Data_Preprocessing.ipynb

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      "cell_type": "markdown",
      "source": [
        "Downloading the data from kaggle API"
      ],
      "metadata": {
        "id": "zpg_zrmoME9T"
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      "source": [
        "!wget 'https://storage.googleapis.com/kaggle-competitions-data/kaggle-v2/27923/3495119/bundle/archive.zip?GoogleAccessId=web-data@kaggle-161607.iam.gserviceaccount.com&Expires=1673129599&Signature=Qnqv6lKODrjvQIN5E01cpdDRq66XMpR9%2BwTW8xoSw3XZOqfGJeQgqPhkK2%2F5kC19fxOjekOab8ehe%2BWEXhbE5dJuoWPDV5UsciSLux%2BkfoxP6uPGseI%2Bx%2FACuZbgc%2BNMx9U8uta8GbpEWIerO3bdlHzJHMK6J%2B%2B4E7sEH5O%2Fj0AbjlcVPPsrXOBs0D5jnptP%2Fja69nG3hr4vnamCgdepZ1AUYMxXgCpK60yjaMFjvfmdB6vFhoyrkyTxn0RHuQUdwh33arXWsbCrZfr023m8iGUwOOCqDGyDjXbBvWX6XQGtIh1Nk0ZDKKl626gJFzB9NY9w%2BbJ2VFWUDCbEqYG3%2Bg%3D%3D&response-content-disposition=attachment%3B+filename%3Duw-madison-gi-tract-image-segmentation.zip' "
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        "outputId": "40177dca-d8ea-4135-eb46-a4d9600a0abe"
      },
      "execution_count": 4,
      "outputs": [
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            "--2023-01-04 22:19:06--  https://storage.googleapis.com/kaggle-competitions-data/kaggle-v2/27923/3495119/bundle/archive.zip?GoogleAccessId=web-data@kaggle-161607.iam.gserviceaccount.com&Expires=1673129599&Signature=Qnqv6lKODrjvQIN5E01cpdDRq66XMpR9%2BwTW8xoSw3XZOqfGJeQgqPhkK2%2F5kC19fxOjekOab8ehe%2BWEXhbE5dJuoWPDV5UsciSLux%2BkfoxP6uPGseI%2Bx%2FACuZbgc%2BNMx9U8uta8GbpEWIerO3bdlHzJHMK6J%2B%2B4E7sEH5O%2Fj0AbjlcVPPsrXOBs0D5jnptP%2Fja69nG3hr4vnamCgdepZ1AUYMxXgCpK60yjaMFjvfmdB6vFhoyrkyTxn0RHuQUdwh33arXWsbCrZfr023m8iGUwOOCqDGyDjXbBvWX6XQGtIh1Nk0ZDKKl626gJFzB9NY9w%2BbJ2VFWUDCbEqYG3%2Bg%3D%3D&response-content-disposition=attachment%3B+filename%3Duw-madison-gi-tract-image-segmentation.zip\n",
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      "cell_type": "code",
      "source": [
        "!unzip '/content/archive.zip?GoogleAccessId=web-data@kaggle-161607.iam.gserviceaccount.com&Expires=1673129599&Signature=Qnqv6lKODrjvQIN5E01cpdDRq66XMpR9+wTW8xoSw3XZOqfGJeQgqPhkK2%2F5kC19fxOjekOab8ehe+WEXhbE5dJuoWPDV5UsciSLux+kfoxP6uPGseI+x%2FACuZbgc+NMx'"
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      "metadata": {
        "id": "VhQz9eQw3ufP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "G11pxHoO10Gp"
      },
      "outputs": [],
      "source": [
        "import numpy as np # linear algebra\n",
        "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
        "\n",
        "# Input data files are available in the read-only \"../input/\" directory\n",
        "# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import cv2\n",
        "import tensorflow as tf\n",
        "import tensorflow.keras as keras\n",
        "import os"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df=pd.read_csv('/content/train.csv')"
      ],
      "metadata": {
        "id": "GhmRvzIO18G8"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#check if the mask is nan\n",
        "def isNaN(num):\n",
        "    return num!= num"
      ],
      "metadata": {
        "id": "TTBwoWPB1-9y"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#find the string in the text\n",
        "def find_string(txt, str1):\n",
        "    return txt.find(str1, txt.find(str1)+1)"
      ],
      "metadata": {
        "id": "njkqXr3v4epK"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Dividing the class column into 3 lists\n",
        "days=[]\n",
        "slices=[]\n",
        "large_bowel=[]\n",
        "small_bowel=[]\n",
        "stomach=[]\n",
        "cases=[]\n",
        "for j in range(0,115488,3):\n",
        "    cases.append(df['id'][j][0:df['id'][j].index('_')])\n",
        "    days.append(df['id'][j][df['id'][j].index('d'):find_string(df['id'][j],('_'))])\n",
        "    slices.append(df['id'][j][find_string(df['id'][j],('s'))::])\n",
        "for k in range(115488):\n",
        "    if(df['class'][k]=='large_bowel'):\n",
        "        large_bowel.append(df['segmentation'][k])\n",
        "\n",
        "    if(df['class'][k]=='small_bowel'):\n",
        "        small_bowel.append(df['segmentation'][k])\n",
        "\n",
        "    if(df['class'][k]=='stomach'):\n",
        "        stomach.append(df['segmentation'][k])"
      ],
      "metadata": {
        "id": "Wl1HCQ4769IB"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Creating new dataFrame"
      ],
      "metadata": {
        "id": "xbsK5Jk7NA3_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df2=0"
      ],
      "metadata": {
        "id": "AdSXX7yl7ABj"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df2=pd.DataFrame(np.array(days),columns =[\"Days\"])"
