Lung_segmentation_from_Chest_X_Ray.ipynb

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      "provenance": [],
      "include_colab_link": true
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    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Mdwij/lung-segmentation/blob/main/Lung_segmentation_from_Chest_X_Ray.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RWAYLIqfbr07"
      },
      "source": [
        "import tensorflow as tf\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import os\n",
        "\n",
        "from keras.layers import Conv2D,Conv2DTranspose,Dense,MaxPool2D,Concatenate,Activation,BatchNormalization,Input\n",
        "from keras.models import Model,load_model,save_model"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-3zd7-cyyFL1",
        "outputId": "121ae625-f56d-4196-e610-43bd2c7c9874"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5LewJ7Nacfho"
      },
      "source": [
        "#Downloading the data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pxJv3tcnckJL"
      },
      "source": [
        "! pip install -q kaggle"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gGqhjKNEctMt"
      },
      "source": [
        "from google.colab import files"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QEc6cCsicvNC",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 90
        },
        "outputId": "e0b237ff-e642-4a1c-e242-cd7f9b706b27"
      },
      "source": [
        "files.upload()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-797140b9-d7d6-4732-8386-0c157f36aa88\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-797140b9-d7d6-4732-8386-0c157f36aa88\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script>// Copyright 2017 Google LLC\n",
              "//\n",
              "// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
              "// you may not use this file except in compliance with the License.\n",
              "// You may obtain a copy of the License at\n",
              "//\n",
              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
              "//\n",
              "// Unless required by applicable law or agreed to in writing, software\n",
              "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
              "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
              "// See the License for the specific language governing permissions and\n",
              "// limitations under the License.\n",
              "\n",
              "/**\n",
              " * @fileoverview Helpers for google.colab Python module.\n",
              " */\n",
              "(function(scope) {\n",
              "function span(text, styleAttributes = {}) {\n",
              "  const element = document.createElement('span');\n",
              "  element.textContent = text;\n",
              "  for (const key of Object.keys(styleAttributes)) {\n",
              "    element.style[key] = styleAttributes[key];\n",
              "  }\n",
              "  return element;\n",
              "}\n",
              "\n",
              "// Max number of bytes which will be uploaded at a time.\n",
              "const MAX_PAYLOAD_SIZE = 100 * 1024;\n",
              "\n",
              "function _uploadFiles(inputId, outputId) {\n",
              "  const steps = uploadFilesStep(inputId, outputId);\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  // Cache steps on the outputElement to make it available for the next call\n",
              "  // to uploadFilesContinue from Python.\n",
              "  outputElement.steps = steps;\n",
              "\n",
              "  return _uploadFilesContinue(outputId);\n",
              "}\n",
              "\n",
              "// This is roughly an async generator (not supported in the browser yet),\n",
              "// where there are multiple asynchronous steps and the Python side is going\n",
              "// to poll for completion of each step.\n",
              "// This uses a Promise to block the python side on completion of each step,\n",
              "// then passes the result of the previous step as the input to the next step.\n",
              "function _uploadFilesContinue(outputId) {\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  const steps = outputElement.steps;\n",
              "\n",
              "  const next = steps.next(outputElement.lastPromiseValue);\n",
              "  return Promise.resolve(next.value.promise).then((value) => {\n",
              "    // Cache the last promise value to make it available to the next\n",
              "    // step of the generator.\n",
              "    outputElement.lastPromiseValue = value;\n",
              "    return next.value.response;\n",
              "  });\n",
              "}\n",
              "\n",
              "/**\n",
              " * Generator function which is called between each async step of the upload\n",
              " * process.\n",
              " * @param {string} inputId Element ID of the input file picker element.\n",
              " * @param {string} outputId Element ID of the output display.\n",
              " * @return {!Iterable<!Object>} Iterable of next steps.\n",
              " */\n",
              "function* uploadFilesStep(inputId, outputId) {\n",
              "  const inputElement = document.getElementById(inputId);\n",
              "  inputElement.disabled = false;\n",
              "\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  outputElement.innerHTML = '';\n",
              "\n",
              "  const pickedPromise = new Promise((resolve) => {\n",
              "    inputElement.addEventListener('change', (e) => {\n",
              "      resolve(e.target.files);\n",
              "    });\n",
              "  });\n",
              "\n",
              "  const cancel = document.createElement('button');\n",
              "  inputElement.parentElement.appendChild(cancel);\n",
              "  cancel.textContent = 'Cancel upload';\n",
              "  const cancelPromise = new Promise((resolve) => {\n",
              "    cancel.onclick = () => {\n",
              "      resolve(null);\n",
              "    };\n",
              "  });\n",
              "\n",
              "  // Wait for the user to pick the files.\n",
              "  const files = yield {\n",
              "    promise: Promise.race([pickedPromise, cancelPromise]),\n",
              "    response: {\n",
              "      action: 'starting',\n",
              "    }\n",
              "  };\n",
              "\n",
              "  cancel.remove();\n",
              "\n",
              "  // Disable the input element since further picks are not allowed.\n",
              "  inputElement.disabled = true;\n",
              "\n",
              "  if (!files) {\n",
              "    return {\n",
              "      response: {\n",
              "        action: 'complete',\n",
              "      }\n",
              "    };\n",
              "  }\n",
