import cv2
import numpy as np
from sklearn.metrics.pairwise import cosine_similarity

def extract_features(video_path):
    """Extract features from the video using OpenCV"""
    cap = cv2.VideoCapture(video_path)
    features = []
    while True:
        ret, frame = cap.read()
        if not ret:
            break
        gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
        features.append(cv2.calcHist([gray], [0], None, [256], [0, 256]))
    cap.release()
    return np.array(features).flatten()

def compare_videos(features1, features2):
    """Compare the features of two videos and return a similarity score"""
    return cosine_similarity(features1.reshape(1, -1), features2.reshape(1, -1))[0][0]

def search_duplicates(path):
    """Search for duplicate videos in the given path"""
    duplicates = []
    processed_videos = []
    for root, dirs, files in os.walk(path):
        for filename in files:
            file_path = os.path.join(root, filename)
            features = extract_features(file_path)
            for processed_video in processed_videos:
                score = compare_videos(features, processed_video[1])
                if score > 0.95:
                    # Found a duplicate video
                    duplicates.append((file_path, processed_video[0]))
            processed_videos.append((file_path, features))
    return duplicates

# Example usage
duplicates = search_duplicates("/path/to/local/storage")
for duplicate in duplicates:
    print("Duplicate found: {} and {}".format(*duplicate))