FetalCns_Final.ipynb

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      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "xIdbyMUcOVca"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "import os"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "zrTC5meqPnVv"
      },
      "outputs": [],
      "source": [
        "#!unzip -u \"/content/drive/MyDrive/Internship/Fetal Cns-20230130T141456Z-001.zip\" -d \"/content/drive/MyDrive/Internship/\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "JsjacsM-P0Cs"
      },
      "outputs": [],
      "source": [
        "#!unzip -u \"/content/drive/MyDrive/Internship/Fetal Cns 2/Dataset/training_set.zip\" -d \"/content/drive/MyDrive/Internship/Fetal Cns 2/Dataset/\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "--bNjUVyPWX_"
      },
      "outputs": [],
      "source": [
        "test_csv = pd.read_csv(\"/content/drive/MyDrive/Internship/Fetal Cns/test_set_pixel_size.csv\")\n",
        "train_csv = pd.read_csv(\"/content/drive/MyDrive/Internship/Fetal Cns/training_set_pixel_size_and_HC.csv\")\n",
        "test_img = \"/content/drive/MyDrive/Internship/Fetal Cns/Dataset/test_set/\"\n",
        "train_img =\"/content/drive/MyDrive/Internship/Fetal Cns/Dataset/training_set/training_set/\""
      ]
    },
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              "     filename  pixel size(mm)  head circumference (mm)\n",
              "0  000_HC.png        0.069136                    44.30\n",
              "1  001_HC.png        0.089659                    56.81\n",
              "2  002_HC.png        0.062033                    68.75\n",
              "3  003_HC.png        0.091291                    69.00\n",
              "4  004_HC.png        0.061240                    59.81"
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              "      <td>002_HC.png</td>\n",
              "      <td>0.062033</td>\n",
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              "      <td>003_HC.png</td>\n",
              "      <td>0.091291</td>\n",
              "      <td>69.00</td>\n",
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              "      <td>0.061240</td>\n",
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          "metadata": {},
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      ],
      "source": [
        "train_csv.head()"
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              "     filename  pixel size(mm)\n",
              "0  000_HC.png        0.235857\n",
              "1  001_HC.png        0.067973\n",
              "2  002_HC.png        0.165202\n",
              "3  003_HC.png        0.096674\n",
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              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ],
      "source": [
        "test_csv.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "V2DJWc8BQm_X"
      },
      "outputs": [],
      "source": [
        "import torch\n",
        "import os\n",
        "import cv2"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pol-UrR-Rzj1",
        "outputId": "3b91640b-5e2f-4656-ac59-59d62192b3e6"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "cuda\n"
          ]
        }
      ],
      "source": [
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "print(device)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "iBufx526SB-A"
      },
      "outputs": [],
      "source": [
        "def mask_generator(im):\n",
        "  imgray = cv2.cvtColor(im,cv2.COLOR_BGR2GRAY)\n",
        "  ret,thresh = cv2.threshold(imgray,127,255,0)\n",
        "  contours, useless1 = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)\n",
        "  ellipse = cv2.fitEllipse(contours[0])\n",
        "  return cv2.ellipse(im,ellipse,(255,255,255),-1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 551
        },
        "id": "GHyiu5Sfu9Os",
        "outputId": "7873a4f1-3c7f-4522-a911-cb251887cea7"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 3000x700 with 4 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "fig = plt.figure(figsize = (30,7))\n",
        "for index in range(2):\n",
        "  file_path = os.path.join(train_img, train_csv.iloc[index, 0].replace('.png','_Annotation.png'))\n",
        "\n",
        "  ax = fig.add_subplot(2, 7, index+1)\n",
        "  plt.imshow(cv2.imread(file_path))\n",
        "\n",
        "  ax = fig.add_subplot(2, 7, index+8)\n",
        "  plt.imshow(mask_generator(cv2.imread(file_path)))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "WA0OXlGoxsel"
      },
      "outputs": [],
      "source": [
        "for index in range(len(train_csv)):\n",
        "    file_path = os.path.join('/content/drive/MyDrive/Internship/Fetal Cns/Dataset/training_set/training_set/', train_csv.iloc[index, 0].replace('.png','_Annotation.png'))\n",
        "    im = cv2.imread(file_path)\n",
        "    mask = mask_generator(im)\n",
        "    cv2.imwrite(file_path, mask)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "7benPG8gilhv"
      },
      "outputs": [],
      "source": [
        "from tqdm import tqdm_notebook\n",
        "import copy\n",
        "import glob\n",
        "from PIL import Image\n",
        "import torchvision\n",
        "import torch.utils.data\n",
        "from torch.utils.data import DataLoader, Dataset, random_split\n",
        "import torchvision.transforms as transforms\n",
        "from torchvision import datasets\n",
        "\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "import torch.optim as optim\n",
        "\n",
        "import numpy as np"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "lSxqbbe5xVzA"
      },
      "outputs": [],
      "source": [
        "def generate_mask(source_file_path, dest_file_path):\n",
        "    file_names = sorted([file for _,_,files in os.walk(source_file_path) for file in files])\n",
        "    for i, file_name in enumerate(file_names):\n",
        "        if i % 2 != 0:\n",
        "            im = cv2.imread(source_file_path+file_name)\n",
        "            imgray = cv2.cvtColor(im,cv2.COLOR_BGR2GRAY)\n",
        "            ret,thresh = cv2.threshold(imgray,127,255,0)\n",
        "            contours, hierarchy = cv2.findContours(thresh,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)\n",
        "            ellipse = cv2.fitEllipse(contours[0])\n",
        "            im1 = cv2.ellipse(im,ellipse,(255,255,255),-1)\n",
        "            image_name = file_name.replace('Annotation', 'Mask')\n",
        "            cv2.imwrite(dest_file_path+image_name, im1)\n",
        "\n",
        "    return 1"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xZL88ojnxmnH",
        "outputId": "712a64e7-8d39-4f12-c54d-9f172bc16a2b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ],
      "source": [
        "generate_mask('/content/drive/MyDrive/Internship/Fetal Cns/Dataset/training_set/training_set/','/content/drive/MyDrive/Internship/Fetal Cns/Dataset/training_set/Masks/')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EOSn_oc9XHjh"
      },
      "source": [
        "\n",
        "\n",
        "#Changing dataset to Torch form"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "wGQ12_JWXFtx"
      },
      "outputs": [],
      "source": [
        "class CHALLENGE18(torch.utils.data.Dataset):\n",
        "  def __init__(self, png_images, transform_X=None,transform_Y = None,train=True):\n",
        "    self.X_train = glob.glob('/content/drive/MyDrive/Internship/Fetal Cns/Dataset/training_set/training_set/*HC.png')\n",
        "\n",
        "    self.Y_train = glob.glob('/content/drive/MyDrive/Internship/Fetal Cns/Dataset/training_set/Masks/*')\n",
        "    self.transform_X = transform_X\n",
        "    self.transform_Y = transform_Y\n",
        "\n",
        "\n",
        "\n",
        "    self.png_images = png_images\n",
        "\n",
        "  def __len__(self):\n",
        "    if self.png_images == \"train\":\n",
        "      return len(self.X_train)\n",
        "    else:\n",
        "      print(\"code is wrong\")\n",
        "\n",
        "  def __getitem__(self,idx):\n",
        "    if self.png_images == \"train\":\n",
        "\n",
        "      X=np.array(Image.open(self.X_train[idx]).resize((572,572))).reshape(572,572,1) #as per U-net paper\n",
