app.py

import gradio as gr
import cv2
import torch
from PIL import Image
from transformers import DetrImageProcessor, DetrForObjectDetection

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

# Function for live object detection from the camera
def live_object_detection(image_pil):
    # Convert the frame to PIL Image
    frame_pil = Image.fromarray(cv2.cvtColor(image_pil, cv2.COLOR_BGR2RGB))

    # Process the frame with the DETR model
    inputs = processor(images=frame_pil, 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([frame_pil.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 = [int(round(i)) for i in box.tolist()]
        cv2.rectangle(image_pil, (box[0], box[1]), (box[2], box[3]), (0, 255, 0), 2)
        cv2.putText(image_pil, f"{model.config.id2label[label.item()]}: {round(score.item(), 3)}",
                    (box[0], box[1] - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)

    return image_pil

# Define the Gradio interface
iface = gr.Interface(
    fn=live_object_detection,
    inputs="image",
    outputs="image",
    live=True,
)

# Launch the Gradio interface
iface.launch()