{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "3feb9d79",
   "metadata": {},
   "source": [
    "# Обнаружение опухолей мозга без дообучения на локальном датасете\n",
    "\n",
    "В работе используется **готовая медицинская модель с Hugging Face** и выполняется **прямое применение без дообучения** к датасету `brain-tumor-object-detection-datasets`.\n",
    "\n",
    "## Выбранная модель\n",
    "Выбрана модель **`LexBwmn/ACE-V1`**:\n",
    "- это готовая модель **object detection** для МРТ-снимков мозга;\n",
    "- в карточке модели указаны веса `ACE-V1.1.pt`, рекомендуемый порог `conf=0.466` и самоназначенные метрики на независимом тесте;\n",
    "- модель ориентирована именно на **детекцию опухоли**, а не на общую детекцию объектов.\n",
    "\n",
    "## Что делает ноутбук\n",
    "1. Находит локальную папку датасета с подпапками `axial_t1wce_2_class`, `coronal_t1wce_2_class`, `sagittal_t1wce_2_class`.\n",
    "2. Скачивает готовые веса модели с Hugging Face.\n",
    "3. Запускает **инференс без дообучения** на тестовых изображениях.\n",
    "4. Считает метрики качества:\n",
    "   - `Precision`\n",
    "   - `Recall`\n",
    "   - `F1`\n",
    "   - `AP@0.50`\n",
    "   - `AP@0.75`\n",
    "   - средний `IoU` по совпавшим детекциям\n",
    "   - среднее время инференса на изображение\n",
    "5. Формирует краткий вывод о применимости модели."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "50dc35e7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[33mWARNING: Ignoring invalid distribution ~ulsectl (/home/konnilol/.pyenv/versions/3.12.12/lib/python3.12/site-packages)\u001b[0m\u001b[33m\n",
      "\u001b[0m\u001b[33mWARNING: Ignoring invalid distribution ~hirp (/home/konnilol/.pyenv/versions/3.12.12/lib/python3.12/site-packages)\u001b[0m\u001b[33m\n",
      "\u001b[0m\u001b[33mWARNING: Ignoring invalid distribution ~ulsectl (/home/konnilol/.pyenv/versions/3.12.12/lib/python3.12/site-packages)\u001b[0m\u001b[33m\n",
      "\u001b[0m\u001b[33mWARNING: Ignoring invalid distribution ~hirp (/home/konnilol/.pyenv/versions/3.12.12/lib/python3.12/site-packages)\u001b[0m\u001b[33m\n",
      "\u001b[0m\u001b[33mWARNING: Ignoring invalid distribution ~ulsectl (/home/konnilol/.pyenv/versions/3.12.12/lib/python3.12/site-packages)\u001b[0m\u001b[33m\n",
      "\u001b[0m\u001b[33mWARNING: Ignoring invalid distribution ~hirp (/home/konnilol/.pyenv/versions/3.12.12/lib/python3.12/site-packages)\u001b[0m\u001b[33m\n",
      "\u001b[0mNote: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "# Если пакеты уже установлены, эту ячейку можно пропустить\n",
    "%pip -q install -U ultralytics huggingface_hub pandas numpy pillow opencv-python matplotlib tqdm pyyaml"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "6a6ce79b",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pathlib import Path\n",
    "import os\n",
    "import time\n",
    "import math\n",
    "import json\n",
    "from collections import defaultdict\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from PIL import Image\n",
    "from tqdm.auto import tqdm\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from huggingface_hub import hf_hub_download\n",
    "from ultralytics import YOLO"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d90a21bb",
   "metadata": {},
   "source": [
    "## 1. Настройка путей\n",
    "\n",
    "Ноутбук **адаптирован под локальный запуск**.  \n",
    "Если автопоиск не найдёт датасет, вручную задайте путь в `MANUAL_DATA_ROOT`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "7d16cb79",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DATASET_ROOT = /home/konnilol/Downloads/brain-tumor-dataset\n",
      "axial_t1wce_2_class -> True\n",
      "coronal_t1wce_2_class -> True\n",
      "sagittal_t1wce_2_class -> True\n"
     ]
    }
   ],
   "source": [
    "# Укажите путь вручную, если нужно:\n",
