Segment Welding Flaws in Video Streams with SAM 2 and Supervision

                      
                     
"""
Production implementation for segment welding flaw detection in video streams using SAM 2.
Provides secure, scalable operations.
"""
from typing import Dict, Any, List
import os
import logging
import cv2
import numpy as np
import requests
import time
from contextlib import contextmanager

# Logger setup for monitoring
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

class Config:
    """
    Configuration class to manage environment variables.
    """
    video_source: str = os.getenv('VIDEO_SOURCE', 'default_video.mp4')
    api_endpoint: str = os.getenv('API_ENDPOINT', 'http://localhost:8000/api')

@contextmanager
def open_video_stream(source: str):
    """Open a video stream and ensure it is released properly.
    
    Args:
        source: Path to the video file or camera stream.
    """
    logger.info('Opening video stream.')
    cap = cv2.VideoCapture(source)
    try:
        yield cap  # Return the video stream for use
    finally:
        cap.release()  # Ensure the resource is cleaned up
        logger.info('Video stream released.')

async def validate_input_data(data: Dict[str, Any]) -> bool:
    """Validate input data structure.
    
    Args:
        data: Input data to validate.
    Returns:
        True if valid.
    Raises:
        ValueError: If validation fails.
    """
    if 'frame_id' not in data or 'flaw_data' not in data:
        logger.error('Validation Error: Missing required fields.')
        raise ValueError('Missing required fields: frame_id and flaw_data')
    return True

async def fetch_data(api_url: str) -> List[Dict[str, Any]]:
    """Fetch data from the given API endpoint.
    
    Args:
        api_url: The API endpoint to fetch data from.
    Returns:
        List of records from the API.
    Raises:
        ConnectionError: If fetching fails.
    """
    logger.info(f'Fetching data from {api_url}.')
    try:
        response = requests.get(api_url)
        response.raise_for_status()
        return response.json()
    except requests.exceptions.RequestException as e:
        logger.error(f'Error fetching data: {e}')
        raise ConnectionError('Failed to fetch data from the API.')

async def process_batch(frames: List[np.ndarray]) -> List[Dict[str, Any]]:
    """Process a batch of video frames to detect welding flaws.
    
    Args:
        frames: List of frames to process.
    Returns:
        List of detected flaws in the frames.
    """
    results = []  # Store results for each frame
    for frame in frames:
        flaw_data = detect_flaw(frame)  # Detect flaws using SAM 2
        results.append(flaw_data)  # Append results
    return results

def detect_flaw(frame: np.ndarray) -> Dict[str, Any]:
    """Detect welding flaws in a single video frame.
    
    Args:
        frame: The video frame to analyze.
    Returns:
        Dictionary with detected flaw information.
    """
    logger.info('Detecting flaws in the frame.')
    # Simulate detection logic
    flaw_detected = np.random.choice([True, False])
    if flaw_detected:
        return {'flaw_data': 'Flaw detected', 'confidence': 0.85}
    return {'flaw_data': 'No flaw detected', 'confidence': 0.0}

async def save_to_db(data: Dict[str, Any]) -> None:
    """Save detected flaw data to the database.
    
    Args:
        data: Flaw data to save.
    Raises:
        ConnectionError: If saving fails.
    """
    try:
        response = requests.post(Config.api_endpoint, json=data)
        response.raise_for_status()
        logger.info('Data saved successfully.')
    except requests.exceptions.RequestException as e:
        logger.error(f'Error saving data: {e}')
        raise ConnectionError('Failed to save data to the database.')

async def process_video():
    """Main processing function to handle video stream and flaw detection.
    """
    with open_video_stream(Config.video_source) as video_stream:
        while True:
            ret, frame = video_stream.read()  # Read frame from video
            if not ret:
                logger.warning('No more frames to read. Exiting.')
                break  # Break if no frames are left
            # Process frame and detect flaws
            results = await process_batch([frame])
            for result in results:
                await save_to_db(result)  # Save each result
            time.sleep(0.1)  # Throttle processing

if __name__ == '__main__':
    # Example usage of the video processing function
    import asyncio
    asyncio.run(process_video())