image_split.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "image_split.ipynb",
      "version": "0.3.2",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "metadata": {
        "id": "l5OLgx1QiA01",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "import os\n",
        "import numpy as np\n",
        "import json\n",
        "from PIL import Image\n",
        "import matplotlib\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "from matplotlib.collections import PatchCollection\n",
        "import cv2 as cv\n",
        "from skimage import io\n",
        "from skimage.segmentation import quickshift\n",
        "import glob\n",
        "import seaborn as sns"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "8rDBAK88lqeE",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import os\n",
        "import glob\n",
        "import zipfile\n",
        "import functools\n",
        "from skimage import io\n",
        "from skimage.segmentation import quickshift\n",
        "import glob\n",
        "from skimage.transform import rescale, resize, downscale_local_mean\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "import matplotlib as mpl\n",
        "mpl.rcParams['axes.grid'] = False\n",
        "mpl.rcParams['figure.figsize'] = (12,12)\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "import matplotlib.image as mpimg\n",
        "import pandas as pd\n",
        "from PIL import Image\n",
        "import cv2 as cv"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "4OlHqFh8ibR4",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "%matplotlib inline"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "a_5ZomZnijZ8",
        "colab_type": "code",
        "outputId": "e2c9ab0e-fe6a-463f-e3f6-3e5f4e961297",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 124
        }
      },
      "cell_type": "code",
      "source": [
        "!apt-get install -y -qq software-properties-common python-software-properties module-init-tools\n",
        "!add-apt-repository -y ppa:alessandro-strada/ppa 2>&1 > /dev/null\n",
        "!apt-get update -qq 2>&1 > /dev/null\n",
        "!apt-get -y install -qq google-drive-ocamlfuse fuse\n",
        "from google.colab import auth\n",
        "auth.authenticate_user()\n",
        "from oauth2client.client import GoogleCredentials\n",
        "creds = GoogleCredentials.get_application_default()\n",
        "import getpass\n",
        "!google-drive-ocamlfuse -headless -id={creds.client_id} -secret={creds.client_secret} < /dev/null 2>&1 | grep URL\n",
        "vcode = getpass.getpass()\n",
        "!echo {vcode} | google-drive-ocamlfuse -headless -id={creds.client_id} -secret={creds.client_secret}\n",
        "\n"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "E: Package 'python-software-properties' has no installation candidate\n",
            "Please, open the following URL in a web browser: https://accounts.google.com/o/oauth2/auth?client_id=32555940559.apps.googleusercontent.com&redirect_uri=urn%3Aietf%3Awg%3Aoauth%3A2.0%3Aoob&scope=https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive&response_type=code&access_type=offline&approval_prompt=force\n",
            "··········\n",
            "Please, open the following URL in a web browser: https://accounts.google.com/o/oauth2/auth?client_id=32555940559.apps.googleusercontent.com&redirect_uri=urn%3Aietf%3Awg%3Aoauth%3A2.0%3Aoob&scope=https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive&response_type=code&access_type=offline&approval_prompt=force\n",
            "Please enter the verification code: Access token retrieved correctly.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "sC8hackhjgtk",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "!mkdir -p drive\n",
        "!google-drive-ocamlfuse drive"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "qbpGT58hi3HU",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "PATH=\"drive/inter_iit/conv_data\"\n",
        "img_dir = os.path.join(PATH, \"sat\")\n",
        "label_dir = os.path.join(PATH, \"gt\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "qvIIArucioU7",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def scale_percentile(matrix):\n",
