app.py

import gradio as gr
import torch
from PIL import Image
from torchvision.transforms import functional as F
from transformers import DetrImageProcessor, DetrForObjectDetection
import cv2
import numpy as np

# Load the pretrained DETR model
processor = DetrImageProcessor.from_pretrained("facebook/detr-resnet-50", revision="no_timm")
model = DetrForObjectDetection.from_pretrained("facebook/detr-resnet-50", revision="no_timm")

# Define the object detection function
def detect_objects(frame):
    # Convert the frame to PIL image
    image = Image.fromarray(frame)

    # Preprocess the image
    inputs = processor(images=image, return_tensors="pt")

    # Perform object detection
    outputs = model(**inputs)

    # Convert outputs to COCO API format
    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 on the frame
    for score, label, box in zip(results["scores"], results["labels"], results["boxes"]):
        box = [round(i, 2) for i in box.tolist()]
        frame = cv2.rectangle(frame, (int(box[0]), int(box[1])), (int(box[2]), int(box[3])), (0, 255, 0), 2)
        frame = cv2.putText(frame, f'{model.config.id2label[label.item()]}: {round(score.item(), 3)}',
                            (int(box[0]), int(box[1]) - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2, cv2.LINE_AA)

    return frame

# Define the Gradio interface
iface = gr.Interface(
    fn=detect_objects,
    inputs=gr.Video(),
    outputs="numpy_image",
    live=True,
)

# Launch the Gradio app
iface.launch()