3190 lines (3189 with data), 396.5 kB

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## U-Net Convolutional Networks Para Segmentação de Imagens de Tomografia Computadorizada (Coração, Pulmão e Traqueia)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Instalando e Carregando Pacotes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Python 3.7.6\r\n"
     ]
    }
   ],
   "source": [
    "# Versão Python usada neste projeto\n",
    "!python --version"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Pacote com modelos de segmentação do PyTorch\n",
    "!pip install -q segmentation_models_pytorch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# OpenCV para Visão Computacional\n",
    "!pip install -q opencv-python==4.2.0.34"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Pacote para dataset augmentation\n",
    "!pip install -q albumentations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Imports\n",
    "import os\n",
    "import io\n",
    "import sys\n",
    "import cv2\n",
    "import time\n",
    "import torch\n",
    "import base64\n",
    "import sklearn\n",
    "import albumentations\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import torch.nn as nn\n",
    "from torch.optim import Adam\n",
    "from torch.optim.lr_scheduler import ReduceLROnPlateau\n",
    "from torch.utils.data import Dataset, DataLoader\n",
    "from albumentations.pytorch import ToTensor, ToTensorV2 \n",
    "from albumentations import HorizontalFlip, VerticalFlip,  Normalize, Compose\n",
    "from segmentation_models_pytorch import Unet\n",
    "from sklearn.model_selection import train_test_split\n",
    "from IPython.display import clear_output\n",
    "import warnings\n",
    "warnings.simplefilter(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Author: Júlio Monteiro\n",
      "\n",
      "numpy         : 1.18.5\n",
      "pandas        : 1.0.3\n",
      "matplotlib    : 3.3.4\n",
      "sklearn       : 1.0.2\n",
      "sys           : 3.7.6 (default, Jan  8 2020, 19:59:22) \n",
      "[GCC 7.3.0]\n",
      "seaborn       : 0.11.2\n",
      "albumentations: 0.5.2\n",
      "torch         : 1.7.0\n",
      "cv2           : 4.2.0\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Versões dos pacotes usados neste jupyter notebook\n",
    "%reload_ext watermark\n",
    "%watermark -a \"Júlio Monteiro\" --iversions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Carregando e Compreendendo os Dados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Classe com variáveis globais de configuração\n",
    "class GlobalConfig:\n",
    "    def __init__(self):\n",
    "        self.seed = 555\n",
    "        self.path_to_csv = '/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/train.csv'\n",
    "        self.path_to_imgs_dir = '/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images'\n",
    "        self.path_to_masks_dir = '/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks'\n",
    "        self.pretrained_model_path = 'modelo/pretreinado/melhor_modelo.pth'\n",
    "        self.train_logs_path = 'modelo/pretreinado/train_log.csv'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Seed\n",
    "def seed_everything(seed: int):\n",
    "    torch.manual_seed(seed)\n",
    "    torch.cuda.manual_seed(seed)\n",
    "    np.random.seed(seed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Inicializa as variáveis globais\n",
    "config = GlobalConfig()\n",
    "seed_everything(config.seed)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Processo de Treinamento"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Classe para preparar o dataset de imagens\n",
    "class PreparaDados(Dataset):\n",
    "    \n",
    "    # Construtor\n",
    "    def __init__(self, \n",
    "                 imgs_dir: str,\n",
    "                 masks_dir:str,\n",
    "                 df: pd.DataFrame,\n",
    "                 phase: str):\n",
    "\n",
    "        self.root_imgs_dir = imgs_dir\n",
    "        self.root_masks_dir = masks_dir\n",
    "        self.df = df\n",
    "        self.augmentations = data_augmentations(phase)\n",
    "        \n",
    "    # Obtém o tamanho\n",
    "    def __len__(self):\n",
    "        return len(self.df)\n",
    "\n",
    "    # Retorna imagem e máscara\n",
    "    def __getitem__(self, idx):\n",
    "        img_name = self.df.loc[idx, \"ImageId\"]\n",
    "        mask_name = self.df.loc[idx, \"MaskId\"]\n",
    "        img_path = os.path.join(self.root_imgs_dir, img_name)\n",
    "        mask_path = os.path.join(self.root_masks_dir, mask_name)\n",
    "        img = cv2.imread(img_path)\n",
    "        mask = cv2.imread(mask_path)\n",
    "        mask[mask < 240] = 0    \n",
    "        mask[mask > 0] = 1\n",
    "        augmented = self.augmentations(image = img, mask = mask.astype(np.float32))\n",
    "        img = augmented['image']\n",
    "        mask = augmented['mask'].permute(2, 0, 1)\n",
    "\n",
    "        return img, mask"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Função para dataset augmentation\n",
    "def data_augmentations(phase, mean: tuple = (0.485, 0.456, 0.406), std: tuple = (0.229, 0.224, 0.225),):\n",
    "    \n",
    "    # Lista\n",
    "    list_transforms = []\n",
    "    \n",
    "    # Se fase de treino, aplicamos transformações\n",
    "    if phase == \"train\":\n",
    "        list_transforms.extend([VerticalFlip(p = 0.5), ])\n",
    "    \n",
    "    # Normalização\n",
    "    list_transforms.extend([Normalize(mean = mean, std = std, p = 1), ToTensorV2(),])\n",
    "    \n",
    "    # Lista final\n",
    "    list_trfms = Compose(list_transforms)\n",
    "    \n",
    "    return list_trfms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Função para dataloader\n",
    "def prepara_dataloader(imgs_dir: str,\n",
    "                       masks_dir: str,\n",
    "                       path_to_csv: str,\n",
    "                       phase: str,\n",
    "                       batch_size: int = 8,\n",
    "                       num_workers: int = 6,\n",
    "                       test_size: float = 0.2,):\n",
    "\n",
    "    # Carrega o arquivo csv\n",
    "    df = pd.read_csv(path_to_csv)\n",
    "    \n",
    "    # Dados de treino e validação\n",
    "    train_df, val_df = train_test_split(df, test_size = test_size, random_state = 69)\n",
    "    \n",
    "    # Reset do índice\n",
    "    train_df, val_df = train_df.reset_index(drop = True), val_df.reset_index(drop = True)\n",
    "\n",
    "    # Define a fase\n",
    "    df = train_df if phase == \"train\" else val_df\n",
    "    \n",
    "    # Prepara o dataset\n",
    "    image_dataset = PreparaDados(imgs_dir, masks_dir, df, phase)\n",
    "    \n",
    "    # Prepara o dataloader\n",
    "    dataloader = DataLoader(image_dataset,\n",
    "                            batch_size = batch_size,\n",
    "                            num_workers = num_workers,\n",
    "                            pin_memory = True,\n",
    "                            shuffle = True,)\n",
    "\n",
    "    return dataloader"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Funções Para os Cálculos das Métricas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Coeficiente Dice\n",
    "def calcula_metrica_dice(probabilities: torch.Tensor,\n",
    "                         truth: torch.Tensor,\n",
    "                         treshold: float = 0.5,\n",
    "                         eps: float = 1e-9) -> np.ndarray:\n",
    "\n",
    "    # Scores\n",
    "    scores = []\n",
    "    \n",
    "    # Shape\n",
    "    num = probabilities.shape[0]\n",
    "    \n",
    "    # Previsões\n",
    "    predictions = (probabilities >= treshold).float()\n",
    "    assert(predictions.shape == truth.shape)\n",
    "    \n",
    "    # Loop\n",
    "    for i in range(num):\n",
    "        prediction = predictions[i]\n",
    "        truth_ = truth[i]\n",
    "        intersection = 2.0 * (truth_ * prediction).sum()\n",
    "        union = truth_.sum() + prediction.sum()\n",
    "        \n",
    "        if truth_.sum() == 0 and prediction.sum() == 0:\n",
    "            scores.append(1.0)\n",
    "        else:\n",
    "            scores.append((intersection + eps) / union)\n",
    "            \n",
    "    return np.mean(scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Coeficiente Jaccard\n",
    "def calcula_metrica_jaccard(probabilities: torch.Tensor,\n",
    "                            truth: torch.Tensor,\n",
    "                            treshold: float = 0.5,\n",
    "                            eps: float = 1e-9) -> np.ndarray:\n",
    "\n",
    "    # Scores\n",
    "    scores = []\n",
    "    \n",
    "    # Shape\n",
    "    num = probabilities.shape[0]\n",
    "    \n",
    "    # Previsões\n",
    "    predictions = (probabilities >= treshold).float()\n",
    "    assert(predictions.shape == truth.shape)\n",
    "\n",
    "    # Loop\n",
    "    for i in range(num):\n",
    "        prediction = predictions[i]\n",
    "        truth_ = truth[i]\n",
    "        intersection = (prediction * truth_).sum()\n",
    "        union = (prediction.sum() + truth_.sum()) - intersection + eps\n",
    "        \n",
    "        if truth_.sum() == 0 and prediction.sum() == 0:\n",
    "            scores.append(1.0)\n",
    "        else:\n",
    "            scores.append((intersection + eps) / union)\n",
    "            \n",
    "    return np.mean(scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Classe para calcular as métricas\n",
    "class Meter:\n",
    "\n",
    "    def __init__(self, treshold: float = 0.5):\n",
    "        self.threshold: float = treshold\n",
    "        self.dice_scores: list = []\n",
    "        self.iou_scores: list = []\n",
    "    \n",
    "    def update(self, logits: torch.Tensor, targets: torch.Tensor):\n",
    "        probs = torch.sigmoid(logits)\n",
    "        dice = calcula_metrica_dice(probs, targets, self.threshold)\n",
    "        iou = calcula_metrica_jaccard(probs, targets, self.threshold)\n",
    "        self.dice_scores.append(dice)\n",
    "        self.iou_scores.append(iou)\n",
    "    \n",
    "    def get_metrics(self) -> np.ndarray:\n",
    "        dice = np.mean(self.dice_scores)\n",
    "        iou = np.mean(self.iou_scores)\n",
    "        return dice, iou"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Função de Perda"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dice Loss\n",
    "class DiceLoss(nn.Module):\n",
    "    \n",
    "    def __init__(self, eps: float = 1e-9):\n",
    "        super(DiceLoss, self).__init__()\n",
    "        self.eps = eps\n",
    "        \n",
    "    def forward(self, logits: torch.Tensor, targets: torch.Tensor) -> torch.Tensor:\n",
    "        \n",
    "        num = targets.size(0)\n",
    "        probability = torch.sigmoid(logits)\n",
    "        probability = probability.view(num, -1)\n",
    "        targets = targets.view(num, -1)\n",
    "        assert(probability.shape == targets.shape)\n",
    "        \n",
    "        intersection = 2.0 * (probability * targets).sum()\n",
    "        union = probability.sum() + targets.sum()\n",
    "        dice_score = (intersection + self.eps) / union\n",
    "\n",
    "        return 1.0 - dice_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# BCE Dice Loss\n",
    "class BCEDiceLoss(nn.Module):\n",
    "\n",
    "    def __init__(self):\n",
    "        super(BCEDiceLoss, self).__init__()\n",
    "        self.bce = nn.BCEWithLogitsLoss()\n",
    "        self.dice = DiceLoss()\n",
    "        \n",
    "    def forward(self, logits: torch.Tensor, targets: torch.Tensor) -> torch.Tensor:\n",
    "        \n",
    "        assert(logits.shape == targets.shape)\n",
    "        dice_loss = self.dice(logits, targets)\n",
    "        bce_loss = self.bce(logits, targets)\n",
    "        \n",
    "        return bce_loss + dice_loss"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Métricas Por Classe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Cálculo do coeficiente dice por classe\n",
    "def calcula_metrica_dice_por_classe(probabilities: np.ndarray,\n",
    "                                    truth: np.ndarray,\n",
    "                                    treshold: float = 0.5,\n",
    "                                    eps: float = 1e-9,\n",
    "                                    classes: list = ['lung', 'heart', 'trachea']) -> np.ndarray:\n",
    "\n",
    "    scores = {key: list() for key in classes}\n",
    "    num = probabilities.shape[0]\n",
    "    num_classes = probabilities.shape[1]\n",
    "    predictions = (probabilities >= treshold).astype(np.float32)\n",
    "    assert(predictions.shape == truth.shape)\n",
    "\n",
    "    # Loop\n",
    "    for i in range(num):\n",
    "        for class_ in range(num_classes):\n",
    "            prediction = predictions[i][class_]\n",
    "            truth_ = truth[i][class_]\n",
    "            intersection = 2.0 * (truth_ * prediction).sum()\n",
    "            union = truth_.sum() + prediction.sum()\n",
    "            if truth_.sum() == 0 and prediction.sum() == 0:\n",
    "                 scores[classes[class_]].append(1.0)\n",
    "            else:\n",
    "                scores[classes[class_]].append((intersection + eps) / union)\n",
    "                \n",
    "    return scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Cálculo do coeficiente jaccard por classe\n",
    "def calcula_metrica_jaccard_por_classe(probabilities: np.ndarray,\n",
    "                                       truth: np.ndarray,\n",