    "# пример: MANUAL_DATA_ROOT = Path('./brain-tumor-object-detection-datasets')\n",
    "MANUAL_DATA_ROOT = None\n",
    "\n",
    "PLANE_DIRS = [\n",
    "    'axial_t1wce_2_class',\n",
    "    'coronal_t1wce_2_class',\n",
    "    'sagittal_t1wce_2_class',\n",
    "]\n",
    "\n",
    "IMAGE_EXTS = {'.jpg', '.jpeg', '.png', '.bmp', '.tif', '.tiff'}\n",
    "\n",
    "def find_dataset_root(manual_root=None):\n",
    "    if manual_root is not None:\n",
    "        root = Path(manual_root).expanduser().resolve()\n",
    "        if root.exists():\n",
    "            return root\n",
    "        raise FileNotFoundError(f'Указанный MANUAL_DATA_ROOT не существует: {root}')\n",
    "\n",
    "    candidates = [\n",
    "        Path('.').resolve(),\n",
    "        Path('./data').resolve(),\n",
    "        Path('./datasets').resolve(),\n",
    "        Path('./brain-tumor-object-detection-datasets').resolve(),\n",
    "        Path('./input').resolve(),\n",
    "    ]\n",
    "\n",
    "    # Сначала ищем папку, внутри которой есть plane-подпапки\n",
    "    for cand in candidates:\n",
    "        if cand.exists() and any((cand / p).exists() for p in PLANE_DIRS):\n",
    "            return cand\n",
    "\n",
    "    # Затем ищем рекурсивно\n",
    "    for base in candidates:\n",
    "        if not base.exists():\n",
    "            continue\n",
    "        for sub in base.rglob('*'):\n",
    "            if sub.is_dir() and any((sub / p).exists() for p in PLANE_DIRS):\n",
    "                return sub\n",
    "\n",
    "    raise FileNotFoundError(\n",
    "        'Не удалось автоматически найти корень датасета. '\n",
    "        'Укажите MANUAL_DATA_ROOT вручную.'\n",
    "    )\n",
    "\n",
    "DATASET_ROOT = find_dataset_root(MANUAL_DATA_ROOT)\n",
    "print('DATASET_ROOT =', DATASET_ROOT)\n",
    "for p in PLANE_DIRS:\n",
    "    print(p, '->', (DATASET_ROOT / p).exists())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "78bc0320",
   "metadata": {},
   "source": [
    "## 2. Сбор тестовых изображений и разметки"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "b2b460ab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.positron.dataexplorer+json": {
       "comm_id": "4672db2a-db64-40a6-96a5-776514779729",
       "shape": {
        "columns": 4,
        "rows": 5
       },
       "source": "pandas",
       "title": "pandas",
       "version": 1
      },
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>plane</th>\n",
       "      <th>image_path</th>\n",
       "      <th>label_path</th>\n",
       "      <th>yaml_path</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>axial_t1wce_2_class</td>\n",
       "      <td>/home/konnilol/Downloads/brain-tumor-dataset/a...</td>\n",
       "      <td>/home/konnilol/Downloads/brain-tumor-dataset/a...</td>\n",
       "      <td>/home/konnilol/Downloads/brain-tumor-dataset/a...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>axial_t1wce_2_class</td>\n",
       "      <td>/home/konnilol/Downloads/brain-tumor-dataset/a...</td>\n",
       "      <td>/home/konnilol/Downloads/brain-tumor-dataset/a...</td>\n",
       "      <td>/home/konnilol/Downloads/brain-tumor-dataset/a...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>axial_t1wce_2_class</td>\n",
       "      <td>/home/konnilol/Downloads/brain-tumor-dataset/a...</td>\n",
       "      <td>/home/konnilol/Downloads/brain-tumor-dataset/a...</td>\n",
       "      <td>/home/konnilol/Downloads/brain-tumor-dataset/a...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>axial_t1wce_2_class</td>\n",
       "      <td>/home/konnilol/Downloads/brain-tumor-dataset/a...</td>\n",
       "      <td>/home/konnilol/Downloads/brain-tumor-dataset/a...</td>\n",
       "      <td>/home/konnilol/Downloads/brain-tumor-dataset/a...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>axial_t1wce_2_class</td>\n",