              "\n",
              "  for (const file of files) {\n",
              "    const li = document.createElement('li');\n",
              "    li.append(span(file.name, {fontWeight: 'bold'}));\n",
              "    li.append(span(\n",
              "        `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n",
              "        `last modified: ${\n",
              "            file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n",
              "                                    'n/a'} - `));\n",
              "    const percent = span('0% done');\n",
              "    li.appendChild(percent);\n",
              "\n",
              "    outputElement.appendChild(li);\n",
              "\n",
              "    const fileDataPromise = new Promise((resolve) => {\n",
              "      const reader = new FileReader();\n",
              "      reader.onload = (e) => {\n",
              "        resolve(e.target.result);\n",
              "      };\n",
              "      reader.readAsArrayBuffer(file);\n",
              "    });\n",
              "    // Wait for the data to be ready.\n",
              "    let fileData = yield {\n",
              "      promise: fileDataPromise,\n",
              "      response: {\n",
              "        action: 'continue',\n",
              "      }\n",
              "    };\n",
              "\n",
              "    // Use a chunked sending to avoid message size limits. See b/62115660.\n",
              "    let position = 0;\n",
              "    do {\n",
              "      const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n",
              "      const chunk = new Uint8Array(fileData, position, length);\n",
              "      position += length;\n",
              "\n",
              "      const base64 = btoa(String.fromCharCode.apply(null, chunk));\n",
              "      yield {\n",
              "        response: {\n",
              "          action: 'append',\n",
              "          file: file.name,\n",
              "          data: base64,\n",
              "        },\n",
              "      };\n",
              "\n",
              "      let percentDone = fileData.byteLength === 0 ?\n",
              "          100 :\n",
              "          Math.round((position / fileData.byteLength) * 100);\n",
              "      percent.textContent = `${percentDone}% done`;\n",
              "\n",
              "    } while (position < fileData.byteLength);\n",
              "  }\n",
              "\n",
              "  // All done.\n",
              "  yield {\n",
              "    response: {\n",
              "      action: 'complete',\n",
              "    }\n",
              "  };\n",
              "}\n",
              "\n",
              "scope.google = scope.google || {};\n",
              "scope.google.colab = scope.google.colab || {};\n",
              "scope.google.colab._files = {\n",
              "  _uploadFiles,\n",
              "  _uploadFilesContinue,\n",
              "};\n",
              "})(self);\n",
              "</script> "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving kaggle.json to kaggle.json\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'kaggle.json': b'{\"username\":\"makvanadwij\",\"key\":\"a3bc6923ad7c8a6bfb577217c61fa743\"}'}"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cXmkJr4ZcyKM"
      },
      "source": [
        "! mkdir ~/.kaggle"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HqBhlzSqc018"
      },
      "source": [
        "! cp kaggle.json ~/.kaggle/"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0g1EPzYcc2zk"
      },
      "source": [
        "! chmod 600 ~/.kaggle/kaggle.json"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VL4A_PPocV0H",
        "outputId": "3fdcd7d4-a7d0-4886-d9e2-e810b99bf5c6"
      },
      "source": [
        "!kaggle datasets download -d nikhilpandey360/chest-xray-masks-and-labels"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading chest-xray-masks-and-labels.zip to /content\n",
            "100% 9.57G/9.58G [07:51<00:00, 16.6MB/s]\n",
            "100% 9.58G/9.58G [07:51<00:00, 21.8MB/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PLuVOqGoc5Ub",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ad0f5dc6-1a72-4ecd-8674-b91201601363"
      },
      "source": [
        "! unzip chest-xray-masks-and-labels.zip -d train"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Archive:  chest-xray-masks-and-labels.zip\n",
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          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3oRlf51R4Pfr"
      },
      "source": [
        "Removing unecessary files"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qMnJxrMf1l7E"
      },
      "source": [
        "%rm -rf '/content/chest-xray-masks-and-labels.zip'"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EYyaO1HU3efu"
      },
      "source": [
        "%rm -rf '/content/train/data'"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aepbVeE22XSa"
      },
      "source": [
        "%rm -rf '/content/train/Lung Segmentation/ClinicalReadings'"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XwcjUVW539BK"
      },
      "source": [
        "%rm -rf '/content/train/Lung Segmentation/NLM-ChinaCXRSet-ReadMe.docx'\n",
        "%rm -rf '/content/train/Lung Segmentation/NLM-MontgomeryCXRSet-ReadMe.pdf'"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OJy3nIVo5XwI"
      },
      "source": [],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3nfe3LEi5X95"
      },
      "source": [
        "#preparing data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mhf1qp_R5mvJ",
        "outputId": "b328b786-923e-497a-e55d-b517440a0808"
      },
      "source": [
        "print(len(os.listdir(\"/content/train/Lung Segmentation/CXR_png\")) == len(os.listdir(\"/content/train/Lung Segmentation/masks\")))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "False\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "id": "uJd4hpoT5_a4",
        "outputId": "b23998fa-ac04-4e8e-99ee-633de7772917"
      },
      "source": [
        "''' Therefore in train data all images don't have their respective mask'''"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "\" Therefore in train data all images don't have their respective mask\""
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "G0VvavnB6XF4"
      },
      "source": [
        "Getting image and mask list"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NrfywIEo6RgF"
      },
      "source": [
        "def remover(item_list,to_remove):\n",
        "  '''\n",
        "  item_list : list of the masks data name\n",
        "  to_remove : dtype=str , it is string that is to remove from each name in item_list\n",
        "  '''\n",
        "\n",
        "  return list(map(lambda x:x.replace(to_remove,\"\"),item_list))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ri6GrCmN7tC0"
      },
      "source": [