    "                                       treshold: float = 0.5,\n",
    "                                       eps: float = 1e-9,\n",
    "                                       classes: list = ['lung', 'heart', 'trachea']) -> np.ndarray:\n",
    "\n",
    "    scores = {key: list() for key in classes}\n",
    "    num = probabilities.shape[0]\n",
    "    num_classes = probabilities.shape[1]\n",
    "    predictions = (probabilities >= treshold).astype(np.float32)\n",
    "    assert(predictions.shape == truth.shape)\n",
    "\n",
    "    # Loop\n",
    "    for i in range(num):\n",
    "        for class_ in range(num_classes):\n",
    "            prediction = predictions[i][class_]\n",
    "            truth_ = truth[i][class_]\n",
    "            intersection = (prediction * truth_).sum()\n",
    "            union = (prediction.sum() + truth_.sum()) - intersection + eps\n",
    "            if truth_.sum() == 0 and prediction.sum() == 0:\n",
    "                 scores[classes[class_]].append(1.0)\n",
    "            else:\n",
    "                scores[classes[class_]].append((intersection + eps) / union)\n",
    "\n",
    "    return scores"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Treinamento do Modelo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Classe para treinar o modelo\n",
    "class TreinaModelo:\n",
    "   \n",
    "    # Construtor\n",
    "    def __init__(self,\n",
    "                 net: nn.Module,\n",
    "                 criterion: nn.Module,\n",
    "                 lr: float,\n",
    "                 accumulation_steps: int,\n",
    "                 batch_size: int,\n",
    "                 num_epochs: int,\n",
    "                 imgs_dir: str,\n",
    "                 masks_dir: str,\n",
    "                 path_to_csv: str,\n",
    "                 display_plot: bool = True):\n",
    "\n",
    "        self.device = 'cuda' if torch.cuda.is_available() else 'cpu'\n",
    "        print(\"device:\", self.device)\n",
    "        self.display_plot = display_plot\n",
    "        self.net = net\n",
    "        self.net = self.net.to(self.device)\n",
    "        self.criterion = criterion\n",
    "        self.optimizer = Adam(self.net.parameters(), lr = lr)\n",
    "        self.scheduler = ReduceLROnPlateau(self.optimizer, mode = \"min\", patience = 3, verbose = True)\n",
    "        self.accumulation_steps = accumulation_steps // batch_size\n",
    "        self.phases = [\"train\", \"val\"]\n",
    "        self.num_epochs = num_epochs\n",
    "\n",
    "        self.dataloaders = {phase: prepara_dataloader(imgs_dir = imgs_dir,\n",
    "                                                      masks_dir = masks_dir,\n",
    "                                                      path_to_csv = path_to_csv,\n",
    "                                                      phase = phase,\n",
    "                                                      batch_size = 8,\n",
    "                                                      num_workers = 6) for phase in self.phases}\n",
    "        \n",
    "        self.best_loss = float(\"inf\")\n",
    "        self.losses = {phase: [] for phase in self.phases}\n",
    "        self.dice_scores = {phase: [] for phase in self.phases}\n",
    "        self.jaccard_scores = {phase: [] for phase in self.phases}\n",
    "         \n",
    "    def _compute_loss_and_outputs(self, images: torch.Tensor, targets: torch.Tensor):\n",
    "        images = images.to(self.device)\n",
    "        targets = targets.to(self.device)\n",
    "        logits = self.net(images)\n",
    "        loss = self.criterion(logits, targets)\n",
    "        return loss, logits\n",
    "        \n",
    "    def _do_epoch(self, epoch: int, phase: str):\n",
    "        print(f\"Fase: {phase} | Epoch: {epoch}\")\n",
    "\n",
    "        self.net.train() if phase == \"train\" else self.net.eval()\n",
    "        meter = Meter()\n",
    "        dataloader = self.dataloaders[phase]\n",
    "        total_batches = len(dataloader)\n",
    "        running_loss = 0.0\n",
    "        self.optimizer.zero_grad()\n",
    "        \n",
    "        # Loop\n",
    "        for itr, (images, targets) in enumerate(dataloader):\n",
    "            loss, logits = self._compute_loss_and_outputs(images, targets)\n",
    "            loss = loss / self.accumulation_steps\n",
    "            if phase == \"train\":\n",
    "                loss.backward()\n",
    "                if (itr + 1) % self.accumulation_steps == 0:\n",
    "                    self.optimizer.step()\n",
    "                    self.optimizer.zero_grad()\n",
    "            running_loss += loss.item()\n",
    "            meter.update(logits.detach().cpu(), targets.detach().cpu())\n",
    "            \n",
    "        epoch_loss = (running_loss * self.accumulation_steps) / total_batches\n",
    "        epoch_dice, epoch_iou = meter.get_metrics()\n",
    "        \n",
    "        self.losses[phase].append(epoch_loss)\n",
    "        self.dice_scores[phase].append(epoch_dice)\n",
    "        self.jaccard_scores[phase].append(epoch_iou)\n",
    "\n",
    "        return epoch_loss\n",
    "        \n",
    "    def treinamento(self):\n",
    "        for epoch in range(self.num_epochs):\n",
    "            self._do_epoch(epoch, \"train\")\n",
    "            with torch.no_grad():\n",
    "                val_loss = self._do_epoch(epoch, \"val\")\n",
    "                self.scheduler.step(val_loss)\n",
    "            if self.display_plot:\n",
    "                self._plot_train_history()\n",
    "                \n",
    "            if val_loss < self.best_loss:\n",
    "                print(f\"\\n{'#'*20}\\nNovo Checkpoint Salvo.\\n{'#'*20}\\n\")\n",
    "                self.best_loss = val_loss\n",
    "                torch.save(self.net.state_dict(), \"modelo/melhor_modelo.pth\")\n",
    "            print()\n",
    "        self._save_train_history()\n",
    "            \n",
    "    def _plot_train_history(self):\n",
    "        data = [self.losses, self.dice_scores, self.jaccard_scores]\n",
    "        colors = ['blue', \"green\"]\n",
    "        labels = [\n",
    "            f\"\"\"\n",
    "            Erro em Treino {self.losses['train'][-1]}\n",
    "            Erro em Validação {self.losses['val'][-1]}\n",
    "            \"\"\",\n",
    "            \n",
    "            f\"\"\"\n",
    "            Dice Score em Treino {self.dice_scores['train'][-1]}\n",
    "            Dice Score em Validação {self.dice_scores['val'][-1]} \n",
    "            \"\"\", \n",
    "                  \n",
    "            f\"\"\"\n",
    "            Jaccard Score em Treino {self.jaccard_scores['train'][-1]}\n",
    "            Jaccard Score em Validação {self.jaccard_scores['val'][-1]}\n",
    "            \"\"\",\n",
    "        ]\n",
    "        \n",
    "        clear_output(True)\n",
    "        \n",
    "        with plt.style.context(\"seaborn-dark-palette\"):\n",
    "            fig, axes = plt.subplots(3, 1, figsize = (8, 10))\n",
    "            for i, ax in enumerate(axes):\n",
    "                ax.plot(data[i]['val'], c=colors[0], label = \"val\")\n",
    "                ax.plot(data[i]['train'], c=colors[-1], label = \"train\")\n",
    "                ax.set_title(labels[i])\n",
    "                ax.legend(loc = \"upper right\")\n",
    "                \n",
    "            plt.tight_layout()\n",
    "            plt.show()\n",
    "            \n",
    "    def load_predtrain_model(self, state_path: str):\n",
    "        self.net.load_state_dict(torch.load(state_path))\n",
    "        print(\"O Modelo Pré-Treinado Foi Carregado!\")\n",
    "        \n",
    "    def _save_train_history(self):\n",
    "        torch.save(self.net.state_dict(), f\"modelo/last_epoch_model.pth\")\n",
    "\n",
    "        logs_ = [self.losses, self.dice_scores, self.jaccard_scores]\n",
    "        log_names_ = [\"_loss\", \"_dice\", \"_jaccard\"]\n",
    "        logs = [logs_[i][key] for i in list(range(len(logs_))) for key in logs_[i]]\n",
    "        log_names = [key+log_names_[i] for i in list(range(len(logs_))) for key in logs_[i]]\n",
    "        \n",
    "        pd.DataFrame(dict(zip(log_names, logs))).to_csv(\"logs/train_log.csv\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Modelo U-Net\n",
    "modelo = Unet('efficientnet-b2', encoder_weights = \"imagenet\", classes = 3, activation = None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "device: cuda\n"
     ]
    }
   ],
   "source": [
    "# Criar o objeto de treinamento\n",
    "treina_modelo = TreinaModelo(net = modelo,\n",
    "                  criterion = BCEDiceLoss(),\n",
    "                  lr = 8e-5,\n",
    "                  accumulation_steps = 32,\n",
    "                  batch_size = 8,\n",
    "                  num_epochs = 1,\n",
    "                  imgs_dir = config.path_to_imgs_dir,\n",
    "                  masks_dir = config.path_to_masks_dir,\n",
    "                  path_to_csv = config.path_to_csv,)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "O Modelo Pré-Treinado Foi Carregado!\n"
     ]
    }
   ],
   "source": [
    "# Se já existir o modelo pré-treinado\n",
    "if config.pretrained_model_path is not None:\n",
    "    \n",
    "    treina_modelo.load_predtrain_model(config.pretrained_model_path)\n",
    "    train_logs = pd.read_csv(config.train_logs_path)\n",
    "    treina_modelo.losses[\"train\"] =  train_logs.loc[:, \"train_loss\"].to_list()\n",
    "    treina_modelo.losses[\"val\"] =  train_logs.loc[:, \"val_loss\"].to_list()\n",
    "    treina_modelo.dice_scores[\"train\"] = train_logs.loc[:, \"train_dice\"].to_list()\n",
    "    treina_modelo.dice_scores[\"val\"] = train_logs.loc[:, \"val_dice\"].to_list()\n",
    "    treina_modelo.jaccard_scores[\"train\"] = train_logs.loc[:, \"train_jaccard\"].to_list()\n",
    "    treina_modelo.jaccard_scores[\"val\"] = train_logs.loc[:, \"val_jaccard\"].to_list()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x720 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "####################\n",
      "Novo Checkpoint Salvo.\n",
      "####################\n",
      "\n",
      "\n",
      "CPU times: user 10min 8s, sys: 2min 3s, total: 12min 11s\n",
      "Wall time: 8min 46s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "treina_modelo.treinamento()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Avaliação do Modelo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calcula os scores por classe\n",
    "def compute_scores_per_classes(model, dataloader, classes):\n",
    "\n",
    "    device = 'cuda' if torch.cuda.is_available() else 'cpu'\n",
    "    dice_scores_per_classes = {key: list() for key in classes}\n",
    "    iou_scores_per_classes = {key: list() for key in classes}\n",
    "\n",
    "    with torch.no_grad():\n",
    "        for i, (imgs, targets) in enumerate(dataloader):\n",
    "            imgs, targets = imgs.to(device), targets.to(device)\n",
    "            logits = model(imgs)\n",
    "            logits = logits.detach().cpu().numpy()\n",
    "            targets = targets.detach().cpu().numpy()\n",
    "            \n",
    "            dice_scores = calcula_metrica_dice_por_classe(logits, targets)\n",
    "            iou_scores = calcula_metrica_jaccard_por_classe(logits, targets)\n",
    "\n",
    "            for key in dice_scores.keys():\n",
    "                dice_scores_per_classes[key].extend(dice_scores[key])\n",
    "\n",
    "            for key in iou_scores.keys():\n",
    "                iou_scores_per_classes[key].extend(iou_scores[key])\n",
    "\n",
    "    return dice_scores_per_classes, iou_scores_per_classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dataloader de validação\n",
    "val_dataloader = prepara_dataloader(imgs_dir = config.path_to_imgs_dir,\n",
    "                                    masks_dir = config.path_to_masks_dir,\n",
    "                                    path_to_csv = config.path_to_csv,\n",
    "                                    phase = \"val\",\n",
    "                                    batch_size = 8,\n",
    "                                    num_workers = 6,\n",
    "                                    test_size = 0.2,)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 46.9 s, sys: 8.28 s, total: 55.2 s\n",
      "Wall time: 49.6 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "dice_scores_per_classes, iou_scores_per_classes = compute_scores_per_classes(modelo, \n",
    "                                                                             val_dataloader, \n",
    "                                                                             ['lung', 'heart', 'trachea'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dataframe Dice\n",
    "dice_df = pd.DataFrame(dice_scores_per_classes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Colunas do dataframe\n",
    "dice_df.columns = ['lung dice', 'heart dice', 'trachea dice']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# IoU\n",
    "iou_df = pd.DataFrame(iou_scores_per_classes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Colunas\n",
    "iou_df.columns = ['lung jaccard', 'heart jaccard', 'trachea jaccard']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Métricas de validação\n",
    "val_metics_df = pd.concat([dice_df, iou_df], axis=1, sort=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Ajusta o dataframe\n",
    "val_metics_df = val_metics_df.loc[:, ['lung dice', 'lung jaccard', \n",
