app.py

import gradio as gr
import torch
import cv2
import numpy as np
import tempfile
import json
from transformers import DetrImageProcessor, DetrForObjectDetection
from PIL import Image

# Load a pretrained object detection model
model_name = "facebook/detr-resnet-50"
processor = DetrImageProcessor.from_pretrained(model_name)
model = DetrForObjectDetection.from_pretrained(model_name)

# Video processing function
def process_video(video_path):
    cap = cv2.VideoCapture(video_path)
    frame_rate = cap.get(cv2.CAP_PROP_FPS)
    timestamped_objects = []

    frame_number = 0
    while cap.isOpened():
        ret, frame = cap.read()
        if not ret:
            break

        # Process every 10th frame for efficiency
        if frame_number % int(frame_rate) == 0:
            timestamp = frame_number / frame_rate
            pil_image = Image.fromarray(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))

            # Model prediction
            inputs = processor(images=pil_image, return_tensors="pt")
            with torch.no_grad():
                outputs = model(**inputs)

            # Extract results
            target_sizes = torch.tensor([pil_image.size])
            results = processor.post_process_object_detection(outputs, target_sizes=target_sizes, threshold=0.7)[0]

            detected_objects = []
            for score, label, box in zip(results["scores"], results["labels"], results["boxes"]):
                label_name = model.config.id2label[label.item()]
                detected_objects.append(label_name)

            if detected_objects:
                timestamped_objects.append({"timestamp": round(timestamp, 2), "objects": list(set(detected_objects))})

        frame_number += 1

    cap.release()
    return json.dumps(timestamped_objects, indent=2)

# Gradio Interface
iface = gr.Interface(
    fn=process_video,
    inputs=gr.Video(label="Upload Video"),
    outputs=gr.JSON(label="Timestamped Object Detections"),
    title="Video Object Detector",
    description="Upload a video and get a timestamped list of detected objects using a pretrained DETR model."
)

# Launch the Gradio app
if __name__ == "__main__":
    iface.launch()