       "      <td>/home/konnilol/Downloads/brain-tumor-dataset/a...</td>\n",
       "      <td>/home/konnilol/Downloads/brain-tumor-dataset/a...</td>\n",
       "      <td>/home/konnilol/Downloads/brain-tumor-dataset/a...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 plane                                         image_path  \\\n",
       "0  axial_t1wce_2_class  /home/konnilol/Downloads/brain-tumor-dataset/a...   \n",
       "1  axial_t1wce_2_class  /home/konnilol/Downloads/brain-tumor-dataset/a...   \n",
       "2  axial_t1wce_2_class  /home/konnilol/Downloads/brain-tumor-dataset/a...   \n",
       "3  axial_t1wce_2_class  /home/konnilol/Downloads/brain-tumor-dataset/a...   \n",
       "4  axial_t1wce_2_class  /home/konnilol/Downloads/brain-tumor-dataset/a...   \n",
       "\n",
       "                                          label_path  \\\n",
       "0  /home/konnilol/Downloads/brain-tumor-dataset/a...   \n",
       "1  /home/konnilol/Downloads/brain-tumor-dataset/a...   \n",
       "2  /home/konnilol/Downloads/brain-tumor-dataset/a...   \n",
       "3  /home/konnilol/Downloads/brain-tumor-dataset/a...   \n",
       "4  /home/konnilol/Downloads/brain-tumor-dataset/a...   \n",
       "\n",
       "                                           yaml_path  \n",
       "0  /home/konnilol/Downloads/brain-tumor-dataset/a...  \n",
       "1  /home/konnilol/Downloads/brain-tumor-dataset/a...  \n",
       "2  /home/konnilol/Downloads/brain-tumor-dataset/a...  \n",
       "3  /home/konnilol/Downloads/brain-tumor-dataset/a...  \n",
       "4  /home/konnilol/Downloads/brain-tumor-dataset/a...  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Всего тестовых изображений: 223\n",
      "plane\n",
      "coronal_t1wce_2_class     78\n",
      "axial_t1wce_2_class       75\n",
      "sagittal_t1wce_2_class    70\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "def collect_test_pairs(dataset_root: Path):\n",
    "    rows = []\n",
    "    for plane in PLANE_DIRS:\n",
    "        plane_root = dataset_root / plane\n",
    "        if not plane_root.exists():\n",
    "            continue\n",
    "\n",
    "        img_root = plane_root / 'images' / 'test'\n",
    "        lbl_root = plane_root / 'labels' / 'test'\n",
    "        yaml_path = plane_root / f'{plane}.yaml'\n",
    "\n",
    "        if not img_root.exists():\n",
    "            print(f'Предупреждение: не найдена папка {img_root}')\n",
    "            continue\n",
    "\n",
    "        for img_path in sorted(img_root.rglob('*')):\n",
    "            if img_path.suffix.lower() not in IMAGE_EXTS:\n",
    "                continue\n",
    "\n",
    "            rel = img_path.relative_to(img_root)\n",
    "            label_path = (lbl_root / rel).with_suffix('.txt')\n",
    "\n",
    "            rows.append({\n",
    "                'plane': plane,\n",
    "                'image_path': str(img_path),\n",
    "                'label_path': str(label_path),\n",
    "                'yaml_path': str(yaml_path) if yaml_path.exists() else None,\n",
    "            })\n",
    "\n",
    "    df = pd.DataFrame(rows)\n",
    "    if df.empty:\n",
    "        raise RuntimeError('Не найдено ни одного тестового изображения.')\n",
    "    return df\n",
    "\n",
    "test_df = collect_test_pairs(DATASET_ROOT)\n",
    "display(test_df.head())\n",
    "print('Всего тестовых изображений:', len(test_df))\n",
    "print(test_df['plane'].value_counts())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1fd62d82",
   "metadata": {},
   "source": [
    "## 3. Загрузка готовой модели с Hugging Face\n",
    "\n",
    "Используется готовый чекпоинт `ACE-V1.1.pt`.  \n",
    "Если основной файл не скачался, ноутбук попробует резервный вариант `ACE-V1.pt`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "00a3535b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Скачаны веса: /home/konnilol/.cache/huggingface/hub/models--LexBwmn--ACE-V1/snapshots/1fbfcfc953345fdff1c82498690efa190419d685/ACE-V1.1.pt\n",
