glaucoma test model

IT20151188/Glaucoma_detection.ipynb

+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Dive mouting"
+      ],
+      "metadata": {
+        "id": "-7qxmsARqk8H"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "NitO_CjRp8FA"
+      },
+      "outputs": [],
+      "source": [
+        "from google.colab import drive\n",
+        "drive.mount(\"/content/drive/\",force_remount=False)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "**Preprosessing**"
+      ],
+      "metadata": {
+        "id": "HN2-UCN2qpYw"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "grayscaling"
+      ],
+      "metadata": {
+        "id": "RaFDyl6qqv-s"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Grayscalling\n",
+        "\n",
+        "import cv2\n",
+        "import os\n",
+        "\n",
+        "# Path to the  glaucoma images\n",
+        "glaucoma_folder = \"/content/drive/MyDrive/Colab_files/KaggelDataSet1/Glaucoma\"\n",
+        "\n",
+        "# Path to the non-glaucoma images\n",
+        "non_glaucoma_folder = \"/content/drive/MyDrive/Colab_files/KaggelDataSet1/NonGlaucoma\"\n",
+        "\n",
+        "# grayscale fucntion\n",
+        "def grayscale_images(input_folder, output_folder):\n",
+        "    # folder eka ndda balana eka\n",
+        "    if not os.path.exists(output_folder):\n",
+        "        os.makedirs(output_folder)\n",
+        "\n",
+        "    # name reading\n",
+        "    image_filenames = os.listdir(input_folder)\n",
+        "\n",
+        "    for filename in image_filenames:\n",
+        "        # Read the image\n",
+        "        image_path = os.path.join(input_folder, filename)\n",
+        "        img = cv2.imread(image_path)\n",
+        "\n",
+        "        # Convert\n",
+        "        gray_img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n",
+        "\n",
+        "        # Save\n",
+        "        output_path = os.path.join(output_folder, filename)\n",
+        "        cv2.imwrite(output_path, gray_img)\n",
+        "\n",
+        "\n",
+        "grayscale_images(glaucoma_folder, \"/content/drive/MyDrive/Colab_files/KaggelDataSet1/Grayscale/Glaucoma\")\n",
+        "\n",
+        "grayscale_images(non_glaucoma_folder, \"/content/drive/MyDrive/Colab_files/KaggelDataSet1/Grayscale/NonGlaucoma\")\n"
+      ],
+      "metadata": {
+        "id": "jo61nmauqDtx"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Treshalding"
+      ],
+      "metadata": {
+        "id": "oF3ZcyD-q0CK"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Tresholding\n",
+        "\n",
+        "# import cv2\n",
+        "# import os\n",
+        "\n",
+        "# Path to the grayscale glaucoma images\n",
+        "glaucoma_folder = \"/content/drive/MyDrive/Colab_files/KaggelDataSet1/Grayscale/Glaucoma\"\n",
+        "\n",
+        "# Path to the grayscale non-glaucoma images\n",
+        "non_glaucoma_folder = \"/content/drive/MyDrive/Colab_files/KaggelDataSet1/Grayscale/NonGlaucoma\"\n",
+        "\n",
+        "# threshalding funciton\n",
+        "def threshold_images(input_folder, output_folder, threshold_value=127, max_value=255):\n",
+        "    \n",
+        "    if not os.path.exists(output_folder):\n",
+        "        os.makedirs(output_folder)\n",
+        "\n",
+        "    # Get the list of image filenames in the input folder\n",
+        "    image_filenames = os.listdir(input_folder)\n",
+        "\n",
+        "    for filename in image_filenames:\n",
+        "        \n",
+        "        image_path = os.path.join(input_folder, filename)\n",
+        "        img = cv2.imread(image_path, cv2.IMREAD_GRAYSCALE)\n",
+        "\n",
+        "        # Thresholding\n",
+        "        _, threshold_img = cv2.threshold(img, threshold_value, max_value, cv2.THRESH_BINARY)\n",
+        "\n",
+        "        # Save \n",
+        "        output_path = os.path.join(output_folder, filename)\n",
+        "        cv2.imwrite(output_path, threshold_img)\n",
+        "\n",
+        "\n",
+        "threshold_images(glaucoma_folder, \"/content/drive/MyDrive/Colab_files/KaggelDataSet1/Thresholded/Glaucoma\")\n",
+        "\n",
+        "\n",
+        "threshold_images(non_glaucoma_folder, \"/content/drive/MyDrive/Colab_files/KaggelDataSet1/Thresholded/NonGlaucoma\")\n"