        "    \"\"\"Fixes the pixel value range to 2%-98% original distribution of values\"\"\"\n",
        "    orig_shape = matrix.shape\n",
        "    matrix = np.reshape(matrix, [matrix.shape[0]*matrix.shape[1], 3]).astype(float)\n",
        "    \n",
        "    # Get 2nd and 98th percentile\n",
        "    mins = np.percentile(matrix, 1, axis=0)\n",
        "    maxs = np.percentile(matrix, 99, axis=0) - mins\n",
        "    \n",
        "    matrix = (matrix - mins[None,:])/maxs[None,:]\n",
        "    matrix = np.reshape(matrix, orig_shape)\n",
        "    matrix = matrix.clip(0,1)\n",
        "    return matrix\n",
        "\n",
        "def scale_percentile_without_normalization(matrix):\n",
        "    \"\"\"Fixes the pixel value range to 2%-98% original distribution of values\"\"\"\n",
        "    orig_shape = matrix.shape\n",
        "    matrix = np.reshape(matrix, [matrix.shape[0]*matrix.shape[1], 3]).astype(float)\n",
        "    \n",
        "    # Get 2nd and 98th percentile\n",
        "    mins = np.percentile(matrix, 1, axis=0)\n",
        "    maxs = np.percentile(matrix, 99, axis=0) - mins\n",
        "    \n",
        "    matrix = (matrix - mins[None,:])/maxs[None,:]\n",
        "    matrix = np.reshape(matrix, orig_shape)\n",
        "    #matrix = matrix.clip(0,1)\n",
        "    return matrix\n",
        "def stretch_8bit(bands, lower_percent=2, higher_percent=98):\n",
        "    out = np.z|eros_like(bands).astype(np.float32)\n",
        "    for i in range(3):\n",
        "        a = 0\n",
        "        b = 1\n",
        "        c = np.percentile(bands[:, :, i], lower_percent)\n",
        "        d = np.percentile(bands[:, :, i], higher_percent)\n",
        "        t = a + (bands[:, :, i] - c) * (b - a) / (d - c)\n",
        "        t[t < a] = a\n",
        "        t[t > b] = b\n",
        "        out[:, :, i] = t\n",
        "    return out.astype(np.float32)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "tbAaXl3vi_tz",
        "colab_type": "code",
        "outputId": "117a1f38-9487-4587-a03f-6f6ce14c0dba",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        }
      },
      "cell_type": "code",
      "source": [
        "'''\n",
        "mypath = 'drive/inter_iit/conv_data/gt'\n",
        "onlyfiles = [f for f in os.listdir(mypath) if os.path.isfile(os.path.join(mypath, f))]\n",
        "sz=(1024,1024)\n",
        "for n in range(0, len(onlyfiles)):\n",
        "    \n",
        "    fname=os.path.join(label_dir, \"{}.png\".format(n))\n",
        "    os.remove(fname)\n",
        "    print(n)\n",
        "\n",
        "  '''  "
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'\\nmypath = \\'drive/inter_iit/conv_data/gt\\'\\nonlyfiles = [f for f in os.listdir(mypath) if os.path.isfile(os.path.join(mypath, f))]\\nsz=(1024,1024)\\nfor n in range(0, len(onlyfiles)):\\n    \\n    fname=os.path.join(label_dir, \"{}.png\".format(n))\\n    os.remove(fname)\\n    print(n)\\n\\n  '"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "metadata": {
        "id": "LlgpTEQJjFN0",
        "colab_type": "code",
        "outputId": "f4c4bce0-2992-4d6f-c911-555a16ef3b7d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        }
      },
      "cell_type": "code",
      "source": [
        "'''\n",
        "mypath2 = 'drive/inter_iit/conv_data/sat'\n",
        "onlyfiles = [f for f in os.listdir(mypath) if os.path.isfile(os.path.join(mypath2, f))]\n",
        "sz=(1024,1024)\n",
        "for n in range(0, len(onlyfiles)):\n",
        "    \n",
        "    fname=os.path.join(img_dir, \"{}.png\".format(n))\n",
        "    os.remove(fname)\n",
        "    print(n)\n",
        "    \n",
        "'''\n"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'\\nmypath2 = \\'drive/inter_iit/conv_data/sat\\'\\nonlyfiles = [f for f in os.listdir(mypath) if os.path.isfile(os.path.join(mypath2, f))]\\nsz=(1024,1024)\\nfor n in range(0, len(onlyfiles)):\\n    \\n    fname=os.path.join(img_dir, \"{}.png\".format(n))\\n    os.remove(fname)\\n    print(n)\\n    \\n'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        }
