{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b8dc159c-47ba-4309-97f1-bef01de80582",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 122635 videos. Starting scan with 64 threads...\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Scanning Videos:   2%|▏         | 1909/122635 [00:48<48:14, 41.72file/s]  "
     ]
    }
   ],
   "source": [
    "import os\n",
    "import cv2\n",
    "from pathlib import Path\n",
    "from concurrent.futures import ThreadPoolExecutor, as_completed\n",
    "from tqdm import tqdm\n",
    "\n",
    "# --- OPENCV LOG SUPPRESSION ---\n",
    "# OpenCV's C++ backend can be very noisy when it finds corrupted videos, \n",
    "# which can visually break the tqdm progress bar. This suppresses those warnings.\n",
    "os.environ[\"OPENCV_LOG_LEVEL\"] = \"FATAL\"\n",
    "os.environ[\"OPENCV_FFMPEG_LOGLEVEL\"] = \"-8\"\n",
    "\n",
    "# --- CONFIGURATION ---\n",
    "TARGET_FOLDER = r\"/workspace/musubi-tuner/dataset/ltxxx\" \n",
    "MAX_THREADS = 64\n",
    "# ---------------------\n",
    "\n",
    "def delete_corrupted(file_path, reason):\n",
    "    \"\"\"Helper function to handle the deletion and message formatting.\"\"\"\n",
    "    result_msg = f\"[CORRUPT] {reason}: {file_path.parent.name}/{file_path.name}\\n\"\n",
    "    result_msg += f\"    -> Deleting {file_path.name}...\"\n",
    "    try:\n",
    "        file_path.unlink()  # Deletes the video\n",
    "        return (\"CORRUPTED\", result_msg + \" SUCCESS.\")\n",
    "    except Exception as del_e:\n",
    "        return (\"CORRUPTED\", result_msg + f\" FAILED: {del_e}\")\n",
    "\n",
    "def process_video(file_path):\n",
    "    \"\"\"\n",
    "    Worker function executed by the threads. \n",
    "    Uses OpenCV to open the container and read the first frame.\n",
    "    \"\"\"\n",
    "    try:\n",
    "        # 1. Attempt to open the video container\n",
    "        cap = cv2.VideoCapture(str(file_path))\n",
    "        \n",
    "        if not cap.isOpened():\n",
    "            cap.release()\n",
    "            return delete_corrupted(file_path, \"OpenCV could not open the container\")\n",
    "        \n",
    "        # 2. Attempt to decode and read the very first frame\n",
    "        ret, frame = cap.read()\n",
    "        cap.release()\n",
    "        \n",
    "        if not ret:\n",
    "            # The container opened, but the video stream is empty or completely broken\n",
    "            return delete_corrupted(file_path, \"OpenCV could not read the first frame\")\n",
    "            \n",
    "        # If it passed both checks, it is a healthy, readable video\n",
    "        return (\"OK\", \"\")\n",
    "        \n",
    "    except Exception as e:\n",
    "        return (\"ERROR\", f\"[ERROR] Unexpected error processing {file_path.name}: {e}\")\n",
    "\n",
    "def check_and_clean_videos(base_path):\n",
    "    base_dir = Path(base_path)\n",
    "    \n",
    "    if not base_dir.is_dir():\n",
    "        print(f\"Error: The path '{base_dir}' is not a valid directory.\")\n",
    "        return\n",
    "\n",
    "    video_extensions = {'.mp4', '.mkv', '.avi', '.mov', '.wmv', '.flv', '.webm', '.m4v'}\n",
    "    files_to_check =[]\n",
    "\n",
    "    # 1. Gather all matching video files first\n",
    "    for folder in base_dir.iterdir():\n",
    "        if folder.is_dir() and folder.name.lower().startswith(\"videos\") and folder.name.lower() != \"videos\":\n",
    "            \n",
    "            # Using rglob(\"*\") to search recursively in case there are subfolders\n",
    "            for file_path in folder.rglob(\"*\"):\n",
    "                if file_path.is_file() and file_path.suffix.lower() in video_extensions:\n",
    "                    files_to_check.append(file_path)\n",
    "\n",
    "    if not files_to_check:\n",
    "        print(\"No videos found matching the criteria.\")\n",
    "        return\n",
    "\n",
    "    print(f\"Found {len(files_to_check)} videos. Starting scan with {MAX_THREADS} threads...\\n\")\n",
    "\n",
    "    # 2. Process the gathered files\n",
    "    with ThreadPoolExecutor(max_workers=MAX_THREADS) as executor:\n",
    "        futures = [executor.submit(process_video, path) for path in files_to_check]\n",
    "        \n",
    "        for future in tqdm(as_completed(futures), total=len(files_to_check), desc=\"Scanning Videos\", unit=\"file\"):\n",
    "            status, message = future.result()\n",
    "            \n",
    "            # If it's anything other than OK (like a corruption or unexpected error), print it.\n",
    "            if status != \"OK\":\n",
    "                tqdm.write(message)\n",
    "\n",
    "if __name__ == \"__main__\":\n",
    "    check_and_clean_videos(TARGET_FOLDER)\n",
    "    print(\"\\nScan complete.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4670a262-884b-45b0-96ed-f4db2dfc429f",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}