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import torch
from PIL import Image, ImageDraw
from torchvision.transforms import Compose, ToTensor, Normalize
from transformers import DetrForObjectDetection, DetrImageProcessor
import gradio as gr
# Load the pre-trained DETR model and processor
model_name = "facebook/detr-resnet-50"
model = DetrForObjectDetection.from_pretrained(model_name)
processor = DetrImageProcessor.from_pretrained(model_name)
# Define fracture detection function
def detect_fractures(image):
"""Detect fractures in the given image using DETR."""
# Convert the input image to a format suitable for the model
inputs = processor(images=image, return_tensors="pt")
# Perform object detection
outputs = model(**inputs)
# Extract predictions
logits = outputs.logits
bboxes = outputs.pred_boxes
scores = logits.softmax(-1)[..., :-1].max(-1)
# Filter predictions
threshold = 0.5 # confidence threshold
keep = scores.values > threshold
filtered_boxes = bboxes[keep].detach().cpu()
filtered_scores = scores.values[keep].detach().cpu().tolist()
# Convert normalized bounding boxes to absolute coordinates
width, height = image.size
filtered_boxes = filtered_boxes * torch.tensor([width, height, width, height])
# Draw bounding boxes on the image
draw = ImageDraw.Draw(image)
for box, score in zip(filtered_boxes, filtered_scores):
x_min, y_min, x_max, y_max = box.tolist()
draw.rectangle(((x_min, y_min), (x_max, y_max)), outline="red", width=3)
draw.text((x_min, y_min), f"Fracture: {score:.2f}", fill="red")
return image
# Define Gradio interface
def infer(image):
"""Run fracture detection and return the result image."""
return detect_fractures(image)
iface = gr.Interface(
fn=infer,
inputs=gr.Image(type="pil"),
outputs=gr.Image(type="pil"),
title="Fracture Detection",
description="Upload an X-ray or medical image to detect fractures using DETR.",
# examples=["example1.jpg", "example2.jpg"],
)
iface.launch()
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