      ]
    },
    {
      "metadata": {
        "id": "ZyqxZsVKjHUr",
        "colab_type": "code",
        "outputId": "b0960f3e-27a7-4168-8fd2-fe30ce86a1ee",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        }
      },
      "cell_type": "code",
      "source": [
        "'''\n",
        "mypath = 'drive/inter_iit/The-Eye-in-the-Sky-dataset/gt'\n",
        "onlyfiles = [f for f in os.listdir(mypath) if os.path.isfile(os.path.join(mypath, f))]\n",
        "sz=(1024,1024)\n",
        "\n",
        "\n",
        "images = []\n",
        "for n in range(0, len(onlyfiles)):\n",
        "    im = io.imread(os.path.join(mypath, onlyfiles[n]))\n",
        "    fname=os.path.join(label_dir, \"{}.png\".format(n))\n",
        "    plt.imsave(fname, im)\n",
        "    im=resize(im, sz)#.reshape(-1)\n",
        "    images.append(im)\n",
        "    print(im.shape)\n",
        "    #df2 = pd.DataFrame(im.reshape(-1))\n",
        "    #df = pd.concat([df, df2], axis=1, ignore_index=True)\n",
        "    k = k + 1\n",
        "    print(k)\n",
        "#print(df)\n",
        "\n",
        "\n",
        "   \n",
        "images_gt = np.asarray(images)\n",
        "\n",
        "print(images_gt.shape)\n",
        "   \n",
        "\n",
        "\n",
        "'''"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'\\nmypath = \\'drive/inter_iit/The-Eye-in-the-Sky-dataset/gt\\'\\nonlyfiles = [f for f in os.listdir(mypath) if os.path.isfile(os.path.join(mypath, f))]\\nsz=(1024,1024)\\n\\n\\nimages = []\\nfor n in range(0, len(onlyfiles)):\\n    im = io.imread(os.path.join(mypath, onlyfiles[n]))\\n    fname=os.path.join(label_dir, \"{}.png\".format(n))\\n    plt.imsave(fname, im)\\n    im=resize(im, sz)#.reshape(-1)\\n    images.append(im)\\n    print(im.shape)\\n    #df2 = pd.DataFrame(im.reshape(-1))\\n    #df = pd.concat([df, df2], axis=1, ignore_index=True)\\n    k = k + 1\\n    print(k)\\n#print(df)\\n\\n\\n   \\nimages_gt = np.asarray(images)\\n\\nprint(images_gt.shape)\\n   \\n\\n\\n'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        }
      ]
    },
    {
      "metadata": {
        "id": "qM80j-9gjJuz",
        "colab_type": "code",
        "outputId": "cc756ded-49b9-4184-9286-c7e0493ff819",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        }
      },
      "cell_type": "code",
      "source": [
        "\n",
        "'''\n",
        "mypath = 'drive/inter_iit/The-Eye-in-the-Sky-dataset/sat'\n",
        "onlyfiles = [f for f in os.listdir(mypath) if os.path.isfile(os.path.join(mypath, f))]\n",
        "\n",
        "\n",
        "images_sat = []\n",
        "\n",
        "for n in range(0, len(onlyfiles)):\n",
        "    im = io.imread(os.path.join(mypath, onlyfiles[n]))\n",
        "    im_rgb=im[...,0:3]\n",
        "    \n",
        "    fixed_im = scale_percentile(im_rgb)\n",
        "    im=fixed_im\n",
        "    print(fixed_im.shape)\n",
        "    fname=os.path.join(img_dir, \"{}.png\".format(n))\n",
        "    plt.imsave(fname, fixed_im)\n",
        "    im=resize(im, sz)#.reshape(-1)\n",
        "    \n",
        "    images_sat.append(im)\n",
        "    print(im.shape)\n",
        "    #df2 = pd.DataFrame(im.reshape(-1))\n",
        "    #df = pd.concat([df, df2], axis=1, ignore_index=True)\n",
        "    \n",
        "    print(n)\n",
        "#print(df)\n",
        "   \n",
        "images_sat = np.asarray(images_sat)\n",
        "print(images_sat[2])\n",
        "\n",
        "print(images_sat.shape)\n",
        "\n",
        "'''\n"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'\\nmypath = \\'drive/inter_iit/The-Eye-in-the-Sky-dataset/sat\\'\\nonlyfiles = [f for f in os.listdir(mypath) if os.path.isfile(os.path.join(mypath, f))]\\n\\n\\nimages_sat = []\\n\\nfor n in range(0, len(onlyfiles)):\\n    im = io.imread(os.path.join(mypath, onlyfiles[n]))\\n    im_rgb=im[...,0:3]\\n    \\n    fixed_im = scale_percentile(im_rgb)\\n    im=fixed_im\\n    print(fixed_im.shape)\\n    fname=os.path.join(img_dir, \"{}.png\".format(n))\\n    plt.imsave(fname, fixed_im)\\n    im=resize(im, sz)#.reshape(-1)\\n    \\n    images_sat.append(im)\\n    print(im.shape)\\n    #df2 = pd.DataFrame(im.reshape(-1))\\n    #df = pd.concat([df, df2], axis=1, ignore_index=True)\\n    \\n    print(n)\\n#print(df)\\n   \\nimages_sat = np.asarray(images_sat)\\nprint(images_sat[2])\\n\\nprint(images_sat.shape)\\n\\n'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        }