      "Модель успешно загружена.\n",
      "Классы: {0: 'tumor'}\n"
     ]
    }
   ],
   "source": [
    "from huggingface_hub import hf_hub_download\n",
    "from ultralytics import YOLO\n",
    "\n",
    "HF_REPO_ID = \"LexBwmn/ACE-V1\"\n",
    "CONF_THRESHOLD = 0.466\n",
    "IMG_SIZE = 640\n",
    "\n",
    "# Пробуем скачать старую .pt-версию из исторических ревизий\n",
    "candidates = [\n",
    "    {\"revision\": \"1fbfcfc\", \"filename\": \"ACE-V1.1.pt\"},\n",
    "    {\"revision\": \"b033d32\", \"filename\": \"archive/ACE_Weights.pt\"},\n",
    "]\n",
    "\n",
    "model_path = None\n",
    "errors = []\n",
    "\n",
    "for item in candidates:\n",
    "    try:\n",
    "        model_path = hf_hub_download(\n",
    "            repo_id=HF_REPO_ID,\n",
    "            filename=item[\"filename\"],\n",
    "            revision=item[\"revision\"],\n",
    "            repo_type=\"model\"\n",
    "        )\n",
    "        print(f\"Скачаны веса: {model_path}\")\n",
    "        break\n",
    "    except Exception as e:\n",
    "        errors.append((item, repr(e)))\n",
    "\n",
    "if model_path is None:\n",
    "    raise RuntimeError(\n",
    "        \"Не удалось скачать совместимые .pt-веса из истории репозитория.\\n\"\n",
    "        f\"Проверенные варианты:\\n{errors}\"\n",
    "    )\n",
    "\n",
    "model = YOLO(model_path)\n",
    "print(\"Модель успешно загружена.\")\n",
    "print(\"Классы:\", getattr(model.model, \"names\", None))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c14b6bc",
   "metadata": {},
   "source": [
    "## 4. Вспомогательные функции для оценки качества"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "20701133",
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_image_size(image_path):\n",
    "    with Image.open(image_path) as img:\n",
    "        return img.size  # (w, h)\n",
    "\n",
    "def yolo_to_xyxy(xc, yc, w, h, img_w, img_h):\n",
    "    x1 = (xc - w / 2.0) * img_w\n",
    "    y1 = (yc - h / 2.0) * img_h\n",
    "    x2 = (xc + w / 2.0) * img_w\n",
    "    y2 = (yc + h / 2.0) * img_h\n",
    "    return [x1, y1, x2, y2]\n",
    "\n",
    "def read_gt_boxes(label_path, img_w, img_h):\n",
    "    label_path = Path(label_path)\n",
    "    if not label_path.exists():\n",
    "        return np.zeros((0, 4), dtype=np.float32)\n",
    "\n",
    "    boxes = []\n",
    "    with open(label_path, 'r', encoding='utf-8') as f:\n",
    "        for line in f:\n",
    "            line = line.strip()\n",
    "            if not line:\n",
    "                continue\n",
    "            parts = line.split()\n",
    "            if len(parts) < 5:\n",
    "                continue\n",
    "            # class_id игнорируем: задача оценивается как детекция опухоли\n",
    "            _, xc, yc, bw, bh = map(float, parts[:5])\n",
    "            boxes.append(yolo_to_xyxy(xc, yc, bw, bh, img_w, img_h))\n",
    "\n",
    "    if not boxes:\n",
    "        return np.zeros((0, 4), dtype=np.float32)\n",
    "    return np.array(boxes, dtype=np.float32)\n",
    "\n",
    "def compute_iou(box_a, box_b):\n",
    "    xA = max(box_a[0], box_b[0])\n",
    "    yA = max(box_a[1], box_b[1])\n",
    "    xB = min(box_a[2], box_b[2])\n",
    "    yB = min(box_a[3], box_b[3])\n",
    "\n",
    "    inter_w = max(0.0, xB - xA)\n",
    "    inter_h = max(0.0, yB - yA)\n",
    "    inter = inter_w * inter_h\n",
    "\n",
    "    area_a = max(0.0, box_a[2] - box_a[0]) * max(0.0, box_a[3] - box_a[1])\n",
    "    area_b = max(0.0, box_b[2] - box_b[0]) * max(0.0, box_b[3] - box_b[1])\n",
    "    union = area_a + area_b - inter + 1e-9\n",
    "    return inter / union\n",
    "\n",
    "def match_predictions(gt_boxes, pred_boxes, pred_scores, iou_thr=0.5):\n",