    "                                      'heart dice', 'heart jaccard', \n",
    "                                      'trachea dice', 'trachea jaccard']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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>lung dice</th>\n",
       "      <th>lung jaccard</th>\n",
       "      <th>heart dice</th>\n",
       "      <th>heart jaccard</th>\n",
       "      <th>trachea dice</th>\n",
       "      <th>trachea jaccard</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.996953</td>\n",
       "      <td>0.993925</td>\n",
       "      <td>0.993187</td>\n",
       "      <td>0.986466</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.307692</td>\n",
       "      <td>0.181818</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.914460</td>\n",
       "      <td>0.842402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.966951</td>\n",
       "      <td>0.936017</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.982185</td>\n",
       "      <td>0.964994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.943973</td>\n",
       "      <td>0.893891</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.934411</td>\n",
       "      <td>0.876896</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3337</th>\n",
       "      <td>0.988596</td>\n",
       "      <td>0.977450</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3338</th>\n",
       "      <td>0.997600</td>\n",
       "      <td>0.995211</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3339</th>\n",
       "      <td>0.994275</td>\n",
       "      <td>0.988614</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.968280</td>\n",
       "      <td>0.938511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3340</th>\n",
       "      <td>0.990581</td>\n",
       "      <td>0.981338</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.907992</td>\n",
       "      <td>0.831488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3341</th>\n",
       "      <td>0.980892</td>\n",
       "      <td>0.962500</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3342 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      lung dice  lung jaccard  heart dice  heart jaccard  trachea dice  \\\n",
       "0      0.996953      0.993925    0.993187       0.986466      1.000000   \n",
       "1      0.307692      0.181818    1.000000       1.000000      0.914460   \n",
       "2      0.966951      0.936017    1.000000       1.000000      0.982185   \n",
       "3      1.000000      1.000000    1.000000       1.000000      0.943973   \n",
       "4      0.934411      0.876896    1.000000       1.000000      1.000000   \n",
       "...         ...           ...         ...            ...           ...   \n",
       "3337   0.988596      0.977450    1.000000       1.000000      1.000000   \n",
       "3338   0.997600      0.995211    1.000000       1.000000      1.000000   \n",
       "3339   0.994275      0.988614    1.000000       1.000000      0.968280   \n",
       "3340   0.990581      0.981338    1.000000       1.000000      0.907992   \n",
       "3341   0.980892      0.962500    1.000000       1.000000      1.000000   \n",
       "\n",
       "      trachea jaccard  \n",
       "0            1.000000  \n",
       "1            0.842402  \n",
       "2            0.964994  \n",
       "3            0.893891  \n",
       "4            1.000000  \n",
       "...               ...  \n",
       "3337         1.000000  \n",
       "3338         1.000000  \n",
       "3339         0.938511  \n",
       "3340         0.831488  \n",
       "3341         1.000000  \n",
       "\n",
       "[3342 rows x 6 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Visualiza\n",
    "val_metics_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot\n",
    "colors = ['#35FCFF', '#FF355A', '#96C503', '#C5035B', '#28B463', '#35FFAF']\n",
    "palette = sns.color_palette(colors, 6)\n",
    "fig, ax = plt.subplots(figsize=(12, 6));\n",
    "sns.barplot(x=val_metics_df.mean().index, y=val_metics_df.mean(), palette=palette, ax=ax);\n",
    "ax.set_xticklabels(val_metics_df.columns, fontsize=14, rotation=15);\n",
    "ax.set_title(\"Coeficientes Dice e Jaccard em Validação\", fontsize=20)\n",
    "\n",
    "for idx, p in enumerate(ax.patches):\n",
    "        percentage = '{:.1f}%'.format(100 * val_metics_df.mean().values[idx])\n",
    "        x = p.get_x() + p.get_width() / 2 - 0.15\n",
    "        y = p.get_y() + p.get_height()\n",
    "        ax.annotate(percentage, (x, y), fontsize=15, fontweight=\"bold\")\n",
    "\n",
    "fig.savefig(\"imagens/resultado1.png\", format=\"png\",  pad_inches=0.2, transparent=False, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot\n",
    "colors = ['#35FCFF', '#FF355A', '#28B463', '#35FFAF', '#96C503', '#C5035B']\n",
    "palette = sns.color_palette(colors[1::], 3)\n",
    "\n",
    "fig, ax = plt.subplots(1, 2, figsize = (20, 5))\n",
    "sns.boxplot(data = dice_df, palette = palette, ax = ax[0])\n",
    "ax[0].set_ylabel(\"Métrica Dice para 3342 slices\", fontsize = 14)\n",
    "ax[0].set_title(\"Métrica Dice em Validação\", fontsize = 20)\n",
    "ax[0].set_xticklabels(dice_df.columns, fontsize = 14)\n",
    "\n",
    "sns.boxplot(data = iou_df, palette = palette, ax = ax[1])\n",
    "ax[1].set_ylabel(\"Métrica Jaccard para 3342 slices\", fontsize = 14)\n",
    "ax[1].set_title(\"Métrica Jaccard em Validação\", fontsize = 20)\n",
    "ax[1].set_xticklabels(iou_df.columns, fontsize=14)\n",
    "plt.tight_layout()\n",
    "\n",
    "fig.savefig(\"imagens/resultado2.png\", format = \"png\",  pad_inches = 0.2, transparent = False, bbox_inches = 'tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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>train_loss</th>\n",
       "      <th>val_loss</th>\n",
       "      <th>train_dice</th>\n",
       "      <th>val_dice</th>\n",
       "      <th>train_jaccard</th>\n",
       "      <th>val_jaccard</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.386394</td>\n",
       "      <td>0.176678</td>\n",
       "      <td>0.697667</td>\n",
       "      <td>0.755589</td>\n",
       "      <td>0.646303</td>\n",
       "      <td>0.717975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.144522</td>\n",
       "      <td>0.123104</td>\n",
       "      <td>0.769935</td>\n",
       "      <td>0.786307</td>\n",
       "      <td>0.731782</td>\n",
       "      <td>0.752826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.112448</td>\n",
       "      <td>0.093943</td>\n",
       "      <td>0.811846</td>\n",
       "      <td>0.829476</td>\n",
       "      <td>0.775296</td>\n",
       "      <td>0.795960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.087972</td>\n",
       "      <td>0.073344</td>\n",
       "      <td>0.838840</td>\n",
       "      <td>0.851835</td>\n",
       "      <td>0.804823</td>\n",
       "      <td>0.819167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.073297</td>\n",
       "      <td>0.064866</td>\n",
       "      <td>0.854065</td>\n",
       "      <td>0.861600</td>\n",
       "      <td>0.821115</td>\n",
       "      <td>0.830928</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   train_loss  val_loss  train_dice  val_dice  train_jaccard  val_jaccard\n",
       "0    0.386394  0.176678    0.697667  0.755589       0.646303     0.717975\n",
       "1    0.144522  0.123104    0.769935  0.786307       0.731782     0.752826\n",
       "2    0.112448  0.093943    0.811846  0.829476       0.775296     0.795960\n",
       "3    0.087972  0.073344    0.838840  0.851835       0.804823     0.819167\n",
       "4    0.073297  0.064866    0.854065  0.861600       0.821115     0.830928"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Logs de treino\n",
    "train_logs = pd.read_csv(config.train_logs_path)\n",
    "train_logs.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1512x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot\n",
    "colors = ['#C042FF', '#03C576FF', '#FF355A', '#03C5BF', '#96C503', '#C5035B']\n",
    "palettes = [sns.color_palette(colors, 2),\n",
    "            sns.color_palette(colors, 4), \n",
    "            sns.color_palette(colors[:2] + colors[-2:] + colors[2:-2], 6)]\n",
    "            \n",
    "fig, ax = plt.subplots(1, 3, figsize = (21, 4))\n",
    "\n",
    "sns.lineplot(data=train_logs.iloc[:, :2], palette=palettes[0], markers=True, ax=ax[0], linewidth=2.5,)\n",
    "ax[0].set_title(\"Erro Durante o Treinamento\", fontsize=14)\n",
    "ax[0].set_xlabel(\"Epoch\", fontsize=14)\n",
    "\n",
    "sns.lineplot(data=train_logs.iloc[:, 2:], palette=palettes[1], markers=True, ax=ax[1], linewidth=2.5, legend=\"full\")\n",
    "ax[1].set_title(\"Métricas Dice e Jaccard Durante Treinamento\", fontsize=14)\n",
    "ax[1].set_xlabel(\"Epoch\", fontsize=14)\n",
    "\n",
    "sns.boxplot(data=val_metics_df.iloc[:,:], palette=palettes[2], ax=ax[2])\n",
    "ax[2].set_title(\"Métricas Dice e Jaccard Durante Validação\", fontsize=14)\n",
    "ax[2].set_xticklabels(val_metics_df.columns, fontsize=10, rotation=15)\n",
    "\n",
    "plt.tight_layout()\n",
    "fig.savefig(\"imagens/resultado3.png\", format=\"png\",  pad_inches=0.2, transparent=False, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Funções Para Geração de Vídeo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Função para obter um slice\n",
    "def get_slice(img_name: str,\n",
    "              mask_name: str,\n",
    "              root_imgs_path: str = \"dados/images/\",\n",
    "              root_masks_path: str = \"dados/masks/\",) -> np.ndarray:\n",
    "\n",
    "    img_path = os.path.join('/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/', img_name)\n",
    "    print(img_path)\n",
    "    mask_path = os.path.join('/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/', mask_name)\n",
    "    print(mask_path)\n",
    "    one_slice_img = cv2.imread(img_path)\n",
    "    one_slice_mask = cv2.imread(mask_path)\n",
    "    one_slice_mask[one_slice_mask < 240] = 0  \n",
    "    one_slice_mask[one_slice_mask >= 240] = 255\n",
    "\n",
    "    return one_slice_img, one_slice_mask"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Função para obter as previsões\n",
    "def get_id_predictions(net: nn.Module,\n",
    "                       ct_scan_id_df: pd.DataFrame,\n",
    "                       root_imgs_dir: str,\n",
    "                       treshold: float = 0.3) -> list:\n",
    "\n",
    "    sigmoid = lambda x: 1 / (1 + np.exp(-x))\n",
    "    images = []\n",
    "    predictions = []\n",
    "    net.eval()\n",
    "    \n",
    "    device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
    "    \n",
    "    print(\"device:\", device)\n",
    "    \n",
    "    with torch.no_grad():\n",
    "        for idx in range(len(ct_scan_id_df)):\n",
    "            img_name = ct_scan_id_df.loc[idx, \"ImageId\"]\n",
    "            path = os.path.join(root_imgs_dir, img_name)\n",
    "\n",
    "            img_ = cv2.imread(path)\n",
    "    \n",
    "            img = Normalize().apply(img_)\n",
    "            tensor = torch.FloatTensor(img).permute(2, 0, 1).unsqueeze(0)\n",
    "            prediction = net.forward(tensor.to(device))\n",
    "            prediction = prediction.cpu().detach().numpy()\n",
    "            prediction = prediction.squeeze(0).transpose(1, 2, 0)\n",
    "            prediction = sigmoid(prediction)\n",
    "            prediction = (prediction >= treshold).astype(np.float32)\n",
    "            predictions.append((prediction * 255).astype(\"uint8\"))\n",
    "            images.append(img_)\n",
    "\n",
    "    return images, predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Função para sobrepor as máscaras nas imagens\n",
    "def sobrepoe_imagem_mascara(one_slice_image: np.ndarray,\n",
    "                            one_slice_mask: np.ndarray, \n",
    "                            w: float = 512,\n",
    "                            h: float = 512, \n",
    "                            dpi: float = 100,\n",
    "                            write: bool = False,\n",
    "                            path_to_save: str = 'imagens/',\n",
    "                            name_to_save: str = 'img_name'):\n",
    "\n",
    "    # Caminho para salvar\n",
    "    path_to_save_ = os.path.join(path_to_save, name_to_save)\n",
    "    \n",
    "    # Máscaras\n",
    "    lung, heart, trachea = [one_slice_mask[:, :, i] for i in range(3)]\n",
    "    \n",
    "    # Figura\n",
    "    figsize = (w / dpi), (h / dpi)\n",
    "    fig = plt.figure(figsize=(figsize))\n",
    "    fig.add_axes([0, 0, 1, 1])\n",
    "\n",
    "    # Imagem\n",
    "    plt.imshow(one_slice_image, cmap = \"bone\")\n",
    "\n",
    "    # Mostra a sobreposição de imagens e máscaras\n",
    "    plt.imshow(np.ma.masked_where(lung == False, lung), cmap='cool', alpha=0.3)\n",
    "    plt.imshow(np.ma.masked_where(heart == False, heart), cmap='autumn', alpha=0.3)\n",
    "    plt.imshow(np.ma.masked_where(trachea == False, trachea), cmap='autumn_r', alpha=0.3) \n",
    "\n",
    "    plt.axis('off')\n",
    "    fig.savefig(f\"{path_to_save_}.png\", bbox_inches = 'tight', pad_inches = 0.0, dpi = dpi,  format = \"png\")\n",
    "    if write:\n",
    "        plt.close()\n",
    "    else:\n",
    "        plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Função para sobreposição das máscaras\n",
    "def sobrepoe_mascaras_full_ctscan(ct_scan_id_df: pd.DataFrame, path_to_save: str):\n",
    "\n",
    "    num_slice = len(ct_scan_id_df)\n",
    "    \n",
    "    for slice_ in range(num_slice):\n",
