Dataset exploration.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "45f35cfb",
   "metadata": {},
   "source": [
    "# Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "790c7c75",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import nibabel as nib\n",
    "import numpy as np\n",
    "import albumentations as A\n",
    "import plotly.graph_objects as go\n",
    "import imageio\n",
    "from PIL import Image\n",
    "import nibabel as nib\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import cv2\n",
    "import winsound\n",
    "import functions"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "08f010c3",
   "metadata": {},
   "source": [
    "# Functions"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "6458853e",
   "metadata": {
    "scrolled": false
   },
   "source": [
    "# BRATS 2023 DATA"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "4425accc",
   "metadata": {},
   "source": [
    "### USEFUL INFO ON DATA"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "e459babc",
   "metadata": {},
   "source": [
    "All BraTS mpMRI scans are available as NIfTI files (.nii.gz) and describe\n",
    "1. native (T1)\n",
    "2. post-contrast T1-weighted (T1Gd)\n",
    "3. T2-weighted (T2), and\n",
    "4. T2 Fluid Attenuated Inversion Recovery (T2-FLAIR) volumes"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "b9fb71b3",
   "metadata": {},
   "source": [
    "Task: Tumor Sub-region Segmentation\n",
    "The sub-regions considered for evaluation are the:\n",
    "1.\"enhancing tumor\" (ET)\n",
    "2.\"tumor core\" (TC)\n",
    "3.\"whole tumor\" (WT) \n",
    "The ET is described by areas that show hyper-intensity in T1Gd when compared to T1, but also when compared to “healthy” white matter in T1Gd.\\\\\n",
    "The TC describes the bulk of the tumor, which is what is typically resected. The TC entails the ET, as well as the necrotic (NCR) parts of the tumor. The appearance of NCR is typically hypo-intense in T1-Gd when compared to T1. The WT describes the complete extent of the disease, as it entails the TC and the peritumoral edematous/invaded tissue (ED), which is typically depicted by hyper-intense signal in FLAIR."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "2addebe0",
   "metadata": {},
   "source": [
    "The submitted methods will be assessed following the configuration of previous BraTS challenges, using the \"Dice Similarity Coefficient\", and the \"Hausdorff distance (95%)\" [4-5]. The aggregate rankings from these two metrics will determine the recipients of monetary awards.\n",
    "\n",
    "Consistent with the BraTS 2021 Challenge, methods will also be assessed using the metrics of \"Sensitivity\" and \"Specificity\" to determine potential over- or under-segmentations of the tumor sub-regions by participating methods. These metrics are provided for participant guidance, and will not impact aggregate rankings."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "e71c6549",
   "metadata": {},
   "source": [
    "The provided segmentation labels have values of:\n",
    "\n",
    "    1 for NCR\n",
    "    2 for ED\n",
    "    3 for ET\n",
    "    0 for everything else.\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "d502e3b0",
   "metadata": {},
   "source": [
    "## Download"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "336ff96d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Welcome, Minty_ines!\n",
      "\n"
     ]
    }
   ],
   "source": [
    "syn = synapseclient.Synapse() \n",
    "syn.login('Minty_ines','s-}q.^!p\"bG4hcH') \n",
    "#files = synapseutils.syncFromSynapse(syn, ' syn51514105 ') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b72e93f9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Welcome, Minty_ines!\n",
      "\n"
     ]
    }
   ],
   "source": [
    "syn = synapseclient.login('Minty_ines','s-}q.^!p\"bG4hcH')\n",
    "dl_list_file_entities = syn.get_download_list()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "78f2de23",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'manifest_1684946314171461800.csv'"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dl_list_file_entities"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "d66d1bfd",
   "metadata": {},
   "source": [
    "## Creating a metadata.csv for training and validation\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "2689c434",
   "metadata": {},
   "outputs": [],
   "source": [
    "path_dataset = r\"C:\\Users\\bouhe\\OneDrive\\Bureau\\pfe\\dataset\\Nifti format BratS\"\n",
    "path_train = r\"C:\\Users\\bouhe\\OneDrive\\Bureau\\pfe\\dataset\\Nifti format BratS\\ASNR-MICCAI-BraTS2023-GLI-Challenge-TrainingData\"\n",
    "path_valid = r\"C:\\Users\\bouhe\\OneDrive\\Bureau\\pfe\\dataset\\Nifti format BratS\\ASNR-MICCAI-BraTS2023-GLI-Challenge-ValidationData\"\n",
    "path_test = \"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "dcf2d95a",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_list_path = os.listdir(path_train)\n",
    "#valid_list_path = os.listdir(path_valid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "1e3e58ce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['BraTS-GLI-00000-000-seg.nii.gz',\n",
       " 'BraTS-GLI-00000-000-t1c.nii.gz',\n",
       " 'BraTS-GLI-00000-000-t1n.nii.gz',\n",
       " 'BraTS-GLI-00000-000-t2f.nii.gz',\n",
       " 'BraTS-GLI-00000-000-t2w.nii.gz']"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.listdir(r'C:\\Users\\bouhe\\OneDrive\\Bureau\\pfe\\dataset\\Nifti format BratS\\ASNR-MICCAI-BraTS2023-GLI-Challenge-TrainingData\\BraTS-GLI-00000-000')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "730fff69",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'BraTS-GLI-00000-000'"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.path.basename(r'C:\\Users\\bouhe\\OneDrive\\Bureau\\pfe\\dataset\\Nifti format BratS\\ASNR-MICCAI-BraTS2023-GLI-Challenge-TrainingData\\BraTS-GLI-00000-000')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "7c8d9719",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Path of the folder containing the dataset\n",
    "path_train = r\"C:\\Users\\bouhe\\OneDrive\\Bureau\\pfe\\dataset\\Nifti format BratS\\ASNR-MICCAI-BraTS2023-GLI-Challenge-TrainingData\"\n",
    "\n",
    "#Returns the list of the folders containing each patient's MRI\n",
    "train_list_path = os.listdir(path_train)\n",
    "\n",
    "#Iterates recursively through the folders and creates a list of images of each MRI sequence and assign respectively\n",
    "seg,t1c,t1n,t2w =[],[],[],[]\n",
    "for dir in train_list_path:\n",
    "\n",
    "    curr_seg = os.path.join(dir,os.path.basename(dir)+'-'+r'seg.nii.gz')\n",
    "    seg.append(curr_seg)\n",
    "    \n",
    "    curr_t1c = os.path.join(dir,os.path.basename(dir)+'-'+r't1c.nii.gz')\n",
    "    t1c.append(curr_t1c)\n",
    "    \n",
    "    curr_t1n = os.path.join(dir,os.path.basename(dir)+'-'+r't1n.nii.gz')\n",
    "    t1n.append(curr_t1n)\n",
    "    \n",
    "    curr_t2w = os.path.join(dir,os.path.basename(dir)+'-'+r't2w.nii.gz')\n",
    "    t2w.append(curr_t2w)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "0438599a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Creates a dataframe for the metadata.csv\n",
    "df = pd.DataFrame({'path_folder' : train_list_path,\n",
    "              'seg' : seg,\n",
    "              't1c' : t1c,\n",
    "              't1n' : t1n,\n",
    "              't2w' : t2w\n",
    "             })"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "8ac0fcc8",
   "metadata": {},
   "outputs": [],
   "source": [
    "df[['a','b','id_patient','index']] = df['path_folder'].str.split('-',expand=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "6fdb52aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(['a', 'b'], axis = 1, inplace = True) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "0387194f",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>path_folder</th>\n",
       "      <th>seg</th>\n",
       "      <th>t1c</th>\n",
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       "      <td>BraTS-GLI-00002-000\\BraTS-GLI-00002-000-t2w.ni...</td>\n",
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       "      <td>BraTS-GLI-00003-000\\BraTS-GLI-00003-000-seg.ni...</td>\n",
       "      <td>BraTS-GLI-00003-000\\BraTS-GLI-00003-000-t1c.ni...</td>\n",
       "      <td>BraTS-GLI-00003-000\\BraTS-GLI-00003-000-t1n.ni...</td>\n",
       "      <td>BraTS-GLI-00003-000\\BraTS-GLI-00003-000-t2w.ni...</td>\n",
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       "      <td>000</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BraTS-GLI-00005-000</td>\n",
       "      <td>BraTS-GLI-00005-000\\BraTS-GLI-00005-000-seg.ni...</td>\n",
       "      <td>BraTS-GLI-00005-000\\BraTS-GLI-00005-000-t1c.ni...</td>\n",
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       "      <td>BraTS-GLI-00005-000\\BraTS-GLI-00005-000-t2w.ni...</td>\n",
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       "      <td>000</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>BraTS-GLI-00006-000</td>\n",
       "      <td>BraTS-GLI-00006-000\\BraTS-GLI-00006-000-seg.ni...</td>\n",
       "      <td>BraTS-GLI-00006-000\\BraTS-GLI-00006-000-t1c.ni...</td>\n",
       "      <td>BraTS-GLI-00006-000\\BraTS-GLI-00006-000-t1n.ni...</td>\n",
       "      <td>BraTS-GLI-00006-000\\BraTS-GLI-00006-000-t2w.ni...</td>\n",
       "      <td>00006</td>\n",
       "      <td>000</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>1246</th>\n",
       "      <td>BraTS-GLI-01662-000</td>\n",
       "      <td>BraTS-GLI-01662-000\\BraTS-GLI-01662-000-seg.ni...</td>\n",
       "      <td>BraTS-GLI-01662-000\\BraTS-GLI-01662-000-t1c.ni...</td>\n",