      ],
      "metadata": {
        "id": "FhXWXwkX7OnV"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df2.insert(1,'Slices',slices,True)"
      ],
      "metadata": {
        "id": "a599qEJg7Qxo"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df2.insert(2,'Stomach',stomach,True)"
      ],
      "metadata": {
        "id": "xXxmYQk67TgY"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df2.insert(3,'Small_bowel',small_bowel,True)"
      ],
      "metadata": {
        "id": "alSKHy0T7WDy"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df2.insert(4,'Large_bowel',large_bowel,True)"
      ],
      "metadata": {
        "id": "1hAMXG_Y7XuI"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df2.insert(5,'Cases',cases,True)"
      ],
      "metadata": {
        "id": "HAtJ7utR7Zsz"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
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        "id": "axRlqsQL7bfV",
        "outputId": "5f953afc-3d15-40de-fa64-8fd45680f9e5"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        Days      Slices Stomach Small_bowel Large_bowel    Cases\n",
              "0      day20  slice_0001     NaN         NaN         NaN  case123\n",
              "1      day20  slice_0002     NaN         NaN         NaN  case123\n",
              "2      day20  slice_0003     NaN         NaN         NaN  case123\n",
              "3      day20  slice_0004     NaN         NaN         NaN  case123\n",
              "4      day20  slice_0005     NaN         NaN         NaN  case123\n",
              "...      ...         ...     ...         ...         ...      ...\n",
              "38491   day0  slice_0140     NaN         NaN         NaN   case30\n",
              "38492   day0  slice_0141     NaN         NaN         NaN   case30\n",
              "38493   day0  slice_0142     NaN         NaN         NaN   case30\n",
              "38494   day0  slice_0143     NaN         NaN         NaN   case30\n",
              "38495   day0  slice_0144     NaN         NaN         NaN   case30\n",
              "\n",
              "[38496 rows x 6 columns]"
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              "    <tr>\n",
              "      <th>38491</th>\n",
              "      <td>day0</td>\n",
              "      <td>slice_0140</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>case30</td>\n",
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              "      <th>38494</th>\n",
              "      <td>day0</td>\n",
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              "      <td>NaN</td>\n",
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              "      <th>38495</th>\n",
              "      <td>day0</td>\n",
              "      <td>slice_0144</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def rle_decode(iter_checker1, iter_checker2, iter_checker3, shape1,shape2):\n",
        "    '''\n",
        "    mask_rle: run-length as string formated (start length)\n",
        "    shape: (height,width) of array to return \n",
        "    Returns numpy array, 1 - mask, 0 - background\n",
        "    \n",
        "    '''\n",
        "    img = np.zeros(shape=(shape1*shape2,1), dtype=np.uint8)\n",
        "#iter_checker is for stomach\n",
        "    if(iter_checker1!=0):\n",
        "\n",
        "        s = iter_checker1.split()\n",
        "        starts, lengths = [np.asarray(x, dtype=int) for x in (s[::2], s[1::2])]\n",
        "        starts -= 1\n",
        "        ends = starts + lengths\n",
        "\n",
        "\n",
        "        for lo, hi in zip(starts, ends):\n",
        "            img[lo:hi] =1\n",
        "\n",
        "#iter_checker2 is for small_bowel\n",
        "    if(iter_checker2!=0):\n",
        "        s = iter_checker2.split()\n",
        "        starts, lengths = [np.asarray(x, dtype=int) for x in (s[::2], s[1::2])]\n",
        "        starts -= 1\n",
        "        ends = starts + lengths\n",
        "\n",
        "\n",
        "        for lo, hi in zip(starts, ends):\n",
        "            img[lo:hi] =2\n",
        "#iter_checker3 is for large_bowel\n",
        "    if(iter_checker3!=0):\n",
        "\n",
        "        s = iter_checker3.split()\n",
        "        starts, lengths = [np.asarray(x, dtype=int) for x in (s[::2], s[1::2])]\n",
        "        starts -= 1\n",
        "        ends = starts + lengths\n",
        "\n",
        "        for lo, hi in zip(starts, ends):\n",
        "            img[lo:hi] =3\n",
        "    return cv2.resize(img.reshape(shape1,shape2,1),(128,128))"
      ],
      "metadata": {
        "id": "FJ6e5ySg7cAT"
      },
      "execution_count": 70,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "all_images=[]\n",
        "all_masks=[]\n",