+      ],
+      "metadata": {
+        "id": "OItsBlPxqK3V"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Demoising"
+      ],
+      "metadata": {
+        "id": "2k4KepOBq2_7"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Denoiseing\n",
+        "# import cv2\n",
+        "# import os\n",
+        "\n",
+        "\n",
+        "glaucoma_folder = \"/content/drive/MyDrive/Colab_files/KaggelDataSet1/Thresholded/Glaucoma\"\n",
+        "\n",
+        "\n",
+        "non_glaucoma_folder = \"/content/drive/MyDrive/Colab_files/KaggelDataSet1/Thresholded/NonGlaucoma\"\n",
+        "\n",
+        "# noice remove\n",
+        "def remove_noise(input_folder, output_folder, kernel_size=3):\n",
+        "    \n",
+        "    if not os.path.exists(output_folder):\n",
+        "        os.makedirs(output_folder)\n",
+        "\n",
+        "    # Get the list of image filenames in the input folder\n",
+        "    image_filenames = os.listdir(input_folder)\n",
+        "\n",
+        "    for filename in image_filenames:\n",
+        "        # Read the image\n",
+        "        image_path = os.path.join(input_folder, filename)\n",
+        "        img = cv2.imread(image_path, cv2.IMREAD_GRAYSCALE)\n",
+        "\n",
+        "        # Remove noise using median filtering\n",
+        "        denoised_img = cv2.medianBlur(img, kernel_size)\n",
+        "\n",
+        "        # Save\n",
+        "        output_path = os.path.join(output_folder, filename)\n",
+        "        cv2.imwrite(output_path, denoised_img)\n",
+        "\n",
+        "\n",
+        "remove_noise(glaucoma_folder, \"/content/drive/MyDrive/Colab_files/KaggelDataSet1/Denoised/Glaucoma\")\n",
+        "\n",
+        "\n",
+        "remove_noise(non_glaucoma_folder, \"/content/drive/MyDrive/Colab_files/KaggelDataSet1/Denoised/NonGlaucoma\")\n"
+      ],
+      "metadata": {
+        "id": "jHssBYS6qPaH"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "**Analizing**"
+      ],
+      "metadata": {
+        "id": "4EqkdJavq7MT"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Highlighting"
+      ],
+      "metadata": {
+        "id": "8D8NfL9LrBfW"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# masking - pre\n",
+        "# Path to the original glaucoma images\n",
+        "glaucoma_folder = \"/content/drive/MyDrive/Colab_files/KaggelDataSet1/Glaucoma\"\n",
+        "\n",
+        "# Path to the original non-glaucoma images\n",
+        "non_glaucoma_folder = \"/content/drive/MyDrive/Colab_files/KaggelDataSet1/NonGlaucoma\"\n",
+        "\n",
+        "# Path to the thresholded glaucoma images\n",
+        "thresholded_glaucoma_folder = \"/content/drive/MyDrive/Colab_files/KaggelDataSet1/Thresholded/Glaucoma\"\n",
+        "\n",
+        "# Path to the thresholded non-glaucoma images\n",
+        "thresholded_non_glaucoma_folder = \"/content/drive/MyDrive/Colab_files/KaggelDataSet1/Thresholded/NonGlaucoma\"\n",
+        "\n",
+        "# analyzing fucntion\n",
+        "def analyze_images(original_folder, thresholded_folder, output_folder):\n",
+        "    \n",
+        "    if not os.path.exists(output_folder):\n",
+        "        os.makedirs(output_folder)\n",
+        "\n",
+        "    # Geting original ffile names\n",
+        "    image_filenames = os.listdir(original_folder)\n",
+        "\n",
+        "    for filename in image_filenames:\n",
+        "        # Reading the original images\n",
+        "        original_image_path = os.path.join(original_folder, filename)\n",
+        "        original_img = cv2.imread(original_image_path)\n",
+        "\n",
+        "        # Reading the thresholded images\n",
+        "        thresholded_image_path = os.path.join(thresholded_folder, filename)\n",
+        "        thresholded_img = cv2.imread(thresholded_image_path, cv2.IMREAD_GRAYSCALE)\n",
+        "\n",
+        "        # Find contours in the thresholded image\n",
+        "        contours, _ = cv2.findContours(thresholded_img, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)\n",
+        "\n",
+        "        if len(contours) > 0:\n",
+        "            # ganna puluwan lokuma area eka gannwa\n",