    "\n",
    "        print(slice_)\n",
    "        \n",
    "        img_name = ct_scan_id_df.loc[slice_, \"ImageId\"]\n",
    "        mask_name = ct_scan_id_df.loc[slice_, \"MaskId\"]\n",
    "\n",
    "        print(img_name)\n",
    "        print(mask_name)\n",
    "        \n",
    "        # Obtém um slice de imagem e um slice de máscara\n",
    "        one_slice_img, one_slice_mask = get_slice(img_name, mask_name)\n",
    "        \n",
    "        # Faz a sobreposição\n",
    "        sobrepoe_imagem_mascara(one_slice_img,\n",
    "                                    one_slice_mask,\n",
    "                                    write = True, \n",
    "                                    path_to_save = path_to_save,\n",
    "                                    name_to_save = str(slice_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Função para criação de video\n",
    "def cria_video(path_to_imgs: str, video_name: str, framerate: int):\n",
    "\n",
    "    img_names = sorted(os.listdir(path_to_imgs), key=lambda x: int(x[:-4])) \n",
    "    img_path = os.path.join(path_to_imgs, img_names[0])\n",
    "    frame_width, frame_height, _ = cv2.imread(img_path).shape\n",
    "    fourc = cv2.VideoWriter_fourcc(*'MP4V')\n",
    "    video = cv2.VideoWriter(video_name + \".mp4\", fourc, framerate, (frame_width, frame_height))\n",
    "\n",
    "    for img_name in img_names:\n",
    "        img_path = os.path.join(path_to_imgs, img_name)\n",
    "        image = cv2.imread(img_path)\n",
    "        video.write(image)\n",
    "            \n",
    "    cv2.destroyAllWindows()\n",
    "    video.release()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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>ImageId</th>\n",
       "      <th>MaskId</th>\n",
       "      <th>Id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ID00400637202305055099402_0.jpg</td>\n",
       "      <td>ID00400637202305055099402_mask_0.jpg</td>\n",
       "      <td>ID00400637202305055099402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ID00400637202305055099402_1.jpg</td>\n",
       "      <td>ID00400637202305055099402_mask_1.jpg</td>\n",
       "      <td>ID00400637202305055099402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ID00400637202305055099402_2.jpg</td>\n",
       "      <td>ID00400637202305055099402_mask_2.jpg</td>\n",
       "      <td>ID00400637202305055099402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ID00400637202305055099402_3.jpg</td>\n",
       "      <td>ID00400637202305055099402_mask_3.jpg</td>\n",
       "      <td>ID00400637202305055099402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ID00400637202305055099402_4.jpg</td>\n",
       "      <td>ID00400637202305055099402_mask_4.jpg</td>\n",
       "      <td>ID00400637202305055099402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260</th>\n",
       "      <td>ID00400637202305055099402_260.jpg</td>\n",
       "      <td>ID00400637202305055099402_mask_260.jpg</td>\n",
       "      <td>ID00400637202305055099402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>261</th>\n",
       "      <td>ID00400637202305055099402_261.jpg</td>\n",
       "      <td>ID00400637202305055099402_mask_261.jpg</td>\n",
       "      <td>ID00400637202305055099402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>262</th>\n",
       "      <td>ID00400637202305055099402_262.jpg</td>\n",
       "      <td>ID00400637202305055099402_mask_262.jpg</td>\n",
       "      <td>ID00400637202305055099402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>263</th>\n",
       "      <td>ID00400637202305055099402_263.jpg</td>\n",
       "      <td>ID00400637202305055099402_mask_263.jpg</td>\n",
       "      <td>ID00400637202305055099402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>264</th>\n",
       "      <td>ID00400637202305055099402_264.jpg</td>\n",
       "      <td>ID00400637202305055099402_mask_264.jpg</td>\n",
       "      <td>ID00400637202305055099402</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>265 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                               ImageId  \\\n",
       "0      ID00400637202305055099402_0.jpg   \n",
       "1      ID00400637202305055099402_1.jpg   \n",
       "2      ID00400637202305055099402_2.jpg   \n",
       "3      ID00400637202305055099402_3.jpg   \n",
       "4      ID00400637202305055099402_4.jpg   \n",
       "..                                 ...   \n",
       "260  ID00400637202305055099402_260.jpg   \n",
       "261  ID00400637202305055099402_261.jpg   \n",
       "262  ID00400637202305055099402_262.jpg   \n",
       "263  ID00400637202305055099402_263.jpg   \n",
       "264  ID00400637202305055099402_264.jpg   \n",
       "\n",
       "                                     MaskId                         Id  \n",
       "0      ID00400637202305055099402_mask_0.jpg  ID00400637202305055099402  \n",
       "1      ID00400637202305055099402_mask_1.jpg  ID00400637202305055099402  \n",
       "2      ID00400637202305055099402_mask_2.jpg  ID00400637202305055099402  \n",
       "3      ID00400637202305055099402_mask_3.jpg  ID00400637202305055099402  \n",
       "4      ID00400637202305055099402_mask_4.jpg  ID00400637202305055099402  \n",
       "..                                      ...                        ...  \n",
       "260  ID00400637202305055099402_mask_260.jpg  ID00400637202305055099402  \n",
       "261  ID00400637202305055099402_mask_261.jpg  ID00400637202305055099402  \n",
       "262  ID00400637202305055099402_mask_262.jpg  ID00400637202305055099402  \n",
       "263  ID00400637202305055099402_mask_263.jpg  ID00400637202305055099402  \n",
       "264  ID00400637202305055099402_mask_264.jpg  ID00400637202305055099402  \n",
       "\n",
       "[265 rows x 3 columns]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Obtém um ID\n",
    "df = pd.read_csv(config.path_to_csv)\n",
    "df[\"Id\"] = df['ImageId'].apply(lambda x: x.split(\"_\")[0])\n",
    "id_ = 'ID00400637202305055099402'\n",
    "full_scan_example = df.loc[df['Id'] == id_].reset_index(drop=True)\n",
    "full_scan_example "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Geração de Vídeo dos Labels Reais"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Caminho para salvar os vídeos\n",
    "PATH_TO_SAVE = \"videos/\" + id_ + \"_ground_truth\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pasta videos/ID00400637202305055099402_ground_truth criada.\n"
     ]
    }
   ],
   "source": [
    "# Salva a pasta do id do vídeo\n",
    "if not os.path.exists(PATH_TO_SAVE):\n",
    "    os.mkdir(PATH_TO_SAVE)\n",
    "    print(f\"Pasta {PATH_TO_SAVE} criada.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "ID00400637202305055099402_0.jpg\n",
      "ID00400637202305055099402_mask_0.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_0.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_0.jpg\n",
      "1\n",
      "ID00400637202305055099402_1.jpg\n",
      "ID00400637202305055099402_mask_1.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_1.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_1.jpg\n",
      "2\n",
      "ID00400637202305055099402_2.jpg\n",
      "ID00400637202305055099402_mask_2.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_2.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_2.jpg\n",
      "3\n",
      "ID00400637202305055099402_3.jpg\n",
      "ID00400637202305055099402_mask_3.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_3.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_3.jpg\n",
      "4\n",
      "ID00400637202305055099402_4.jpg\n",
      "ID00400637202305055099402_mask_4.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_4.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_4.jpg\n",
      "5\n",
      "ID00400637202305055099402_5.jpg\n",
      "ID00400637202305055099402_mask_5.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_5.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_5.jpg\n",
      "6\n",
      "ID00400637202305055099402_6.jpg\n",
      "ID00400637202305055099402_mask_6.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_6.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_6.jpg\n",
      "7\n",
      "ID00400637202305055099402_7.jpg\n",
      "ID00400637202305055099402_mask_7.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_7.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_7.jpg\n",
      "8\n",
      "ID00400637202305055099402_8.jpg\n",
      "ID00400637202305055099402_mask_8.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_8.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_8.jpg\n",
      "9\n",
      "ID00400637202305055099402_9.jpg\n",
      "ID00400637202305055099402_mask_9.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_9.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_9.jpg\n",
      "10\n",
      "ID00400637202305055099402_10.jpg\n",
      "ID00400637202305055099402_mask_10.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_10.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_10.jpg\n",
      "11\n",
      "ID00400637202305055099402_11.jpg\n",
      "ID00400637202305055099402_mask_11.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_11.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_11.jpg\n",
      "12\n",
      "ID00400637202305055099402_12.jpg\n",
      "ID00400637202305055099402_mask_12.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_12.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_12.jpg\n",
      "13\n",
      "ID00400637202305055099402_13.jpg\n",
      "ID00400637202305055099402_mask_13.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_13.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_13.jpg\n",
      "14\n",
      "ID00400637202305055099402_14.jpg\n",
      "ID00400637202305055099402_mask_14.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_14.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_14.jpg\n",
      "15\n",
      "ID00400637202305055099402_15.jpg\n",
      "ID00400637202305055099402_mask_15.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_15.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_15.jpg\n",
      "16\n",
      "ID00400637202305055099402_16.jpg\n",
      "ID00400637202305055099402_mask_16.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_16.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_16.jpg\n",
      "17\n",
      "ID00400637202305055099402_17.jpg\n",
      "ID00400637202305055099402_mask_17.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_17.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_17.jpg\n",
      "18\n",
      "ID00400637202305055099402_18.jpg\n",
      "ID00400637202305055099402_mask_18.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_18.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_18.jpg\n",
      "19\n",
      "ID00400637202305055099402_19.jpg\n",
      "ID00400637202305055099402_mask_19.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_19.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_19.jpg\n",
      "20\n",
      "ID00400637202305055099402_20.jpg\n",
      "ID00400637202305055099402_mask_20.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_20.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_20.jpg\n",
      "21\n",
      "ID00400637202305055099402_21.jpg\n",
      "ID00400637202305055099402_mask_21.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_21.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_21.jpg\n",
      "22\n",
      "ID00400637202305055099402_22.jpg\n",
      "ID00400637202305055099402_mask_22.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_22.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_22.jpg\n",
      "23\n",
      "ID00400637202305055099402_23.jpg\n",
      "ID00400637202305055099402_mask_23.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_23.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_23.jpg\n",
      "24\n",
      "ID00400637202305055099402_24.jpg\n",
      "ID00400637202305055099402_mask_24.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_24.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_24.jpg\n",
      "25\n",
      "ID00400637202305055099402_25.jpg\n",
      "ID00400637202305055099402_mask_25.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_25.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_25.jpg\n",
      "26\n",
      "ID00400637202305055099402_26.jpg\n",
      "ID00400637202305055099402_mask_26.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_26.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_26.jpg\n",
      "27\n",
      "ID00400637202305055099402_27.jpg\n",
      "ID00400637202305055099402_mask_27.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_27.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_27.jpg\n",
      "28\n",
      "ID00400637202305055099402_28.jpg\n",
      "ID00400637202305055099402_mask_28.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_28.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_28.jpg\n",
      "29\n",
      "ID00400637202305055099402_29.jpg\n",
      "ID00400637202305055099402_mask_29.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_29.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_29.jpg\n",
      "30\n",
      "ID00400637202305055099402_30.jpg\n",
      "ID00400637202305055099402_mask_30.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_30.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_30.jpg\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "31\n",
      "ID00400637202305055099402_31.jpg\n",