       "      <td>BraTS-GLI-01662-000\\BraTS-GLI-01662-000-t1n.ni...</td>\n",
       "      <td>BraTS-GLI-01662-000\\BraTS-GLI-01662-000-t2w.ni...</td>\n",
       "      <td>01662</td>\n",
       "      <td>000</td>\n",
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       "      <th>1247</th>\n",
       "      <td>BraTS-GLI-01663-000</td>\n",
       "      <td>BraTS-GLI-01663-000\\BraTS-GLI-01663-000-seg.ni...</td>\n",
       "      <td>BraTS-GLI-01663-000\\BraTS-GLI-01663-000-t1c.ni...</td>\n",
       "      <td>BraTS-GLI-01663-000\\BraTS-GLI-01663-000-t1n.ni...</td>\n",
       "      <td>BraTS-GLI-01663-000\\BraTS-GLI-01663-000-t2w.ni...</td>\n",
       "      <td>01663</td>\n",
       "      <td>000</td>\n",
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       "      <td>BraTS-GLI-01664-000</td>\n",
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       "      <td>BraTS-GLI-01664-000\\BraTS-GLI-01664-000-t1c.ni...</td>\n",
       "      <td>BraTS-GLI-01664-000\\BraTS-GLI-01664-000-t1n.ni...</td>\n",
       "      <td>BraTS-GLI-01664-000\\BraTS-GLI-01664-000-t2w.ni...</td>\n",
       "      <td>01664</td>\n",
       "      <td>000</td>\n",
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       "      <td>BraTS-GLI-01665-000</td>\n",
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       "      <td>BraTS-GLI-01665-000\\BraTS-GLI-01665-000-t1c.ni...</td>\n",
       "      <td>BraTS-GLI-01665-000\\BraTS-GLI-01665-000-t1n.ni...</td>\n",
       "      <td>BraTS-GLI-01665-000\\BraTS-GLI-01665-000-t2w.ni...</td>\n",
       "      <td>01665</td>\n",
       "      <td>000</td>\n",
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       "      <td>BraTS-GLI-01666-000\\BraTS-GLI-01666-000-t1c.ni...</td>\n",
       "      <td>BraTS-GLI-01666-000\\BraTS-GLI-01666-000-t1n.ni...</td>\n",
       "      <td>BraTS-GLI-01666-000\\BraTS-GLI-01666-000-t2w.ni...</td>\n",
       "      <td>01666</td>\n",
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      "text/plain": [
       "              path_folder                                                seg   \n",
       "0     BraTS-GLI-00000-000  BraTS-GLI-00000-000\\BraTS-GLI-00000-000-seg.ni...  \\\n",
       "1     BraTS-GLI-00002-000  BraTS-GLI-00002-000\\BraTS-GLI-00002-000-seg.ni...   \n",
       "2     BraTS-GLI-00003-000  BraTS-GLI-00003-000\\BraTS-GLI-00003-000-seg.ni...   \n",
       "3     BraTS-GLI-00005-000  BraTS-GLI-00005-000\\BraTS-GLI-00005-000-seg.ni...   \n",
       "4     BraTS-GLI-00006-000  BraTS-GLI-00006-000\\BraTS-GLI-00006-000-seg.ni...   \n",
       "...                   ...                                                ...   \n",
       "1246  BraTS-GLI-01662-000  BraTS-GLI-01662-000\\BraTS-GLI-01662-000-seg.ni...   \n",
       "1247  BraTS-GLI-01663-000  BraTS-GLI-01663-000\\BraTS-GLI-01663-000-seg.ni...   \n",
       "1248  BraTS-GLI-01664-000  BraTS-GLI-01664-000\\BraTS-GLI-01664-000-seg.ni...   \n",
       "1249  BraTS-GLI-01665-000  BraTS-GLI-01665-000\\BraTS-GLI-01665-000-seg.ni...   \n",
       "1250  BraTS-GLI-01666-000  BraTS-GLI-01666-000\\BraTS-GLI-01666-000-seg.ni...   \n",
       "\n",
       "                                                    t1c   \n",
       "0     BraTS-GLI-00000-000\\BraTS-GLI-00000-000-t1c.ni...  \\\n",
       "1     BraTS-GLI-00002-000\\BraTS-GLI-00002-000-t1c.ni...   \n",
       "2     BraTS-GLI-00003-000\\BraTS-GLI-00003-000-t1c.ni...   \n",
       "3     BraTS-GLI-00005-000\\BraTS-GLI-00005-000-t1c.ni...   \n",
       "4     BraTS-GLI-00006-000\\BraTS-GLI-00006-000-t1c.ni...   \n",
       "...                                                 ...   \n",
       "1246  BraTS-GLI-01662-000\\BraTS-GLI-01662-000-t1c.ni...   \n",
       "1247  BraTS-GLI-01663-000\\BraTS-GLI-01663-000-t1c.ni...   \n",
       "1248  BraTS-GLI-01664-000\\BraTS-GLI-01664-000-t1c.ni...   \n",
       "1249  BraTS-GLI-01665-000\\BraTS-GLI-01665-000-t1c.ni...   \n",
       "1250  BraTS-GLI-01666-000\\BraTS-GLI-01666-000-t1c.ni...   \n",
       "\n",
       "                                                    t1n   \n",
       "0     BraTS-GLI-00000-000\\BraTS-GLI-00000-000-t1n.ni...  \\\n",
       "1     BraTS-GLI-00002-000\\BraTS-GLI-00002-000-t1n.ni...   \n",
       "2     BraTS-GLI-00003-000\\BraTS-GLI-00003-000-t1n.ni...   \n",
       "3     BraTS-GLI-00005-000\\BraTS-GLI-00005-000-t1n.ni...   \n",
       "4     BraTS-GLI-00006-000\\BraTS-GLI-00006-000-t1n.ni...   \n",
       "...                                                 ...   \n",
       "1246  BraTS-GLI-01662-000\\BraTS-GLI-01662-000-t1n.ni...   \n",
       "1247  BraTS-GLI-01663-000\\BraTS-GLI-01663-000-t1n.ni...   \n",
       "1248  BraTS-GLI-01664-000\\BraTS-GLI-01664-000-t1n.ni...   \n",
       "1249  BraTS-GLI-01665-000\\BraTS-GLI-01665-000-t1n.ni...   \n",
       "1250  BraTS-GLI-01666-000\\BraTS-GLI-01666-000-t1n.ni...   \n",
       "\n",
       "                                                    t2w id_patient index  \n",
       "0     BraTS-GLI-00000-000\\BraTS-GLI-00000-000-t2w.ni...      00000   000  \n",
       "1     BraTS-GLI-00002-000\\BraTS-GLI-00002-000-t2w.ni...      00002   000  \n",
       "2     BraTS-GLI-00003-000\\BraTS-GLI-00003-000-t2w.ni...      00003   000  \n",
       "3     BraTS-GLI-00005-000\\BraTS-GLI-00005-000-t2w.ni...      00005   000  \n",
       "4     BraTS-GLI-00006-000\\BraTS-GLI-00006-000-t2w.ni...      00006   000  \n",
       "...                                                 ...        ...   ...  \n",
       "1246  BraTS-GLI-01662-000\\BraTS-GLI-01662-000-t2w.ni...      01662   000  \n",
       "1247  BraTS-GLI-01663-000\\BraTS-GLI-01663-000-t2w.ni...      01663   000  \n",
       "1248  BraTS-GLI-01664-000\\BraTS-GLI-01664-000-t2w.ni...      01664   000  \n",
       "1249  BraTS-GLI-01665-000\\BraTS-GLI-01665-000-t2w.ni...      01665   000  \n",
       "1250  BraTS-GLI-01666-000\\BraTS-GLI-01666-000-t2w.ni...      01666   000  \n",
       "\n",
       "[1251 rows x 7 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "59949aa6",
   "metadata": {},
   "outputs": [],
   "source": [
    "metadata_train_path =r\"C:\\Users\\bouhe\\OneDrive\\Bureau\\pfe\\dataset\\Nifti format BratS\\metadata_training.csv\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "fcbfb215",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv(metadata_train_path,index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "fc5c70a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "t1c,t1n,t2f,t2w =[],[],[],[]\n",
    "for dir in valid_list_path:\n",
    "    curr_t1c = os.path.join(dir,os.path.basename(dir)+'-'+r't1c.nii.gz')\n",
    "    t1c.append(curr_t1c)\n",
    "    curr_t1n = os.path.join(dir,os.path.basename(dir)+'-'+r't1n.nii.gz')\n",
    "    t1n.append(curr_t1n)\n",
    "    curr_t2f = os.path.join(dir,os.path.basename(dir)+'-'+r't2f.nii.gz')\n",
    "    t2f.append(curr_t2f)\n",
    "    curr_t2w = os.path.join(dir,os.path.basename(dir)+'-'+r't2w.nii.gz')\n",
    "    t2w.append(curr_t2w)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "13672a0a",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "df = pd.DataFrame({'path_folder' : valid_list_path,\n",
    "              't1c' : t1c,\n",
    "              't1n' : t1n,\n",
    "              't2f' : t2f,\n",
    "              't2w' : t2w\n",
    "             })"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "2c9e1bca",
   "metadata": {},
   "outputs": [],
   "source": [
    "df[['a','b','id_patient','index']] = df['path_folder'].str.split('-',expand=True)\n",
    "df.drop(['a', 'b'], axis = 1, inplace = True) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "92c11da0",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>path_folder</th>\n",
       "      <th>t1c</th>\n",
       "      <th>t1n</th>\n",
       "      <th>t2f</th>\n",
       "      <th>t2w</th>\n",
       "      <th>id_patient</th>\n",
       "      <th>index</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>BraTS-GLI-00001-000</td>\n",
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       "      <td>BraTS-GLI-00001-001\\BraTS-GLI-00001-001-t2w.ni...</td>\n",
       "      <td>00001</td>\n",
       "      <td>001</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>BraTS-GLI-00013-000</td>\n",
       "      <td>BraTS-GLI-00013-000\\BraTS-GLI-00013-000-t1c.ni...</td>\n",
       "      <td>BraTS-GLI-00013-000\\BraTS-GLI-00013-000-t1n.ni...</td>\n",
       "      <td>BraTS-GLI-00013-000\\BraTS-GLI-00013-000-t2f.ni...</td>\n",
       "      <td>BraTS-GLI-00013-000\\BraTS-GLI-00013-000-t2w.ni...</td>\n",
       "      <td>00013</td>\n",
       "      <td>000</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BraTS-GLI-00013-001</td>\n",
       "      <td>BraTS-GLI-00013-001\\BraTS-GLI-00013-001-t1c.ni...</td>\n",
       "      <td>BraTS-GLI-00013-001\\BraTS-GLI-00013-001-t1n.ni...</td>\n",
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       "      <td>BraTS-GLI-00013-001\\BraTS-GLI-00013-001-t2w.ni...</td>\n",
       "      <td>00013</td>\n",
       "      <td>001</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>BraTS-GLI-00015-000</td>\n",
       "      <td>BraTS-GLI-00015-000\\BraTS-GLI-00015-000-t1c.ni...</td>\n",
       "      <td>BraTS-GLI-00015-000\\BraTS-GLI-00015-000-t1n.ni...</td>\n",
       "      <td>BraTS-GLI-00015-000\\BraTS-GLI-00015-000-t2f.ni...</td>\n",
       "      <td>BraTS-GLI-00015-000\\BraTS-GLI-00015-000-t2w.ni...</td>\n",
       "      <td>00015</td>\n",
       "      <td>000</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",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>214</th>\n",
       "      <td>BraTS-GLI-01774-000</td>\n",
       "      <td>BraTS-GLI-01774-000\\BraTS-GLI-01774-000-t1c.ni...</td>\n",