+        "            largest_contour = max(contours, key=cv2.contourArea)\n",
+        "\n",
+        "            # Draw a circle around the optic nerve head\n",
+        "            cv2.drawContours(original_img, [largest_contour], -1, (0, 255, 0), 2)\n",
+        "\n",
+        "        # Save\n",
+        "        output_path = os.path.join(output_folder, filename)\n",
+        "        cv2.imwrite(output_path, original_img)\n",
+        "# out put glucoma\n",
+        "analyze_images(glaucoma_folder, thresholded_glaucoma_folder, \"/content/drive/MyDrive/Colab_files/KaggelDataSet1/Analyzed/Glaucoma\")\n",
+        "\n",
+        "# output non-gocoma\n",
+        "analyze_images(non_glaucoma_folder, thresholded_non_glaucoma_folder, \"/content/drive/MyDrive/Colab_files/KaggelDataSet1/Analyzed/NonGlaucoma\")\n"
+      ],
+      "metadata": {
+        "id": "vg-FUEGoqTMc"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Resizing"
+      ],
+      "metadata": {
+        "id": "iK1d5DX6rpZa"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# import cv2\n",
+        "# import os\n",
+        "\n",
+        "# glaucoma_folder = \"/content/drive/MyDrive/Colab_files/KaggelDataSet1/Analyzed/Glaucoma\"\n",
+        "# non_glaucoma_folder = \"/content/drive/MyDrive/Colab_files/KaggelDataSet1/Analyzed/NonGlaucoma\"\n",
+        "\n",
+        "# def resize_images(input_folder, output_folder, target_size=(256, 256)):\n",
+        "#     if not os.path.exists(output_folder):\n",
+        "#         os.makedirs(output_folder)\n",
+        "#     image_filenames = os.listdir(input_folder)\n",
+        "#     for filename in image_filenames:\n",
+        "#         image_path = os.path.join(input_folder, filename)\n",
+        "#         img = cv2.imread(image_path)\n",
+        "#         resized_img = cv2.resize(img, target_size)\n",
+        "#         output_path = os.path.join(output_folder, filename)\n",
+        "#         cv2.imwrite(output_path, resized_img)\n",
+        "\n",
+        "# resize_images(glaucoma_folder, \"/content/drive/MyDrive/Colab_files/KaggelDataSet1/Resized/Glaucoma\")\n",
+        "# resize_images(non_glaucoma_folder, \"/content/drive/MyDrive/Colab_files/KaggelDataSet1/Resized/NonGlaucoma\")\n"
+      ],
+      "metadata": {
+        "id": "ih5KreLIrtDq"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Augmentation"
+      ],
+      "metadata": {
+        "id": "ckhE_NVIrvIU"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# import os\n",
+        "# from keras.preprocessing.image import ImageDataGenerator\n",
+        "\n",
+        "# glaucoma_folder = \"/content/drive/MyDrive/Colab_files/KaggelDataSet1/Resized/Glaucoma\"\n",
+        "# non_glaucoma_folder = \"/content/drive/MyDrive/Colab_files/KaggelDataSet1/Resized/NonGlaucoma\"\n",
+        "\n",
+        "# def augment_images(input_folder, output_folder, augmentation_parameters):\n",
+        "#     if not os.path.exists(output_folder):\n",
+        "#         os.makedirs(output_folder)\n",
+        "\n",
+        "#     data_generator = ImageDataGenerator(**augmentation_parameters)\n",
+        "\n",
+        "#     image_filenames = os.listdir(input_folder)\n",
+        "\n",
+        "#     for filename in image_filenames:\n",
+        "#         image_path = os.path.join(input_folder, filename)\n",
+        "#         img = cv2.imread(image_path)\n",
+        "#         img = np.expand_dims(img, axis=0)\n",
+        "#         augmented_images = data_generator.flow(img, batch_size=1, save_to_dir=output_folder, save_prefix=\"aug\", save_format=\"jpg\")\n",
+        "#         for i, augmented_image in enumerate(augmented_images):\n",
+        "#             if i >= 5:\n",
+        "#                 break\n",
+        "\n",
+        "#     data_generator.close()\n",
+        "\n",
+        "# augmentation_parameters = {\n",
+        "#     'rotation_range': 10,\n",
+        "#     'width_shift_range': 0.1,\n",
+        "#     'height_shift_range': 0.1,\n",
+        "#     'shear_range': 0.2,\n",
+        "#     'zoom_range': 0.2,\n",
+        "#     'horizontal_flip': True,\n",
+        "#     'vertical_flip': True\n",
+        "# }\n",
+        "\n",