        "def adder(item_list):\n",
        "  '''\n",
        "  reverse of remover\n",
        "\n",
        "  '''\n",
        "  return list(map(lambda x:x.replace(\".\",\"_mask.\"),item_list))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Rs1J1_GT8Inj"
      },
      "source": [
        "image_set = set(os.listdir('/content/train/Lung Segmentation/CXR_png'))\n",
        "mask_set = set(remover(os.listdir(\"/content/train/Lung Segmentation/masks\"),\"_mask\"))\n",
        "required_list = list(image_set.intersection(mask_set))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QFCBVcnW90Lp",
        "outputId": "8efbea86-ec7b-4ab0-e255-27cc5548fc30"
      },
      "source": [
        "print(len(required_list))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "704\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d5EhaAtEDGgU"
      },
      "source": [
        "def preprocessing_image(image_path):\n",
        "  image = plt.imread(image_path)\n",
        "  if image.shape[-1] != 3:\n",
        "    new_image = np.zeros((image.shape[0],image.shape[1],3))\n",
        "    new_image[:,:,0] += image\n",
        "    new_image[:,:,1] += image\n",
        "    new_image[:,:,2] += image \n",
        "    image = new_image\n",
        "  image = tf.cast(image,dtype=tf.float32)\n",
        "  image = tf.image.resize(image,(128,128))\n",
        "  return image"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WGpTsRs-DUiC"
      },
      "source": [
        "def preprocessing_mask(mask_path):\n",
        "  try:\n",
        "    mask = plt.imread(mask_path)\n",
        "  except:\n",
        "    mask_path = mask_path.replace(\"_mask\",\"\")\n",
        "    mask = plt.imread(mask_path)\n",
        "  \n",
        "  mask = tf.expand_dims(mask,axis=-1)\n",
        "  mask = tf.image.resize(mask,(128,128))\n",
        "  mask = tf.cast(mask,dtype=tf.int32)\n",
        "  return mask"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "loGIpa6j_ypk"
      },
      "source": [
        "\n",
        "def prepare_dataset(image_dir,mask_dir,required_list):\n",
        "\n",
        "  images_list = required_list\n",
        "  masks_list = adder(required_list)\n",
        "\n",
        "  images_path = list(map(lambda x:os.path.join(image_dir,x),images_list))\n",
        "  masks_path =  list(map(lambda x:os.path.join(mask_dir,x),masks_list))\n",
        "\n",
        "  images = list(map(preprocessing_image,images_path))\n",
        "  masks = list(map(preprocessing_mask,masks_path))\n",
        "\n",
        "  images = tf.stack(images)\n",
        "  masks = tf.stack(masks)\n",
        "\n",
        "  return images,masks"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "R9MOWfFeGC8m"
      },
      "source": [
        "train_images,train_masks =  prepare_dataset('/content/train/Lung Segmentation/CXR_png','/content/train/Lung Segmentation/masks',required_list)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Zv38IYCD4IiC"
      },
      "source": [
        "!rm -rf '/content/train'"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dkk0ZVsJE5hw",
        "outputId": "00d77d23-725e-4787-d133-c1374d5f6d8e"
      },
      "source": [
        "train_images.shape,train_images.dtype,train_masks.shape,train_masks.dtype"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(TensorShape([704, 128, 128, 3]),\n",
              " tf.float32,\n",
              " TensorShape([704, 128, 128, 1]),\n",
              " tf.int32)"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i3VAlbatFV7w"
      },
      "source": [
        "train_images = train_images.numpy()\n",
        "train_masks = train_masks.numpy()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Original_train_images = train_images\n",
        "Original_train_masks = train_masks"
      ],
      "metadata": {
        "id": "aycHvAY8cGaD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "ZepxAGFjcN0A"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cpsNsoni3niF"
      },
      "source": [
        "#Data Augmentation with imgaug library"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-XIZDIXX3wJD"
      },
      "source": [
        "from imgaug import augmenters as iaa\n",
        "from imgaug.augmentables.segmaps import SegmentationMapOnImage"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "edbPXxYv3x-E"
      },
      "source": [
        "seq = iaa.Sequential([\n",
        "    iaa.Affine(rotate=(-30, 30)), # rotate the image\n",
        "    iaa.Fliplr(1.0)\n",
        "])\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fTLUTYrB5zGF",
        "outputId": "a7ee80fb-6b00-48fc-87b0-0ed2e0104cdb"
      },
      "source": [
        "sample_image = train_images[23]\n",
        "sample_mask = SegmentationMapOnImage(train_masks[23],shape=train_masks[23].shape,nb_classes=2)\n",
        "sample_image.shape,sample_mask.shape\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/imgaug/imgaug.py:184: DeprecationWarning: Function `SegmentationMapOnImage()` is deprecated. Use `SegmentationMapsOnImage` instead. (Note the plural 'Maps' instead of old 'Map'.).\n",
            "  warn_deprecated(msg, stacklevel=3)\n",
            "/usr/local/lib/python3.8/dist-packages/imgaug/imgaug.py:106: DeprecationWarning: Providing nb_classes to SegmentationMapsOnImage is no longer necessary and hence deprecated. The argument is ignored and can be safely removed.\n",
            "  warn(msg, category=DeprecationWarning, stacklevel=stacklevel)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((128, 128, 3), (128, 128, 1))"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iVbBE04G4E1S"
      },
      "source": [
        "image_aug, segmap_aug = seq(image=sample_image, segmentation_maps=sample_mask)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C6LecCm27fXr",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "66f9d036-7657-40b2-875a-0e1d75994204"
      },
      "source": [
        "segmap_aug = segmap_aug.get_arr_int()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/imgaug/imgaug.py:184: DeprecationWarning: Method `SegmentationMapsOnImage.get_arr_int()` is deprecated. Use `SegmentationMapsOnImage.get_arr()` instead.\n",
            "  warn_deprecated(msg, stacklevel=3)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "A2ALWDDV76ju",
        "outputId": "5853039c-f5d5-4a33-bf49-da46f5070ee2"
      },
      "source": [
        "type(segmap_aug)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "numpy.ndarray"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RWtYXeG4L6IX",
        "outputId": "ac690549-4444-49e8-96ff-caa40a946fd1"
      },
      "source": [
        "plt.imshow(image_aug)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7fa20c8a0730>"
            ]
          },
          "metadata": {},
          "execution_count": 35
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wl8U9zMf61WS",
        "outputId": "91fcd689-6351-422f-8b79-95d7c0070a05"
      },
      "source": [
