Detect Quality Defects in Video Streams with Grounded SAM 2 and Supervision

                      
                     
"""
Production implementation for detecting quality defects in video streams.
This service utilizes Grounded SAM 2 and supervision for real-time analysis.
"""

from typing import Dict, Any, List, Tuple
import os
import logging
import time
import cv2
import numpy as np
from fastapi import FastAPI, HTTPException, UploadFile, File
from pydantic import BaseModel, ValidationError
from sqlalchemy import create_engine, Column, Integer, String
from sqlalchemy.ext.declarative import declarative_base
from sqlalchemy.orm import sessionmaker, scoped_session

logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

# Configuration class for environment variables
class Config:
    database_url: str = os.getenv('DATABASE_URL', 'sqlite:///./test.db')

# Database setup
Base = declarative_base()
engine = create_engine(Config.database_url)
SessionLocal = scoped_session(sessionmaker(autocommit=False, autoflush=False, bind=engine))

class VideoQuality(Base):
    __tablename__ = 'video_quality'
    id = Column(Integer, primary_key=True, index=True)
    status = Column(String, index=True)
    timestamp = Column(String)

Base.metadata.create_all(bind=engine)

# Input model
class VideoInput(BaseModel):
    video_file: UploadFile = File(...)

async def validate_input(video_file: UploadFile) -> None:
    """Validate the video file input.
    
    Args:
        video_file: Input video file
    Raises:
        ValueError: If validation fails
    """
    if video_file.content_type not in ['video/mp4', 'video/x-msvideo']:
        raise ValueError('Unsupported video format.')

async def process_video(video_file: UploadFile) -> List[Tuple[str, Any]]:
    """Process the video for quality defects.
    
    Args:
        video_file: Video file to process
    Returns:
        List of detected defects and their timestamps
    """
    defects = []  # List to hold defects
    # Simulate video processing
    logger.info('Starting video processing...')
    cap = cv2.VideoCapture(video_file.file)
    frame_count = 0

    while cap.isOpened():
        ret, frame = cap.read()
        if not ret:
            break
        frame_count += 1
        # Simulate defect detection logic
        if frame_count % 30 == 0:  # Every 30 frames
            defects.append(('Defect detected', frame_count))
    cap.release()
    logger.info('Finished processing video.')
    return defects

async def save_to_db(defects: List[Tuple[str, Any]]) -> None:
    """Save detected defects to the database.
    
    Args:
        defects: List of defects to save
    """
    db = SessionLocal()
    try:
        for defect in defects:
            db.add(VideoQuality(status=defect[0], timestamp=str(defect[1])))
        db.commit()
        logger.info('Defects saved to database.')
    except Exception as e:
        db.rollback()
        logger.error(f'Error saving to DB: {e}')
        raise
    finally:
        db.close()

async def format_output(defects: List[Tuple[str, Any]]) -> Dict[str, Any]:
    """Format the output for the API response.
    
    Args:
        defects: List of defects
    Returns:
        Formatted response
    """
    return {'defects': [{'status': d[0], 'timestamp': d[1]} for d in defects]}

app = FastAPI()

@app.post('/upload_video/', response_model=Dict[str, Any])
async def upload_video(video: VideoInput) -> Dict[str, Any]:
    """API endpoint to upload a video and detect quality defects.
    
    Args:
        video: Video input model
    Returns:
        Detection results
    """
    # Validate input
    await validate_input(video.video_file)
    # Process video
    defects = await process_video(video.video_file)
    # Save results to the database
    await save_to_db(defects)
    # Format output
    return await format_output(defects)

if __name__ == '__main__':
    import uvicorn
    uvicorn.run(app, host='0.0.0.0', port=8000)