        "k=-1\n",
        "for u in range(0,38495):\n",
        "    \n",
        "    pat='/content/train/'+df2['Cases'][u]+'/'+df2['Cases'][u]+'_'+df2['Days'][u]+'/'+'scans'\n",
        "    li=len(os.listdir(pat))\n",
        "    if(k<(li-1)):\n",
        "        k=k+1\n",
        "    else:\n",
        "        k=0\n",
        "    f = os.path.join(pat, os.listdir(pat)[k])\n",
        "    rt=os.listdir(pat)[k]\n",
        "    z=int(rt[rt.index('_')+1:find_string(rt,('_'))])\n",
        "    slice_int = int(os.listdir(pat)[k][6:10])\n",
        "    slice_number= int(df2['Slices'][u][-4::])-slice_int\n",
        "    x1=int(f[-21:-18])\n",
        "    x2=int(f[-17:-14])\n",
        "    t=u-slice_number\n",
        "#     chech if there is a mask in any of these images\n",
        "    iter_checker1=0\n",
        "    iter_checker2=0\n",
        "    iter_checker3=0\n",
        "    mask = np.zeros(shape=(128,128))\n",
        "    if(isNaN(df2.loc[t]['Stomach'])==False):\n",
        "        iter_checker1=df2.loc[t]['Stomach']\n",
        "\n",
        "        \n",
        "    if(isNaN(df2.loc[t]['Small_bowel'])==False):\n",
        "\n",
        "        iter_checker2=df2.loc[t]['Small_bowel']\n",
        "        \n",
        "    if(isNaN(df2.loc[t]['Large_bowel'])==False):\n",
        "        iter_checker3=df2.loc[t]['Large_bowel']\n",
        "        \n",
        "    if(iter_checker1!=0 or iter_checker2!=0 or iter_checker3!=0):\n",
        "        history1.append(t)\n",
        "        temp1 = cv2.imread(f)\n",
        "        temp1=cv2.resize(temp1, (128, 128 ))\n",
        "        all_images.append(temp1)\n",
        "\n",
        "        all_masks.append(rle_decode(iter_checker1, iter_checker2, iter_checker3, cv2.imread(f).shape[0],cv2.imread(f).shape[1]))\n",
        "        "
      ],
      "metadata": {
        "id": "j6QykCfD7gaV"
      },
      "execution_count": 71,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "len(all_images)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6qu5n2Ck8nCJ",
        "outputId": "eaa5efa7-5e19-44bf-c819-ed13d0e27a7c"
      },
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "16590"
            ]
          },
          "metadata": {},
          "execution_count": 72
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "imgs1=all_images[0:2000]\n",
        "imgs2=all_images[2000:4000]\n",
        "imgs3=all_images[6000:8000]\n",
        "imgs4=all_images[10000:12000]\n",
        "imgs5=all_images[14000:15000]"
      ],
      "metadata": {
        "id": "XzJWMVeZBFIQ"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "masks1=all_masks[0:2000]\n",
        "masks2=all_masks[2000:4000]\n",
        "masks3=all_masks[6000:8000]\n",
        "masks4=all_masks[10000:12000]\n",
        "masks5=all_masks[14000:15000]"
      ],
      "metadata": {
        "id": "kf6OSBB5C4zn"
      },
      "execution_count": 73,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "We are taking only half of the images due to the computational contrains of colab (when we took the whole data, colab crashed) "
      ],
      "metadata": {
        "id": "gah4o8VPBwfe"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "imgs_axis_one=np.zeros(shape=(8000+1000,128,128))"
      ],
      "metadata": {
        "id": "tPLgtVm3-Qgz"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for i in range(2000):\n",
        "  for j in range(128):\n",
        "    for g in range(128):\n",
        "      imgs_axis_one[i][j][g]=imgs1[i][j][g][0]"
      ],
      "metadata": {
        "id": "HOk_ls4rBAmU"
      },
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for i in range(2000):\n",
        "  for j in range(128):\n",
        "    for g in range(128):\n",
        "      imgs_axis_one[i+2000][j][g]=imgs2[i][j][g][0]"
      ],
      "metadata": {
        "id": "bJU8gg_aBWmn"
      },
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for i in range(2000):\n",
        "  for j in range(128):\n",
        "    for g in range(128):\n",
        "      imgs_axis_one[i+4000][j][g]=imgs3[i][j][g][0]"
      ],
      "metadata": {
        "id": "BdcfYaWVBaFe"
      },
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for i in range(2000):\n",
        "  for j in range(128):\n",
        "    for g in range(128):\n",
        "      imgs_axis_one[i+6000][j][g]=imgs4[i][j][g][0]"
      ],
      "metadata": {
        "id": "LU9TGJ9eBcWW"
      },
      "execution_count": 77,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for i in range(1000):\n",
        "  for j in range(128):\n",
        "    for g in range(128):\n",
        "      imgs_axis_one[i+8000][j][g]=imgs5[i][j][g][0]"
      ],
      "metadata": {
        "id": "UMb9TiLZBqPn"
      },