    "    if len(pred_boxes) == 0:\n",
    "        return [], 0, 0, len(gt_boxes), []\n",
    "\n",
    "    order = np.argsort(-pred_scores)\n",
    "    pred_boxes = pred_boxes[order]\n",
    "    pred_scores = pred_scores[order]\n",
    "\n",
    "    matched_gt = set()\n",
    "    pred_records = []\n",
    "    matched_ious = []\n",
    "\n",
    "    for box, score in zip(pred_boxes, pred_scores):\n",
    "        best_iou = 0.0\n",
    "        best_gt = -1\n",
    "        for j, gt in enumerate(gt_boxes):\n",
    "            if j in matched_gt:\n",
    "                continue\n",
    "            iou = compute_iou(box, gt)\n",
    "            if iou > best_iou:\n",
    "                best_iou = iou\n",
    "                best_gt = j\n",
    "\n",
    "        is_tp = int(best_gt >= 0 and best_iou >= iou_thr)\n",
    "        if is_tp:\n",
    "            matched_gt.add(best_gt)\n",
    "            matched_ious.append(best_iou)\n",
    "\n",
    "        pred_records.append({\n",
    "            'score': float(score),\n",
    "            'tp': is_tp,\n",
    "            'iou': float(best_iou),\n",
    "        })\n",
    "\n",
    "    tp = sum(r['tp'] for r in pred_records)\n",
    "    fp = len(pred_records) - tp\n",
    "    fn = len(gt_boxes) - tp\n",
    "    return pred_records, tp, fp, fn, matched_ious\n",
    "\n",
    "def compute_ap_from_records(pred_records, total_gt):\n",
    "    if total_gt == 0:\n",
    "        return np.nan\n",
    "\n",
    "    if len(pred_records) == 0:\n",
    "        return 0.0\n",
    "\n",
    "    pred_records = sorted(pred_records, key=lambda x: x['score'], reverse=True)\n",
    "    tps = np.array([r['tp'] for r in pred_records], dtype=np.float32)\n",
    "    fps = 1.0 - tps\n",
    "\n",
    "    tp_cum = np.cumsum(tps)\n",
    "    fp_cum = np.cumsum(fps)\n",
    "\n",
    "    recalls = tp_cum / (total_gt + 1e-9)\n",
    "    precisions = tp_cum / (tp_cum + fp_cum + 1e-9)\n",
    "\n",
    "    # interpolation envelope\n",
    "    mrec = np.concatenate(([0.0], recalls, [1.0]))\n",
    "    mpre = np.concatenate(([0.0], precisions, [0.0]))\n",
    "    for i in range(len(mpre) - 1, 0, -1):\n",
    "        mpre[i - 1] = max(mpre[i - 1], mpre[i])\n",
    "\n",
    "    idx = np.where(mrec[1:] != mrec[:-1])[0]\n",
    "    ap = np.sum((mrec[idx + 1] - mrec[idx]) * mpre[idx + 1])\n",
    "    return float(ap)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "763ac14c",
   "metadata": {},
   "source": [
    "## 5. Инференс без дообучения и расчёт метрик"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "01eb5c57",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Инференс: 100%|██████████| 223/223 [00:02<00:00, 110.10it/s]\n"
     ]
    },
    {
     "data": {
      "application/vnd.positron.dataexplorer+json": {
       "comm_id": "98e62a88-83da-475b-a5f6-41d1bae6f599",
       "shape": {
        "columns": 2,
        "rows": 7
       },
       "source": "pandas",
       "title": "pandas",
       "version": 1
      },
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Метрика</th>\n",
       "      <th>Значение</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Precision@0.50</td>\n",
       "      <td>0.867347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Recall@0.50</td>\n",
       "      <td>0.352697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>F1@0.50</td>\n",
       "      <td>0.501475</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AP@0.50</td>\n",
       "      <td>0.309928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AP@0.75</td>\n",
       "      <td>0.073004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Mean IoU (matched, 0.50)</td>\n",
       "      <td>0.731495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Среднее время инференса, мс/изобр.</td>\n",