       "      <td>BraTS-GLI-01774-000\\BraTS-GLI-01774-000-t1n.ni...</td>\n",
       "      <td>BraTS-GLI-01774-000\\BraTS-GLI-01774-000-t2f.ni...</td>\n",
       "      <td>BraTS-GLI-01774-000\\BraTS-GLI-01774-000-t2w.ni...</td>\n",
       "      <td>01774</td>\n",
       "      <td>000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>215</th>\n",
       "      <td>BraTS-GLI-01775-000</td>\n",
       "      <td>BraTS-GLI-01775-000\\BraTS-GLI-01775-000-t1c.ni...</td>\n",
       "      <td>BraTS-GLI-01775-000\\BraTS-GLI-01775-000-t1n.ni...</td>\n",
       "      <td>BraTS-GLI-01775-000\\BraTS-GLI-01775-000-t2f.ni...</td>\n",
       "      <td>BraTS-GLI-01775-000\\BraTS-GLI-01775-000-t2w.ni...</td>\n",
       "      <td>01775</td>\n",
       "      <td>000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>216</th>\n",
       "      <td>BraTS-GLI-01776-000</td>\n",
       "      <td>BraTS-GLI-01776-000\\BraTS-GLI-01776-000-t1c.ni...</td>\n",
       "      <td>BraTS-GLI-01776-000\\BraTS-GLI-01776-000-t1n.ni...</td>\n",
       "      <td>BraTS-GLI-01776-000\\BraTS-GLI-01776-000-t2f.ni...</td>\n",
       "      <td>BraTS-GLI-01776-000\\BraTS-GLI-01776-000-t2w.ni...</td>\n",
       "      <td>01776</td>\n",
       "      <td>000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>217</th>\n",
       "      <td>BraTS-GLI-01789-000</td>\n",
       "      <td>BraTS-GLI-01789-000\\BraTS-GLI-01789-000-t1c.ni...</td>\n",
       "      <td>BraTS-GLI-01789-000\\BraTS-GLI-01789-000-t1n.ni...</td>\n",
       "      <td>BraTS-GLI-01789-000\\BraTS-GLI-01789-000-t2f.ni...</td>\n",
       "      <td>BraTS-GLI-01789-000\\BraTS-GLI-01789-000-t2w.ni...</td>\n",
       "      <td>01789</td>\n",
       "      <td>000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>218</th>\n",
       "      <td>BraTS-GLI-01790-000</td>\n",
       "      <td>BraTS-GLI-01790-000\\BraTS-GLI-01790-000-t1c.ni...</td>\n",
       "      <td>BraTS-GLI-01790-000\\BraTS-GLI-01790-000-t1n.ni...</td>\n",
       "      <td>BraTS-GLI-01790-000\\BraTS-GLI-01790-000-t2f.ni...</td>\n",
       "      <td>BraTS-GLI-01790-000\\BraTS-GLI-01790-000-t2w.ni...</td>\n",
       "      <td>01790</td>\n",
       "      <td>000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>219 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             path_folder                                                t1c   \n",
       "0    BraTS-GLI-00001-000  BraTS-GLI-00001-000\\BraTS-GLI-00001-000-t1c.ni...  \\\n",
       "1    BraTS-GLI-00001-001  BraTS-GLI-00001-001\\BraTS-GLI-00001-001-t1c.ni...   \n",
       "2    BraTS-GLI-00013-000  BraTS-GLI-00013-000\\BraTS-GLI-00013-000-t1c.ni...   \n",
       "3    BraTS-GLI-00013-001  BraTS-GLI-00013-001\\BraTS-GLI-00013-001-t1c.ni...   \n",
       "4    BraTS-GLI-00015-000  BraTS-GLI-00015-000\\BraTS-GLI-00015-000-t1c.ni...   \n",
       "..                   ...                                                ...   \n",
       "214  BraTS-GLI-01774-000  BraTS-GLI-01774-000\\BraTS-GLI-01774-000-t1c.ni...   \n",
       "215  BraTS-GLI-01775-000  BraTS-GLI-01775-000\\BraTS-GLI-01775-000-t1c.ni...   \n",
       "216  BraTS-GLI-01776-000  BraTS-GLI-01776-000\\BraTS-GLI-01776-000-t1c.ni...   \n",
       "217  BraTS-GLI-01789-000  BraTS-GLI-01789-000\\BraTS-GLI-01789-000-t1c.ni...   \n",
       "218  BraTS-GLI-01790-000  BraTS-GLI-01790-000\\BraTS-GLI-01790-000-t1c.ni...   \n",
       "\n",
       "                                                   t1n   \n",
       "0    BraTS-GLI-00001-000\\BraTS-GLI-00001-000-t1n.ni...  \\\n",
       "1    BraTS-GLI-00001-001\\BraTS-GLI-00001-001-t1n.ni...   \n",
       "2    BraTS-GLI-00013-000\\BraTS-GLI-00013-000-t1n.ni...   \n",
       "3    BraTS-GLI-00013-001\\BraTS-GLI-00013-001-t1n.ni...   \n",
       "4    BraTS-GLI-00015-000\\BraTS-GLI-00015-000-t1n.ni...   \n",
       "..                                                 ...   \n",
       "214  BraTS-GLI-01774-000\\BraTS-GLI-01774-000-t1n.ni...   \n",
       "215  BraTS-GLI-01775-000\\BraTS-GLI-01775-000-t1n.ni...   \n",
       "216  BraTS-GLI-01776-000\\BraTS-GLI-01776-000-t1n.ni...   \n",
       "217  BraTS-GLI-01789-000\\BraTS-GLI-01789-000-t1n.ni...   \n",
       "218  BraTS-GLI-01790-000\\BraTS-GLI-01790-000-t1n.ni...   \n",
       "\n",
       "                                                   t2f   \n",
       "0    BraTS-GLI-00001-000\\BraTS-GLI-00001-000-t2f.ni...  \\\n",
       "1    BraTS-GLI-00001-001\\BraTS-GLI-00001-001-t2f.ni...   \n",
       "2    BraTS-GLI-00013-000\\BraTS-GLI-00013-000-t2f.ni...   \n",
       "3    BraTS-GLI-00013-001\\BraTS-GLI-00013-001-t2f.ni...   \n",
       "4    BraTS-GLI-00015-000\\BraTS-GLI-00015-000-t2f.ni...   \n",
       "..                                                 ...   \n",
       "214  BraTS-GLI-01774-000\\BraTS-GLI-01774-000-t2f.ni...   \n",
       "215  BraTS-GLI-01775-000\\BraTS-GLI-01775-000-t2f.ni...   \n",
       "216  BraTS-GLI-01776-000\\BraTS-GLI-01776-000-t2f.ni...   \n",
       "217  BraTS-GLI-01789-000\\BraTS-GLI-01789-000-t2f.ni...   \n",
       "218  BraTS-GLI-01790-000\\BraTS-GLI-01790-000-t2f.ni...   \n",
       "\n",
       "                                                   t2w id_patient index  \n",
       "0    BraTS-GLI-00001-000\\BraTS-GLI-00001-000-t2w.ni...      00001   000  \n",
       "1    BraTS-GLI-00001-001\\BraTS-GLI-00001-001-t2w.ni...      00001   001  \n",
       "2    BraTS-GLI-00013-000\\BraTS-GLI-00013-000-t2w.ni...      00013   000  \n",
       "3    BraTS-GLI-00013-001\\BraTS-GLI-00013-001-t2w.ni...      00013   001  \n",
       "4    BraTS-GLI-00015-000\\BraTS-GLI-00015-000-t2w.ni...      00015   000  \n",
       "..                                                 ...        ...   ...  \n",
       "214  BraTS-GLI-01774-000\\BraTS-GLI-01774-000-t2w.ni...      01774   000  \n",
       "215  BraTS-GLI-01775-000\\BraTS-GLI-01775-000-t2w.ni...      01775   000  \n",
       "216  BraTS-GLI-01776-000\\BraTS-GLI-01776-000-t2w.ni...      01776   000  \n",
       "217  BraTS-GLI-01789-000\\BraTS-GLI-01789-000-t2w.ni...      01789   000  \n",
       "218  BraTS-GLI-01790-000\\BraTS-GLI-01790-000-t2w.ni...      01790   000  \n",
       "\n",
       "[219 rows x 7 columns]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "a415b3fd",
   "metadata": {},
   "outputs": [],
   "source": [
    "metadata_valid_path = r\"C:\\Users\\bouhe\\OneDrive\\Bureau\\pfe\\dataset\\Nifti format BratS\\metadata_validation.csv\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "bfb99c2e",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv(metadata_valid_path, index=False)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "4e51932e",
   "metadata": {},
   "source": [
    "## Visualizing the Images (nifti format)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "cec002ef",
   "metadata": {},
   "outputs": [],
   "source": [
    "metadata_train_path=r\"C:\\Users\\bouhe\\OneDrive\\Bureau\\pfe\\dataset\\Nifti format BratS\\metadata_training.csv\"\n",
    "metadata_valid_path=r\"C:\\Users\\bouhe\\OneDrive\\Bureau\\pfe\\dataset\\Nifti format BratS\\metadata_validation.csv\"\n",
    "parent_dir_train = r'C:\\Users\\bouhe\\OneDrive\\Bureau\\pfe\\dataset\\Nifti format BratS\\ASNR-MICCAI-BraTS2023-GLI-Challenge-TrainingData'\n",
    "parent_dir_valid = r'C:\\Users\\bouhe\\OneDrive\\Bureau\\pfe\\dataset\\Nifti format BratS\\ASNR-MICCAI-BraTS2023-GLI-Challenge-ValidationData'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "a1b73aff",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Reading the metadata\n",
    "dt = pd.read_csv(metadata_train_path) #training metadata\n",
    "#dv = pd.read_csv(metadata_valid_path) #validation metadata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "1000eae7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              path_folder                                                seg   \n",
      "1009  BraTS-GLI-01307-000  BraTS-GLI-01307-000\\BraTS-GLI-01307-000-seg.ni...  \\\n",
      "\n",
      "                                                    t1c   \n",
      "1009  BraTS-GLI-01307-000\\BraTS-GLI-01307-000-t1c.ni...  \\\n",
      "\n",
      "                                                    t1n   \n",
      "1009  BraTS-GLI-01307-000\\BraTS-GLI-01307-000-t1n.ni...  \\\n",
      "\n",
      "                                                    t2f   \n",
      "1009  BraTS-GLI-01307-000\\BraTS-GLI-01307-000-t2f.ni...  \\\n",
      "\n",
      "                                                    t2w  id_patient  index  \n",
      "1009  BraTS-GLI-01307-000\\BraTS-GLI-01307-000-t2w.ni...        1307      0  \n"
     ]
    }
   ],
   "source": [
    "dt_row = dt.sample(n=1)\n",
    "print(dt_row)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "41f29615",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             path_folder                                                seg   \n",
      "264  BraTS-GLI-00329-000  BraTS-GLI-00329-000\\BraTS-GLI-00329-000-seg.ni...  \\\n",
      "\n",
      "                                                   t1c   \n",
      "264  BraTS-GLI-00329-000\\BraTS-GLI-00329-000-t1c.ni...  \\\n",
      "\n",
      "                                                   t1n   \n",
      "264  BraTS-GLI-00329-000\\BraTS-GLI-00329-000-t1n.ni...  \\\n",
      "\n",
      "                                                   t2f   \n",
      "264  BraTS-GLI-00329-000\\BraTS-GLI-00329-000-t2f.ni...  \\\n",
      "\n",
      "                                                   t2w  id_patient  index  \n",
      "264  BraTS-GLI-00329-000\\BraTS-GLI-00329-000-t2w.ni...         329      0  \n"
     ]
    }
   ],
   "source": [
    "dt_row = dt.sample(n=1)\n",
