_pairs: | |
| if part in part_labels and damage in damage_labels: | |
| part_index = part_labels.index(part) | |
| damage_index = damage_labels.index(damage) | |
| vector_index = part_index * len(damage_labels) + damage_index | |
| vector[vector_index] = 1 | |
| return vector | |
| def visualize_results(image, part_mask, damage_mask): | |
| img = Image.fromarray(image) | |
| draw = ImageDraw.Draw(img) | |
| for i in range(0, img.width, 10): # Sample every 10th pixel for efficiency | |
| for j in range(0, img.height, 10): | |
| part = part_labels[part_mask[j, i]] | |
| damage = damage_labels[damage_mask[j, i]] | |
| draw.point((i, j), fill="red") | |
| return img | |
| def process_image(image): | |
| # Mock inference | |
| part_mask, damage_mask = mock_inference(image) | |
| # Combine masks | |
| part_damage_pairs = combine_masks(part_mask, damage_mask) | |
| # Create one-hot encoded vector | |
| one_hot_vector = create_one_hot_vector(part_damage_pairs) | |
| # Visualize results | |
| result_image = visualize_results(image, part_mask, damage_mask) | |
| return result_image, part_damage_pairs, one_hot_vector.tolist() | |
| def gradio_interface(input_image): | |
| result_image, part_damage_pairs, one_hot_vector = process_image(input_image) | |
| # Convert part_damage_pairs to a string for display | |
| damage_description = "\n".join([f"{part} : {damage}" for part, damage in part_damage_pairs]) | |
| return result_image, damage_description, str(one_hot_vector) | |
| iface = gr.Interface( | |
| fn=gradio_interface, | |
| inputs=gr.Image(type="numpy"), | |
| outputs=[ | |
| gr.Image(type="pil", label="Detected Damage (Mocked)"), | |
| gr.Textbox(label="Damage Description"), | |
| gr.Textbox(label="One-hot Encoded Vector") | |
| ], | |
| title="Car Damage Assessment (Demo)", | |
| description="Upload an image of a damaged car to get a mocked assessment of the damage. Note: This is a demo using random predictions, not actual model inference." | |
| ) | |
| iface.launch() |