      ]
    },
    {
      "metadata": {
        "id": "H5UHd6TPsBwp",
        "colab_type": "code",
        "outputId": "7cc63216-a53b-45f9-dc5a-837ad669841c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 331
        }
      },
      "cell_type": "code",
      "source": [
        "mypath = 'drive/inter_iit/conv_data/gt'\n",
        "onlyfiles = [f for f in os.listdir(mypath) if os.path.isfile(os.path.join(mypath, f))]\n",
        "sz=(1024,1024)\n",
        "\n",
        "\n",
        "images = []\n",
        "for n in range(0, len(onlyfiles)):\n",
        "    im = io.imread(os.path.join(mypath, onlyfiles[n]))[...,0:3]\n",
        "    fname=os.path.join(label_dir, \"{}.png\".format(n))\n",
        "    #plt.imsave(fname, im)\n",
        "    im=resize(im, sz)#.reshape(-1)\n",
        "    images.append(im)\n",
        "    print(im.shape)\n",
        "    #df2 = pd.DataFrame(im.reshape(-1))\n",
        "    #df = pd.concat([df, df2], axis=1, ignore_index=True)\n",
        "    n\n",
        "#print(df)\n",
        "\n",
        "\n",
        "   \n",
        "images_gt = np.asarray(images)\n",
        "\n",
        "print(images_gt.shape)"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/skimage/transform/_warps.py:84: UserWarning: The default mode, 'constant', will be changed to 'reflect' in skimage 0.15.\n",
            "  warn(\"The default mode, 'constant', will be changed to 'reflect' in \"\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "(1024, 1024, 3)\n",
            "(1024, 1024, 3)\n",
            "(1024, 1024, 3)\n",
            "(1024, 1024, 3)\n",
            "(1024, 1024, 3)\n",
            "(1024, 1024, 3)\n",
            "(1024, 1024, 3)\n",
            "(1024, 1024, 3)\n",
            "(1024, 1024, 3)\n",
            "(1024, 1024, 3)\n",
            "(1024, 1024, 3)\n",
            "(1024, 1024, 3)\n",
            "(1024, 1024, 3)\n",
            "(1024, 1024, 3)\n",
            "(14, 1024, 1024, 3)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "d1qFbW39rtjJ",
        "colab_type": "code",
        "outputId": "c29d5d03-61af-45fe-82bd-fdca0c2c31ca",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 331
        }
      },
      "cell_type": "code",
      "source": [
        "mypath = 'drive/inter_iit/conv_data/sat'\n",
        "onlyfiles = [f for f in os.listdir(mypath) if os.path.isfile(os.path.join(mypath, f))]\n",
        "\n",
        "\n",
        "images_sat = []\n",
        "\n",
        "for n in range(0, len(onlyfiles)):\n",
        "    im = io.imread(os.path.join(mypath, onlyfiles[n]))[...,0:3]\n",
        "    \n",
        "    \n",
        "    im=resize(im, sz)#.reshape(-1)\n",
        "    \n",
        "    images_sat.append(im)\n",
        "    print(im.shape)\n",
        "    n\n",
        "    \n",
        "\n",
        "images_sat = np.asarray(images_sat)\n",
        "#print(images_sat[2])\n",
        "\n",
        "print(images_sat.shape)"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/skimage/transform/_warps.py:84: UserWarning: The default mode, 'constant', will be changed to 'reflect' in skimage 0.15.\n",
            "  warn(\"The default mode, 'constant', will be changed to 'reflect' in \"\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "(1024, 1024, 3)\n",
            "(1024, 1024, 3)\n",
            "(1024, 1024, 3)\n",
            "(1024, 1024, 3)\n",
            "(1024, 1024, 3)\n",
            "(1024, 1024, 3)\n",
            "(1024, 1024, 3)\n",
            "(1024, 1024, 3)\n",
            "(1024, 1024, 3)\n",
            "(1024, 1024, 3)\n",
            "(1024, 1024, 3)\n",
            "(1024, 1024, 3)\n",
            "(1024, 1024, 3)\n",
            "(1024, 1024, 3)\n",
