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Running
| import requests | |
| from PIL import Image | |
| import gradio as gr | |
| from segmentation import segment_image | |
| from classification import get_segments_for_garment | |
| def process_url(url, selected_classes, show_original, show_segmentation, show_overlay, fixed_size=(400, 400)): | |
| """Process an image from a URL""" | |
| try: | |
| image = Image.open(requests.get(url, stream=True).raw) | |
| return segment_image(image, selected_classes, show_original, show_segmentation, show_overlay, fixed_size) | |
| except Exception as e: | |
| return [gr.update(value=None)] * 4, f"Error: {str(e)}" | |
| def process_person_and_garment(person_image, garment_image, show_original, show_segmentation, show_overlay, fixed_size=(400, 400)): | |
| """Process person and garment images for targeted segmentation""" | |
| if person_image is None or garment_image is None: | |
| return [gr.update(value=None)] * 4, "Please provide both person and garment images" | |
| try: | |
| # Get segments that should be included based on the garment | |
| selected_class, segformer_idx, result_text = get_segments_for_garment(garment_image) | |
| if selected_class is None: | |
| return [gr.update(value=None)] * 4, result_text | |
| # Process the person image with the selected garment classes | |
| result_images = segment_image(person_image, selected_class, show_original, show_segmentation, show_overlay, fixed_size) | |
| return result_images, result_text | |
| except Exception as e: | |
| return [gr.update(value=None)] * 4, f"Error: {str(e)}" | |