      "ID00400637202305055099402_mask_31.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_31.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_31.jpg\n",
      "32\n",
      "ID00400637202305055099402_32.jpg\n",
      "ID00400637202305055099402_mask_32.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_32.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_32.jpg\n",
      "33\n",
      "ID00400637202305055099402_33.jpg\n",
      "ID00400637202305055099402_mask_33.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_33.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_33.jpg\n",
      "34\n",
      "ID00400637202305055099402_34.jpg\n",
      "ID00400637202305055099402_mask_34.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_34.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_34.jpg\n",
      "35\n",
      "ID00400637202305055099402_35.jpg\n",
      "ID00400637202305055099402_mask_35.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_35.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_35.jpg\n",
      "36\n",
      "ID00400637202305055099402_36.jpg\n",
      "ID00400637202305055099402_mask_36.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_36.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_36.jpg\n",
      "37\n",
      "ID00400637202305055099402_37.jpg\n",
      "ID00400637202305055099402_mask_37.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_37.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_37.jpg\n",
      "38\n",
      "ID00400637202305055099402_38.jpg\n",
      "ID00400637202305055099402_mask_38.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_38.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_38.jpg\n",
      "39\n",
      "ID00400637202305055099402_39.jpg\n",
      "ID00400637202305055099402_mask_39.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_39.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_39.jpg\n",
      "40\n",
      "ID00400637202305055099402_40.jpg\n",
      "ID00400637202305055099402_mask_40.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_40.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_40.jpg\n",
      "41\n",
      "ID00400637202305055099402_41.jpg\n",
      "ID00400637202305055099402_mask_41.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_41.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_41.jpg\n",
      "42\n",
      "ID00400637202305055099402_42.jpg\n",
      "ID00400637202305055099402_mask_42.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_42.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_42.jpg\n",
      "43\n",
      "ID00400637202305055099402_43.jpg\n",
      "ID00400637202305055099402_mask_43.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_43.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_43.jpg\n",
      "44\n",
      "ID00400637202305055099402_44.jpg\n",
      "ID00400637202305055099402_mask_44.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_44.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_44.jpg\n",
      "45\n",
      "ID00400637202305055099402_45.jpg\n",
      "ID00400637202305055099402_mask_45.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_45.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_45.jpg\n",
      "46\n",
      "ID00400637202305055099402_46.jpg\n",
      "ID00400637202305055099402_mask_46.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_46.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_46.jpg\n",
      "47\n",
      "ID00400637202305055099402_47.jpg\n",
      "ID00400637202305055099402_mask_47.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_47.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_47.jpg\n",
      "48\n",
      "ID00400637202305055099402_48.jpg\n",
      "ID00400637202305055099402_mask_48.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_48.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_48.jpg\n",
      "49\n",
      "ID00400637202305055099402_49.jpg\n",
      "ID00400637202305055099402_mask_49.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_49.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_49.jpg\n",
      "50\n",
      "ID00400637202305055099402_50.jpg\n",
      "ID00400637202305055099402_mask_50.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_50.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_50.jpg\n",
      "51\n",
      "ID00400637202305055099402_51.jpg\n",
      "ID00400637202305055099402_mask_51.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_51.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_51.jpg\n",
      "52\n",
      "ID00400637202305055099402_52.jpg\n",
      "ID00400637202305055099402_mask_52.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_52.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_52.jpg\n",
      "53\n",
      "ID00400637202305055099402_53.jpg\n",
      "ID00400637202305055099402_mask_53.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_53.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_53.jpg\n",
      "54\n",
      "ID00400637202305055099402_54.jpg\n",
      "ID00400637202305055099402_mask_54.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_54.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_54.jpg\n",
      "55\n",
      "ID00400637202305055099402_55.jpg\n",
      "ID00400637202305055099402_mask_55.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_55.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_55.jpg\n",
      "56\n",
      "ID00400637202305055099402_56.jpg\n",
      "ID00400637202305055099402_mask_56.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_56.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_56.jpg\n",
      "57\n",
      "ID00400637202305055099402_57.jpg\n",
      "ID00400637202305055099402_mask_57.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_57.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_57.jpg\n",
      "58\n",
      "ID00400637202305055099402_58.jpg\n",
      "ID00400637202305055099402_mask_58.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_58.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_58.jpg\n",
      "59\n",
      "ID00400637202305055099402_59.jpg\n",
      "ID00400637202305055099402_mask_59.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_59.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_59.jpg\n",
      "60\n",
      "ID00400637202305055099402_60.jpg\n",
      "ID00400637202305055099402_mask_60.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_60.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_60.jpg\n",
      "61\n",
      "ID00400637202305055099402_61.jpg\n",
      "ID00400637202305055099402_mask_61.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_61.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_61.jpg\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "62\n",
      "ID00400637202305055099402_62.jpg\n",
      "ID00400637202305055099402_mask_62.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_62.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_62.jpg\n",
      "63\n",
      "ID00400637202305055099402_63.jpg\n",
      "ID00400637202305055099402_mask_63.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_63.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_63.jpg\n",
      "64\n",
      "ID00400637202305055099402_64.jpg\n",
      "ID00400637202305055099402_mask_64.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_64.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_64.jpg\n",
      "65\n",
      "ID00400637202305055099402_65.jpg\n",
      "ID00400637202305055099402_mask_65.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_65.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_65.jpg\n",
      "66\n",
      "ID00400637202305055099402_66.jpg\n",
      "ID00400637202305055099402_mask_66.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_66.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_66.jpg\n",
      "67\n",
      "ID00400637202305055099402_67.jpg\n",
      "ID00400637202305055099402_mask_67.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_67.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_67.jpg\n",
      "68\n",
      "ID00400637202305055099402_68.jpg\n",
      "ID00400637202305055099402_mask_68.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_68.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_68.jpg\n",
      "69\n",
      "ID00400637202305055099402_69.jpg\n",
      "ID00400637202305055099402_mask_69.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_69.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_69.jpg\n",
      "70\n",
      "ID00400637202305055099402_70.jpg\n",
      "ID00400637202305055099402_mask_70.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_70.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_70.jpg\n",
      "71\n",
      "ID00400637202305055099402_71.jpg\n",
      "ID00400637202305055099402_mask_71.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_71.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_71.jpg\n",
      "72\n",
      "ID00400637202305055099402_72.jpg\n",
      "ID00400637202305055099402_mask_72.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_72.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_72.jpg\n",
      "73\n",
      "ID00400637202305055099402_73.jpg\n",
      "ID00400637202305055099402_mask_73.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_73.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_73.jpg\n",
      "74\n",
      "ID00400637202305055099402_74.jpg\n",
      "ID00400637202305055099402_mask_74.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_74.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_74.jpg\n",
      "75\n",
      "ID00400637202305055099402_75.jpg\n",
      "ID00400637202305055099402_mask_75.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_75.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_75.jpg\n",
      "76\n",
      "ID00400637202305055099402_76.jpg\n",
      "ID00400637202305055099402_mask_76.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_76.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_76.jpg\n",
      "77\n",
      "ID00400637202305055099402_77.jpg\n",
      "ID00400637202305055099402_mask_77.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_77.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_77.jpg\n",
      "78\n",
      "ID00400637202305055099402_78.jpg\n",
      "ID00400637202305055099402_mask_78.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_78.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_78.jpg\n",
      "79\n",
      "ID00400637202305055099402_79.jpg\n",
      "ID00400637202305055099402_mask_79.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_79.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_79.jpg\n",
      "80\n",
      "ID00400637202305055099402_80.jpg\n",
      "ID00400637202305055099402_mask_80.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_80.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_80.jpg\n",
      "81\n",
      "ID00400637202305055099402_81.jpg\n",
      "ID00400637202305055099402_mask_81.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_81.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_81.jpg\n",
      "82\n",
      "ID00400637202305055099402_82.jpg\n",
      "ID00400637202305055099402_mask_82.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_82.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_82.jpg\n",
      "83\n",
      "ID00400637202305055099402_83.jpg\n",
      "ID00400637202305055099402_mask_83.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_83.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_83.jpg\n",
      "84\n",
      "ID00400637202305055099402_84.jpg\n",
      "ID00400637202305055099402_mask_84.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_84.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_84.jpg\n",
      "85\n",
      "ID00400637202305055099402_85.jpg\n",
      "ID00400637202305055099402_mask_85.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_85.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_85.jpg\n",
      "86\n",
      "ID00400637202305055099402_86.jpg\n",
      "ID00400637202305055099402_mask_86.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_86.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_86.jpg\n",
      "87\n",
      "ID00400637202305055099402_87.jpg\n",
      "ID00400637202305055099402_mask_87.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_87.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_87.jpg\n",
      "88\n",
      "ID00400637202305055099402_88.jpg\n",
      "ID00400637202305055099402_mask_88.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_88.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_88.jpg\n",
      "89\n",
      "ID00400637202305055099402_89.jpg\n",
      "ID00400637202305055099402_mask_89.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_89.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_89.jpg\n",
      "90\n",
      "ID00400637202305055099402_90.jpg\n",
      "ID00400637202305055099402_mask_90.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_90.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_90.jpg\n",
      "91\n",
      "ID00400637202305055099402_91.jpg\n",
      "ID00400637202305055099402_mask_91.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_91.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_91.jpg\n",
      "92\n",
      "ID00400637202305055099402_92.jpg\n",
      "ID00400637202305055099402_mask_92.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_92.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_92.jpg\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "93\n",
      "ID00400637202305055099402_93.jpg\n",