      "execution_count": 78,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "All_masks = np.zeros(shape=(8000+1000,128,128))"
      ],
      "metadata": {
        "id": "dYH6t7eNCgpP"
      },
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "All_masks[0:2000]=masks1"
      ],
      "metadata": {
        "id": "5qDe48mtIEfZ"
      },
      "execution_count": 74,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "All_masks[2000:4000]=masks2\n",
        "All_masks[4000:6000]=masks3\n",
        "All_masks[6000:8000]=masks4\n",
        "All_masks[8000:9000]=masks5"
      ],
      "metadata": {
        "id": "HQ6slfQYIMyn"
      },
      "execution_count": 75,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.imshow(All_masks[10],cmap='gray')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 285
        },
        "id": "rWmfyhAuIofh",
        "outputId": "5523a8ff-fccd-4c8a-c27b-31e2206c0710"
      },
      "execution_count": 86,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f0fad4a6df0>"
            ]
          },
          "metadata": {},
          "execution_count": 86
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from keras.utils import normalize"
      ],
      "metadata": {
        "id": "lSIfvYcPDJrH"
      },
      "execution_count": 76,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "imgs_normalized=normalize(imgs_axis_one)"
      ],
      "metadata": {
        "id": "lziR0XF7IrsY"
      },
      "execution_count": 79,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "imgs_normalized = np.expand_dims(imgs_normalized, axis=3)"
      ],
      "metadata": {
        "id": "3dkUYMGfJI4R"
      },
      "execution_count": 80,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "imgs_normalized.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ovei1r8YJPHo",
        "outputId": "4fe71dbd-1fe8-4332-d2c5-d50027240b2b"
      },
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(9000, 128, 128, 1)"
            ]
          },
          "metadata": {},
          "execution_count": 81
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.imshow(masks1[10])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 285
        },
        "id": "GWC8Nl_2EiUP",
        "outputId": "f9b00bad-010a-467f-acac-c091bc56ea68"
      },
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f0fad82d220>"
            ]
          },
          "metadata": {},
          "execution_count": 61
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.imshow(all_images[10])\n",
        "# plt.imshow(all_masks[0], alpha=0.2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 285
        },
        "id": "7JLhRA6xFQ8c",
        "outputId": "7b1d168d-2731-4865-cdae-9002481ea24d"
      },
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f0fad7fd880>"
            ]
          },
          "metadata": {},
          "execution_count": 62
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.imshow(all_images[10])\n",
        "plt.imshow(all_masks[10], alpha=0.2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 285
        },
        "id": "IpGgKSdjGY-E",
        "outputId": "fc241ae8-7010-4fbd-cc15-2eb874d83ff9"
      },
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f0fad8125b0>"
            ]
          },
          "metadata": {},
          "execution_count": 63
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from numpy import save\n",
        "save('Final_images.npy', All_masks)"
      ],
      "metadata": {
        "id": "WrIHjl_IDNXK"
      },
      "execution_count": 87,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from numpy import save\n",
        "save('Final_masks.npy', All_masks)"
      ],
      "metadata": {
        "id": "RSUmX1KgKbHI"
      },
      "execution_count": 88,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "We saved our images and masks in the drive"
      ],
      "metadata": {
        "id": "16aeUABWKw7B"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "\n",
        "drive.mount('/content/gdrive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fbocKu_kKwWl",
        "outputId": "28ff528c-48c5-4f23-bb8b-d189c9d95128"
      },
      "execution_count": 89,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/gdrive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!cp Final_images.npy '/content/gdrive/MyDrive/DL/DeepLearning_Project.ipynb/used_data'"
      ],
      "metadata": {
        "id": "Z6-LwMAaKpCX"
      },
      "execution_count": 91,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!cp Final_masks.npy '/content/gdrive/MyDrive/DL/DeepLearning_Project.ipynb/used_data'"
      ],
      "metadata": {
        "id": "3jDDnwRALKnO"
      },
      "execution_count": 92,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "K1_lTHnpLYJ2"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}