33a165a615
- app.py: Streamlit UI for video generation workflow - main_flow.py: CLI tool with argparse support - modules/: Business logic modules (script_gen, image_gen, video_gen, composer, etc.) - config.py: Configuration with API keys and paths - requirements.txt: Python dependencies - docs/: System prompt documentation
61 lines
1.8 KiB
Python
61 lines
1.8 KiB
Python
"""
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MatchMe Studio - Ingest Module (Video Processing)
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"""
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import cv2
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import os
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import logging
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from pathlib import Path
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from typing import List, Tuple
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import config
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from modules import storage
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logger = logging.getLogger(__name__)
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def process_uploaded_video(video_path: str) -> Tuple[List[str], str]:
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"""
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Process uploaded video:
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1. Upload raw video to R2.
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2. Extract 3 keyframes (10%, 50%, 90%).
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3. Return local frame paths and R2 video URL.
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"""
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if not Path(video_path).exists():
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raise FileNotFoundError(f"Video not found: {video_path}")
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logger.info(f"Processing video: {video_path}")
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# 1. Upload to R2
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video_url = storage.upload_file(video_path)
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if not video_url:
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raise RuntimeError("Failed to upload video to R2")
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# 2. Extract Frames
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cap = cv2.VideoCapture(video_path)
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if not cap.isOpened():
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raise IOError(f"Cannot open video: {video_path}")
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total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
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frame_indices = [
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int(total_frames * 0.1),
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int(total_frames * 0.5),
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int(total_frames * 0.9)
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]
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frame_urls = []
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for i, idx in enumerate(frame_indices):
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cap.set(cv2.CAP_PROP_POS_FRAMES, idx)
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ret, frame = cap.read()
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if ret:
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frame_name = f"frame_{Path(video_path).stem}_{i}.jpg"
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frame_path = config.TEMP_DIR / frame_name
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cv2.imwrite(str(frame_path), frame)
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# Upload frame to R2 immediately
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frame_url = storage.upload_file(str(frame_path))
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if frame_url:
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frame_urls.append(frame_url)
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cap.release()
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logger.info(f"Extracted and uploaded {len(frame_urls)} frames")
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return frame_urls, video_url
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