      "ID00400637202305055099402_mask_93.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_93.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_93.jpg\n",
      "94\n",
      "ID00400637202305055099402_94.jpg\n",
      "ID00400637202305055099402_mask_94.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_94.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_94.jpg\n",
      "95\n",
      "ID00400637202305055099402_95.jpg\n",
      "ID00400637202305055099402_mask_95.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_95.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_95.jpg\n",
      "96\n",
      "ID00400637202305055099402_96.jpg\n",
      "ID00400637202305055099402_mask_96.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_96.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_96.jpg\n",
      "97\n",
      "ID00400637202305055099402_97.jpg\n",
      "ID00400637202305055099402_mask_97.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_97.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_97.jpg\n",
      "98\n",
      "ID00400637202305055099402_98.jpg\n",
      "ID00400637202305055099402_mask_98.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_98.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_98.jpg\n",
      "99\n",
      "ID00400637202305055099402_99.jpg\n",
      "ID00400637202305055099402_mask_99.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_99.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_99.jpg\n",
      "100\n",
      "ID00400637202305055099402_100.jpg\n",
      "ID00400637202305055099402_mask_100.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_100.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_100.jpg\n",
      "101\n",
      "ID00400637202305055099402_101.jpg\n",
      "ID00400637202305055099402_mask_101.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_101.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_101.jpg\n",
      "102\n",
      "ID00400637202305055099402_102.jpg\n",
      "ID00400637202305055099402_mask_102.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_102.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_102.jpg\n",
      "103\n",
      "ID00400637202305055099402_103.jpg\n",
      "ID00400637202305055099402_mask_103.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_103.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_103.jpg\n",
      "104\n",
      "ID00400637202305055099402_104.jpg\n",
      "ID00400637202305055099402_mask_104.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_104.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_104.jpg\n",
      "105\n",
      "ID00400637202305055099402_105.jpg\n",
      "ID00400637202305055099402_mask_105.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_105.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_105.jpg\n",
      "106\n",
      "ID00400637202305055099402_106.jpg\n",
      "ID00400637202305055099402_mask_106.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_106.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_106.jpg\n",
      "107\n",
      "ID00400637202305055099402_107.jpg\n",
      "ID00400637202305055099402_mask_107.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_107.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_107.jpg\n",
      "108\n",
      "ID00400637202305055099402_108.jpg\n",
      "ID00400637202305055099402_mask_108.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_108.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_108.jpg\n",
      "109\n",
      "ID00400637202305055099402_109.jpg\n",
      "ID00400637202305055099402_mask_109.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_109.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_109.jpg\n",
      "110\n",
      "ID00400637202305055099402_110.jpg\n",
      "ID00400637202305055099402_mask_110.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_110.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_110.jpg\n",
      "111\n",
      "ID00400637202305055099402_111.jpg\n",
      "ID00400637202305055099402_mask_111.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_111.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_111.jpg\n",
      "112\n",
      "ID00400637202305055099402_112.jpg\n",
      "ID00400637202305055099402_mask_112.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_112.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_112.jpg\n",
      "113\n",
      "ID00400637202305055099402_113.jpg\n",
      "ID00400637202305055099402_mask_113.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_113.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_113.jpg\n",
      "114\n",
      "ID00400637202305055099402_114.jpg\n",
      "ID00400637202305055099402_mask_114.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_114.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_114.jpg\n",
      "115\n",
      "ID00400637202305055099402_115.jpg\n",
      "ID00400637202305055099402_mask_115.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_115.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_115.jpg\n",
      "116\n",
      "ID00400637202305055099402_116.jpg\n",
      "ID00400637202305055099402_mask_116.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_116.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_116.jpg\n",
      "117\n",
      "ID00400637202305055099402_117.jpg\n",
      "ID00400637202305055099402_mask_117.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_117.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_117.jpg\n",
      "118\n",
      "ID00400637202305055099402_118.jpg\n",
      "ID00400637202305055099402_mask_118.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_118.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_118.jpg\n",
      "119\n",
      "ID00400637202305055099402_119.jpg\n",
      "ID00400637202305055099402_mask_119.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_119.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_119.jpg\n",
      "120\n",
      "ID00400637202305055099402_120.jpg\n",
      "ID00400637202305055099402_mask_120.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_120.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_120.jpg\n",
      "121\n",
      "ID00400637202305055099402_121.jpg\n",
      "ID00400637202305055099402_mask_121.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_121.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_121.jpg\n",
      "122\n",
      "ID00400637202305055099402_122.jpg\n",
      "ID00400637202305055099402_mask_122.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_122.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_122.jpg\n",
      "123\n",
      "ID00400637202305055099402_123.jpg\n",
      "ID00400637202305055099402_mask_123.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_123.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_123.jpg\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "124\n",
      "ID00400637202305055099402_124.jpg\n",
      "ID00400637202305055099402_mask_124.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_124.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_124.jpg\n",
      "125\n",
      "ID00400637202305055099402_125.jpg\n",
      "ID00400637202305055099402_mask_125.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_125.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_125.jpg\n",
      "126\n",
      "ID00400637202305055099402_126.jpg\n",
      "ID00400637202305055099402_mask_126.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_126.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_126.jpg\n",
      "127\n",
      "ID00400637202305055099402_127.jpg\n",
      "ID00400637202305055099402_mask_127.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_127.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_127.jpg\n",
      "128\n",
      "ID00400637202305055099402_128.jpg\n",
      "ID00400637202305055099402_mask_128.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_128.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_128.jpg\n",
      "129\n",
      "ID00400637202305055099402_129.jpg\n",
      "ID00400637202305055099402_mask_129.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_129.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_129.jpg\n",
      "130\n",
      "ID00400637202305055099402_130.jpg\n",
      "ID00400637202305055099402_mask_130.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_130.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_130.jpg\n",
      "131\n",
      "ID00400637202305055099402_131.jpg\n",
      "ID00400637202305055099402_mask_131.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_131.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_131.jpg\n",
      "132\n",
      "ID00400637202305055099402_132.jpg\n",
      "ID00400637202305055099402_mask_132.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_132.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_132.jpg\n",
      "133\n",
      "ID00400637202305055099402_133.jpg\n",
      "ID00400637202305055099402_mask_133.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_133.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_133.jpg\n",
      "134\n",
      "ID00400637202305055099402_134.jpg\n",
      "ID00400637202305055099402_mask_134.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_134.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_134.jpg\n",
      "135\n",
      "ID00400637202305055099402_135.jpg\n",
      "ID00400637202305055099402_mask_135.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_135.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_135.jpg\n",
      "136\n",
      "ID00400637202305055099402_136.jpg\n",
      "ID00400637202305055099402_mask_136.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_136.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_136.jpg\n",
      "137\n",
      "ID00400637202305055099402_137.jpg\n",
      "ID00400637202305055099402_mask_137.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_137.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_137.jpg\n",
      "138\n",
      "ID00400637202305055099402_138.jpg\n",
      "ID00400637202305055099402_mask_138.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_138.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_138.jpg\n",
      "139\n",
      "ID00400637202305055099402_139.jpg\n",
      "ID00400637202305055099402_mask_139.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_139.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_139.jpg\n",
      "140\n",
      "ID00400637202305055099402_140.jpg\n",
      "ID00400637202305055099402_mask_140.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_140.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_140.jpg\n",
      "141\n",
      "ID00400637202305055099402_141.jpg\n",
      "ID00400637202305055099402_mask_141.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_141.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_141.jpg\n",
      "142\n",
      "ID00400637202305055099402_142.jpg\n",
      "ID00400637202305055099402_mask_142.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_142.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_142.jpg\n",
      "143\n",
      "ID00400637202305055099402_143.jpg\n",
      "ID00400637202305055099402_mask_143.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_143.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_143.jpg\n",
      "144\n",
      "ID00400637202305055099402_144.jpg\n",
      "ID00400637202305055099402_mask_144.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_144.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_144.jpg\n",
      "145\n",
      "ID00400637202305055099402_145.jpg\n",
      "ID00400637202305055099402_mask_145.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_145.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_145.jpg\n",
      "146\n",
      "ID00400637202305055099402_146.jpg\n",
      "ID00400637202305055099402_mask_146.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_146.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_146.jpg\n",
      "147\n",
      "ID00400637202305055099402_147.jpg\n",
      "ID00400637202305055099402_mask_147.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_147.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_147.jpg\n",
      "148\n",
      "ID00400637202305055099402_148.jpg\n",
      "ID00400637202305055099402_mask_148.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_148.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_148.jpg\n",
      "149\n",
      "ID00400637202305055099402_149.jpg\n",
      "ID00400637202305055099402_mask_149.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_149.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_149.jpg\n",
      "150\n",
      "ID00400637202305055099402_150.jpg\n",
      "ID00400637202305055099402_mask_150.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_150.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_150.jpg\n",
      "151\n",
      "ID00400637202305055099402_151.jpg\n",
      "ID00400637202305055099402_mask_151.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_151.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_151.jpg\n",
      "152\n",
      "ID00400637202305055099402_152.jpg\n",
      "ID00400637202305055099402_mask_152.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_152.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_152.jpg\n",
      "153\n",
      "ID00400637202305055099402_153.jpg\n",
      "ID00400637202305055099402_mask_153.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_153.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_153.jpg\n",