       "      <td>8.644933</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              Метрика  Значение\n",
       "0                      Precision@0.50  0.867347\n",
       "1                         Recall@0.50  0.352697\n",
       "2                             F1@0.50  0.501475\n",
       "3                             AP@0.50  0.309928\n",
       "4                             AP@0.75  0.073004\n",
       "5            Mean IoU (matched, 0.50)  0.731495\n",
       "6  Среднее время инференса, мс/изобр.  8.644933"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Всего GT-боксов: 241\n",
      "TP@0.50 = 85 FP@0.50 = 13 FN@0.50 = 156\n"
     ]
    }
   ],
   "source": [
    "all_pred_records_50 = []\n",
    "all_pred_records_75 = []\n",
    "\n",
    "total_gt = 0\n",
    "total_tp_50 = 0\n",
    "total_fp_50 = 0\n",
    "total_fn_50 = 0\n",
    "\n",
    "total_tp_75 = 0\n",
    "total_fp_75 = 0\n",
    "total_fn_75 = 0\n",
    "\n",
    "matched_ious_50 = []\n",
    "matched_ious_75 = []\n",
    "\n",
    "image_level = []\n",
    "\n",
    "per_plane_stats = defaultdict(lambda: {\n",
    "    'n_images': 0,\n",
    "    'gt_boxes': 0,\n",
    "    'tp_50': 0,\n",
    "    'fp_50': 0,\n",
    "    'fn_50': 0,\n",
    "    'tp_75': 0,\n",
    "    'fp_75': 0,\n",
    "    'fn_75': 0,\n",
    "    'inference_times_ms': [],\n",
    "    'matched_ious_50': [],\n",
    "})\n",
    "\n",
    "for row in tqdm(test_df.itertuples(index=False), total=len(test_df), desc='Инференс'):\n",
    "    image_path = Path(row.image_path)\n",
    "    label_path = Path(row.label_path)\n",
    "    plane = row.plane\n",
    "\n",
    "    img_w, img_h = load_image_size(image_path)\n",
    "    gt_boxes = read_gt_boxes(label_path, img_w, img_h)\n",
    "\n",
    "    t0 = time.perf_counter()\n",
    "    result = model.predict(\n",
    "        source=str(image_path),\n",
    "        imgsz=IMG_SIZE,\n",
    "        conf=CONF_THRESHOLD,\n",
    "        verbose=False,\n",
    "        save=False\n",
    "    )[0]\n",
    "    elapsed_ms = (time.perf_counter() - t0) * 1000.0\n",
    "\n",
    "    if result.boxes is None or len(result.boxes) == 0:\n",
    "        pred_boxes = np.zeros((0, 4), dtype=np.float32)\n",
    "        pred_scores = np.zeros((0,), dtype=np.float32)\n",
    "    else:\n",
    "        pred_boxes = result.boxes.xyxy.cpu().numpy().astype(np.float32)\n",
    "        pred_scores = result.boxes.conf.cpu().numpy().astype(np.float32)\n",
    "\n",
    "    records_50, tp_50, fp_50, fn_50, ious_50 = match_predictions(\n",
    "        gt_boxes, pred_boxes, pred_scores, iou_thr=0.50\n",
    "    )\n",
    "    records_75, tp_75, fp_75, fn_75, ious_75 = match_predictions(\n",
    "        gt_boxes, pred_boxes, pred_scores, iou_thr=0.75\n",
    "    )\n",
    "\n",
    "    all_pred_records_50.extend(records_50)\n",
    "    all_pred_records_75.extend(records_75)\n",
    "\n",
    "    total_gt += len(gt_boxes)\n",
    "    total_tp_50 += tp_50\n",
    "    total_fp_50 += fp_50\n",
    "    total_fn_50 += fn_50\n",
    "    total_tp_75 += tp_75\n",
    "    total_fp_75 += fp_75\n",
    "    total_fn_75 += fn_75\n",
    "\n",
    "    matched_ious_50.extend(ious_50)\n",
    "    matched_ious_75.extend(ious_75)\n",
    "\n",
    "    # image-level presence metrics\n",
    "    gt_positive = int(len(gt_boxes) > 0)\n",
    "    pred_positive = int(len(pred_boxes) > 0)\n",
    "    image_level.append({\n",
    "        'plane': plane,\n",
    "        'image_path': str(image_path),\n",
    "        'gt_positive': gt_positive,\n",
    "        'pred_positive': pred_positive,\n",
    "        'n_gt_boxes': len(gt_boxes),\n",
    "        'n_pred_boxes': len(pred_boxes),\n",
    "        'inference_time_ms': elapsed_ms,\n",
    "    })\n",
    "\n",
    "    st = per_plane_stats[plane]\n",
    "    st['n_images'] += 1\n",
    "    st['gt_boxes'] += len(gt_boxes)\n",