            "(14, 1024, 1024, 3)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "_QnCp4CkuFOj",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "df_x = pd.DataFrame(images_sat.flatten())\n",
        "df_y = pd.DataFrame(images_gt.flatten())"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "NPz8Vx8e0LTV",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "ee81a258-4ac0-470a-cb4b-10b0d71d27db"
      },
      "cell_type": "code",
      "source": [
        "print(df_x.shape)"
      ],
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(44040192, 1)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "9eBbwFI40ZvQ",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "a=df_x.values"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "t51zPXi_0dyP",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 166
        },
        "outputId": "32ef0bbd-ef9d-4400-da90-65a9e692c1eb"
      },
      "cell_type": "code",
      "source": [
        "a=a.reshape(14,256,256,3)"
      ],
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-60-edc30c4d37ea>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0ma\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m14\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m256\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m256\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m4\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m: cannot reshape array of size 44040192 into shape (14,256,256,4)"
          ]
        }
      ]
    },
    {
      "metadata": {
        "id": "xzXpFOX_jQA0",
        "colab_type": "code",
        "outputId": "252500df-9d2b-4b3b-94e1-ba1d4b16b46f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "x_train_filenames = []\n",
        "y_train_filenames = []\n",
        "for index in range( 14):\n",
        "  \n",
        "  x_train_filenames.append(os.path.join(img_dir, \"{}.png\".format(index)))\n",
        "  y_train_filenames.append(os.path.join(label_dir, \"{}.png\".format(index)))\n",
        "  #print(index)\n",
        "\n",
        "print(len(x_train_filenames))  "
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "14\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "QL7A02vtqU43",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "\n",
        "display_num = 5\n",
        "nu=len(x_train_filenames)\n",
        "r_choices = np.random.choice(nu, display_num)\n",
        "\n",
        "plt.figure(figsize=(10, 15))\n",
        "for i in range(0, display_num * 2, 2):\n",
        "  img_num = r_choices[i // 2]\n",
        "  x_pathname = x_train_filenames[img_num]\n",
        "  y_pathname = y_train_filenames[img_num]\n",
        "  \n",
        "  plt.subplot(display_num, 2, i + 1)\n",
        "  print(io.imread(x_train_filenames[img_num])[...,0:3].shape)\n",
        "  plt.imshow((io.imread(x_pathname)[...,0:3]))\n",
        "  plt.title(\"Original Image\")\n",
        "  \n",
        "  example_labels = Image.open(y_pathname)\n",
        "  label_vals = np.unique(example_labels)\n",
        "  #print(np.unique(io.imread(y_train_filenames[img_num])))\n",
        "  plt.subplot(display_num, 2, i + 2)\n",
        "  plt.imshow(example_labels)\n",
        "  plt.title(\"Masked Image\")  \n",
        "  \n",
        "plt.suptitle(\"Examples of Images and their Masks\")\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "jAJiS5Wb3Nk0",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "PATH=\"drive/inter_iit/split_data\"\n",
        "img_dir = os.path.join(PATH, \"sat\")\n",
        "label_dir = os.path.join(PATH, \"gt\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "wBi-UNqqKs6z",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "2sDr-dxhvcGK",
        "colab_type": "code",
        "outputId": "ddda8dc9-2e6a-456b-86a6-2ebafe2839e8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 259
        }
      },
      "cell_type": "code",
      "source": [
        "\n",
        "h,w=256,256\n",
        "\n",
        "split_sat=[]\n",
        "split_gt=[]\n",
        "t=0\n",
        "for i in range(0, 14):\n",
        "  \n",
        "  img_num=i\n",
        "  x,y=0,0\n",