        "plt.imshow(segmap_aug[:,:,0])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7fa20c834070>"
            ]
          },
          "metadata": {},
          "execution_count": 36
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HzY6EQzuMiR9"
      },
      "source": [
        "adding sequences for each type"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LG6gxuyMMcqE"
      },
      "source": [
        "horizontal_flip_seq = iaa.Sequential([\n",
        "    iaa.Fliplr(1.0)\n",
        "])\n",
        "\n",
        "rotation_seq = seq = iaa.Sequential([\n",
        "    iaa.Affine(rotate=(-30, 30))])\n",
        "\n",
        "shear_flip_seq = seq = iaa.Sequential([iaa.Affine(shear=1.0),\n",
        "                                       iaa.Fliplr(1.0)\n",
        "                                       ])\n",
        "rotation_flip_seq = iaa.Sequential([\n",
        "    iaa.Affine(rotate=(-30, 30)), # rotate the image\n",
        "    iaa.Fliplr(1.0)\n",
        "])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lNGhCvKdOaKf"
      },
      "source": [
        "sequences = [horizontal_flip_seq,rotation_seq,shear_flip_seq,rotation_flip_seq]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qmrFk8jkOlxP"
      },
      "source": [
        "horizontal_flip_images = []\n",
        "rotation_images = []\n",
        "shear_flip_images = []\n",
        "rotation_flip_images = []\n",
        "\n",
        "aug_images  = [horizontal_flip_images,rotation_images,shear_flip_images,rotation_flip_images]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_oaVgDDkSPBi"
      },
      "source": [
        "horizontal_flip_masks = []\n",
        "rotation_masks = []\n",
        "shear_flip_masks = []\n",
        "rotation_flip_masks = []\n",
        "\n",
        "aug_masks  = [horizontal_flip_masks,rotation_masks,shear_flip_masks,rotation_flip_masks]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JY2p72BVO5Qq"
      },
      "source": [
        "for count,current_images in enumerate(aug_images):\n",
        "  \n",
        "  duplicate_images = train_images.copy()\n",
        "  duplicate_masks = train_masks.copy()\n",
        "\n",
        "  m = duplicate_images.shape[0]\n",
        "\n",
        "  current_masks = aug_masks[count]\n",
        "\n",
        "  for i in range(m):\n",
        "\n",
        "    segmap_to_aug = SegmentationMapOnImage(duplicate_masks[i],shape=duplicate_masks[i].shape,nb_classes=2)\n",
        "\n",
        "    current_seq = sequences[count]\n",
        "\n",
        "    image_aug, segmap_aug = current_seq(image=duplicate_images[i], segmentation_maps=segmap_to_aug)\n",
        "\n",
        "    segmap_aug = segmap_aug.get_arr_int()\n",
        "    segmap_aug = np.expand_dims(segmap_aug,axis=-1)\n",
        "\n",
        "    current_images.append(image_aug)\n",
        "    current_masks.append(segmap_aug)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hBQtgD9dVEXZ"
      },
      "source": [
        "checking whether it is done properly or not by visualizing"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pceC59HxT8-g"
      },
      "source": [
        "i=1"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YkJU8iM-TGA8",
        "outputId": "089424c2-6941-40b8-9a35-a69af461e9da"
      },
      "source": [
        "for count,current_images in enumerate(aug_images):\n",
        "\n",
        "  plt.subplot(4,2,i)\n",
        "  plt.imshow(current_images[23])\n",
        "  \n",
        "  i+=1 \n",
        "\n",
        "  plt.subplot(4,2,i)\n",
        "  plt.imshow(aug_masks[count][23].squeeze(axis=-1)[:,:,0])\n",
        "\n",
        "  i+=1"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 8 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P3T4aFWgVNbT"
      },
      "source": [
        "train_images = np.concatenate((Original_train_images,np.stack(aug_images[0]),np.stack(aug_images[1]),np.stack(aug_images[2]),np.stack(aug_images[3])))\n",
        "train_masks = np.concatenate((Original_train_masks,np.stack(aug_masks[0])[:,:,:,0],np.stack(aug_masks[1])[:,:,:,0],np.stack(aug_masks[2])[:,:,:,0],np.stack(aug_masks[3])[:,:,:,0]))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "E6XFzHMVXKVi",
        "outputId": "8efa2dd1-dc07-482d-eb6c-de37e7fc856c"
      },
      "source": [
        "train_images.shape,train_masks.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((3520, 128, 128, 3), (3520, 128, 128, 1))"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4nIyoxh8YMkm"
      },
      "source": [
        "shuufle data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JT3UyuxfYLTr",
        "outputId": "b42a48f8-ca9a-4540-97f9-a65b803aedd7"
      },
      "source": [
        "from sklearn.utils import shuffle\n",
        "train_images,train_masks = shuffle(train_images,train_masks)\n",
        "train_images.shape,train_masks.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((3520, 128, 128, 3), (3520, 128, 128, 1))"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Hqxn6wT3YTj4",
        "outputId": "9ddf0118-51cb-4b7f-ba21-58b056768611"
      },
      "source": [
        "# checking that data don't get mismatched\n",
        "plt.subplot(1,2,1)\n",
        "plt.imshow(train_images[23])\n",
        "plt.subplot(1,2,2)\n",
        "plt.imshow(train_masks[23].squeeze(axis=-1))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7fa20c115fd0>"
            ]
          },
          "metadata": {},
          "execution_count": 47
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yrATChCJwELT"
      },
      "source": [
        "#Building and traing Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZCG9dMJcDdtD"
      },
      "source": [
        "from keras import backend as K\n",
        "\n",
        "\n",
        "def jaccard_distance(y_true, y_pred, smooth=100):\n",
        "    \"\"\"Jaccard distance for semantic segmentation.\n",
        "    Also known as the intersection-over-union loss.\n",
        "    This loss is useful when you have unbalanced numbers of pixels within an image\n",
        "    because it gives all classes equal weight. However, it is not the defacto\n",
        "    standard for image segmentation.\n",
        "    For example, assume you are trying to predict if\n",
        "    each pixel is cat, dog, or background.\n",
        "    You have 80% background pixels, 10% dog, and 10% cat.\n",
        "    If the model predicts 100% background\n",
        "    should it be be 80% right (as with categorical cross entropy)\n",
        "    or 30% (with this loss)?\n",
        "    The loss has been modified to have a smooth gradient as it converges on zero.\n",
        "    This has been shifted so it converges on 0 and is smoothed to avoid exploding\n",
        "    or disappearing gradient.\n",
        "    Jaccard = (|X & Y|)/ (|X|+ |Y| - |X & Y|)\n",
        "            = sum(|A*B|)/(sum(|A|)+sum(|B|)-sum(|A*B|))\n",
        "    # Arguments\n",
        "        y_true: The ground truth tensor.\n",