        "      X = self.transform_X(X)\n",
        "      # print(idx)\n",
        "      # print(self.X_train[idx])\n",
        "      name = self.X_train[idx].split('/')\n",
        "      name2 = self.Y_train[idx].split('/')\n",
        "      # print(name[7])\n",
        "      # print(name2[7])\n",
        "      Y=np.array(Image.open(self.Y_train[idx].replace(name2[7], name[7]).replace('_Mask.png','_Mask.png')).convert('L').resize((572,572))).reshape(572,572,1)\n",
        "      Y = self.transform_Y(Y)\n",
        "\n",
        "      #print(name2[8])\n",
        "      #print(self.Y_train[idx].replace(name2[5], name[3]).replace('.png','_Mask.png'))\n",
        "\n",
        "      return X, Y\n",
        "\n",
        "    else:\n",
        "      print(\"code is wrong\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eaCBtlTNMkhp"
      },
      "source": [
        "#Normalizing image between [0,1]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "pvYs4E5SloLF"
      },
      "outputs": [],
      "source": [
        "transform_X = transforms.Compose([transforms.ToTensor(),transforms.Normalize((0.5, ), (0.5, ))])\n",
        "transform_Y = transforms.Compose([transforms.ToTensor()])\n",
        "total_images = CHALLENGE18(\"train\",transform_Y=transform_Y, transform_X = transform_X)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iuoWMYWbGH5-",
        "outputId": "7e9a1757-0281-46bc-cf28-e59307bfe30c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "999"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ],
      "source": [
        "len(total_images)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "IDkYByL0HL8r"
      },
      "outputs": [],
      "source": [
        "x, y =total_images[555]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0VD6VdtEGmfc"
      },
      "source": [
        "**Cross-checking the shape of x and y to being sure**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mQN5W4G7byX8",
        "outputId": "d318c370-141c-4e7c-844b-c3fc11654805"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.Size([1, 572, 572])"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ],
      "source": [
        "x.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OC_Ws7v9bzaG",
        "outputId": "17a140bf-b045-42ea-f49a-b1ad10e47890"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.Size([1, 572, 572])"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ],
      "source": [
        "y.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Un8BBfKPNC58"
      },
      "source": [
        "#for cross-validation, dividing the whole set in (90:10) ratio"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "id": "VsFHHVWnpFiK"
      },
      "outputs": [],
      "source": [
        "train_images = int(0.9 * len(total_images))\n",
        "test_images = len(total_images)- train_images\n",
        "\n",
        "train_data, validation_data = random_split(total_images,[train_images, test_images])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aZUIw-VnJhVD"
      },
      "source": [
        "**Creating only train_loader & validation_loader here**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "id": "8SR_Y065p4HS"
      },
      "outputs": [],
      "source": [
        "batch_size = 2\n",
        "\n",
        "train_loader = torch.utils.data.DataLoader(train_data, batch_size=batch_size, shuffle=True, num_workers=2)\n",
        "validation_loader = torch.utils.data.DataLoader(validation_data, batch_size=batch_size, shuffle=True, num_workers=2)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "id": "t2lUJYk1qkRi"
      },
      "outputs": [],
      "source": [
        "def imshow(img, title):\n",
        "    img = img.clone().detach().numpy()\n",
        "    print('npimage shape',img.shape)\n",
        "    plt.show()\n",
        "    return img"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "id": "4CcQ1lPNq07T"
      },
      "outputs": [],
      "source": [
        "def show_train_batch_images(dataloader):\n",
        "    x, y = next(iter(dataloader))\n",
        "    print(x.shape)\n",
        "    return imshow(x[0],'d')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "id": "ORJ_qJf1q8rV",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b41b2cc9-f1ca-4f98-e09b-aab0b6e8974e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "torch.Size([2, 1, 572, 572])\n",
            "npimage shape (1, 572, 572)\n"
          ]
        }
      ],
      "source": [
        "p =show_train_batch_images(validation_loader)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "id": "OnkIavrcqyBX",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6e4e54fa-7d3e-4646-8090-6910879ed2fe"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "torch.Size([2, 1, 572, 572])\n",
            "npimage shape (1, 572, 572)\n"
          ]
        }
      ],
      "source": [
        "q= show_train_batch_images(train_loader)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "id": "l9dsNlGqL2Mn",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a2d61d05-307e-41f5-b5b5-0add46d91796"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1, 572, 572)"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ],
      "source": [
        "p.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "id": "lxRJDArFMDM6"
      },
      "outputs": [],
      "source": [
        "p=p.reshape(572,572)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "id": "07OFr-ZWMF3Z",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 452
        },
        "outputId": "475d11b9-1003-4dc5-fc03-89b57dacb8d7"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7a120c70dc00>"
            ]
          },
          "metadata": {},
          "execution_count": 31
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.imshow(p)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Y1rz4QcJ0PIb"
      },
      "source": [
        "#preparing the U-net architecture"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "id": "iR8rSzRPCalm"
      },
      "outputs": [],
      "source": [
        "class double_conv(nn.Module):\n",
        "    '''(conv => BN => ReLU) * 2'''\n",
        "    def __init__(self, in_ch, out_ch):\n",
        "        super(double_conv, self).__init__()\n",
        "        self.conv = nn.Sequential(\n",
        "            nn.Conv2d(in_ch, out_ch, 3, padding=1),\n",
        "            nn.BatchNorm2d(out_ch),\n",
        "            nn.ReLU(inplace=True),\n",
        "            nn.Conv2d(out_ch, out_ch, 3, padding=1),\n",
        "            nn.BatchNorm2d(out_ch),\n",
        "            nn.ReLU(inplace=True)\n",
        "        )\n",
        "\n",
        "    def forward(self, x):\n",
        "        x = self.conv(x)\n",
        "        return x\n",
        "\n",
        "\n",
        "class inconv(nn.Module):\n",
        "    def __init__(self, in_ch, out_ch):\n",
        "        super(inconv, self).__init__()\n",
        "        self.conv = double_conv(in_ch, out_ch)\n",
        "\n",
        "    def forward(self, x):\n",
        "        x = self.conv(x)\n",
        "        return x\n",
        "\n",
        "\n",
        "class down(nn.Module):\n",
        "    def __init__(self, in_ch, out_ch):\n",
        "        super(down, self).__init__()\n",
        "        self.mpconv = nn.Sequential(\n",
        "            nn.MaxPool2d(2),\n",
        "            double_conv(in_ch, out_ch)\n",
        "        )\n",
        "\n",
        "    def forward(self, x):\n",
        "        x = self.mpconv(x)\n",
        "        return x\n",
        "\n",
        "\n",
        "class up(nn.Module):\n",
        "    def __init__(self, in_ch, out_ch, bilinear=True):\n",
        "        super(up, self).__init__()\n",
        "\n",
        "\n",
        "        if bilinear:\n",
        "            self.up = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True)\n",
        "        else:\n",
        "            self.up = nn.ConvTranspose2d(in_ch//2, in_ch//2, 2, stride=2)\n",
        "\n",
        "        self.conv = double_conv(in_ch, out_ch)\n",
        "\n",
        "    def forward(self, x1, x2):\n",
        "        x1 = self.up(x1)\n",
        "\n",
        "        # input is CHW\n",
        "        diffY = x2.size()[2] - x1.size()[2]\n",