      "154\n",
      "ID00400637202305055099402_154.jpg\n",
      "ID00400637202305055099402_mask_154.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_154.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_154.jpg\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "155\n",
      "ID00400637202305055099402_155.jpg\n",
      "ID00400637202305055099402_mask_155.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_155.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_155.jpg\n",
      "156\n",
      "ID00400637202305055099402_156.jpg\n",
      "ID00400637202305055099402_mask_156.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_156.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_156.jpg\n",
      "157\n",
      "ID00400637202305055099402_157.jpg\n",
      "ID00400637202305055099402_mask_157.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_157.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_157.jpg\n",
      "158\n",
      "ID00400637202305055099402_158.jpg\n",
      "ID00400637202305055099402_mask_158.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_158.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_158.jpg\n",
      "159\n",
      "ID00400637202305055099402_159.jpg\n",
      "ID00400637202305055099402_mask_159.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_159.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_159.jpg\n",
      "160\n",
      "ID00400637202305055099402_160.jpg\n",
      "ID00400637202305055099402_mask_160.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_160.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_160.jpg\n",
      "161\n",
      "ID00400637202305055099402_161.jpg\n",
      "ID00400637202305055099402_mask_161.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_161.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_161.jpg\n",
      "162\n",
      "ID00400637202305055099402_162.jpg\n",
      "ID00400637202305055099402_mask_162.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_162.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_162.jpg\n",
      "163\n",
      "ID00400637202305055099402_163.jpg\n",
      "ID00400637202305055099402_mask_163.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_163.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_163.jpg\n",
      "164\n",
      "ID00400637202305055099402_164.jpg\n",
      "ID00400637202305055099402_mask_164.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_164.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_164.jpg\n",
      "165\n",
      "ID00400637202305055099402_165.jpg\n",
      "ID00400637202305055099402_mask_165.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_165.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_165.jpg\n",
      "166\n",
      "ID00400637202305055099402_166.jpg\n",
      "ID00400637202305055099402_mask_166.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_166.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_166.jpg\n",
      "167\n",
      "ID00400637202305055099402_167.jpg\n",
      "ID00400637202305055099402_mask_167.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_167.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_167.jpg\n",
      "168\n",
      "ID00400637202305055099402_168.jpg\n",
      "ID00400637202305055099402_mask_168.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_168.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_168.jpg\n",
      "169\n",
      "ID00400637202305055099402_169.jpg\n",
      "ID00400637202305055099402_mask_169.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_169.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_169.jpg\n",
      "170\n",
      "ID00400637202305055099402_170.jpg\n",
      "ID00400637202305055099402_mask_170.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_170.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_170.jpg\n",
      "171\n",
      "ID00400637202305055099402_171.jpg\n",
      "ID00400637202305055099402_mask_171.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_171.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_171.jpg\n",
      "172\n",
      "ID00400637202305055099402_172.jpg\n",
      "ID00400637202305055099402_mask_172.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_172.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_172.jpg\n",
      "173\n",
      "ID00400637202305055099402_173.jpg\n",
      "ID00400637202305055099402_mask_173.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_173.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_173.jpg\n",
      "174\n",
      "ID00400637202305055099402_174.jpg\n",
      "ID00400637202305055099402_mask_174.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_174.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_174.jpg\n",
      "175\n",
      "ID00400637202305055099402_175.jpg\n",
      "ID00400637202305055099402_mask_175.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_175.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_175.jpg\n",
      "176\n",
      "ID00400637202305055099402_176.jpg\n",
      "ID00400637202305055099402_mask_176.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_176.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_176.jpg\n",
      "177\n",
      "ID00400637202305055099402_177.jpg\n",
      "ID00400637202305055099402_mask_177.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_177.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_177.jpg\n",
      "178\n",
      "ID00400637202305055099402_178.jpg\n",
      "ID00400637202305055099402_mask_178.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_178.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_178.jpg\n",
      "179\n",
      "ID00400637202305055099402_179.jpg\n",
      "ID00400637202305055099402_mask_179.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_179.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_179.jpg\n",
      "180\n",
      "ID00400637202305055099402_180.jpg\n",
      "ID00400637202305055099402_mask_180.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_180.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_180.jpg\n",
      "181\n",
      "ID00400637202305055099402_181.jpg\n",
      "ID00400637202305055099402_mask_181.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_181.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_181.jpg\n",
      "182\n",
      "ID00400637202305055099402_182.jpg\n",
      "ID00400637202305055099402_mask_182.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_182.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_182.jpg\n",
      "183\n",
      "ID00400637202305055099402_183.jpg\n",
      "ID00400637202305055099402_mask_183.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_183.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_183.jpg\n",
      "184\n",
      "ID00400637202305055099402_184.jpg\n",
      "ID00400637202305055099402_mask_184.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_184.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_184.jpg\n",
      "185\n",
      "ID00400637202305055099402_185.jpg\n",
      "ID00400637202305055099402_mask_185.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_185.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_185.jpg\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "186\n",
      "ID00400637202305055099402_186.jpg\n",
      "ID00400637202305055099402_mask_186.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_186.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_186.jpg\n",
      "187\n",
      "ID00400637202305055099402_187.jpg\n",
      "ID00400637202305055099402_mask_187.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_187.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_187.jpg\n",
      "188\n",
      "ID00400637202305055099402_188.jpg\n",
      "ID00400637202305055099402_mask_188.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_188.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_188.jpg\n",
      "189\n",
      "ID00400637202305055099402_189.jpg\n",
      "ID00400637202305055099402_mask_189.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_189.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_189.jpg\n",
      "190\n",
      "ID00400637202305055099402_190.jpg\n",
      "ID00400637202305055099402_mask_190.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_190.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_190.jpg\n",
      "191\n",
      "ID00400637202305055099402_191.jpg\n",
      "ID00400637202305055099402_mask_191.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_191.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_191.jpg\n",
      "192\n",
      "ID00400637202305055099402_192.jpg\n",
      "ID00400637202305055099402_mask_192.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_192.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_192.jpg\n",
      "193\n",
      "ID00400637202305055099402_193.jpg\n",
      "ID00400637202305055099402_mask_193.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_193.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_193.jpg\n",
      "194\n",
      "ID00400637202305055099402_194.jpg\n",
      "ID00400637202305055099402_mask_194.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_194.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_194.jpg\n",
      "195\n",
      "ID00400637202305055099402_195.jpg\n",
      "ID00400637202305055099402_mask_195.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_195.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_195.jpg\n",
      "196\n",
      "ID00400637202305055099402_196.jpg\n",
      "ID00400637202305055099402_mask_196.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_196.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_196.jpg\n",
      "197\n",
      "ID00400637202305055099402_197.jpg\n",
      "ID00400637202305055099402_mask_197.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_197.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_197.jpg\n",
      "198\n",
      "ID00400637202305055099402_198.jpg\n",
      "ID00400637202305055099402_mask_198.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_198.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_198.jpg\n",
      "199\n",
      "ID00400637202305055099402_199.jpg\n",
      "ID00400637202305055099402_mask_199.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_199.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_199.jpg\n",
      "200\n",
      "ID00400637202305055099402_200.jpg\n",
      "ID00400637202305055099402_mask_200.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_200.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_200.jpg\n",
      "201\n",
      "ID00400637202305055099402_201.jpg\n",
      "ID00400637202305055099402_mask_201.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_201.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_201.jpg\n",
      "202\n",
      "ID00400637202305055099402_202.jpg\n",
      "ID00400637202305055099402_mask_202.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_202.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_202.jpg\n",
      "203\n",
      "ID00400637202305055099402_203.jpg\n",
      "ID00400637202305055099402_mask_203.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_203.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_203.jpg\n",
      "204\n",
      "ID00400637202305055099402_204.jpg\n",
      "ID00400637202305055099402_mask_204.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_204.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_204.jpg\n",
      "205\n",
      "ID00400637202305055099402_205.jpg\n",
      "ID00400637202305055099402_mask_205.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_205.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_205.jpg\n",
      "206\n",
      "ID00400637202305055099402_206.jpg\n",
      "ID00400637202305055099402_mask_206.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_206.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_206.jpg\n",
      "207\n",
      "ID00400637202305055099402_207.jpg\n",
      "ID00400637202305055099402_mask_207.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_207.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_207.jpg\n",
      "208\n",
      "ID00400637202305055099402_208.jpg\n",
      "ID00400637202305055099402_mask_208.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_208.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_208.jpg\n",
      "209\n",
      "ID00400637202305055099402_209.jpg\n",
      "ID00400637202305055099402_mask_209.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_209.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_209.jpg\n",
      "210\n",
      "ID00400637202305055099402_210.jpg\n",
      "ID00400637202305055099402_mask_210.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_210.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_210.jpg\n",
      "211\n",
      "ID00400637202305055099402_211.jpg\n",
      "ID00400637202305055099402_mask_211.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_211.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_211.jpg\n",
      "212\n",
      "ID00400637202305055099402_212.jpg\n",
      "ID00400637202305055099402_mask_212.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_212.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_212.jpg\n",
      "213\n",
      "ID00400637202305055099402_213.jpg\n",
      "ID00400637202305055099402_mask_213.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_213.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_213.jpg\n",
      "214\n",
      "ID00400637202305055099402_214.jpg\n",