        "        y_pred: The predicted tensor\n",
        "        smooth: Smoothing factor. Default is 100.\n",
        "    # Returns\n",
        "        The Jaccard distance between the two tensors.\n",
        "    # References\n",
        "        - [What is a good evaluation measure for semantic segmentation?](\n",
        "           http://www.bmva.org/bmvc/2013/Papers/paper0032/paper0032.pdf)\n",
        "    \"\"\"\n",
        "    intersection = K.sum(K.abs(y_true * y_pred), axis=-1)+\\\n",
        "    sum_ = K.sum(K.abs(y_true) + K.abs(y_pred), axis=-1)\n",
        "    jac = (intersection + smooth) / (sum_ - intersection + smooth)\n",
        "    return (1 - jac) * smooth"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HimLmMwvsX-b"
      },
      "source": [
        "\n",
        "def conv_block(input, num_filters):\n",
        "  x = Conv2D(num_filters, 3, padding=\"same\")(input)\n",
        "  x = BatchNormalization()(x)\n",
        "  x = Activation(\"relu\")(x)\n",
        "\n",
        "  x = Conv2D(num_filters, 3, padding=\"same\")(x)\n",
        "  x = BatchNormalization()(x)\n",
        "  x = Activation(\"relu\")(x)\n",
        "\n",
        "  return x\n",
        "\n",
        "def encoder_block(input, num_filters):\n",
        "  x = conv_block(input, num_filters)\n",
        "  p = MaxPool2D((2, 2))(x)\n",
        "  return x, p\n",
        "\n",
        "def decoder_block(input, skip_features, num_filters):\n",
        "  x = Conv2DTranspose(num_filters, (2, 2), strides=2, padding=\"same\")(input)\n",
        "  x = Concatenate()([x, skip_features])\n",
        "  x = conv_block(x, num_filters)\n",
        "  return x\n",
        "\n",
        "def build_unet(input_shape):\n",
        "  inputs = Input(input_shape)\n",
        "\n",
        "  s1, p1 = encoder_block(inputs, 64)\n",
        "  s2, p2 = encoder_block(p1, 128)\n",
        "  s3, p3 = encoder_block(p2, 256)\n",
        "  s4, p4 = encoder_block(p3, 512)\n",
        "\n",
        "  b1 = conv_block(p4, 1024)\n",
        "\n",
        "  d1 = decoder_block(b1, s4, 512)\n",
        "  d2 = decoder_block(d1, s3, 256)\n",
        "  d3 = decoder_block(d2, s2, 128)\n",
        "  d4 = decoder_block(d3, s1, 64)\n",
        "\n",
        "  outputs = Conv2D(1, 1, padding=\"same\", activation=\"sigmoid\")(d4)\n",
        "  \n",
        "  model = Model(inputs=[inputs], outputs=[outputs])\n",
        "  model.compile(optimizer='adam', loss=jaccard_distance,metrics=[\"accuracy\"])\n",
        "  return model\n",
        "\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wjIgWLz9tJ5g"
      },
      "source": [
        "U_Net_model = build_unet((128,128,3))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-eUiuP7NtN6Y",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "761a97cc-e1ef-49be-89a3-8142c1479ed5"
      },
      "source": [
        "U_Net_model.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model\"\n",
            "__________________________________________________________________________________________________\n",
            " Layer (type)                   Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            " input_1 (InputLayer)           [(None, 128, 128, 3  0           []                               \n",
            "                                )]                                                                \n",
            "                                                                                                  \n",
            " conv2d (Conv2D)                (None, 128, 128, 64  1792        ['input_1[0][0]']                \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " batch_normalization (BatchNorm  (None, 128, 128, 64  256        ['conv2d[0][0]']                 \n",
            " alization)                     )                                                                 \n",
            "                                                                                                  \n",
            " activation (Activation)        (None, 128, 128, 64  0           ['batch_normalization[0][0]']    \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " conv2d_1 (Conv2D)              (None, 128, 128, 64  36928       ['activation[0][0]']             \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " batch_normalization_1 (BatchNo  (None, 128, 128, 64  256        ['conv2d_1[0][0]']               \n",
            " rmalization)                   )                                                                 \n",
            "                                                                                                  \n",
            " activation_1 (Activation)      (None, 128, 128, 64  0           ['batch_normalization_1[0][0]']  \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " max_pooling2d (MaxPooling2D)   (None, 64, 64, 64)   0           ['activation_1[0][0]']           \n",
            "                                                                                                  \n",
            " conv2d_2 (Conv2D)              (None, 64, 64, 128)  73856       ['max_pooling2d[0][0]']          \n",
            "                                                                                                  \n",
            " batch_normalization_2 (BatchNo  (None, 64, 64, 128)  512        ['conv2d_2[0][0]']               \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " activation_2 (Activation)      (None, 64, 64, 128)  0           ['batch_normalization_2[0][0]']  \n",
            "                                                                                                  \n",
            " conv2d_3 (Conv2D)              (None, 64, 64, 128)  147584      ['activation_2[0][0]']           \n",
            "                                                                                                  \n",
            " batch_normalization_3 (BatchNo  (None, 64, 64, 128)  512        ['conv2d_3[0][0]']               \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " activation_3 (Activation)      (None, 64, 64, 128)  0           ['batch_normalization_3[0][0]']  \n",
            "                                                                                                  \n",
            " max_pooling2d_1 (MaxPooling2D)  (None, 32, 32, 128)  0          ['activation_3[0][0]']           \n",
            "                                                                                                  \n",
            " conv2d_4 (Conv2D)              (None, 32, 32, 256)  295168      ['max_pooling2d_1[0][0]']        \n",
            "                                                                                                  \n",
            " batch_normalization_4 (BatchNo  (None, 32, 32, 256)  1024       ['conv2d_4[0][0]']               \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " activation_4 (Activation)      (None, 32, 32, 256)  0           ['batch_normalization_4[0][0]']  \n",
