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

                      
                     
import cv2
import numpy as np
import os
from typing import Tuple

# Configuration
VIDEO_SOURCE = os.getenv('VIDEO_SOURCE', 'video.mp4')  # Path to video file
OUTPUT_PATH = os.getenv('OUTPUT_PATH', 'output.mp4')  # Output video file

# Function to detect quality defects
def detect_defects(frame: np.ndarray) -> Tuple[bool, str]:
    try:
        # Simulate defect detection logic
        grayscale = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
        # Thresholding to detect defects
        _, threshold = cv2.threshold(grayscale, 128, 255, cv2.THRESH_BINARY)
        defect_found = np.sum(threshold) > 10000  # Arbitrary threshold for defect detection
        return defect_found, 'Defect found' if defect_found else 'No defect'
    except Exception as e:
        return False, str(e)

# Main processing function
def process_video():
    cap = cv2.VideoCapture(VIDEO_SOURCE)
    fourcc = cv2.VideoWriter_fourcc(*'MP4V')
    out = cv2.VideoWriter(OUTPUT_PATH, fourcc, 20.0, (640, 480))

    while cap.isOpened():
        ret, frame = cap.read()
        if not ret:
            break
        defect, message = detect_defects(frame)
        print(message)  # Log the defect message
        # Write the frame into output video
        out.write(frame)

    # Cleanup
    cap.release()
    out.release()
    print('Video processing completed.')

if __name__ == '__main__':
    process_video()