        "        diffX = x2.size()[3] - x1.size()[3]\n",
        "\n",
        "        x1 = F.pad(x1, (diffX // 2, diffX - diffX//2,\n",
        "                        diffY // 2, diffY - diffY//2))\n",
        "\n",
        "\n",
        "\n",
        "        x = torch.cat([x2, x1], dim=1)\n",
        "        x = self.conv(x)\n",
        "        return x\n",
        "\n",
        "\n",
        "class outconv(nn.Module):\n",
        "    def __init__(self, in_ch, out_ch):\n",
        "        super(outconv, self).__init__()\n",
        "        self.conv = nn.Conv2d(in_ch, out_ch, 1)\n",
        "\n",
        "    def forward(self, x):\n",
        "        x = self.conv(x)\n",
        "        return x"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "id": "DbP5YsYyCrbh"
      },
      "outputs": [],
      "source": [
        "class UNET(nn.Module):\n",
        "    def __init__(self, n_channels, n_classes):\n",
        "        super(UNET, self).__init__()\n",
        "        self.inc = inconv(n_channels, 64)\n",
        "        self.down1 = down(64, 128)\n",
        "        self.down2 = down(128, 256)\n",
        "        self.down3 = down(256, 512)\n",
        "        self.down4 = down(512, 512)\n",
        "        self.up1 = up(1024, 256)\n",
        "        self.up2 = up(512, 128)\n",
        "        self.up3 = up(256, 64)\n",
        "        self.up4 = up(128, 64)\n",
        "        self.outc = outconv(64, n_classes)\n",
        "\n",
        "    def forward(self, x):\n",
        "        x1 = self.inc(x)\n",
        "        x2 = self.down1(x1)\n",
        "        x3 = self.down2(x2)\n",
        "        x4 = self.down3(x3)\n",
        "        x5 = self.down4(x4)\n",
        "        x = self.up1(x5, x4)\n",
        "        x = self.up2(x, x3)\n",
        "        x = self.up3(x, x2)\n",
        "        x = self.up4(x, x1)\n",
        "        x = self.outc(x)\n",
        "        return torch.sigmoid(x)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "id": "A0v3H3cpDGO1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "90c3b990-a592-48e4-c6fe-d59af1b425ac"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "UNET(\n",
              "  (inc): inconv(\n",
              "    (conv): double_conv(\n",
              "      (conv): Sequential(\n",
              "        (0): Conv2d(1, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "        (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (2): ReLU(inplace=True)\n",
              "        (3): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "        (4): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (5): ReLU(inplace=True)\n",
              "      )\n",
              "    )\n",
              "  )\n",
              "  (down1): down(\n",
              "    (mpconv): Sequential(\n",
              "      (0): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
              "      (1): double_conv(\n",
              "        (conv): Sequential(\n",
              "          (0): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "          (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (2): ReLU(inplace=True)\n",
              "          (3): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "          (4): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (5): ReLU(inplace=True)\n",
              "        )\n",
              "      )\n",
              "    )\n",
              "  )\n",
              "  (down2): down(\n",
              "    (mpconv): Sequential(\n",
              "      (0): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
              "      (1): double_conv(\n",
              "        (conv): Sequential(\n",
              "          (0): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "          (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (2): ReLU(inplace=True)\n",
              "          (3): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "          (4): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (5): ReLU(inplace=True)\n",
              "        )\n",
              "      )\n",
              "    )\n",
              "  )\n",
              "  (down3): down(\n",
              "    (mpconv): Sequential(\n",
              "      (0): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
              "      (1): double_conv(\n",
              "        (conv): Sequential(\n",
              "          (0): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "          (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (2): ReLU(inplace=True)\n",
              "          (3): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "          (4): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (5): ReLU(inplace=True)\n",
              "        )\n",
              "      )\n",
              "    )\n",
              "  )\n",
              "  (down4): down(\n",
              "    (mpconv): Sequential(\n",
              "      (0): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
              "      (1): double_conv(\n",
              "        (conv): Sequential(\n",
              "          (0): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "          (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (2): ReLU(inplace=True)\n",
              "          (3): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "          (4): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (5): ReLU(inplace=True)\n",
              "        )\n",
              "      )\n",
              "    )\n",
              "  )\n",
              "  (up1): up(\n",
              "    (up): Upsample(scale_factor=2.0, mode='bilinear')\n",
              "    (conv): double_conv(\n",
              "      (conv): Sequential(\n",
              "        (0): Conv2d(1024, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "        (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (2): ReLU(inplace=True)\n",
              "        (3): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "        (4): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (5): ReLU(inplace=True)\n",
              "      )\n",
              "    )\n",
              "  )\n",
              "  (up2): up(\n",
              "    (up): Upsample(scale_factor=2.0, mode='bilinear')\n",
              "    (conv): double_conv(\n",
              "      (conv): Sequential(\n",
              "        (0): Conv2d(512, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "        (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (2): ReLU(inplace=True)\n",
              "        (3): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "        (4): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (5): ReLU(inplace=True)\n",
              "      )\n",
              "    )\n",
              "  )\n",
              "  (up3): up(\n",
              "    (up): Upsample(scale_factor=2.0, mode='bilinear')\n",
              "    (conv): double_conv(\n",
              "      (conv): Sequential(\n",
              "        (0): Conv2d(256, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "        (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (2): ReLU(inplace=True)\n",
              "        (3): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "        (4): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (5): ReLU(inplace=True)\n",
              "      )\n",
              "    )\n",
              "  )\n",
              "  (up4): up(\n",
              "    (up): Upsample(scale_factor=2.0, mode='bilinear')\n",
              "    (conv): double_conv(\n",
              "      (conv): Sequential(\n",
              "        (0): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "        (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (2): ReLU(inplace=True)\n",
              "        (3): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "        (4): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (5): ReLU(inplace=True)\n",
              "      )\n",
              "    )\n",
              "  )\n",
              "  (outc): outconv(\n",
              "    (conv): Conv2d(64, 1, kernel_size=(1, 1), stride=(1, 1))\n",
              "  )\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ],
      "source": [
        "model = UNET(1,1)\n",