      "ID00400637202305055099402_mask_214.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_214.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_214.jpg\n",
      "215\n",
      "ID00400637202305055099402_215.jpg\n",
      "ID00400637202305055099402_mask_215.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_215.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_215.jpg\n",
      "216\n",
      "ID00400637202305055099402_216.jpg\n",
      "ID00400637202305055099402_mask_216.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_216.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_216.jpg\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "217\n",
      "ID00400637202305055099402_217.jpg\n",
      "ID00400637202305055099402_mask_217.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_217.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_217.jpg\n",
      "218\n",
      "ID00400637202305055099402_218.jpg\n",
      "ID00400637202305055099402_mask_218.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_218.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_218.jpg\n",
      "219\n",
      "ID00400637202305055099402_219.jpg\n",
      "ID00400637202305055099402_mask_219.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_219.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_219.jpg\n",
      "220\n",
      "ID00400637202305055099402_220.jpg\n",
      "ID00400637202305055099402_mask_220.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_220.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_220.jpg\n",
      "221\n",
      "ID00400637202305055099402_221.jpg\n",
      "ID00400637202305055099402_mask_221.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_221.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_221.jpg\n",
      "222\n",
      "ID00400637202305055099402_222.jpg\n",
      "ID00400637202305055099402_mask_222.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_222.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_222.jpg\n",
      "223\n",
      "ID00400637202305055099402_223.jpg\n",
      "ID00400637202305055099402_mask_223.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_223.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_223.jpg\n",
      "224\n",
      "ID00400637202305055099402_224.jpg\n",
      "ID00400637202305055099402_mask_224.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_224.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_224.jpg\n",
      "225\n",
      "ID00400637202305055099402_225.jpg\n",
      "ID00400637202305055099402_mask_225.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_225.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_225.jpg\n",
      "226\n",
      "ID00400637202305055099402_226.jpg\n",
      "ID00400637202305055099402_mask_226.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_226.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_226.jpg\n",
      "227\n",
      "ID00400637202305055099402_227.jpg\n",
      "ID00400637202305055099402_mask_227.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_227.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_227.jpg\n",
      "228\n",
      "ID00400637202305055099402_228.jpg\n",
      "ID00400637202305055099402_mask_228.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_228.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_228.jpg\n",
      "229\n",
      "ID00400637202305055099402_229.jpg\n",
      "ID00400637202305055099402_mask_229.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_229.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_229.jpg\n",
      "230\n",
      "ID00400637202305055099402_230.jpg\n",
      "ID00400637202305055099402_mask_230.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_230.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_230.jpg\n",
      "231\n",
      "ID00400637202305055099402_231.jpg\n",
      "ID00400637202305055099402_mask_231.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_231.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_231.jpg\n",
      "232\n",
      "ID00400637202305055099402_232.jpg\n",
      "ID00400637202305055099402_mask_232.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_232.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_232.jpg\n",
      "233\n",
      "ID00400637202305055099402_233.jpg\n",
      "ID00400637202305055099402_mask_233.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_233.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_233.jpg\n",
      "234\n",
      "ID00400637202305055099402_234.jpg\n",
      "ID00400637202305055099402_mask_234.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_234.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_234.jpg\n",
      "235\n",
      "ID00400637202305055099402_235.jpg\n",
      "ID00400637202305055099402_mask_235.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_235.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_235.jpg\n",
      "236\n",
      "ID00400637202305055099402_236.jpg\n",
      "ID00400637202305055099402_mask_236.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_236.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_236.jpg\n",
      "237\n",
      "ID00400637202305055099402_237.jpg\n",
      "ID00400637202305055099402_mask_237.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_237.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_237.jpg\n",
      "238\n",
      "ID00400637202305055099402_238.jpg\n",
      "ID00400637202305055099402_mask_238.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_238.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_238.jpg\n",
      "239\n",
      "ID00400637202305055099402_239.jpg\n",
      "ID00400637202305055099402_mask_239.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_239.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_239.jpg\n",
      "240\n",
      "ID00400637202305055099402_240.jpg\n",
      "ID00400637202305055099402_mask_240.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_240.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_240.jpg\n",
      "241\n",
      "ID00400637202305055099402_241.jpg\n",
      "ID00400637202305055099402_mask_241.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_241.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_241.jpg\n",
      "242\n",
      "ID00400637202305055099402_242.jpg\n",
      "ID00400637202305055099402_mask_242.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_242.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_242.jpg\n",
      "243\n",
      "ID00400637202305055099402_243.jpg\n",
      "ID00400637202305055099402_mask_243.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_243.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_243.jpg\n",
      "244\n",
      "ID00400637202305055099402_244.jpg\n",
      "ID00400637202305055099402_mask_244.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_244.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_244.jpg\n",
      "245\n",
      "ID00400637202305055099402_245.jpg\n",
      "ID00400637202305055099402_mask_245.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_245.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_245.jpg\n",
      "246\n",
      "ID00400637202305055099402_246.jpg\n",
      "ID00400637202305055099402_mask_246.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_246.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_246.jpg\n",
      "247\n",
      "ID00400637202305055099402_247.jpg\n",
      "ID00400637202305055099402_mask_247.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_247.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_247.jpg\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "248\n",
      "ID00400637202305055099402_248.jpg\n",
      "ID00400637202305055099402_mask_248.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_248.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_248.jpg\n",
      "249\n",
      "ID00400637202305055099402_249.jpg\n",
      "ID00400637202305055099402_mask_249.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_249.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_249.jpg\n",
      "250\n",
      "ID00400637202305055099402_250.jpg\n",
      "ID00400637202305055099402_mask_250.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_250.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_250.jpg\n",
      "251\n",
      "ID00400637202305055099402_251.jpg\n",
      "ID00400637202305055099402_mask_251.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_251.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_251.jpg\n",
      "252\n",
      "ID00400637202305055099402_252.jpg\n",
      "ID00400637202305055099402_mask_252.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_252.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_252.jpg\n",
      "253\n",
      "ID00400637202305055099402_253.jpg\n",
      "ID00400637202305055099402_mask_253.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_253.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_253.jpg\n",
      "254\n",
      "ID00400637202305055099402_254.jpg\n",
      "ID00400637202305055099402_mask_254.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_254.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_254.jpg\n",
      "255\n",
      "ID00400637202305055099402_255.jpg\n",
      "ID00400637202305055099402_mask_255.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_255.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_255.jpg\n",
      "256\n",
      "ID00400637202305055099402_256.jpg\n",
      "ID00400637202305055099402_mask_256.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_256.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_256.jpg\n",
      "257\n",
      "ID00400637202305055099402_257.jpg\n",
      "ID00400637202305055099402_mask_257.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_257.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_257.jpg\n",
      "258\n",
      "ID00400637202305055099402_258.jpg\n",
      "ID00400637202305055099402_mask_258.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_258.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_258.jpg\n",
      "259\n",
      "ID00400637202305055099402_259.jpg\n",
      "ID00400637202305055099402_mask_259.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_259.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_259.jpg\n",
      "260\n",
      "ID00400637202305055099402_260.jpg\n",
      "ID00400637202305055099402_mask_260.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_260.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_260.jpg\n",
      "261\n",
      "ID00400637202305055099402_261.jpg\n",
      "ID00400637202305055099402_mask_261.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_261.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_261.jpg\n",
      "262\n",
      "ID00400637202305055099402_262.jpg\n",
      "ID00400637202305055099402_mask_262.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_262.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_262.jpg\n",
      "263\n",
      "ID00400637202305055099402_263.jpg\n",
      "ID00400637202305055099402_mask_263.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_263.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_263.jpg\n",
      "264\n",
      "ID00400637202305055099402_264.jpg\n",
      "ID00400637202305055099402_mask_264.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/images/images/ID00400637202305055099402_264.jpg\n",
      "/media/datasets/IAMED/Cap09/Mini-Projeto6/dados/masks/masks/ID00400637202305055099402_mask_264.jpg\n"
     ]
    }
   ],
   "source": [
    "# Executa a função\n",
    "sobrepoe_mascaras_full_ctscan(ct_scan_id_df = full_scan_example, path_to_save = PATH_TO_SAVE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 1.85 s, sys: 24.5 ms, total: 1.87 s\n",
      "Wall time: 1.87 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "cria_video(path_to_imgs = PATH_TO_SAVE, video_name = \"videos/\" + id_ + \"_ground_truth\", framerate = 30)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Geração de Vídeo das Previsões do Modelo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "device: cuda\n"
     ]
    }
   ],
   "source": [
    "# Previsões\n",
    "imgs, predictions = get_id_predictions(net = modelo,\n",
    "                                       ct_scan_id_df = full_scan_example,\n",
    "                                       root_imgs_dir = config.path_to_imgs_dir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pasta videos/ID00400637202305055099402_predictions criada.\n",
      "CPU times: user 56.1 s, sys: 90.7 ms, total: 56.2 s\n",
      "Wall time: 56.2 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "PATH_TO_SAVE = \"videos/\" + id_ + \"_predictions\"\n",
    "\n",
    "if not os.path.exists(PATH_TO_SAVE):\n",
    "    os.mkdir(PATH_TO_SAVE)\n",
    "    print(f\"Pasta {PATH_TO_SAVE} criada.\")\n",
    "\n",
    "_= [sobrepoe_imagem_mascara(one_slice_image = image,\n",
    "                            one_slice_mask = mask, \n",
    "                            write = True,\n",
    "                            path_to_save = PATH_TO_SAVE,\n",
    "                            name_to_save = str(i_name)) \n",
    "    for i_name, (image, mask) in enumerate(zip(imgs, predictions))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 1.82 s, sys: 32.3 ms, total: 1.85 s\n",
      "Wall time: 1.85 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "cria_video(path_to_imgs = PATH_TO_SAVE, \n",
    "           video_name = \"videos/\" + id_ + \"_predictions\", framerate = 30)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}