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import gradio as gr
from transformers import DetrImageProcessor, DetrForObjectDetection
from PIL import Image, ImageDraw
# Load pre-trained model and image processor
model_name = "facebook/detr-resnet-50"
model = DetrForObjectDetection.from_pretrained(model_name)
processor = DetrImageProcessor.from_pretrained(model_name)
# Define function for object detection
def detect_objects(image):
inputs = processor(images=image, return_tensors="pt")
outputs = model(**inputs)
# Get predictions
target_sizes = [image.size[::-1]] # (height, width)
results = processor.post_process_object_detection(outputs, target_sizes=target_sizes, threshold=0.9)[0]
# Draw bounding boxes on the image
draw = ImageDraw.Draw(image)
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=3)
label_name = model.config.id2label[label.item()]
draw.text((box[0], box[1]), f"{label_name} ({score:.2f})", fill="red")
return image
# Create Gradio interface
interface = gr.Interface(
fn=detect_objects,
inputs=gr.Image(type="pil"),
outputs=gr.Image(type="pil"),
title="Object Detection App",
description="Upload an image to detect objects using the DETR model."
)
# Launch the app
if __name__ == "__main__":
interface.launch()