+        "# augment_images(glaucoma_folder, \"/content/drive/MyDrive/Colab_files/KaggelDataSet1/Augmented/Glaucoma\", augmentation_parameters)\n",
+        "\n",
+        "# augment_images(non_glaucoma_folder, \"/content/drive/MyDrive/Colab_files/KaggelDataSet1/Augmented/NonGlaucoma\", augmentation_parameters)\n"
+      ],
+      "metadata": {
+        "id": "LjKrA-yprxN0"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Augmentation and resizing"
+      ],
+      "metadata": {
+        "id": "5idbO770rIV8"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# augmentaion and resizing\n",
+        "\n",
+        "import os\n",
+        "import cv2\n",
+        "import numpy as np\n",
+        "from keras.preprocessing.image import ImageDataGenerator\n",
+        "\n",
+        "# Path to the analyzed glaucoma images\n",
+        "glaucoma_folder = \"/content/drive/MyDrive/Colab_files/KaggelDataSet1/Analyzed/Glaucoma\"\n",
+        "\n",
+        "# Path to the analyzed non-glaucoma images\n",
+        "non_glaucoma_folder = \"/content/drive/MyDrive/Colab_files/KaggelDataSet1/Analyzed/NonGlaucoma\"\n",
+        "\n",
+        "\n",
+        "def resize_and_augment_images(input_folder, output_folder, augmentation_parameters, target_size=(256, 256)):\n",
+        "    \n",
+        "    if not os.path.exists(output_folder):\n",
+        "        os.makedirs(output_folder)\n",
+        "\n",
+        "    # Createing ImageDataGenerator\n",
+        "    data_generator = ImageDataGenerator(**augmentation_parameters)\n",
+        "\n",
+        "    # name list\n",
+        "    image_filenames = os.listdir(input_folder)\n",
+        "\n",
+        "    for filename in image_filenames:\n",
+        "        # Read the image\n",
+        "        image_path = os.path.join(input_folder, filename)\n",
+        "        img = cv2.imread(image_path)\n",
+        "\n",
+        "        # Resize the image\n",
+        "        resized_img = cv2.resize(img, target_size)\n",
+        "\n",
+        "        # expanding image to fit to genarator\n",
+        "        resized_img = np.expand_dims(resized_img, axis=0)\n",
+        "\n",
+        "        # generate augmented images\n",
+        "        augmented_images = data_generator.flow(resized_img, batch_size=1, save_to_dir=output_folder, save_prefix=\"aug\", save_format=\"jpg\")\n",
+        "\n",
+        "        # generate and save augmented images\n",
+        "        for i, augmented_image in enumerate(augmented_images):\n",
+        "            if i >= 5:  # Generate 5 augmented images per input image\n",
+        "                break\n",
+        "\n",
+        "# augmentation parameters\n",
+        "augmentation_parameters = {\n",
+        "    'rotation_range': 10,\n",
+        "    'width_shift_range': 0.1,\n",
+        "    'height_shift_range': 0.1,\n",
+        "    'shear_range': 0.2,\n",
+        "    'zoom_range': 0.2,\n",
+        "    'horizontal_flip': True,\n",
+        "    'vertical_flip': True\n",
+        "}\n",
+        "\n",
+        "\n",
+        "resize_and_augment_images(glaucoma_folder, \"/content/drive/MyDrive/Colab_files/KaggelDataSet1/Resized_Augmented/Glaucoma\", augmentation_parameters)\n",
+        "\n",
+        "\n",
+        "resize_and_augment_images(non_glaucoma_folder, \"/content/drive/MyDrive/Colab_files/KaggelDataSet1/Resized_Augmented/NonGlaucoma\", augmentation_parameters)\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "LdBdEzHJqX3Z"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "**Model Building**"
+      ],
+      "metadata": {
+        "id": "4FecUyrqrN4q"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Appling U-net model with normalization"
+      ],
+      "metadata": {
+        "id": "2E68zneqrRwO"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# new model \n",
+        "\n",
+        "import os\n",
+        "import cv2\n",
+        "import numpy as np\n",
+        "from keras.models import Model\n",
+        "from keras.layers import Input, Conv2D, MaxPooling2D, Dropout, UpSampling2D, concatenate, Flatten, Dense\n",
+        "from keras.optimizers import Adam\n",
+        "import tensorflow as tf\n",
+        "\n",
+        "def load_and_normalize_images(input_folder):\n",
+        "    image_filenames = os.listdir(input_folder)\n",
