203 lines
6.9 KiB
Python
203 lines
6.9 KiB
Python
import os
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import sys
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import glob
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import time
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import argparse
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from pathlib import Path
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import cv2 # opencv-python
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import ollama
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# Configuration
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# 'moondream' is extremely lightweight (1.6B params).
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# If you want more accuracy at the cost of speed, change this to 'llava' (7B params).
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MODEL_NAME = 'moondream'
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# How many seconds to skip between frames.
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# Higher = Faster processing, but might miss short actions.
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FRAME_INTERVAL_SECONDS = 5
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VIDEO_EXTENSIONS = {'.mp4', '.mov', '.avi', '.mkv', '.webm', '.flv'}
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def analyze_frame(frame_bytes):
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"""Sends a single image frame to the local model for description."""
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try:
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response = ollama.chat(model=MODEL_NAME, messages=[
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{
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'role': 'user',
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'content': 'Describe the sexual activity in this image in detail. Be specific about whether there is oral sex or penetration.',
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'images': [frame_bytes]
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}
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])
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return response['message']['content']
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except Exception as e:
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print(f"Error communicating with Ollama: {e}")
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return ""
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def process_video(video_path):
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"""Extracts frames and aggregates analysis."""
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cap = cv2.VideoCapture(str(video_path))
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if not cap.isOpened():
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print(f"Error opening video: {video_path}")
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return None, False
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fps = cap.get(cv2.CAP_PROP_FPS)
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frame_interval = int(fps * FRAME_INTERVAL_SECONDS)
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frame_count = 0
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descriptions = []
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print(f"Scanning {video_path.name} (taking 1 frame every {FRAME_INTERVAL_SECONDS}s)...")
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while cap.isOpened():
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ret, frame = cap.read()
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if not ret:
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break
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if frame_count % frame_interval == 0:
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# Convert frame to bytes for Ollama
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_, buffer = cv2.imencode('.jpg', frame)
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frame_bytes = buffer.tobytes()
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# Analyze frame
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desc = analyze_frame(frame_bytes)
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if desc:
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descriptions.append(desc)
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# print(f" [Frame {frame_count}] {desc[:50]}...") # Uncomment for debug noise
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frame_count += 1
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cap.release()
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if not descriptions:
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return "No frames analyzed.", False
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# Aggregate Logic
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# We join all descriptions and check keywords.
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# This is a naive heuristic because the model doesn't have "memory" of the whole video context,
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# just individual snapshots.
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full_text = " ".join(descriptions).lower()
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has_oral = 'oral' in full_text or 'blowjob' in full_text or 'sucking' in full_text or 'fellatio' in full_text
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has_penetration = 'penetration' in full_text or 'sex' in full_text or 'intercourse' in full_text or 'vaginal' in full_text or 'anal' in full_text or 'fucking' in full_text
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# Refined logic: simple keyword matching can be prone to false positives/negatives with small models.
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# However, for an automated script, this is the baseline.
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summary = f"Analyzed {len(descriptions)} frames.\n\nCombined Observations:\n{full_text}"
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# Criteria: Only Blowjobs (Oral), NO Sex (Penetration)
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# Note: 'sex' is a broad term. Small models might use it generically.
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# You might need to tune 'has_penetration' keywords based on model behavior.
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is_match = has_oral and not has_penetration
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return summary, is_match
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def update_html_report(report_path, video_name, summary, flag):
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file_exists = os.path.exists(report_path)
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with open(report_path, 'a', encoding='utf-8') as f:
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if not file_exists:
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f.write("""
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<!DOCTYPE html>
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<html>
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<head>
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<style>
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table { border-collapse: collapse; width: 100%; }
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th, td { border: 1px solid #ddd; padding: 8px; text-align: left; vertical-align: top;}
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tr:nth-child(even) { background-color: #f2f2f2; }
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.flagged { color: red; font-weight: bold; }
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.summary-text { max-height: 200px; overflow-y: auto; display: block; }
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</style>
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</head>
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<body>
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<h2>Local Video Analysis Report</h2>
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<table>
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<tr>
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<th>File Name</th>
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<th>Summary (Frame Aggregation)</th>
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<th>Criteria Met (Only BJ, No Sex)</th>
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</tr>
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""")
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row_class = ' class="flagged"' if flag else ""
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# Truncate summary for HTML display to avoid massive cells
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display_summary = summary[:1000] + "..." if len(summary) > 1000 else summary
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f.write(f" <tr>\n <td>{video_name}</td>\n <td><div class='summary-text'>{display_summary}</div></td>\n <td{row_class}>{flag}</td>\n </tr>\n")
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def close_html_report(report_path):
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if os.path.exists(report_path):
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with open(report_path, 'a', encoding='utf-8') as f:
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f.write("</table>\n</body>\n</html>")
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def main():
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parser = argparse.ArgumentParser(description="Analyze videos locally using Ollama.")
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parser.add_argument("directory", help="Target directory containing videos")
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args = parser.parse_args()
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target_dir = Path(args.directory)
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if not target_dir.is_dir():
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print(f"Directory not found: {target_dir}")
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sys.exit(1)
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files_to_delete = []
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video_files = [
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f for f in target_dir.iterdir()
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if f.is_file() and f.suffix.lower() in VIDEO_EXTENSIONS
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]
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print(f"Found {len(video_files)} videos in {target_dir}")
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print(f"Using local model: {MODEL_NAME}")
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report_path = target_dir / "local_analysis_report.html"
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if report_path.exists():
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os.remove(report_path)
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for video_path in video_files:
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print(f"\nProcessing: {video_path.name}")
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summary, criteria_met = process_video(video_path)
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if summary:
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# Save text summary
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txt_path = video_path.with_suffix('.txt')
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with open(txt_path, 'w', encoding='utf-8') as f:
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f.write(summary)
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print(f"Saved summary to {txt_path.name}")
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# Update HTML
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update_html_report(report_path, video_path.name, summary, criteria_met)
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if criteria_met:
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print("--> MATCHES CRITERIA: Only blowjobs, no sex.")
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files_to_delete.append(video_path)
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else:
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print("--> Does not match deletion criteria.")
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else:
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print("Skipped (no content analyzing)")
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close_html_report(report_path)
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print(f"\nAnalysis complete. Report saved to {report_path}")
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if files_to_delete:
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print("\n" + "="*40)
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print(f"Found {len(files_to_delete)} files matching 'Only Blowjobs, No Sex':")
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for f in files_to_delete:
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print(f"- {f.name}")
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print("="*40)
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confirm = input("\nDo you want to DELETE these files? (yes/no): ").lower()
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if confirm == 'yes':
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for f in files_to_delete:
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try:
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os.remove(f)
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print(f"Deleted: {f.name}")
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except OSError as e:
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print(f"Error deleting {f.name}: {e}")
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else:
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print("Deletion cancelled.")
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else:
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print("\nNo files matched the deletion criteria.")
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if __name__ == "__main__":
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main()
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