        "model.to(device)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7QH4mb75HxJ1"
      },
      "source": [
        "**In the above cell using \"model\" name as to represent U-Net model..**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EZYSYNRoHHRa"
      },
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {
        "id": "BPSoNFx6bdH4"
      },
      "outputs": [],
      "source": [
        "\n",
        "import torch.optim as optim\n",
        "\n",
        "criterion = nn.BCELoss()\n",
        "\n",
        "optimizer = torch.optim.Adam(model.parameters(), lr=0.00015)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2P0fk0zHlKud"
      },
      "source": [
        "#Dice coefficient"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {
        "id": "6I716J-tgfi6"
      },
      "outputs": [],
      "source": [
        "def dice_coeff(y_pred, y_targets):\n",
        "\n",
        "\n",
        "    smooth = 1\n",
        "    num = y_targets.size(0)\n",
        "    m1 = y_pred.view(num, -1)\n",
        "    m2 = y_targets.view(num, -1)\n",
        "    intersection = (m1 * m2)\n",
        "\n",
        "    score = 2. * (intersection.sum(1) + smooth) / (m1.sum(1) + m2.sum(1) + smooth)\n",
        "    score = score.sum() / num\n",
        "    return score.item()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {
        "id": "J7zYHVC8e08L"
      },
      "outputs": [],
      "source": [
        "def dice_loss(y_predict, y_train):\n",
        "\n",
        "  dice_loss = 1 -(dice_coeff(y_predict, y_train))\n",
        "\n",
        "  return dice_loss"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "klz34VhE5NO6"
      },
      "source": [
        "#Training of the network"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "metadata": {
        "id": "KsNW1X7NfqZH",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "34d14d91-2a29-4315-d80e-b2178fc87701"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch:  1 Batch:  0 Current Loss:  0.6521528959274292\n",
            "Epoch:  1 Batch:  50 Current Loss:  0.4898240566253662\n",
            "Epoch:  1 Batch:  100 Current Loss:  0.3873606324195862\n",
            "Epoch:  1 Batch:  150 Current Loss:  0.34647300839424133\n",
            "Epoch:  1 Batch:  200 Current Loss:  0.3849681615829468\n",
            "Epoch:  1 Batch:  250 Current Loss:  0.3725150227546692\n",
            "Epoch:  1 Batch:  300 Current Loss:  0.378578245639801\n",
            "Epoch:  1 Batch:  350 Current Loss:  0.30669519305229187\n",
            "Epoch:  1 Batch:  400 Current Loss:  0.34648197889328003\n",
            "================================================================================\n",
            "Epoch 1 completed\n",
            "Average train loss is 0.39810943530665505: \n",
            "Average validation loss is 0.3208438548445702\n",
            "================================================================================\n",
            "Epoch:  2 Batch:  0 Current Loss:  0.3621903657913208\n",
            "Epoch:  2 Batch:  50 Current Loss:  0.3073126971721649\n",
            "Epoch:  2 Batch:  100 Current Loss:  0.32078102231025696\n",
            "Epoch:  2 Batch:  150 Current Loss:  0.33381354808807373\n",
            "Epoch:  2 Batch:  200 Current Loss:  0.3368663787841797\n",
            "Epoch:  2 Batch:  250 Current Loss:  0.2907828688621521\n",
            "Epoch:  2 Batch:  300 Current Loss:  0.2898692190647125\n",
            "Epoch:  2 Batch:  350 Current Loss:  0.3288010358810425\n",
            "Epoch:  2 Batch:  400 Current Loss:  0.374511182308197\n",
            "================================================================================\n",
            "Epoch 2 completed\n",
            "Average train loss is 0.34398109078407285: \n",
            "Average validation loss is 0.3072110477089882\n",
            "================================================================================\n"
          ]
        }
      ],
      "source": [
        "epochs = 2\n",
        "train_running_loss_history = []\n",
        "validation_running_loss_history =[]\n",
        "\n",
        "for e in range(epochs):\n",
        "  train_running_loss = 0.0\n",
        "  validation_running_loss = 0.0\n",
        "\n",
        "  model.train()\n",
        "\n",
        "  for ith_batch, sample_batched in enumerate(train_loader):\n",
        "    X_train, y_train = sample_batched\n",
        "    X_train= X_train.to(device)\n",
        "    y_train= y_train.to(device)\n",
        "\n",
        "    optimizer.zero_grad()\n",
        "    y_pred = model(X_train)\n",
        "\n",
        "    loss = 0.30 * dice_loss(y_pred, y_train) +  0.70 * criterion(y_pred, y_train)\n",
        "\n",
        "    loss.backward()\n",
        "    optimizer.step()\n",
        "\n",
        "    if ith_batch % 50 == 0:\n",
        "      print('Epoch: ', e + 1, 'Batch: ', ith_batch, 'Current Loss: ', loss.item())\n",
        "\n",
        "    train_running_loss += loss.item()\n",
        "  else:\n",
        "    with torch.no_grad():\n",
        "\n",
        "      model.eval()\n",
        "\n",
        "      for ith_batch, sample_batched in enumerate(validation_loader):\n",
        "          X_val, y_val = sample_batched\n",
        "          X_val = X_val.to(device)\n",
        "          y_val = y_val.to(device)\n",
        "\n",
        "          y_out = model(X_val)\n",
        "          out_val = (y_out + 0.5).int().float()\n",
        "\n",
        "          val_loss = 0.3 * dice_loss(out_val, y_val)  + 0.7 * criterion(y_out, y_val)\n",
        "\n",
        "          validation_running_loss += val_loss.item()\n",
        "      print(\"================================================================================\")\n",
        "      print(\"Epoch {} completed\".format(e + 1))\n",
        "\n",
        "      train_epoch_loss = train_running_loss / len(train_loader)\n",
        "      validation_epoch_loss = validation_running_loss / len(validation_loader)\n",
        "\n",
        "      print(\"Average train loss is {}: \".format(train_epoch_loss))\n",
        "      print(\"Average validation loss is {}\".format(validation_epoch_loss))\n",
        "      print(\"================================================================================\")\n",
        "      train_running_loss_history.append(train_epoch_loss)\n",
        "      validation_running_loss_history.append(validation_epoch_loss)\n",
        "\n",
        "  torch.cuda.empty_cache()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3cr6A99EmaYX"
      },
      "source": [
        "#plot for validation & training loss respectively"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "metadata": {
        "id": "VgUPzC96QulB",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "outputId": "b0dbded6-4922-448c-c639-112b9d02d16a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7a120c03a830>"
            ]
          },
          "metadata": {},
          "execution_count": 39
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.plot(validation_running_loss_history, label = 'Validation Loss', color='green')\n",
        "plt.plot(train_running_loss_history, label = 'Train Loss',  color='red')\n",
        "plt.legend()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-A0-yFfkIf9z"
      },
      "source": [
        "**We can see that both the losses are decreasing**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "metadata": {
        "id": "Sa-0C9ZERM87",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "99e43b79-d115-4a6c-9abc-e55982299f69"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.Size([1, 1, 572, 572])"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ],
      "source": [
        "model.eval()\n",
        "X_train.size()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 41,
      "metadata": {
        "id": "AmAma6jqRQXD"
      },
      "outputs": [],
      "source": [
        "out = (y_pred + 0.5).int().float()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 42,
      "metadata": {
        "id": "snlNjKRqRWAh",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ad351d52-6a4a-44e8-b469-2003fb6232cf"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.7757141590118408\n",
            "0.22428584098815918\n"
          ]