    "print(dt_row)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "5493893f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['BraTS-GLI-00329-000\\\\BraTS-GLI-00329-000-seg.nii.gz', 'BraTS-GLI-00329-000\\\\BraTS-GLI-00329-000-t1c.nii.gz', 'BraTS-GLI-00329-000\\\\BraTS-GLI-00329-000-t1n.nii.gz', 'BraTS-GLI-00329-000\\\\BraTS-GLI-00329-000-t2f.nii.gz', 'BraTS-GLI-00329-000\\\\BraTS-GLI-00329-000-t2w.nii.gz']\n",
      "[329, 0]\n",
      "['seg', 't1c', 't1n', 't2w']\n"
     ]
    }
   ],
   "source": [
    "image_paths_abs = get_paths(parent_dir_train,dt_row)\n",
    "values_mris = df_row2list(dt_row,beg=-2)\n",
    "scan_type = [path.split('-')[-1].split('.')[0] for path in image_paths_abs]\n",
    "scan_type.pop(-2)\n",
    "image_paths_abs.pop(-2)\n",
    "\n",
    "print(values_mris)\n",
    "print(scan_type)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "d60d004e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x2000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "load_the_mris(image_paths_abs,scan_type,60,values_mris)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "65e63f54",
   "metadata": {},
   "outputs": [],
   "source": [
    "img_path= r\"C:\\Users\\bouhe\\OneDrive\\Bureau\\pfe\\dataset\\Nifti format BratS\\ASNR-MICCAI-BraTS2023-GLI-Challenge-TrainingData\\BraTS-GLI-00000-000\\BraTS-GLI-00000-000-t1c.nii.gz\"\n",
    "img_path_seg= r\"C:\\Users\\bouhe\\OneDrive\\Bureau\\pfe\\dataset\\Nifti format BratS\\ASNR-MICCAI-BraTS2023-GLI-Challenge-TrainingData\\BraTS-GLI-00000-000\\BraTS-GLI-00000-000-seg.nii.gz\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "75f08ddb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_nifti(img_path_seg,80)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "72bf7390",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_nifti(img_path,100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "2fc463f2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "13eee21b03b6458690f62016c853d312",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(IntSlider(value=77, description='layer', max=154), Output()), _dom_classes=('widget-inte…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "animate_img(img_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "36a920da",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ad7787d82f244fa88895ef75e72ab8b7",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(IntSlider(value=77, description='layer', max=154), Output()), _dom_classes=('widget-inte…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "animate_labels(img_path_seg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2de92bcf",
   "metadata": {},
   "outputs": [],
   "source": [
    "show_all_layers(img_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "9c1a34c8",
   "metadata": {},
   "outputs": [],
   "source": [
    "filename_to_convert=r\"C:\\Users\\bouhe\\OneDrive\\Bureau\\pfe\\image.gif\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "6053b0e9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "create_gif(nib.load(img_path), title='.gif', filename=filename_to_convert)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "052e749f",
   "metadata": {},
   "outputs": [],
   "source": [
    "image_0_seg = r\"C:\\Users\\bouhe\\OneDrive\\Bureau\\pfe\\dataset\\Nifti format BratS\\ASNR-MICCAI-BraTS2023-GLI-Challenge-TrainingData\\BraTS-GLI-00087-000\\BraTS-GLI-00087-000-seg.nii.gz\"\n",
    "image_1_seg = r\"C:\\Users\\bouhe\\OneDrive\\Bureau\\pfe\\dataset\\Nifti format BratS\\ASNR-MICCAI-BraTS2023-GLI-Challenge-TrainingData\\BraTS-GLI-00087-001\\BraTS-GLI-00087-001-seg.nii.gz\"\n",
    "image_0_t1c = r\"C:\\Users\\bouhe\\OneDrive\\Bureau\\pfe\\dataset\\Nifti format BratS\\ASNR-MICCAI-BraTS2023-GLI-Challenge-TrainingData\\BraTS-GLI-00087-000\\BraTS-GLI-00087-000-t1c.nii.gz\"\n",
    "image_1_t1c = r\"C:\\Users\\bouhe\\OneDrive\\Bureau\\pfe\\dataset\\Nifti format BratS\\ASNR-MICCAI-BraTS2023-GLI-Challenge-TrainingData\\BraTS-GLI-00087-001\\BraTS-GLI-00087-001-t1c.nii.gz\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "41aee757",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_nifti(image_0_t1c, 70)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "016003ee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_nifti(image_1_t1c, 70)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "95b32274",
   "metadata": {},
   "outputs": [],
   "source": [
    "loaded_0_seg =load_nifti(image_0_seg)\n",
    "loaded_1_seg =load_nifti(image_1_seg)\n",
    "slice_0 = get_slice(loaded_0_seg,60)\n",
    "slice_1 = get_slice(loaded_1_seg,60)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "cd4a2dc3",
   "metadata": {},
   "outputs": [],
   "source": [
    "unique, counts = np.unique(slice_0, return_counts=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "aa35bfa3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0.0: 55556, 1.0: 115, 2.0: 1199, 3.0: 730}"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dict(zip(unique, counts))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "0765f9a2",
   "metadata": {},
   "outputs": [],
   "source": [
    "loaded_0_t1c =nib.load(image_0_t1c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "4d3e5e41",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'nibabel.nifti1.Nifti1Header'> object, endian='<'\n",
      "sizeof_hdr      : 348\n",
      "data_type       : b''\n",
      "db_name         : b''\n",
      "extents         : 0\n",
      "session_error   : 0\n",
      "regular         : b'r'\n",
      "dim_info        : 0\n",
      "dim             : [  3 240 240 155   1   1   1   1]\n",
      "intent_p1       : 0.0\n",
      "intent_p2       : 0.0\n",
      "intent_p3       : 0.0\n",
      "intent_code     : none\n",
      "datatype        : int16\n",
      "bitpix          : 16\n",
      "slice_start     : 0\n",
      "pixdim          : [1. 1. 1. 1. 0. 0. 0. 0.]\n",
      "vox_offset      : 0.0\n",
      "scl_slope       : nan\n",
      "scl_inter       : nan\n",
      "slice_end       : 0\n",
      "slice_code      : unknown\n",
      "xyzt_units      : 2\n",
      "cal_max         : 0.0\n",
      "cal_min         : 0.0\n",
      "slice_duration  : 0.0\n",
      "toffset         : 0.0\n",
      "glmax           : 0\n",
      "glmin           : 0\n",
      "descrip         : b''\n",
      "aux_file        : b''\n",
      "qform_code      : scanner\n",
      "sform_code      : scanner\n",
      "quatern_b       : 0.0\n",
      "quatern_c       : 0.0\n",
      "quatern_d       : 1.0\n",
      "qoffset_x       : 0.0\n",
      "qoffset_y       : 239.0\n",
      "qoffset_z       : 0.0\n",
      "srow_x          : [-1. -0. -0.  0.]\n",
      "srow_y          : [ -0.  -1.  -0. 239.]\n",
      "srow_z          : [0. 0. 1. 0.]\n",
      "intent_name     : b''\n",
      "magic           : b'n+1'\n"
     ]
    }
   ],
   "source": [
    "print(loaded_0_t1c.header)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "20970150",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The scan data array has the shape:  (240, 240, 155)\n",
      "The scan header is as follows: \n",
      " <class 'nibabel.nifti1.Nifti1Header'> object, endian='<'\n",
      "sizeof_hdr      : 348\n",
      "data_type       : b''\n",
      "db_name         : b''\n",
      "extents         : 0\n",
      "session_error   : 0\n",
      "regular         : b'r'\n",
      "dim_info        : 0\n",
      "dim             : [  3 240 240 155   1   1   1   1]\n",
      "intent_p1       : 0.0\n",
      "intent_p2       : 0.0\n",
      "intent_p3       : 0.0\n",
      "intent_code     : none\n",
      "datatype        : int16\n",
      "bitpix          : 16\n",
      "slice_start     : 0\n",
      "pixdim          : [1. 1. 1. 1. 0. 0. 0. 0.]\n",
      "vox_offset      : 0.0\n",
      "scl_slope       : nan\n",
      "scl_inter       : nan\n",
      "slice_end       : 0\n",
      "slice_code      : unknown\n",
      "xyzt_units      : 2\n",
      "cal_max         : 0.0\n",
      "cal_min         : 0.0\n",
      "slice_duration  : 0.0\n",
      "toffset         : 0.0\n",
      "glmax           : 0\n",
      "glmin           : 0\n",
      "descrip         : b''\n",
      "aux_file        : b''\n",
      "qform_code      : scanner\n",
      "sform_code      : scanner\n",
      "quatern_b       : 0.0\n",
      "quatern_c       : 0.0\n",
      "quatern_d       : 1.0\n",
      "qoffset_x       : 0.0\n",
      "qoffset_y       : 239.0\n",
      "qoffset_z       : 0.0\n",
      "srow_x          : [-1. -0. -0.  0.]\n",
      "srow_y          : [ -0.  -1.  -0. 239.]\n",
      "srow_z          : [0. 0. 1. 0.]\n",
      "intent_name     : b''\n",
      "magic           : b'n+1'\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Define the filepath to your NIfTI scan\n",
    "scanFilePath = r\"C:\\Users\\bouhe\\OneDrive\\Bureau\\pfe\\dataset\\Nifti format BratS\\ASNR-MICCAI-BraTS2023-GLI-Challenge-TrainingData\\BraTS-GLI-00000-000\\BraTS-GLI-00000-000-t1c.nii.gz\"\n",
    " \n",
    "#Load the scan and extract data using nibabel \n",
    "scan = nib.load(scanFilePath)\n",
    "scanArray = scan.get_fdata()\n",
    "\n",
    "#Get and print the scan's shape \n",
    "scanArrayShape = scanArray.shape\n",
    "print('The scan data array has the shape: ', scanArrayShape)\n",
    " \n",
    "#Get and print the scan's header\n",
    "scanHeader = scan.header\n",
    "print('The scan header is as follows: \\n', scanHeader)\n",
    " \n",
    "#Display scan array's middle slices\n",
    "fig, axs = plt.subplots(1,3)\n",
    "fig.suptitle('Scan Array (Middle Slices)')\n",
    "axs[0].imshow(scanArray[scanArrayShape[0]//2,:,:], cmap='gray')\n",
    "axs[1].imshow(scanArray[:,scanArrayShape[1]//2,:], cmap='gray')\n",
    "axs[2].imshow(scanArray[:,:,scanArrayShape[2]//2], cmap='gray')\n",
