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import gradio as gr
from transformers import DetrImageProcessor, DetrForObjectDetection
import torch
from PIL import Image, ImageDraw
# Load the DETR layout model
model_name = "cmarkea/detr-layout-detection"
processor = DetrImageProcessor.from_pretrained(model_name)
model = DetrForObjectDetection.from_pretrained(model_name)
def detect_layout(image):
inputs = processor(images=image, return_tensors="pt")
outputs = model(**inputs)
target_sizes = torch.tensor([image.size[::-1]]) # H x W
results = processor.post_process_object_detection(outputs, target_sizes=target_sizes, threshold=0.7)[0]
draw = ImageDraw.Draw(image)
labels = []
for score, label, box in zip(results["scores"], results["labels"], results["boxes"]):
box = [round(i, 2) for i in box.tolist()]
draw.rectangle(box, outline="red", width=2)
label_name = model.config.id2label[label.item()]
draw.text((box[0] + 4, box[1]), f"{label_name} ({round(score.item(), 2)})", fill="red")
labels.append({"label": label_name, "score": round(score.item(), 2), "box": box})
return image, labels
iface = gr.Interface(
fn=detect_layout,
inputs=gr.Image(type="pil"),
outputs=[gr.Image(type="pil"), gr.JSON()],
title="Image to Figma Layers (with DETR)",
description="Upload a PNG or JPEG UI image to detect editable layers using a layout-aware DETR model."
)
iface.launch()
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