        }
      ],
      "source": [
        "print(dice_coeff(y_train, out))\n",
        "print(dice_loss(y_train, out))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 43,
      "metadata": {
        "id": "xd6SvL9SVF4r"
      },
      "outputs": [],
      "source": [
        "class HC18_test(Dataset):\n",
        "    def __init__(self, root_dir, transform_X = None):\n",
        "        self.pixel_file = test_csv\n",
        "        self.root_dir = root_dir\n",
        "        self.transform_X = transform_X\n",
        "\n",
        "    def __len__(self):\n",
        "        return len(self.pixel_file)\n",
        "\n",
        "    def __getitem__(self, index):\n",
        "        imx_name = os.path.join(self.root_dir, '', self.pixel_file.iloc[index, 0])\n",
        "\n",
        "        imx = Image.open(imx_name)\n",
        "\n",
        "        f_name = self.pixel_file.iloc[index, 0]\n",
        "\n",
        "        if self.transform_X :\n",
        "            imx = self.transform_X(imx)\n",
        "\n",
        "        sample = {'image': imx, 'f_name': f_name}\n",
        "        return sample"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 49,
      "metadata": {
        "id": "K-v88WF9VGEa"
      },
      "outputs": [],
      "source": [
        "test_img = HC18_test('/content/drive/MyDrive/Internship/Fetal Cns/Dataset/test_set', transform_X = transform_X)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8VI8b8mtJS6m"
      },
      "source": [
        "**Creating test-loader to work with test images**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 50,
      "metadata": {
        "id": "sWmR982rVGM_"
      },
      "outputs": [],
      "source": [
        "test_loader = torch.utils.data.DataLoader(test_img, batch_size=batch_size,shuffle=False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 51,
      "metadata": {
        "id": "cUMDDQ-OETti"
      },
      "outputs": [],
      "source": [
        "def avg_dice_index(dataloader):\n",
        "\n",
        "    dice = 0.0\n",
        "    model.eval()\n",
        "    with torch.no_grad():\n",
        "\n",
        "        for ith_batch, sample_batched in enumerate(dataloader):\n",
        "            X_train , y_train = sample_batched\n",
        "            X_train = X_train.to(device)\n",
        "            y_train = y_train.to(device)\n",
        "            # print(model(X_train))\n",
        "\n",
        "            y_predict = (model(X_train) + 0.5).int().float()\n",
        "            # print(y_predict)\n",
        "\n",
        "            dice += dice_coeff(y_predict, y_train)\n",
        "\n",
        "    avg_dice = dice / len(dataloader)\n",
        "    # print(avg_dice)\n",
        "    return avg_dice"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 52,
      "metadata": {
        "id": "QVpaY9VvZTlN"
      },
      "outputs": [],
      "source": [
        "test_loader = DataLoader(dataset = test_img, batch_size = 1, shuffle = True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lXIe_SW4hteW"
      },
      "source": [
        "#Writing in the Drive"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 53,
      "metadata": {
        "id": "hnLgBOyph8fS",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2688317c-e866-47ca-ab9b-2c7ed5141aa0"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "task to write file is complete\n"
          ]
        }
      ],
      "source": [
        "transform = transforms.Compose([transforms.ToPILImage(),transforms.Resize((540, 800)),transforms.ToTensor()])\n",
        "\n",
        "with torch.no_grad():\n",
        "\n",
        "      for ith_batch , data in enumerate(test_loader):\n",
        "        X_test = data['image']\n",
        "        X_test = X_test.to(device)\n",
        "\n",
        "        y_pred = (model(X_test) + 0.5).int().float().cpu()\n",
        "\n",
        "        file_names = data['f_name']\n",
        "        Result = transform(y_pred[0])\n",
        "        torchvision.utils.save_image(Result , os.path.join('/content/drive/MyDrive/Internship/Fetal Cns/Dataset/segmentation_test_2/', file_names[0]))\n",
        "      print(\"task to write file is complete\")\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WeWxmP-9PYtn"
      },
      "source": [
        "**Function to create mask of test_set**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 54,
      "metadata": {
        "id": "qTcUVKPWh8XQ"
      },
      "outputs": [],
      "source": [
        "def test_ellipse(im):\n",
        "    imgray = cv2.cvtColor(im,cv2.COLOR_BGR2GRAY)\n",
        "    ret,thresh = cv2.threshold(imgray,127,255,0)\n",
        "    # im2,\n",
        "    contours, hierarchy = cv2.findContours(thresh,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)\n",
        "    contour_sizes = [len(contour) for contour in contours]\n",
        "    ellipse = cv2.fitEllipse(contours[np.argmax(contour_sizes)])\n",
        "    return ellipse\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CISHgt5yPkZP"
      },
      "source": [
        "# **Formula to Calculate Head Circumference**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TAxMOfXC0i4h"
      },
      "source": [
        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)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 55,
      "metadata": {
        "id": "hJP11ppz7Ekd"
      },
      "outputs": [],
      "source": [
        "# occipitofrontal diameter (OFD)\n",
        "#BPD (biparietal diameter), the diameter of your baby's head\n",
        "#HC = 1.62 × (BPD + OFD) 3\n",
        "# OFD = 2(semi_axes_a)\n",
        "# BPD = 2(semi_axes_b)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YiRO6S9b0r71"
      },
      "source": [
        "#Creating a csv file to store the Data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 56,
      "metadata": {
        "id": "FBo0AxH9h7oo",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f06115ca-4b44-4972-a5aa-7bb1bbeb89e1"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Required .csv file generated\n",
            "((379.1164855957031, 268.57366943359375), (397.6134033203125, 598.98291015625), 82.05652618408203)\n"
          ]
        }
      ],
      "source": [
        "submission = {'filename': [], 'center_x_mm': [], 'center_y_mm': [], 'semi_axes_a_mm': [],\n",
        "             'semi_axes_b_mm': [], 'angle_rad': [], 'Index': []}\n",
        "\n",
        "\n",
        "\n",
        "file_names = sorted([file for _,_,files in os.walk('/content/drive/MyDrive/Internship/Fetal Cns/Dataset/segmentation_test_2/') for file in files])\n",
        "\n",
        "for i, file_name in enumerate(file_names):\n",
        "    im = cv2.imread('/content/drive/MyDrive/Internship/Fetal Cns/Dataset/segmentation_test_2/'+file_name)\n",
        "\n",
        "    ellipse = test_ellipse(im)\n",
        "    im_mask = cv2.ellipse(np.zeros_like(im),ellipse,(255,255,255),-1,100)\n",
        "    image_name = file_name.split('.')[0] + '_Annotation_fit.png'\n",
        "\n",
        "\n",
        "    pixel_size = test_csv.loc[test_csv['filename'] == file_name, 'pixel size(mm)'].iloc[0]\n",
        "    submission['filename'].extend([file_name])\n",
        "    submission['Index'].extend([int(file_name.split('_')[0])])\n",
        "    center_x,  center_y = ellipse[0]\n",
        "    submission['center_x_mm'].extend([pixel_size*center_x])\n",
        "    submission['center_y_mm'].extend([pixel_size*center_y])\n",
        "    semi_axes_b, semi_axes_a = ellipse[1]\n",
        "    if semi_axes_b > semi_axes_a:\n",
        "        semi_axes_b = semi_axes_b + semi_axes_a\n",
        "        semi_axes_a = semi_axes_b - semi_axes_a\n",
        "        semi_axes_b -= semi_axes_a\n",
        "    submission['semi_axes_a_mm'].extend([semi_axes_a*pixel_size/2])\n",
        "    submission['semi_axes_b_mm'].extend([semi_axes_b*pixel_size/2])\n",
        "    angle = ellipse[2]\n",
        "    if angle < 90:\n",
        "        angle += 90\n",
        "    else:\n",
        "        angle -= 90\n",
        "    submission['angle_rad'].extend([np.deg2rad(angle)])\n",
        "    #OFD,  BPD = ellipse[3]\n",
        "    #submission['OFD'].extend([semi_axes_a_mm*2])\n",
        "    #submission['BPD'].extend([semi_axes_b*2])\n",
