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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)}"
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