    "    st['tp_50'] += tp_50\n",
    "    st['fp_50'] += fp_50\n",
    "    st['fn_50'] += fn_50\n",
    "    st['tp_75'] += tp_75\n",
    "    st['fp_75'] += fp_75\n",
    "    st['fn_75'] += fn_75\n",
    "    st['inference_times_ms'].append(elapsed_ms)\n",
    "    st['matched_ious_50'].extend(ious_50)\n",
    "\n",
    "ap50 = compute_ap_from_records(all_pred_records_50, total_gt)\n",
    "ap75 = compute_ap_from_records(all_pred_records_75, total_gt)\n",
    "\n",
    "precision_50 = total_tp_50 / (total_tp_50 + total_fp_50 + 1e-9)\n",
    "recall_50 = total_tp_50 / (total_tp_50 + total_fn_50 + 1e-9)\n",
    "f1_50 = 2 * precision_50 * recall_50 / (precision_50 + recall_50 + 1e-9)\n",
    "mean_iou_50 = float(np.mean(matched_ious_50)) if matched_ious_50 else np.nan\n",
    "avg_inference_ms = float(np.mean([x['inference_time_ms'] for x in image_level])) if image_level else np.nan\n",
    "\n",
    "summary_df = pd.DataFrame({\n",
    "    'Метрика': [\n",
    "        'Precision@0.50',\n",
    "        'Recall@0.50',\n",
    "        'F1@0.50',\n",
    "        'AP@0.50',\n",
    "        'AP@0.75',\n",
    "        'Mean IoU (matched, 0.50)',\n",
    "        'Среднее время инференса, мс/изобр.'\n",
    "    ],\n",
    "    'Значение': [\n",
    "        precision_50,\n",
    "        recall_50,\n",
    "        f1_50,\n",
    "        ap50,\n",
    "        ap75,\n",
    "        mean_iou_50,\n",
    "        avg_inference_ms,\n",
    "    ]\n",
    "})\n",
    "\n",
    "display(summary_df)\n",
    "print('Всего GT-боксов:', total_gt)\n",
    "print('TP@0.50 =', total_tp_50, 'FP@0.50 =', total_fp_50, 'FN@0.50 =', total_fn_50)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "53fe838b",
   "metadata": {},
   "source": [
    "## 6. Метрики по плоскостям MRI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "c8474eb2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.positron.dataexplorer+json": {
       "comm_id": "9fa7ad0d-2372-4cd3-b60a-0718c50a1dba",
       "shape": {
        "columns": 8,
        "rows": 3
       },
       "source": "pandas",
       "title": "pandas",
       "version": 1
      },
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>plane</th>\n",
       "      <th>n_images</th>\n",
       "      <th>gt_boxes</th>\n",
       "      <th>precision@0.50</th>\n",
       "      <th>recall@0.50</th>\n",
       "      <th>f1@0.50</th>\n",
       "      <th>mean_iou@0.50</th>\n",
       "      <th>avg_inference_ms</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>axial_t1wce_2_class</td>\n",
       "      <td>75</td>\n",
       "      <td>81</td>\n",
       "      <td>0.861111</td>\n",
       "      <td>0.382716</td>\n",
       "      <td>0.529915</td>\n",
       "      <td>0.683783</td>\n",
       "      <td>12.185616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>coronal_t1wce_2_class</td>\n",
       "      <td>78</td>\n",
       "      <td>83</td>\n",
       "      <td>0.964286</td>\n",
       "      <td>0.325301</td>\n",
       "      <td>0.486486</td>\n",
       "      <td>0.765150</td>\n",
       "      <td>6.808148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>sagittal_t1wce_2_class</td>\n",
       "      <td>70</td>\n",
       "      <td>77</td>\n",
       "      <td>0.794118</td>\n",
       "      <td>0.350649</td>\n",
       "      <td>0.486486</td>\n",
       "      <td>0.752622</td>\n",
       "      <td>6.898048</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    plane  n_images  gt_boxes  precision@0.50  recall@0.50  \\\n",
       "0     axial_t1wce_2_class        75        81        0.861111     0.382716   \n",
       "1   coronal_t1wce_2_class        78        83        0.964286     0.325301   \n",
       "2  sagittal_t1wce_2_class        70        77        0.794118     0.350649   \n",
       "\n",
       "    f1@0.50  mean_iou@0.50  avg_inference_ms  \n",