    "fig.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "28d4f72a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The required aspect ratios are:  [1.0, 1.0, 1.0]\n"
     ]
    }
   ],
   "source": [
    "#Calculate proper aspect ratios\n",
    "pixDim = scanHeader['pixdim'][1:4]\n",
    "aspectRatios = [pixDim[1]/pixDim[2],pixDim[0]/pixDim[2],pixDim[0]/pixDim[1]]\n",
    "print('The required aspect ratios are: ', aspectRatios)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "46fbc121",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Display scan array's middle slices with proper aspect ratio\n",
    "fig, axs = plt.subplots(1,3)\n",
    "fig.suptitle('Scan Array w/ Proper Aspect Ratio (Middle Slices)')\n",
    "axs[0].imshow(scanArray[scanArrayShape[0]//2,:,:], cmap='gray')\n",
    "axs[1].imshow(scanArray[:,scanArrayShape[1]//2,:], cmap='gray')\n",
    "axs[2].imshow(scanArray[:,:,scanArrayShape[2]//2], cmap='gray')\n",
    "fig.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "d941d9f4",
   "metadata": {},
   "source": [
    "### Folder structures"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "7db19f9a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#name_folders_test = dv[\"path_folder\"].tolist()\n",
    "name_folders_train = dt[\"path_folder\"].tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "cd837253",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bouhe\\OneDrive\\Bureau\\pfe\\dataset\\png_format\\Train\n"
     ]
    }
   ],
   "source": [
    "%cd C:\\Users\\bouhe\\OneDrive\\Bureau\\pfe\\dataset\\png_format\\Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "1fdb5d9a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Le volume dans le lecteur C n'a pas de nom.\n",
      " Le num�ro de s�rie du volume est E4A6-EB66\n",
      "\n",
      " R�pertoire de c:\\Users\\bouhe\\OneDrive\\Bureau\\pfe\\dataset\\png_format\\Test\n",
      "\n",
      "31/05/2023  23:11    <DIR>          .\n",
      "31/05/2023  23:11    <DIR>          ..\n",
      "               0 fichier(s)                0 octets\n",
      "               2 R�p(s)  65�769�582�592 octets libres\n"
     ]
    }
   ],
   "source": [
    "%ls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "ab223cda",
   "metadata": {},
   "outputs": [],
   "source": [
    "#for name in name_folders_test:\n",
    "    #!mkdir $name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "685a02f9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c:\\Users\\bouhe\\OneDrive\\Bureau\\pfe\\dataset\\png_format\\Test\n"
     ]
    }
   ],
   "source": [
    "%cd Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "323d7636",
   "metadata": {},
   "outputs": [],
   "source": [
    "for name in name_folders_test:\n",
    "    %cd $name\n",
    "    #!rm -r 'seg'\n",
    "    %cd .."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "17020feb",
   "metadata": {},
   "outputs": [],
   "source": [
    "output_parent_dir_path_train = r\"C:\\Users\\bouhe\\OneDrive\\Bureau\\pfe\\dataset\\png_format\\Train\"\n",
    "output_parent_dir_path_test = r\"C:\\Users\\bouhe\\OneDrive\\Bureau\\pfe\\dataset\\png_format\\Test\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "36b5d743",
   "metadata": {},
   "outputs": [],
   "source": [
    "_,t1c,t1n,t2f,t2w = images_based_on_scan_type(dv,'test')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7da914cd",
   "metadata": {},
   "outputs": [],
   "source": [
    "t1n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "77c82676",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'C:\\\\Users\\\\bouhe\\\\OneDrive\\\\Bureau\\\\pfe\\\\dataset\\\\Nifti format BratS\\\\ASNR-MICCAI-BraTS2023-GLI-Challenge-ValidationData'"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "parent_dir_valid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "1f603d50",
   "metadata": {},
   "outputs": [],
   "source": [
    "savin_dims(t1c,'t1c',output_parent_dir_path_test,parent_dir_valid,name_folders_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "544128a3",
   "metadata": {},
   "outputs": [],
   "source": [
    "savin_dims(t1n,'t1n',output_parent_dir_path_test,parent_dir_valid,name_folders_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "2d1a7f69",
   "metadata": {},
   "outputs": [],
   "source": [
    "savin_dims(t2f,'ta2f',output_parent_dir_path_test,parent_dir_valid,name_folders_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "9308267e",
   "metadata": {},
   "outputs": [],
   "source": [
    "savin_dims(t2w,'t2w',output_parent_dir_path_test,parent_dir_valid,name_folders_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "9cfce0c1",
   "metadata": {},
   "outputs": [],
   "source": [
    "name_folders_seg = os.listdir(r'C:\\Users\\bouhe\\OneDrive\\Bureau\\pfe\\dataset\\png_format\\train_all_labels')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "48eb2d55",
   "metadata": {},
   "outputs": [],
   "source": [
    "savin_dims(seg,'seg',r'C:\\Users\\bouhe\\OneDrive\\Bureau\\pfe\\dataset\\png_format\\train_all_labels',parent_dir_train,name_folders_seg)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "5d605b64",
   "metadata": {},
   "source": [
    "### Adjusting the generated slices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "bb5844de",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1251"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(name_folders_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "119bff6b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bouhe\\OneDrive\\Bureau\\pfe\\dataset\\png_format\\Train\n"
     ]
    }
   ],
   "source": [
    "%cd C:\\Users\\bouhe\\OneDrive\\Bureau\\pfe\\dataset\\png_format\\Train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "0e8788be",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bouhe\\OneDrive\\Bureau\\pfe\\dataset\\png_format\\Train\\BraTS-GLI-00140-000\n"
     ]
    }
   ],
   "source": [
    "%cd BraTS-GLI-00140-000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "c1b623f9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'BraTS-GLI-00140-000'"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "name_folders_train[130]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "8ccc63b2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bouhe\\OneDrive\\Bureau\\pfe\\dataset\\png_format\\Train\n"
     ]
    }
   ],
   "source": [
    "%cd C:\\Users\\bouhe\\OneDrive\\Bureau\\pfe\\dataset\\png_format\\Train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "50e8e966",
   "metadata": {},
   "outputs": [],
   "source": [
    "for name in name_folders_train[129:132]:\n",
    "    %cd $name\n",
    "    refs = os.listdir('seg')\n",
    "    print(refs)\n",
    "    full_slices = os.listdir('t1c')\n",
    "    print(full_slices)\n",
    "    rm_useless_slices('t1c',refs, full_slices)\n",
    "    print('*********************************************************************************************')\n",
    "\n",
    "    full_slices = os.listdir('t1n')\n",
    "    rm_useless_slices('t1n',refs, full_slices)\n",
    "    print('*********************************************************************************************')\n",
    "\n",
    "    full_slices = os.listdir('t2f')\n",
    "    rm_useless_slices('t2f',refs, full_slices)\n",
    "    print('*********************************************************************************************')\n",
    "\n",
    "    full_slices = os.listdir('t2w')\n",
    "    rm_useless_slices('t2w',refs, full_slices)\n",
    "    print('*********************************************************************************************')\n",
    "    \n",
    "    %cd ..\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4ff9a0bf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Images copied to respective folders.\n"
     ]
    }
   ],
   "source": [
    "source_folder = r'C:\\Users\\bouhe\\OneDrive\\Bureau\\pfe\\dataset\\Nifti format BratS\\ASNR-MICCAI-BraTS2023-GLI-Challenge-TrainingData'\n",
    "labels_folder = r'C:\\Users\\bouhe\\OneDrive\\Bureau\\pfe\\dataset\\Nifti format BratS\\labels'\n",
    "images_folder = r'C:\\Users\\bouhe\\OneDrive\\Bureau\\pfe\\dataset\\Nifti format BratS\\images'\n",
    "\n",
    "copy_images(source_folder, labels_folder, images_folder)\n",
    "\n",
    "print(\"Images copied to respective folders.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dce4aa7b",
   "metadata": {},
   "outputs": [],
   "source": [
    "input_directory = r\"C:\\Users\\bouhe\\OneDrive\\Bureau\\pfe\\dataset\\Nifti format BratS\\images\"\n",
    "output_directory = r\"C:\\Users\\bouhe\\OneDrive\\Bureau\\pfe\\dataset\\Nifti format BratS\"\n",
    "organize_images_by_modality(input_directory, output_directory)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "e821774c",
   "metadata": {},
   "source": [
    "### Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "8bc4de4b",
   "metadata": {},
   "outputs": [],
   "source": [
    "dir_preprocess = r\"C:\\Users\\bouhe\\OneDrive\\Bureau\\pfe\\dataset\\png_format\\train\"\n",
    "dir_postprocess = r\"C:\\Users\\bouhe\\OneDrive\\Bureau\\pfe\\dataset\\preprocessed_images\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "7dbc7af2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bouhe\\OneDrive\\Bureau\\pfe\\dataset\\preprocessed_images\n"
     ]
    }
   ],
   "source": [
    "%cd $dir_postprocess"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "b275668d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bouhe\\OneDrive\\Bureau\\pfe\\dataset\\png_format\\train_all_labels\n"
     ]
    }
   ],
   "source": [
    "%cd \"C:\\Users\\bouhe\\OneDrive\\Bureau\\pfe\\dataset\\png_format\\train_all_labels\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "65a91aa1",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "12293fc3",
   "metadata": {},
   "outputs": [],