+        "    normalized_images = []\n",
+        "    for filename in image_filenames:\n",
+        "        image_path = os.path.join(input_folder, filename)\n",
+        "        img = cv2.imread(image_path)\n",
+        "        normalized_img = img / 255.0\n",
+        "        normalized_images.append(normalized_img)\n",
+        "    normalized_images = np.array(normalized_images)\n",
+        "    return normalized_images\n",
+        "\n",
+        "glaucoma_folder = \"/content/drive/MyDrive/Colab_files/tepmT/Resized_Augmented/Glaucoma\"\n",
+        "non_glaucoma_folder = \"/content/drive/MyDrive/Colab_files/tepmT/Resized_Augmented/NonGlaucoma\"\n",
+        "glaucoma_images = load_and_normalize_images(glaucoma_folder)\n",
+        "non_glaucoma_images = load_and_normalize_images(non_glaucoma_folder)\n",
+        "all_images = np.concatenate((glaucoma_images, non_glaucoma_images))\n",
+        "glaucoma_labels = np.ones((glaucoma_images.shape[0],), dtype=int)\n",
+        "non_glaucoma_labels = np.zeros((non_glaucoma_images.shape[0],), dtype=int)\n",
+        "all_labels = np.concatenate((glaucoma_labels, non_glaucoma_labels))\n",
+        "random_indices = np.random.permutation(all_images.shape[0])\n",
+        "all_images = all_images[random_indices]\n",
+        "all_labels = all_labels[random_indices]\n",
+        "\n",
+        "def unet_model(input_shape):\n",
+        "    inputs = Input(input_shape)\n",
+        "    conv1 = Conv2D(64, 3, activation='relu', padding='same')(inputs)\n",
+        "    conv1 = Conv2D(64, 3, activation='relu', padding='same')(conv1)\n",
+        "    pool1 = MaxPooling2D(pool_size=(2, 2))(conv1)\n",
+        "    conv2 = Conv2D(128, 3, activation='relu', padding='same')(pool1)\n",
+        "    conv2 = Conv2D(128, 3, activation='relu', padding='same')(conv2)\n",
+        "    pool2 = MaxPooling2D(pool_size=(2, 2))(conv2)\n",
+        "    conv3 = Conv2D(256, 3, activation='relu', padding='same')(pool2)\n",
+        "    conv3 = Conv2D(256, 3, activation='relu', padding='same')(conv3)\n",
+        "    up1 = UpSampling2D(size=(2, 2))(conv3)\n",
+        "    up1 = Conv2D(128, 2, activation='relu', padding='same')(up1)\n",
+        "    merge1 = concatenate([conv2, up1], axis=3)\n",
+        "    conv4 = Conv2D(128, 3, activation='relu', padding='same')(merge1)\n",
+        "    conv4 = Conv2D(128, 3, activation='relu', padding='same')(conv4)\n",
+        "    up2 = UpSampling2D(size=(2, 2))(conv4)\n",
+        "    up2 = Conv2D(64, 2, activation='relu', padding='same')(up2)\n",
+        "    merge2 = concatenate([conv1, up2], axis=3)\n",
+        "    conv5 = Conv2D(64, 3, activation='relu', padding='same')(merge2)\n",
+        "    conv5 = Conv2D(64, 3, activation='relu', padding='same')(conv5)\n",
+        "    flatten = Flatten()(conv5)\n",
+        "    output = Dense(1, activation='sigmoid')(flatten)\n",
+        "    model = Model(inputs=inputs, outputs=output)\n",
+        "    return model\n",
+        "\n",
+        "input_shape = all_images[0].shape\n",
+        "model = unet_model(input_shape)\n",
+        "model.compile(optimizer=Adam(lr=1e-4), loss='binary_crossentropy', metrics=['accuracy'])\n",
+        "\n",
+        "with tf.device('/GPU:0'):\n",
+        "    model.fit(all_images, all_labels, batch_size=32, epochs=10, validation_split=0.2)\n",
+        "\n",
+        "\n",
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "# Plot accuracy curve\n",
+        "plt.plot(model.history.history['accuracy'])\n",
+        "plt.plot(model.history.history['val_accuracy'])\n",
+        "plt.title('Model Accuracy')\n",
+        "plt.xlabel('Epoch')\n",
+        "plt.ylabel('Accuracy')\n",
+        "plt.legend(['train', 'val'], loc='upper left')\n",
+        "plt.show()\n",
+        "\n",
+        "plt.plot(model.history.history['loss'])\n",
+        "plt.plot(model.history.history['val_loss'])\n",
+        "plt.title('Model Loss')\n",
+        "plt.xlabel('Epoch')\n",
+        "plt.ylabel('Loss')\n",
+        "plt.legend(['train', 'val'], loc='upper left')\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "id": "DUNO1LxiLbUo"
+      },
+      "execution_count": null,
+      "outputs": []
+    }
+  ]
+}
\ No newline at end of file