        "    #submission['Head Circumfrence'].extend([1.62*(BPD + OFD)^3])\n",
        "submission = pd.DataFrame(submission)\n",
        "submission = submission[['filename', 'center_x_mm', 'center_y_mm',\n",
        "                         'semi_axes_a_mm', 'semi_axes_b_mm', 'angle_rad', 'Index']]\n",
        "submission = submission.sort_values(['Index'])\n",
        "submission = submission.drop('Index', axis=1)\n",
        "submission.to_csv(\"/content/drive/MyDrive/Internship/Fetal Cns/final1_result.csv\", index=False)\n",
        "print('Required .csv file generated')\n",
        "print(ellipse)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 57,
      "metadata": {
        "id": "h3fRXZtCjqr5"
      },
      "outputs": [],
      "source": [
        "\n",
        "final_result = pd.read_csv('/content/drive/MyDrive/Internship/Fetal Cns/final1_result.csv')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 58,
      "metadata": {
        "id": "lFquierTY18S"
      },
      "outputs": [],
      "source": [
        "#HeadCircumfrence = 1.62*(BPD + OFD)^3\n",
        "#HeadCircumfrence"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 59,
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        "id": "i9x0IkjLkaj_",
        "colab": {
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          "height": 206
        },
        "outputId": "1d156846-92c5-4529-973a-6849e41b5472"
      },
      "outputs": [
        {
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          "data": {
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              "     filename  center_x_mm  center_y_mm  semi_axes_a_mm  semi_axes_b_mm  \\\n",
              "0  000_HC.png    90.010766    63.376299       50.143532       47.266806   \n",
              "1  001_HC.png    25.278797    19.246907       17.384347       11.411411   \n",
              "2  002_HC.png    68.187725    44.128207       46.946380       33.769523   \n",
              "3  003_HC.png    40.579349    25.770146       31.012453       20.715353   \n",
              "4  004_HC.png    77.817254    48.772644       58.174265       43.440736   \n",
              "\n",
              "   angle_rad  \n",
              "0   0.917420  \n",
              "1   0.185200  \n",
              "2   2.972059  \n",
              "3   3.104870  \n",
              "4   3.061607  "
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              "      <th>0</th>\n",
              "      <td>000_HC.png</td>\n",
              "      <td>90.010766</td>\n",
              "      <td>63.376299</td>\n",
              "      <td>50.143532</td>\n",
              "      <td>47.266806</td>\n",
              "      <td>0.917420</td>\n",
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              "      <th>1</th>\n",
              "      <td>001_HC.png</td>\n",
              "      <td>25.278797</td>\n",
              "      <td>19.246907</td>\n",
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              "      <td>11.411411</td>\n",
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              "      <th>2</th>\n",
              "      <td>002_HC.png</td>\n",
              "      <td>68.187725</td>\n",
              "      <td>44.128207</td>\n",
              "      <td>46.946380</td>\n",
              "      <td>33.769523</td>\n",
              "      <td>2.972059</td>\n",
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              "      <th>3</th>\n",
              "      <td>003_HC.png</td>\n",
              "      <td>40.579349</td>\n",
              "      <td>25.770146</td>\n",
              "      <td>31.012453</td>\n",
              "      <td>20.715353</td>\n",
              "      <td>3.104870</td>\n",
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              "      <td>004_HC.png</td>\n",
              "      <td>77.817254</td>\n",
              "      <td>48.772644</td>\n",
              "      <td>58.174265</td>\n",
              "      <td>43.440736</td>\n",
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              "      <script>\n",
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              "          document.querySelector('#df-be2a8c8b-a53f-4095-919f-b4cb7e74d4f8 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-be2a8c8b-a53f-4095-919f-b4cb7e74d4f8');\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 59
        }
      ],
      "source": [
        "final_result.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 60,
      "metadata": {
        "id": "M73KNKOlkc-v",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2ceb2618-9069-4bf3-e8cd-4e61138762ea"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "335"
            ]
          },
          "metadata": {},
          "execution_count": 60
        }
      ],
      "source": [
        "len(final_result)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RNms8Sos1A2W"
      },
      "source": [
        "##Caculating The Head Circumfrence"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 61,
      "metadata": {
        "id": "6b8_W84BQB3X"
      },
      "outputs": [],
      "source": [
        "df = pd.read_csv(\"/content/drive/MyDrive/Internship/Fetal Cns/final1_result.csv\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 62,
      "metadata": {
        "id": "BP7pw4pYSQAm"
      },
      "outputs": [],
      "source": [
        "df['OFD']= np.round(df.semi_axes_a_mm*2,1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 63,
      "metadata": {
        "id": "g0Zyxl-bSje4"
      },
      "outputs": [],
      "source": [
        "df['BPD']= np.round(df.semi_axes_b_mm*2,1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 64,
      "metadata": {
        "id": "J94cOb_TRIWL"
      },
      "outputs": [],
      "source": [
        "b = np.round(df.BPD,1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 65,
      "metadata": {
        "id": "KQlbL51_TtqE"
      },
      "outputs": [],
      "source": [
        "c= np.round(df.OFD,1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 66,
      "metadata": {
        "id": "2ReWtrvGTz4Z"
      },
      "outputs": [],
      "source": [
        "d = b+c\n",
        "e=d*d*d\n",
        "f=(1.62*e)\n",
        "g=np.round(f**(1/3),1)\n",
        "h=g/10"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 67,
      "metadata": {
        "id": "J3DbWTvTUae1"
      },
      "outputs": [],
      "source": [
        "df['Head_Circumfrence_cm']= h"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 68,
      "metadata": {
        "id": "yFYZoMtERatb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "b9b996d1-6451-4e85-9cb4-75235bd54c20"
      },
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          "data": {
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              "     filename  center_x_mm  center_y_mm  semi_axes_a_mm  semi_axes_b_mm  \\\n",
              "0  000_HC.png    90.010766    63.376299       50.143532       47.266806   \n",
              "1  001_HC.png    25.278797    19.246907       17.384347       11.411411   \n",
              "2  002_HC.png    68.187725    44.128207       46.946380       33.769523   \n",
              "3  003_HC.png    40.579349    25.770146       31.012453       20.715353   \n",
              "4  004_HC.png    77.817254    48.772644       58.174265       43.440736   \n",
              "\n",
              "   angle_rad    OFD   BPD  Head_Circumfrence_cm  \n",
              "0   0.917420  100.3  94.5                 22.88  \n",
              "1   0.185200   34.8  22.8                  6.76  \n",
              "2   2.972059   93.9  67.5                 18.96  \n",
              "3   3.104870   62.0  41.4                 12.14  \n",
              "4   3.061607  116.3  86.9                 23.87  "
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              "      <th>0</th>\n",
              "      <td>000_HC.png</td>\n",
              "      <td>90.010766</td>\n",
              "      <td>63.376299</td>\n",
              "      <td>50.143532</td>\n",
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              "      <td>0.917420</td>\n",
              "      <td>100.3</td>\n",
              "      <td>94.5</td>\n",
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              "      <th>1</th>\n",