   "source": [
    "folders = os.listdir(r\"C:\\Users\\bouhe\\OneDrive\\Bureau\\pfe\\dataset\\Nifti format BratS\\ASNR-MICCAI-BraTS2023-GLI-Challenge-TrainingData\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "13d0d283",
   "metadata": {},
   "outputs": [],
   "source": [
    "for name in folders: \n",
    "    print(name)\n",
    "    !mkdir $name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c19dda2d",
   "metadata": {},
   "outputs": [],
   "source": [
    "for name in folders:\n",
    "    %cd $name\n",
    "    !mkdir seg\n",
    "    #!mkdir t1c\n",
    "    #!mkdir t1n\n",
    "    #!mkdir t2f\n",
    "    #!mkdir t2w\n",
    "    %cd .."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4579a7fa",
   "metadata": {},
   "source": [
    "#### Train_all_labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a9231a3a",
   "metadata": {},
   "outputs": [],
   "source": [
    "same_size_tests =[]\n",
    "dir_preprocess_2 = r'C:\\Users\\bouhe\\OneDrive\\Bureau\\pfe\\dataset\\png_format\\train_all_labels'\n",
    "dir_postprocess = r'C:\\Users\\bouhe\\OneDrive\\Bureau\\pfe\\dataset\\png_format\\train_all_labels'\n",
    "for name in folders:\n",
    "    seg_dir = join_paths(dir_preprocess_2,name)\n",
    "    print(seg_dir)\n",
    "    tmp_seg_dir = join_paths(seg_dir,r'seg')\n",
    "    seg_images = os.listdir(tmp_seg_dir)\n",
    "    for name_im in seg_images:\n",
    "        seg_image = join_paths(tmp_seg_dir,name_im)\n",
    "        im_array = np.array(Image.open(seg_image))\n",
    "        replace_pixels(im_array)\n",
    "        image = Image.fromarray(im_array)\n",
    "        new_im_path = join_paths(dir_postprocess,'\\\\'.join(seg_image.split('\\\\')[-3::]))\n",
    "        path_whole = seg_image.split('\\\\')\n",
    "        new_im_path = ('\\\\').join(path_whole[0:8]+['train_all_labels']+path_whole[9::])\n",
    "        image.save(new_im_path)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "029f2acb",
   "metadata": {},
   "outputs": [],
   "source": [
    "from PIL import Image as I"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6e30632d",
   "metadata": {},
   "outputs": [],
   "source": [
    "same_size_tests =[]\n",
    "for name in folders:\n",
    "    seg_dir = join_paths(dir_preprocess,name)\n",
    "    tmp_seg_dir = join_paths(seg_dir,r'seg')\n",
    "    seg_images = os.listdir(tmp_seg_dir)\n",
    "    for name_im in seg_images:\n",
    "        seg_image = join_paths(tmp_seg_dir,name_im)\n",
    "        im_array = np.array(Image.open(seg_image))\n",
    "        #replace_non_zero_pixels(im_array)\n",
    "        image = Image.fromarray(im_array)\n",
    "        #new_im_path = join_paths(dir_postprocess,'\\\\'.join(seg_image.split('\\\\')[-3::]))\n",
    "        path_whole = seg_image.split('\\\\')\n",
    "        new_im_path = ('\\\\').join(path_whole[0:8]+['train_seg_one_mask']+path_whole[9::])\n",
    "        print(new_im_path)\n",
    "        image.save(new_im_path)\n",
    "        break\n",
    "    break\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "1e20983e",
   "metadata": {},
   "outputs": [],
   "source": [
    "ex = r'C:\\\\Users\\\\bouhe\\\\OneDrive\\\\Bureau\\\\pfe\\\\dataset\\\\png_format\\\\train_seg_one_mask\\\\BraTS-GLI-01489-000\\\\seg\\\\Slice_dim1_89.png'\n",
    "ex_pre = r'C:\\\\Users\\\\bouhe\\\\OneDrive\\\\Bureau\\\\pfe\\\\dataset\\\\png_format\\\\train\\\\BraTS-GLI-01489-000\\\\seg\\\\Slice_dim1_89.png'\n",
    "im_array = np.array(Image.open(ex_pre))\n",
    "replace_non_zero_pixels(im_array)        \n",
    "image.save(ex)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "3f9efb2d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1])"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "im = I.open(r'C:\\Users\\bouhe\\OneDrive\\Bureau\\pfe\\dataset\\png_format\\train_seg_one_mask\\BraTS-GLI-01666-000\\seg\\Slice_dim2_98.png')\n",
    "ar = np.array(im)\n",
    "np.unique(ar)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "9250c0b2",
   "metadata": {},
   "outputs": [],
   "source": [
    "from tifffile import imsave"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "71955b51",
   "metadata": {},
   "outputs": [],
   "source": [
    "size_t1cs =[]\n",
    "for name in folders:\n",
    "    t1c_dir = join_paths(dir_preprocess,name)\n",
    "    tmp_t1c_dir = join_paths(t1c_dir,r't1c')\n",
    "    t1c_images = os.listdir(tmp_t1c_dir)\n",
    "    for name_im in t1c_images:\n",
    "        t1c_image = join_paths(tmp_t1c_dir,name_im)\n",
    "        im_array = np.array(Image.open(t1c_image))\n",
    "        new_im = normalize_image(im_array)\n",
    "        image = Image.fromarray(new_im)\n",
    "        new_im_path = join_paths(dir_postprocess,'\\\\'.join(t1c_image.split('\\\\')[-3::]))\n",
    "        imsave(new_im_path[:-3:]+'tif',image)\n",
    "        size_t1cs.append(new_im.shape)\n",
    "    print(name)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "84c896f7",
   "metadata": {},
   "source": [
    "### New metadata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "20a66ac1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bouhe\\OneDrive\\Bureau\\pfe\\dataset\\png_format\\Train\n"
     ]
    }
   ],
   "source": [
    "%cd C:\\Users\\bouhe\\OneDrive\\Bureau\\pfe\\dataset\\png_format\\Train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "de036cb3",
   "metadata": {},
   "outputs": [],
   "source": [
    "seg_list = metadata_rows('seg',path_folders,'train')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "98ead801",
   "metadata": {},
   "outputs": [],
   "source": [
    "t1c_list = metadata_rows('t1c',path_folders,'train')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "af404032",
   "metadata": {},
   "outputs": [],
   "source": [
    "t1c_list = metadata_rows('t1c',path_folders,'train')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e58bef32",
   "metadata": {},
   "outputs": [],
   "source": [
    "t1n_list = metadata_rows('t1n',path_folders,'train')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "18a1f5bc",
   "metadata": {},
   "outputs": [],
   "source": [
    "t2f_list = metadata_rows('t2f',path_folders,'train')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "92ba4a84",
   "metadata": {},
   "outputs": [],
   "source": [
    "t2w_list = metadata_rows('t2w',path_folders,'train')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0a616b86",
   "metadata": {},
   "outputs": [],
   "source": [
    "seg_list\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "873bd116",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bouhe\\OneDrive\\Bureau\\pfe\\dataset\\png_format\\Test\n"
     ]
    }
   ],
   "source": [
    "%cd C:\\Users\\bouhe\\OneDrive\\Bureau\\pfe\\dataset\\png_format\\Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "5c871e4f",
   "metadata": {},
   "outputs": [],
   "source": [
    "path_folders_test = os.listdir()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "db7a2981",
   "metadata": {},
   "outputs": [],
   "source": [
    "t1c_list_test = metadata_rows('t1c',path_folders_test,'test')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5ca99347",
   "metadata": {},
   "outputs": [],
   "source": [
    "t1n_list_test = metadata_rows('t1n',path_folders_test,'test')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4f8242e3",
   "metadata": {},
   "outputs": [],
   "source": [
    "t2f_list_test = metadata_rows('t2f',path_folders_test,'test')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9a602a97",
   "metadata": {},
   "outputs": [],
   "source": [
    "t2w_list_test = metadata_rows('t2w',path_folders_test,'test')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3cd7a405",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import pandas as pd\n",
    "import pandas as pd \n",
    "    \n",
    "# List1 \n",
    "lst = seg_list+t1c_list+t1n_list+t2f_list+t2w_list+t1c_list_test+t1n_list_test+t2f_list_test+t2w_list_test\n",
    "    \n",
    "df = pd.DataFrame(lst, columns =['image_name', 'patient_id', 'time_stamp','scan_type','split'])\n",
    "df.sort_values(by=[\"patient_id\"], inplace = True)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "3ff189e8",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv(r'C:\\Users\\bouhe\\OneDrive\\Bureau\\pfe\\dataset\\png_format\\metadata.csv',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "3535c906",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv(r'C:\\Users\\bouhe\\OneDrive\\Bureau\\pfe\\dataset\\png_format\\metadata.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "e269810e",
   "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>image_name</th>\n",
       "      <th>patient_id</th>\n",
       "      <th>time_stamp</th>\n",
       "      <th>scan_type</th>\n",
       "      <th>split</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Slice_dim0_118.png</td>\n",
       "      <td>00000</td>\n",
       "      <td>000</td>\n",
       "      <td>seg</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>257504</th>\n",
       "      <td>Slice_dim1_113.png</td>\n",
       "      <td>00000</td>\n",
       "      <td>000</td>\n",
       "      <td>t1c</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>257503</th>\n",
       "      <td>Slice_dim1_112.png</td>\n",
       "      <td>00000</td>\n",
       "      <td>000</td>\n",
       "      <td>t1c</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>257502</th>\n",
       "      <td>Slice_dim1_111.png</td>\n",
       "      <td>00000</td>\n",
       "      <td>000</td>\n",
       "      <td>t1c</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>257501</th>\n",
       "      <td>Slice_dim1_110.png</td>\n",
       "      <td>00000</td>\n",
       "      <td>000</td>\n",