            "                                                                                                  \n",
            " conv2d_5 (Conv2D)              (None, 32, 32, 256)  590080      ['activation_4[0][0]']           \n",
            "                                                                                                  \n",
            " batch_normalization_5 (BatchNo  (None, 32, 32, 256)  1024       ['conv2d_5[0][0]']               \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " activation_5 (Activation)      (None, 32, 32, 256)  0           ['batch_normalization_5[0][0]']  \n",
            "                                                                                                  \n",
            " max_pooling2d_2 (MaxPooling2D)  (None, 16, 16, 256)  0          ['activation_5[0][0]']           \n",
            "                                                                                                  \n",
            " conv2d_6 (Conv2D)              (None, 16, 16, 512)  1180160     ['max_pooling2d_2[0][0]']        \n",
            "                                                                                                  \n",
            " batch_normalization_6 (BatchNo  (None, 16, 16, 512)  2048       ['conv2d_6[0][0]']               \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " activation_6 (Activation)      (None, 16, 16, 512)  0           ['batch_normalization_6[0][0]']  \n",
            "                                                                                                  \n",
            " conv2d_7 (Conv2D)              (None, 16, 16, 512)  2359808     ['activation_6[0][0]']           \n",
            "                                                                                                  \n",
            " batch_normalization_7 (BatchNo  (None, 16, 16, 512)  2048       ['conv2d_7[0][0]']               \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " activation_7 (Activation)      (None, 16, 16, 512)  0           ['batch_normalization_7[0][0]']  \n",
            "                                                                                                  \n",
            " max_pooling2d_3 (MaxPooling2D)  (None, 8, 8, 512)   0           ['activation_7[0][0]']           \n",
            "                                                                                                  \n",
            " conv2d_8 (Conv2D)              (None, 8, 8, 1024)   4719616     ['max_pooling2d_3[0][0]']        \n",
            "                                                                                                  \n",
            " batch_normalization_8 (BatchNo  (None, 8, 8, 1024)  4096        ['conv2d_8[0][0]']               \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " activation_8 (Activation)      (None, 8, 8, 1024)   0           ['batch_normalization_8[0][0]']  \n",
            "                                                                                                  \n",
            " conv2d_9 (Conv2D)              (None, 8, 8, 1024)   9438208     ['activation_8[0][0]']           \n",
            "                                                                                                  \n",
            " batch_normalization_9 (BatchNo  (None, 8, 8, 1024)  4096        ['conv2d_9[0][0]']               \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " activation_9 (Activation)      (None, 8, 8, 1024)   0           ['batch_normalization_9[0][0]']  \n",
            "                                                                                                  \n",
            " conv2d_transpose (Conv2DTransp  (None, 16, 16, 512)  2097664    ['activation_9[0][0]']           \n",
            " ose)                                                                                             \n",
            "                                                                                                  \n",
            " concatenate (Concatenate)      (None, 16, 16, 1024  0           ['conv2d_transpose[0][0]',       \n",
            "                                )                                 'activation_7[0][0]']           \n",
            "                                                                                                  \n",
            " conv2d_10 (Conv2D)             (None, 16, 16, 512)  4719104     ['concatenate[0][0]']            \n",
            "                                                                                                  \n",
            " batch_normalization_10 (BatchN  (None, 16, 16, 512)  2048       ['conv2d_10[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " activation_10 (Activation)     (None, 16, 16, 512)  0           ['batch_normalization_10[0][0]'] \n",
            "                                                                                                  \n",
            " conv2d_11 (Conv2D)             (None, 16, 16, 512)  2359808     ['activation_10[0][0]']          \n",
            "                                                                                                  \n",
            " batch_normalization_11 (BatchN  (None, 16, 16, 512)  2048       ['conv2d_11[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " activation_11 (Activation)     (None, 16, 16, 512)  0           ['batch_normalization_11[0][0]'] \n",
            "                                                                                                  \n",
            " conv2d_transpose_1 (Conv2DTran  (None, 32, 32, 256)  524544     ['activation_11[0][0]']          \n",
            " spose)                                                                                           \n",
            "                                                                                                  \n",
            " concatenate_1 (Concatenate)    (None, 32, 32, 512)  0           ['conv2d_transpose_1[0][0]',     \n",
            "                                                                  'activation_5[0][0]']           \n",
            "                                                                                                  \n",
            " conv2d_12 (Conv2D)             (None, 32, 32, 256)  1179904     ['concatenate_1[0][0]']          \n",
            "                                                                                                  \n",
            " batch_normalization_12 (BatchN  (None, 32, 32, 256)  1024       ['conv2d_12[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " activation_12 (Activation)     (None, 32, 32, 256)  0           ['batch_normalization_12[0][0]'] \n",
            "                                                                                                  \n",
            " conv2d_13 (Conv2D)             (None, 32, 32, 256)  590080      ['activation_12[0][0]']          \n",