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              "      <td>25.278797</td>\n",
              "      <td>19.246907</td>\n",
              "      <td>17.384347</td>\n",
              "      <td>11.411411</td>\n",
              "      <td>0.185200</td>\n",
              "      <td>34.8</td>\n",
              "      <td>22.8</td>\n",
              "      <td>6.76</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>002_HC.png</td>\n",
              "      <td>68.187725</td>\n",
              "      <td>44.128207</td>\n",
              "      <td>46.946380</td>\n",
              "      <td>33.769523</td>\n",
              "      <td>2.972059</td>\n",
              "      <td>93.9</td>\n",
              "      <td>67.5</td>\n",
              "      <td>18.96</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>003_HC.png</td>\n",
              "      <td>40.579349</td>\n",
              "      <td>25.770146</td>\n",
              "      <td>31.012453</td>\n",
              "      <td>20.715353</td>\n",
              "      <td>3.104870</td>\n",
              "      <td>62.0</td>\n",
              "      <td>41.4</td>\n",
              "      <td>12.14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>004_HC.png</td>\n",
              "      <td>77.817254</td>\n",
              "      <td>48.772644</td>\n",
              "      <td>58.174265</td>\n",
              "      <td>43.440736</td>\n",
              "      <td>3.061607</td>\n",
              "      <td>116.3</td>\n",
              "      <td>86.9</td>\n",
              "      <td>23.87</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9d81b917-0ad4-493f-a607-ba19c8360884')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
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              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "\n",
              "\n",
              "\n",
              "    <div id=\"df-55e2f18d-6872-4257-9d9d-af521c61aff8\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-55e2f18d-6872-4257-9d9d-af521c61aff8')\"\n",
              "              title=\"Suggest charts.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "    background-color: #E8F0FE;\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: #1967D2;\n",
              "    height: 32px;\n",
              "    padding: 0 0 0 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: #E2EBFA;\n",
              "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: #174EA6;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
              "    background-color: #434B5C;\n",
              "    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "    fill: #FFFFFF;\n",
              "  }\n",
              "</style>\n",
              "\n",
              "    <script>\n",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-55e2f18d-6872-4257-9d9d-af521c61aff8 button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-9d81b917-0ad4-493f-a607-ba19c8360884 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-9d81b917-0ad4-493f-a607-ba19c8360884');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 68
        }
      ],
      "source": [
        "df.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ymE4pW7X1Iio"
      },
      "source": [
        "## Adding Gestational Age Data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 69,
      "metadata": {
        "id": "moo1zBn5cddS",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "outputId": "c0811502-423a-4625-a11d-496fc1cbd854"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     filename  center_x_mm  center_y_mm  semi_axes_a_mm  semi_axes_b_mm  \\\n",
              "0  000_HC.png    90.010766    63.376299       50.143532       47.266806   \n",
              "1  001_HC.png    25.278797    19.246907       17.384347       11.411411   \n",
              "2  002_HC.png    68.187725    44.128207       46.946380       33.769523   \n",
              "3  003_HC.png    40.579349    25.770146       31.012453       20.715353   \n",
              "4  004_HC.png    77.817254    48.772644       58.174265       43.440736   \n",
              "5  005_HC.png    49.425677    29.466279       29.249023       23.654686   \n",
              "6  006_HC.png    57.613703    35.955519       41.175181       29.072821   \n",
              "7  007_HC.png    58.971240    36.724520       34.757010       28.950953   \n",
              "8  008_HC.png    53.441893    48.383977       36.762264       28.599815   \n",
              "9  009_HC.png   129.586484    88.491433       66.511369       57.252505   \n",
              "\n",
              "   angle_rad    OFD    BPD  Head_Circumfrence_cm  \\\n",
              "0   0.917420  100.3   94.5                 22.88   \n",
              "1   0.185200   34.8   22.8                  6.76   \n",
              "2   2.972059   93.9   67.5                 18.96   \n",
              "3   3.104870   62.0   41.4                 12.14   \n",
              "4   3.061607  116.3   86.9                 23.87   \n",
              "5   0.037229   58.5   47.3                 12.43   \n",
              "6   0.182108   82.4   58.1                 16.50   \n",
              "7   3.103859   69.5   57.9                 14.96   \n",
              "8   2.076130   73.5   57.2                 15.35   \n",
              "9   2.355646  133.0  114.5                 29.07   \n",
              "\n",
              "               Gestational_Age_In_Weeks  \n",
              "0                                    24  \n",
              "1  Fetus is less than 8 Menstrual Weeks  \n",
              "2                                    20  \n",
              "3                                    15  \n",
              "4                                    25  \n",
              "5                                    15  \n",
              "6                                     0  \n",
              "7                                    17  \n",
              "8                                    18  \n",
              "9                                    31  "
            ],
            "text/html": [
              "\n",
              "\n",
              "  <div id=\"df-9c9f0e82-ded8-4692-8ebe-10fe348a6e0f\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>filename</th>\n",
              "      <th>center_x_mm</th>\n",
              "      <th>center_y_mm</th>\n",
              "      <th>semi_axes_a_mm</th>\n",
              "      <th>semi_axes_b_mm</th>\n",
              "      <th>angle_rad</th>\n",
              "      <th>OFD</th>\n",
              "      <th>BPD</th>\n",
              "      <th>Head_Circumfrence_cm</th>\n",
              "      <th>Gestational_Age_In_Weeks</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>000_HC.png</td>\n",
              "      <td>90.010766</td>\n",
              "      <td>63.376299</td>\n",
              "      <td>50.143532</td>\n",
              "      <td>47.266806</td>\n",
              "      <td>0.917420</td>\n",
              "      <td>100.3</td>\n",
              "      <td>94.5</td>\n",
              "      <td>22.88</td>\n",
              "      <td>24</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>001_HC.png</td>\n",
              "      <td>25.278797</td>\n",
              "      <td>19.246907</td>\n",
              "      <td>17.384347</td>\n",
              "      <td>11.411411</td>\n",
              "      <td>0.185200</td>\n",
              "      <td>34.8</td>\n",
              "      <td>22.8</td>\n",
              "      <td>6.76</td>\n",
              "      <td>Fetus is less than 8 Menstrual Weeks</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>002_HC.png</td>\n",
              "      <td>68.187725</td>\n",
              "      <td>44.128207</td>\n",
              "      <td>46.946380</td>\n",
              "      <td>33.769523</td>\n",
              "      <td>2.972059</td>\n",
              "      <td>93.9</td>\n",
              "      <td>67.5</td>\n",
              "      <td>18.96</td>\n",
              "      <td>20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>003_HC.png</td>\n",
              "      <td>40.579349</td>\n",
              "      <td>25.770146</td>\n",
              "      <td>31.012453</td>\n",
              "      <td>20.715353</td>\n",
