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import cv2
import requests
import tempfile
import os
from urllib.parse import urlparse, parse_qs
import yt_dlp
class VideoParser:
def __init__(self):
self.temp_dir = tempfile.mkdtemp()
def download_youtube_video(self, url: str) -> str:
"""Download YouTube video and return local path"""
ydl_opts = {
'format': 'best[height<=720]',
'outtmpl': os.path.join(self.temp_dir, '%(title)s.%(ext)s'),
'quiet': True,
'cookiesfrombrowser': ('chrome',),
'extractor_args': {'youtube': {'skip': ['dash', 'hls']}}
}
with yt_dlp.YoutubeDL(ydl_opts) as ydl:
info = ydl.extract_info(url, download=True)
return ydl.prepare_filename(info)
def analyze_video_frames(self, video_path: str, sample_rate: int = 30):
"""Analyze video frames for object detection/counting"""
cap = cv2.VideoCapture(video_path)
frame_count = 0
results = []
while cap.isOpened():
ret, frame = cap.read()
if not ret:
break
if frame_count % sample_rate == 0:
# Basic frame analysis - you'd integrate with object detection here
results.append({
'frame': frame_count,
'timestamp': frame_count / cap.get(cv2.CAP_PROP_FPS),
'frame_data': frame
})
frame_count += 1
cap.release()
return results
def extract_audio(self, video_path: str) -> str:
"""Extract audio from video for speech analysis"""
audio_path = video_path.rsplit('.', 1)[0] + '.wav'
# Use ffmpeg to extract audio
import subprocess
subprocess.run([
'ffmpeg', '-i', video_path, '-vn', '-acodec', 'pcm_s16le',
'-ar', '16000', '-ac', '1', audio_path, '-y'
], capture_output=True)
return audio_path
def cleanup(self):
"""Clean up temporary files"""
import shutil
shutil.rmtree(self.temp_dir, ignore_errors=True) |