       "0  0.529915       0.683783         12.185616  \n",
       "1  0.486486       0.765150          6.808148  \n",
       "2  0.486486       0.752622          6.898048  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rows = []\n",
    "for plane, st in per_plane_stats.items():\n",
    "    p = st['tp_50'] / (st['tp_50'] + st['fp_50'] + 1e-9)\n",
    "    r = st['tp_50'] / (st['tp_50'] + st['fn_50'] + 1e-9)\n",
    "    f1 = 2 * p * r / (p + r + 1e-9)\n",
    "    miou = float(np.mean(st['matched_ious_50'])) if st['matched_ious_50'] else np.nan\n",
    "    t_ms = float(np.mean(st['inference_times_ms'])) if st['inference_times_ms'] else np.nan\n",
    "\n",
    "    rows.append({\n",
    "        'plane': plane,\n",
    "        'n_images': st['n_images'],\n",
    "        'gt_boxes': st['gt_boxes'],\n",
    "        'precision@0.50': p,\n",
    "        'recall@0.50': r,\n",
    "        'f1@0.50': f1,\n",
    "        'mean_iou@0.50': miou,\n",
    "        'avg_inference_ms': t_ms,\n",
    "    })\n",
    "\n",
    "plane_metrics_df = pd.DataFrame(rows).sort_values('plane').reset_index(drop=True)\n",
    "display(plane_metrics_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3382b58d",
   "metadata": {},
   "source": [
    "## 7. Небольшая визуальная проверка результатов"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "73d4443d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x2400 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sample_n = min(6, len(test_df))\n",
    "sample_df = test_df.sample(sample_n, random_state=42).reset_index(drop=True)\n",
    "\n",
    "fig, axes = plt.subplots(sample_n, 2, figsize=(12, 4 * sample_n))\n",
    "if sample_n == 1:\n",
    "    axes = np.array([axes])\n",
    "\n",
    "for i, row in sample_df.iterrows():\n",
    "    image_path = row['image_path']\n",
    "    img = Image.open(image_path).convert('RGB')\n",
    "    result = model.predict(\n",
    "        source=image_path,\n",
    "        imgsz=IMG_SIZE,\n",
    "        conf=CONF_THRESHOLD,\n",
    "        verbose=False,\n",
    "        save=False\n",
    "    )[0]\n",
    "\n",
    "    axes[i, 0].imshow(img)\n",
    "    axes[i, 0].set_title(f'Исходное изображение\\n{row[\"plane\"]}')\n",
    "    axes[i, 0].axis('off')\n",
    "\n",
    "    rendered = result.plot()\n",
    "    rendered = rendered[..., ::-1]  # BGR -> RGB\n",
    "    axes[i, 1].imshow(rendered)\n",
    "    axes[i, 1].set_title('Предсказания модели')\n",
    "    axes[i, 1].axis('off')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "92ba1a41",
   "metadata": {},
   "source": [
    "## 8. Сохранение метрик"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "44147f4e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Сохранены файлы:\n",
      "- hf_brain_tumor_zero_shot_metrics.csv\n",
      "- hf_brain_tumor_zero_shot_metrics_by_plane.csv\n"
     ]
    }
   ],
   "source": [
    "summary_df.to_csv('hf_brain_tumor_zero_shot_metrics.csv', index=False, encoding='utf-8-sig')\n",
    "plane_metrics_df.to_csv('hf_brain_tumor_zero_shot_metrics_by_plane.csv', index=False, encoding='utf-8-sig')\n",
    "\n",
    "print('Сохранены файлы:')\n",
    "print('- hf_brain_tumor_zero_shot_metrics.csv')\n",
    "print('- hf_brain_tumor_zero_shot_metrics_by_plane.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5165c47b",
   "metadata": {},
   "source": [
    "вывод:\n",
    "без дообучения выбранная Hugging Face-модель показала ограниченную применимость: она даёт высокую точность срабатываний, но низкую полноту, то есть пропускает значительную долю опухолей. Поэтому её можно рассматривать только как вспомогательный инструмент предварительного обнаружения, но не как надёжную автономную систему медицинской диагностики."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "name": "python"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}