              "      <td>3.104870</td>\n",
              "      <td>62.0</td>\n",
              "      <td>41.4</td>\n",
              "      <td>12.14</td>\n",
              "      <td>15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>004_HC.png</td>\n",
              "      <td>77.817254</td>\n",
              "      <td>48.772644</td>\n",
              "      <td>58.174265</td>\n",
              "      <td>43.440736</td>\n",
              "      <td>3.061607</td>\n",
              "      <td>116.3</td>\n",
              "      <td>86.9</td>\n",
              "      <td>23.87</td>\n",
              "      <td>25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>005_HC.png</td>\n",
              "      <td>49.425677</td>\n",
              "      <td>29.466279</td>\n",
              "      <td>29.249023</td>\n",
              "      <td>23.654686</td>\n",
              "      <td>0.037229</td>\n",
              "      <td>58.5</td>\n",
              "      <td>47.3</td>\n",
              "      <td>12.43</td>\n",
              "      <td>15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>006_HC.png</td>\n",
              "      <td>57.613703</td>\n",
              "      <td>35.955519</td>\n",
              "      <td>41.175181</td>\n",
              "      <td>29.072821</td>\n",
              "      <td>0.182108</td>\n",
              "      <td>82.4</td>\n",
              "      <td>58.1</td>\n",
              "      <td>16.50</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>007_HC.png</td>\n",
              "      <td>58.971240</td>\n",
              "      <td>36.724520</td>\n",
              "      <td>34.757010</td>\n",
              "      <td>28.950953</td>\n",
              "      <td>3.103859</td>\n",
              "      <td>69.5</td>\n",
              "      <td>57.9</td>\n",
              "      <td>14.96</td>\n",
              "      <td>17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>008_HC.png</td>\n",
              "      <td>53.441893</td>\n",
              "      <td>48.383977</td>\n",
              "      <td>36.762264</td>\n",
              "      <td>28.599815</td>\n",
              "      <td>2.076130</td>\n",
              "      <td>73.5</td>\n",
              "      <td>57.2</td>\n",
              "      <td>15.35</td>\n",
              "      <td>18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>009_HC.png</td>\n",
              "      <td>129.586484</td>\n",
              "      <td>88.491433</td>\n",
              "      <td>66.511369</td>\n",
              "      <td>57.252505</td>\n",
              "      <td>2.355646</td>\n",
              "      <td>133.0</td>\n",
              "      <td>114.5</td>\n",
              "      <td>29.07</td>\n",
              "      <td>31</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9c9f0e82-ded8-4692-8ebe-10fe348a6e0f')\"\n",
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              "              style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "\n",
              "\n",
              "\n",
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              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-cdbc166f-d16c-4ceb-bd8e-7b684d974c78')\"\n",
              "              title=\"Suggest charts.\"\n",
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              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    </g>\n",
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              "      </button>\n",
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              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
              "    background-color: #434B5C;\n",
              "    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "    fill: #FFFFFF;\n",
              "  }\n",
              "</style>\n",
              "\n",
              "    <script>\n",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-cdbc166f-d16c-4ceb-bd8e-7b684d974c78 button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-9c9f0e82-ded8-4692-8ebe-10fe348a6e0f button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-9c9f0e82-ded8-4692-8ebe-10fe348a6e0f');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 69
        }
      ],
      "source": [
        "import numpy as np\n",
        "# Gestational Age\n",
        "conditions = [\n",
        "    (df['Head_Circumfrence_cm'] < 8.00),\n",
        "    (df['Head_Circumfrence_cm'] >= 8.00) & (df['Head_Circumfrence_cm'] <= 9.00),#week13\n",
        "    (df['Head_Circumfrence_cm'] > 9.01) & (df['Head_Circumfrence_cm'] <= 10.49),#week14\n",
        "    (df['Head_Circumfrence_cm'] >10.50 ) & (df['Head_Circumfrence_cm'] <= 12.49),#week15\n",
        "    (df['Head_Circumfrence_cm'] >12.50 ) & (df['Head_Circumfrence_cm'] <= 13.49),#week16\n",
        "    (df['Head_Circumfrence_cm'] >13.50 ) & (df['Head_Circumfrence_cm'] <= 14.99),#week17\n",
        "    (df['Head_Circumfrence_cm'] >15.00 ) & (df['Head_Circumfrence_cm'] <= 16.49),#week18\n",
        "    (df['Head_Circumfrence_cm'] >16.50 ) & (df['Head_Circumfrence_cm'] <= 17.49),#week19\n",
        "    (df['Head_Circumfrence_cm'] >17.50 ) & (df['Head_Circumfrence_cm'] <= 18.99),#week20\n",
        "    (df['Head_Circumfrence_cm'] >19.00 ) & (df['Head_Circumfrence_cm'] <= 19.99),#week21\n",
        "    (df['Head_Circumfrence_cm'] >20.00 ) & (df['Head_Circumfrence_cm'] <= 20.99),#week22\n",
        "    (df['Head_Circumfrence_cm'] >21.00 ) & (df['Head_Circumfrence_cm'] <= 22.49),#week23\n",
        "    (df['Head_Circumfrence_cm'] >22.50 ) & (df['Head_Circumfrence_cm'] <= 22.99),#week24\n",
        "    (df['Head_Circumfrence_cm'] >= 23.00) & (df['Head_Circumfrence_cm'] <= 23.99),#week25\n",
        "    (df['Head_Circumfrence_cm'] > 24.00) & (df['Head_Circumfrence_cm'] <= 24.79),#week26\n",
        "    (df['Head_Circumfrence_cm'] >24.80 ) & (df['Head_Circumfrence_cm'] <= 25.60),#week27\n",
        "    (df['Head_Circumfrence_cm'] >25.61 ) & (df['Head_Circumfrence_cm'] <= 26.75),#week28\n",
        "    (df['Head_Circumfrence_cm'] >26.76 ) & (df['Head_Circumfrence_cm'] <= 27.75),#week29\n",
        "    (df['Head_Circumfrence_cm'] >27.76 ) & (df['Head_Circumfrence_cm'] <= 28.85),#week30\n",
        "    (df['Head_Circumfrence_cm'] >28.86 ) & (df['Head_Circumfrence_cm'] <= 29.60),#week31\n",
        "    (df['Head_Circumfrence_cm'] >29.61 ) & (df['Head_Circumfrence_cm'] <= 30.40),#week32\n",
        "    (df['Head_Circumfrence_cm'] >30.41 ) & (df['Head_Circumfrence_cm'] <= 31.20),#week33\n",
        "    (df['Head_Circumfrence_cm'] >31.21 ) & (df['Head_Circumfrence_cm'] <= 31.80),#week34\n",
        "    (df['Head_Circumfrence_cm'] >31.81 ) & (df['Head_Circumfrence_cm'] <= 32.50),#week35\n",
        "    (df['Head_Circumfrence_cm'] >32.51 ) & (df['Head_Circumfrence_cm'] <= 33.00),#week36\n",
        "    (df['Head_Circumfrence_cm'] >33.01 ) & (df['Head_Circumfrence_cm'] <= 33.70),#week37\n",
        "    (df['Head_Circumfrence_cm'] >33.71 ) & (df['Head_Circumfrence_cm'] <= 34.20),#week38\n",
        "    (df['Head_Circumfrence_cm'] >34.21 ) & (df['Head_Circumfrence_cm'] <= 35.00),#week39\n",
        "    (df['Head_Circumfrence_cm'] >35.00 ) & (df['Head_Circumfrence_cm'] <= 36.00),#week40\n",
        "    (df['Head_Circumfrence_cm'] > 36)\n",
        "    ]\n",
        "\n",
        "# create a list of the values we want to assign for each condition\n",
        "values = ['Fetus is less than 8 Menstrual Weeks','13','14','15','16','17','18','19','20','21','22',\n",
        "          '23','24','25', '26', '27', '28','29','30','31','32','33','34','35','36','37','38','39','40','Abnormal']\n",
        "\n",
        "\n",
        "df['Gestational_Age_In_Weeks'] = np.select(conditions, values)\n",
        "\n",
        "\n",
        "df.head(10)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 70,
      "metadata": {
        "id": "Fs3ZnTbVahUv"
      },
      "outputs": [],
      "source": [
        "df.to_csv(\"/content/drive/MyDrive/Internship/Fetal Cns/Head_Circumfrence.csv\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 70,
      "metadata": {
        "id": "CJISxQKF43w9"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "provenance": []
    },
    "gpuClass": "standard",
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}