       "      <td>t1c</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1385082</th>\n",
       "      <td>Slice_dim2_98.png</td>\n",
       "      <td>01790</td>\n",
       "      <td>000</td>\n",
       "      <td>t1c</td>\n",
       "      <td>test</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1385081</th>\n",
       "      <td>Slice_dim2_97.png</td>\n",
       "      <td>01790</td>\n",
       "      <td>000</td>\n",
       "      <td>t1c</td>\n",
       "      <td>test</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1385080</th>\n",
       "      <td>Slice_dim2_96.png</td>\n",
       "      <td>01790</td>\n",
       "      <td>000</td>\n",
       "      <td>t1c</td>\n",
       "      <td>test</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1482329</th>\n",
       "      <td>Slice_dim0_103.png</td>\n",
       "      <td>01790</td>\n",
       "      <td>000</td>\n",
       "      <td>t1n</td>\n",
       "      <td>test</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1678137</th>\n",
       "      <td>Slice_dim2_99.png</td>\n",
       "      <td>01790</td>\n",
       "      <td>000</td>\n",
       "      <td>t2w</td>\n",
       "      <td>test</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1678138 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 image_name patient_id time_stamp scan_type  split\n",
       "0        Slice_dim0_118.png      00000        000       seg  train\n",
       "257504   Slice_dim1_113.png      00000        000       t1c  train\n",
       "257503   Slice_dim1_112.png      00000        000       t1c  train\n",
       "257502   Slice_dim1_111.png      00000        000       t1c  train\n",
       "257501   Slice_dim1_110.png      00000        000       t1c  train\n",
       "...                     ...        ...        ...       ...    ...\n",
       "1385082   Slice_dim2_98.png      01790        000       t1c   test\n",
       "1385081   Slice_dim2_97.png      01790        000       t1c   test\n",
       "1385080   Slice_dim2_96.png      01790        000       t1c   test\n",
       "1482329  Slice_dim0_103.png      01790        000       t1n   test\n",
       "1678137   Slice_dim2_99.png      01790        000       t2w   test\n",
       "\n",
       "[1678138 rows x 5 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from IPython.display import display\n",
    "\n",
    "display(df)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "9aeaf802",
   "metadata": {},
   "source": [
    "### Upload the dataset to huggin face"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d3608e49",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: huggingface_hub in c:\\users\\bouhe\\anaconda3\\envs\\pytorch\\lib\\site-packages (0.14.1)\n",
      "Requirement already satisfied: filelock in c:\\users\\bouhe\\appdata\\roaming\\python\\python311\\site-packages (from huggingface_hub) (3.12.0)\n",
      "Requirement already satisfied: fsspec in c:\\users\\bouhe\\anaconda3\\envs\\pytorch\\lib\\site-packages (from huggingface_hub) (2023.4.0)\n",
      "Requirement already satisfied: requests in c:\\users\\bouhe\\appdata\\roaming\\python\\python311\\site-packages (from huggingface_hub) (2.31.0)\n",
      "Requirement already satisfied: tqdm>=4.42.1 in c:\\users\\bouhe\\anaconda3\\envs\\pytorch\\lib\\site-packages (from huggingface_hub) (4.65.0)\n",
      "Requirement already satisfied: pyyaml>=5.1 in c:\\users\\bouhe\\anaconda3\\envs\\pytorch\\lib\\site-packages (from huggingface_hub) (6.0)\n",
      "Requirement already satisfied: typing-extensions>=3.7.4.3 in c:\\users\\bouhe\\anaconda3\\envs\\pytorch\\lib\\site-packages (from huggingface_hub) (4.5.0)\n",
      "Requirement already satisfied: packaging>=20.9 in c:\\users\\bouhe\\anaconda3\\envs\\pytorch\\lib\\site-packages (from huggingface_hub) (23.0)\n",
      "Requirement already satisfied: colorama in c:\\users\\bouhe\\anaconda3\\envs\\pytorch\\lib\\site-packages (from tqdm>=4.42.1->huggingface_hub) (0.4.4)\n",
      "Requirement already satisfied: charset-normalizer<4,>=2 in c:\\users\\bouhe\\appdata\\roaming\\python\\python311\\site-packages (from requests->huggingface_hub) (3.1.0)\n",
      "Requirement already satisfied: idna<4,>=2.5 in c:\\users\\bouhe\\appdata\\roaming\\python\\python311\\site-packages (from requests->huggingface_hub) (3.4)\n",
      "Requirement already satisfied: urllib3<3,>=1.21.1 in c:\\users\\bouhe\\anaconda3\\envs\\pytorch\\lib\\site-packages (from requests->huggingface_hub) (1.26.15)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in c:\\users\\bouhe\\appdata\\roaming\\python\\python311\\site-packages (from requests->huggingface_hub) (2023.5.7)\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "!pip install huggingface_hub"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "4293dac1",
   "metadata": {},
   "outputs": [],
   "source": [
    "from datasets import load_dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b15e249b",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = load_dataset(\"Train_folder\", data_dir=r\"C:\\Users\\bouhe\\OneDrive\\Bureau\\pfe\\dataset\\png_format\\Train\", split=\"train\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "01412445",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = load_dataset(\"Minty-Ines/BraTs-2023\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d3b2a7ee",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset.push_to_hub(r\"C:\\Users\\bouhe\\OneDrive\\Bureau\\pfe\\dataset\\png_format\\Test\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "a18de146",
   "metadata": {},
   "source": [
    "## Plotting 3D MRI scans: (IN PROGRESS)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "5f805eb8",
   "metadata": {},
   "source": [
    "To plot them, we need to rasterizd images into a point cloud with reduced dimensionality.  \n",
    "Passing each scanned pixel into our visualization would net us more than a million points,\n",
    "so we need to:  \n",
    "1. resize every image to 128x128  \n",
    "2. downsample space without tumor for brevity.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "b4d0466e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import albumentations as A\n",
    "class ImageReader:\n",
    "    def __init__(self, root:str, img_size:int=256, normalize:bool=False, single_class:bool=False):\n",
    "        pad_size = 256 if img_size > 256 else 224\n",
    "        self.resize = A.Compose(\n",
    "            [\n",
    "                A.PadIfNeeded(min_height=pad_size, min_width=pad_size, value=0),\n",
    "                A.Resize(img_size, img_size)\n",
    "            ]\n",
    "        )\n",
    "        self.normalize=normalize\n",
    "        self.single_class=single_class\n",
    "        self.root=root\n",
    "        \n",
    "    def read_file(self, path:str) -> dict:\n",
    "        scan_type = path.split('-')[-1]\n",
    "        print(\"********************************************************\")\n",
    "        print(scan_type)\n",
    "        print(\"********************************************************\")\n",
    "        print(path)\n",
    "        raw_image = nib.load(path).get_fdata()\n",
    "        raw_mask = nib.load(path.replace(scan_type, 'seg.nii.gz')).get_fdata()\n",
    "        processed_frames, processed_masks = [], []\n",
    "        for frame_idx in range(raw_image.shape[2]):\n",
    "            frame = raw_image[:, :, frame_idx]\n",
    "            mask = raw_mask[:, :, frame_idx]\n",
    "            if self.normalize:\n",
    "                if frame.max() > 0:\n",
    "                    frame = frame/frame.max()\n",
    "                frame = frame.astype(np.float32)\n",
    "            else:\n",
    "                frame = frame.astype(np.uint8)\n",
    "            resized = self.resize(image=frame, mask=mask)\n",
    "            processed_frames.append(resized['image'])\n",
    "            processed_masks.append(1*(resized['mask'] > 0) if self.single_class else resized['mask'])\n",
    "        return {\n",
    "            'scan': np.stack(processed_frames, 0),\n",
    "            'segmentation': np.stack(processed_masks, 0),\n",
    "            'orig_shape': raw_image.shape\n",
    "        }\n",
    "    \n",
    "    def load_patient_scan(self, idp:int, idx:int, scan_type:str='flair') -> dict:\n",
    "        patient_id = str(idp).zfill(5)\n",
    "        index = str(idx).zfill(3)\n",
    "        scan_filename = f\"{self.root}\\\\BraTS-GLI-{patient_id}-{index}\\\\BraTS-GLI-{patient_id}-{index}-{scan_type}.nii.gz\"\n",
    "        print(scan_filename)\n",
    "        return self.read_file(scan_filename)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "cd974653",
   "metadata": {},
   "outputs": [],
   "source": [
    "def generate_3d_scatter(\n",
    "    x:np.array, y:np.array, z:np.array, colors:np.array,\n",
    "    size:int=3, opacity:float=0.2, scale:str='Teal',\n",
    "    hover:str='skip', name:str='MRI'\n",
    ") -> go.Scatter3d:\n",
    "    return go.Scatter3d(\n",
    "        x=x, y=y, z=z,\n",
    "        mode='markers', hoverinfo=hover,\n",
    "        marker = dict(\n",
    "            size=size, opacity=opacity,\n",
    "            color=colors, colorscale=scale\n",
    "        ),\n",
    "        name=name\n",
    "    )\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "a320769d",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "class ImageViewer3d():\n",
    "    def __init__(\n",
    "        self, reader:ImageReader, mri_downsample:int=10, mri_colorscale:str='Ice'\n",
    "    ) -> None:\n",
    "        self.reader = reader\n",
    "        self.mri_downsample = mri_downsample\n",
    "        self.mri_colorscale = mri_colorscale\n",
    "\n",
    "    def load_clean_mri(self, image:np.array, orig_dim:int) -> dict:\n",