            "                                                                                                  \n",
            " batch_normalization_13 (BatchN  (None, 32, 32, 256)  1024       ['conv2d_13[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " activation_13 (Activation)     (None, 32, 32, 256)  0           ['batch_normalization_13[0][0]'] \n",
            "                                                                                                  \n",
            " conv2d_transpose_2 (Conv2DTran  (None, 64, 64, 128)  131200     ['activation_13[0][0]']          \n",
            " spose)                                                                                           \n",
            "                                                                                                  \n",
            " concatenate_2 (Concatenate)    (None, 64, 64, 256)  0           ['conv2d_transpose_2[0][0]',     \n",
            "                                                                  'activation_3[0][0]']           \n",
            "                                                                                                  \n",
            " conv2d_14 (Conv2D)             (None, 64, 64, 128)  295040      ['concatenate_2[0][0]']          \n",
            "                                                                                                  \n",
            " batch_normalization_14 (BatchN  (None, 64, 64, 128)  512        ['conv2d_14[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " activation_14 (Activation)     (None, 64, 64, 128)  0           ['batch_normalization_14[0][0]'] \n",
            "                                                                                                  \n",
            " conv2d_15 (Conv2D)             (None, 64, 64, 128)  147584      ['activation_14[0][0]']          \n",
            "                                                                                                  \n",
            " batch_normalization_15 (BatchN  (None, 64, 64, 128)  512        ['conv2d_15[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " activation_15 (Activation)     (None, 64, 64, 128)  0           ['batch_normalization_15[0][0]'] \n",
            "                                                                                                  \n",
            " conv2d_transpose_3 (Conv2DTran  (None, 128, 128, 64  32832      ['activation_15[0][0]']          \n",
            " spose)                         )                                                                 \n",
            "                                                                                                  \n",
            " concatenate_3 (Concatenate)    (None, 128, 128, 12  0           ['conv2d_transpose_3[0][0]',     \n",
            "                                8)                                'activation_1[0][0]']           \n",
            "                                                                                                  \n",
            " conv2d_16 (Conv2D)             (None, 128, 128, 64  73792       ['concatenate_3[0][0]']          \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " batch_normalization_16 (BatchN  (None, 128, 128, 64  256        ['conv2d_16[0][0]']              \n",
            " ormalization)                  )                                                                 \n",
            "                                                                                                  \n",
            " activation_16 (Activation)     (None, 128, 128, 64  0           ['batch_normalization_16[0][0]'] \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " conv2d_17 (Conv2D)             (None, 128, 128, 64  36928       ['activation_16[0][0]']          \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " batch_normalization_17 (BatchN  (None, 128, 128, 64  256        ['conv2d_17[0][0]']              \n",
            " ormalization)                  )                                                                 \n",
            "                                                                                                  \n",
            " activation_17 (Activation)     (None, 128, 128, 64  0           ['batch_normalization_17[0][0]'] \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " conv2d_18 (Conv2D)             (None, 128, 128, 1)  65          ['activation_17[0][0]']          \n",
            "                                                                                                  \n",
            "==================================================================================================\n",
            "Total params: 31,055,297\n",
            "Trainable params: 31,043,521\n",
            "Non-trainable params: 11,776\n",
            "__________________________________________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DkLFqhrJU9VQ"
      },
      "source": [
        "train_images = tf.convert_to_tensor(train_images)\n",
        "train_masks = tf.convert_to_tensor(train_masks)\n",
        "train_masks = tf.cast(train_masks,tf.float32)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k4tj1E-RVjKf"
      },
      "source": [
        "Earlystopper = tf.keras.callbacks.EarlyStopping(patience=3,restore_best_weights=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iaNC5OL-uDoK",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "04771992-d03d-45cf-d1e5-5b5e7f0a33e3"
      },
      "source": [
        "U_Net_model.fit(train_images,train_masks,validation_split=0.08,epochs=6,callbacks=[Earlystopper])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/6\n",
            "102/102 [==============================] - 70s 513ms/step - loss: 0.0521 - accuracy: 0.9601 - val_loss: 0.1929 - val_accuracy: 0.8305\n",
            "Epoch 2/6\n",
            "102/102 [==============================] - 45s 439ms/step - loss: 0.0239 - accuracy: 0.9788 - val_loss: 0.2496 - val_accuracy: 0.7505\n",
            "Epoch 3/6\n",
            "102/102 [==============================] - 45s 444ms/step - loss: 0.0214 - accuracy: 0.9799 - val_loss: 0.0845 - val_accuracy: 0.9161\n",
            "Epoch 4/6\n",
            "102/102 [==============================] - 45s 442ms/step - loss: 0.0211 - accuracy: 0.9796 - val_loss: 0.0414 - val_accuracy: 0.9595\n",
            "Epoch 5/6\n",
            "102/102 [==============================] - 45s 443ms/step - loss: 0.0195 - accuracy: 0.9809 - val_loss: 0.0230 - val_accuracy: 0.9773\n",
            "Epoch 6/6\n",
            "102/102 [==============================] - 45s 444ms/step - loss: 0.0191 - accuracy: 0.9812 - val_loss: 0.0228 - val_accuracy: 0.9774\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7fa20b634190>"
            ]
          },
          "metadata": {},
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hOWlyAEMX6A8"
      },
      "source": [
