Python - Faster Media Scanner.txt

import concurrent.futures
import time

import cv2
import numpy as np
import os

def extract_frames(video_path, num_frames):
    # Open the video file
    video = cv2.VideoCapture(video_path)

    # Find the number of frames in the video file
    video_length = int(video.get(cv2.CAP_PROP_FRAME_COUNT)) - 1
    print(f'Number of frames in the video: {video_length}')

    # Calculate the interval between frames
    interval = video_length // num_frames

    # Initialize variables
    count = 0
    frames = []
    success = True

    # Iterate over the frames of the video
    while success:
        success, frame = video.read()
        if count % interval == 0:
            # Resize the frame and add it to the list
            frame = cv2.resize(frame, (640, 360))
            frames.append(frame)
        count += 1

    # Release the video file
    video.release()

    return frames

def extract_frames_parallel(video_path, num_frames, num_threads):
    with concurrent.futures.ThreadPoolExecutor(max_workers=num_threads) as executor:
        frames = list(executor.map(extract_frames, [video_path] * num_threads, [num_frames] * num_threads))

    return frames

if __name__ == '__main__':
    # Set the video path and the number of frames to extract
    video_path = 'path/to/video.mp4'
    num_frames = 10

    # Extract the frames in parallel using 4 threads
    start = time.perf_counter()
    frames = extract_frames_parallel(video_path, num_frames, num_threads=4)
    end = time.perf_counter()

    # Print the elapsed time
    print(f'Elapsed time: {end - start:.2f} seconds')

    # Save the frames to disk
    for i, frame in enumerate(frames):
        cv2.imwrite(f'frame_{i}.jpg', frame)