    "        shape_offset = image.shape[1]/orig_dim\n",
    "        z, x, y = (image > 0).nonzero()\n",
    "        # only (1/mri_downsample) is sampled for the resulting image\n",
    "        x, y, z = x[::self.mri_downsample], y[::self.mri_downsample], z[::self.mri_downsample]\n",
    "        colors = image[z, x, y]\n",
    "        return dict(x=x/shape_offset, y=y/shape_offset, z=z, colors=colors)\n",
    "    \n",
    "    def load_tumor_segmentation(self, image:np.array, orig_dim:int) -> dict:\n",
    "        tumors = {}\n",
    "        shape_offset = image.shape[1]/orig_dim\n",
    "        # 1/1, 1/3 and 1/5 pixels for tumor tissue classes 1(core), 2(invaded) and 3(enhancing)\n",
    "        sampling = {\n",
    "            1: 1, 2: 3, 3: 5\n",
    "        }\n",
    "        for class_idx in sampling:\n",
    "            z, x, y = (image == class_idx).nonzero()\n",
    "            x, y, z = x[::sampling[class_idx]], y[::sampling[class_idx]], z[::sampling[class_idx]]\n",
    "            tumors[class_idx] = dict(\n",
    "                x=x/shape_offset, y=y/shape_offset, z=z,\n",
    "                colors=class_idx/4\n",
    "            )\n",
    "        return tumors\n",
    "    \n",
    "    def collect_patient_data(self, scan:dict) -> tuple:\n",
    "        clean_mri = self.load_clean_mri(scan['scan'], scan['orig_shape'][0])\n",
    "        tumors = self.load_tumor_segmentation(scan['segmentation'], scan['orig_shape'][0])\n",
    "        markers_created = clean_mri['x'].shape[0] + sum(tumors[class_idx]['x'].shape[0] for class_idx in tumors)\n",
    "        return [\n",
    "            generate_3d_scatter(**clean_mri, scale=self.mri_colorscale, opacity=0.3, hover='skip', name='Brain MRI'),\n",
    "            generate_3d_scatter(**tumors[1], opacity=0.8, hover='all', name='Necrotic tumor core'),\n",
    "            generate_3d_scatter(**tumors[2], opacity=0.4, hover='all', name='Peritumoral invaded tissue'),\n",
    "            generate_3d_scatter(**tumors[3], opacity=0.4, hover='all', name='GD-enhancing tumor'),\n",
    "        ], markers_created\n",
    "    \n",
    "    def get_3d_scan(self, patient_idx:int,index:int, scan_type:str='flair') -> go.Figure:\n",
    "        scan = self.reader.load_patient_scan(patient_idx,index, scan_type)\n",
    "        data, num_markers = self.collect_patient_data(scan)\n",
    "        fig = go.Figure(data=data)\n",
    "        fig.update_layout(\n",
    "            title=f\"[Patient id:{patient_idx}] brain MRI scan ({num_markers} points)\",\n",
    "            legend_title=\"Pixel class (click to enable/disable)\",\n",
    "            font=dict(\n",
    "                family=\"Courier New, monospace\",\n",
    "                size=14,\n",
    "            ),\n",
    "            margin=dict(\n",
    "                l=0,r=0,b=0,t=30\n",
    "            ),\n",
    "            legend=dict(itemsizing='constant')\n",
    "        )\n",
    "        return fig\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "61fdec86",
   "metadata": {},
   "outputs": [],
   "source": [
    "folder = r\"C:\\Users\\bouhe\\OneDrive\\Bureau\\pfe\\dataset\\Nifti format BratS\\ASNR-MICCAI-BraTS2023-GLI-Challenge-TrainingData\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "e2a8c3d4",
   "metadata": {},
   "outputs": [],
   "source": [
    "reader = ImageReader(folder, img_size=128, normalize=True, single_class=False)\n",
    "viewer = ImageViewer3d(reader, mri_downsample=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "f6c1e5e3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bouhe\\OneDrive\\Bureau\\pfe\\dataset\\Nifti format BratS\\ASNR-MICCAI-BraTS2023-GLI-Challenge-TrainingData\\BraTS-GLI-00000-000\\BraTS-GLI-00000-000-t1c.nii.gz\n",
      "********************************************************\n",
      "t1c.nii.gz\n",
      "********************************************************\n",
      "C:\\Users\\bouhe\\OneDrive\\Bureau\\pfe\\dataset\\Nifti format BratS\\ASNR-MICCAI-BraTS2023-GLI-Challenge-TrainingData\\BraTS-GLI-00000-000\\BraTS-GLI-00000-000-t1c.nii.gz\n"
     ]
    }
   ],
   "source": [
    "fig = viewer.get_3d_scan(0,0, 't1c')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8d9cf5dc",
   "metadata": {},
   "outputs": [],
   "source": [
    "pip install git+https://github.com/bsun0802/Zero-Learning-Fast-Medical-Image-Fusion.git"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "9731ce8d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c:\\Users\\bouhe\\OneDrive\\Documents\\Python Scripts\\work_internship mitacs_backup\\Zero-Learning-Fast-Medical-Image-Fusion-master\n"
     ]
    }
   ],
   "source": [
    "%cd Zero-Learning-Fast-Medical-Image-Fusion-master"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "68fca804",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c:\\Users\\bouhe\\OneDrive\\Documents\\Python Scripts\\work_internship mitacs_backup\\Zero-Learning-Fast-Medical-Image-Fusion-master\\code\n"
     ]
    }
   ],
   "source": [
    "%cd code"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6cf09da3",
   "metadata": {},
   "source": [
    "## Fusion T1C AND T2W"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "aeb4aabf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bouhe\\OneDrive\\Bureau\\pfe\\dataset\\png_format\\train_fused\n"
     ]
    }
   ],
   "source": [
    "%cd C:\\Users\\bouhe\\OneDrive\\Bureau\\pfe\\dataset\\png_format\\train_fused"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "f2a0d6d0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bouhe\\OneDrive\\Bureau\\pfe\\dataset\\png_format\\predicted\n"
     ]
    }
   ],
   "source": [
    "%cd C:\\Users\\bouhe\\OneDrive\\Bureau\\pfe\\dataset\\png_format\\predicted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "4e3edb01",
   "metadata": {},
   "outputs": [],
   "source": [
    "for name in name_folders_train:\n",
    "    #!mkdir $name\n",
    "    #!mkdir t1c\n",
    "    #!mkdir t1n\n",
    "    #!mkdir t2f\n",
    "    #!mkdir t2w\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "7e901a6b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1251"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(name_folders_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2eed522d",
   "metadata": {},
   "source": [
    "Splitting the data into 70% 30% train test  \n",
    "876 + 375\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "64e9e26b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'BraTS-GLI-01173-000'"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "name_folders_train[875]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "3e094bb3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bouhe\\OneDrive\\Documents\\Python Scripts\\work_internship mitacs_backup\\Imfusion-main\n"
     ]
    }
   ],
   "source": [
    "%cd C:\\Users\\bouhe\\OneDrive\\Documents\\Python Scripts\\work_internship mitacs_backup\\Imfusion-main"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "c4f674f7",
   "metadata": {},
   "outputs": [],
   "source": [
    "import fusion_main as f"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "56f2929d",
   "metadata": {},
   "outputs": [],
   "source": [
    "dir_fusion = r'C:\\Users\\bouhe\\OneDrive\\Bureau\\pfe\\dataset\\png_format\\train_fused'\n",
    "dir_normal = r'C:\\Users\\bouhe\\OneDrive\\Bureau\\pfe\\dataset\\png_format\\train' #T1C T2W"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "87118d7f",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_dir = r'C:\\Users\\bouhe\\OneDrive\\Documents\\Python Scripts\\work_internship mitacs_backup\\Imfusion-main\\demo'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "7418da98",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'BraTS-GLI-01173-000'"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "name_folders_train[875]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "511cf108",
   "metadata": {},
   "outputs": [],
   "source": [
    "name = name_folders_train[875]\n",
    "T1 = os.path.join(*[dir_normal,name,'t1c'])\n",
    "T2 = os.path.join(*[dir_normal,name,'t2w'])\n",
    "list_t1 = os.listdir(T1)\n",
    "list_t2 = os.listdir(T2)\n",
    "flist_t1, flist_t2 = [ os.path.join(T1, path) for path in list_t1 ],  [ os.path.join(T2, path) for path in list_t2 ]\n",
    "for name1,name2 in zip(flist_t1, flist_t2):\n",
    "    correct_name = '\\\\' + name1.split('\\\\')[-1]\n",
    "    res = os.path.join(dir_fusion,name) + correct_name\n",
    "    print(res)\n",
    "    generatedImage = f.fusion(name1, name2, res)\n",
    "    print(generatedImage)\n",
    "    print('***************** IMAGE GENERATED ******************')\n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "8456a62f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "C:/Users/bouhe/OneDrive/Documents/Python Scripts/work_internship mitacs_backup/Zero-Learning-Fast-Medical-Image-Fusion-master/images/Slice_dim0_143.1.png\n",
      "C:/Users/bouhe/OneDrive/Documents/Python Scripts/work_internship mitacs_backup/Zero-Learning-Fast-Medical-Image-Fusion-master/images/Slice_dim0_143.2.png\n",
      "(256, 256, 3)\n",
      "(256, 256, 3)\n",
      "demo/out1985.jpg\n"
     ]
    }
   ],
   "source": [
    "!python3 manual_fusion.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8c805634",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ea41e3b9",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "532b8dd2",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "32360575",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8ef7aab8",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d118d774",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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