        "#Evaluating Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xBd9KF3yYqf1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "dcb26d4c-5bb5-45ed-bfc0-e30d97bd874c"
      },
      "source": [
        "idx = 23\n",
        "sample_pred = U_Net_model.predict(tf.expand_dims(train_images[idx],axis=0))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 1s 1s/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OVLAQ1gSar-3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6349dd05-2cc0-4462-fe0a-dbca58ae1fa8"
      },
      "source": [
        "np.max(sample_pred),np.min(sample_pred)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0.99999726, 2.2965605e-05)"
            ]
          },
          "metadata": {},
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tiynWAx0a8VE"
      },
      "source": [
        "sample_pred = sample_pred.reshape(128,128)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nWFGTavLafok"
      },
      "source": [
        "sample_pred = (sample_pred>0.05).astype(np.uint8)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BcY0_xfRbUz0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1a8d3a23-13e7-4b79-bed1-57292f9ebd1f"
      },
      "source": [
        "np.min(sample_pred),sample_pred.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0, (128, 128))"
            ]
          },
          "metadata": {},
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WwDmarq4Z0jD",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 220
        },
        "outputId": "31207355-2b1c-480e-fa25-58d631720e53"
      },
      "source": [
        "plt.subplot(1,2,1)\n",
        "plt.imshow(sample_pred.reshape(128,128))\n",
        "plt.subplot(1,2,2)\n",
        "plt.imshow(tf.reshape(train_masks[idx],(128,128)))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7fa20b48efd0>"
            ]
          },
          "metadata": {},
          "execution_count": 60
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3sn04vAJX8Hp"
      },
      "source": [
        "n_classes=2\n",
        "def class_wise_metrics(y_true, y_pred):\n",
        "  '''\n",
        "  Computes the class-wise IOU and Dice Score.\n",
        "\n",
        "  Args:\n",
        "    y_true (tensor) - ground truth label maps\n",
        "    y_pred (tensor) - predicted label maps\n",
        "  '''\n",
        "  class_wise_iou = []\n",
        "  class_wise_dice_score = []\n",
        "\n",
        "  smoothing_factor = 0.00001\n",
        "  \n",
        "  for i in range(n_classes):\n",
        "    intersection = np.sum((y_pred == i) * (y_true == i))\n",
        "    y_true_area = np.sum((y_true == i))\n",
        "    y_pred_area = np.sum((y_pred == i))\n",
        "    combined_area = y_true_area + y_pred_area\n",
        "    \n",
        "    iou = (intersection) / (combined_area - intersection + smoothing_factor)\n",
        "    class_wise_iou.append(iou)\n",
        "    \n",
        "    dice_score =  2 * ((intersection) / (combined_area + smoothing_factor))\n",
        "    class_wise_dice_score.append(dice_score)\n",
        "\n",
        "  return class_wise_iou, class_wise_dice_score\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uH168_zNYV6v"
      },
      "source": [
        "width=height=128"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z14thGtR733r"
      },
      "source": [
        "class_wise_iou,class_wise_dice_score = class_wise_metrics(train_masks[23].numpy().reshape(width,height),sample_pred)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ytn3LdOB8mCs",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fb41efb2-c6c4-4bb9-db57-8f19dd25a92e"
      },
      "source": [
        "print(class_wise_iou[1])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.8971937374603515\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CPdbLAd68ugl"
      },
      "source": [
        "class_names = ['background','segmented_area']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UyoC2VOn99uL"
      },
      "source": [
        "def calc_average_class_metrics(train_images,train_labels,model,class_metric_fnc,class_names):\n",
        "\n",
        "  average_iou = [0.0,0,0]\n",
        "  average_dice_score = [0.0,0.0]\n",
        "\n",
        "  m=train_images.shape[0]\n",
        "\n",
        "  y_predicteds = model.predict(train_images)\n",
        "  y_predicteds = y_predicteds.squeeze(axis=-1)\n",
        "  y_predicteds = (y_predicteds > 0.1).astype(np.int32)\n",
        "\n",
        "  train_labels = train_labels.squeeze(axis=-1)\n",
        "\n",
        "  for i in range(m):\n",
        "    iou,dice_score = class_metric_fnc(train_labels[i],y_predicteds[i])\n",
        "    \n",
        "    average_iou[0]+=iou[0]\n",
        "    average_dice_score[0]+=dice_score[0]\n",
        "    \n",
        "    average_iou[1]+=iou[1]\n",
        "    average_dice_score[1]+=dice_score[1]\n",
        "\n",
        "  \n",
        "  average_iou = list(map(lambda x:x/m,average_iou))\n",
        "  average_dice_score = list(map(lambda x:x/m,average_dice_score))\n",
        "  \n",
        "  print(\"========================IOU score=============================\")\n",
        "  for i,class_name in enumerate(class_names):\n",
        "    print(f\"{class_name}: {average_iou[i]}\")\n",
        "  \n",
        "  print(\"---------------------------------------------------------------------------------\")\n",
        "\n",
        "  print(\"========================Dice score=============================\")\n",
        "  for i,class_name in enumerate(class_names):\n",
        "    print(f\"{class_name}: {average_dice_score[i]}\")\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k81X6g5j-tur",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b3498ae5-12cf-4dbc-a752-5f5676e2791e"
      },
      "source": [
        "calc_average_class_metrics(train_images.numpy(),train_masks.numpy(),U_Net_model,class_wise_metrics,class_names)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "110/110 [==============================] - 15s 137ms/step\n",
            "========================IOU score=============================\n",
            "background: 0.9709664580228283\n",
            "segmented_area: 0.9159324461155075\n",
            "---------------------------------------------------------------------------------\n",
            "========================Dice score=============================\n",
            "background: 0.9851728237381832\n",
            "segmented_area: 0.9553031408546632\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VHHK1ZkO-7NV",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6f93f892-7fce-4631-b35f-c6a97d2250e7"
      },
      "source": [
        "'''IOU score is 92.59834225118458 % which is pretty high'''"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'IOU score is 92.59834225118458 % which is pretty high'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wIGR67v7aeHg"
      },
      "source": [],
      "execution_count": null,
      "outputs": []
    }
  ]
}