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from transformers import DetrImageProcessor, DetrForObjectDetection
import torch
from PIL import Image, ImageDraw
import gradio as gr
import requests
import random
def detect_objects(image):
# Load the pre-trained DETR model
processor = DetrImageProcessor.from_pretrained("facebook/detr-resnet-50")
model = DetrForObjectDetection.from_pretrained("facebook/detr-resnet-50")
inputs = processor(images=image, return_tensors="pt")
outputs = model(**inputs)
# convert outputs (bounding boxes and class logits) to COCO API
# let's only keep detections with score > 0.9
target_sizes = torch.tensor([image.size[::-1]])
results = processor.post_process_object_detection(outputs, target_sizes=target_sizes, threshold=0.9)[0]
# Draw bounding boxes and labels on the image
draw = ImageDraw.Draw(image)
for i, (score, label, box) in enumerate(zip(results["scores"], results["labels"], results["boxes"])):
box = [round(i, 2) for i in box.tolist()]
color = (random.randint(0, 255), random.randint(0, 255), random.randint(0, 255))
draw.rectangle(box, outline=color, width=3)
draw.text((box[0], box[1]), model.config.id2label[label.item()], fill=color)
return image
def upload_image(file):
image = Image.open(file.name)
image_with_boxes = detect_objects(image)
return image_with_boxes
iface = gr.Interface(
fn=upload_image,
inputs="file",
outputs="image",
title="Object Detection",
description="Upload an image and detect objects using DETR model.",
allow_flagging=False
)
iface.launch()
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