        "  x_pathname = x_train_filenames[img_num]\n",
        "  y_pathname = y_train_filenames[img_num]\n",
        "  im_sat=io.imread(x_pathname)[...,0:3]\n",
        "  im_gt=io.imread(y_pathname)[...,0:3]\n",
        "  n=i\n",
        "  print(i)\n",
        "  \n",
        "  while 1:\n",
        "    x=0\n",
        "    while 1:\n",
        "      \n",
        "      fname_lab=os.path.join(label_dir, \"{}_{}_{}.png\".format(n,x,y))\n",
        "      fname_img=os.path.join(img_dir, \"{}_{}_{}.png\".format(n,x,y))\n",
        "      a,b,c=im_sat[y:y+h, x:x+w].shape\n",
        "      c,d,f=im_gt[y:y+h, x:x+w].shape\n",
        "      \n",
        "      \n",
        "      if(a==256 and b==256 and c==256 and d==256):\n",
        "        \n",
        "        t=t+1\n",
        "        \n",
        "        #print(t)\n",
        "        if(t==1):\n",
        "          crop_img_x = im_sat[y:y+h, x:x+w]#.flatten()\n",
        "          a=crop_img_x.reshape(1,h*w*3)\n",
        "          #df_sat=pd.DataFrame(a)\n",
        "          crop_img_y = im_gt[y:y+h, x:x+w] \n",
        "          b=crop_img_y.reshape(1,h*w*3)\n",
        "          #df_gt=pd.DataFrame(b)\n",
        "          #print(\"dddddddddd\")\n",
        "        else:\n",
        "          crop_img_x = im_sat[y:y+h, x:x+w]#.flatten()\n",
        "          a=crop_img_x.reshape(1,h*w*3)\n",
        "          #temp=pd.DataFrame(a)\n",
        "          #df_sat = pd.concat([df_sat, temp], axis=0, ignore_index=True)\n",
        "          #plt.imsave(fname_img,crop_img_x)\n",
        "          #print(crop_img_y.shape)\n",
        "          split_sat.append(crop_img_x)\n",
        "\n",
        "          crop_img_y = im_gt[y:y+h, x:x+w]#.flatten()\n",
        "          b=crop_img_y.reshape(1,h*w*3)\n",
        "          #temp2=pd.DataFrame(b)\n",
        "          #df_gt = pd.concat([df_gt, temp2], axis=0, ignore_index=True)\n",
        "          #plt.imsave(fname_lab,crop_img_y)\n",
        "          split_gt.append(crop_img_y)\n",
        "      x=x+40\n",
        "     # print(x)\n",
        "      if(x>=750):\n",
        "        break\n",
        "    y=y+40\n",
        "    if(y>=750):\n",
        "      break\n",
        "      \n",
        "    \n",
        "  \n",
        " "
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0\n",
            "1\n",
            "2\n",
            "3\n",
            "4\n",
            "5\n",
            "6\n",
            "7\n",
            "8\n",
            "9\n",
            "10\n",
            "11\n",
            "12\n",
            "13\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "e1bAcVjsArLB",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "\n",
        "split_sat = np.asarray(split_sat)\n",
        "split_gt = np.asarray(split_gt)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "lYc4aLjY9jD-",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "unWflTud9jRa",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "print(df_sat.shape)\n",
        "print(df_sat)\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "joMQCFcv9Fnm",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "df_sat.to_csv('drive/inter_iit/split_data/sat/df_sat.csv')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "uVqb6ouY9ZNC",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "df_gt.to_csv('drive/inter_iit/split_data/gt/df_gt.csv')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "Qa-YTKeMgwpG",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "949d3df0-6007-473a-b82d-cacb0706a8ef"
      },
      "cell_type": "code",
      "source": [
        "print(split_sat.shape)"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(3724, 256, 256, 3)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "qIwVI0EvKfBr",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "df_x = pd.DataFrame.from_records(split_sat)\n",
        "df_y = pd.DataFrame.from_records(split_gt)\n",
        "print(df_x[1])\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "xvZjitZIQ5I4",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "\n",
        "img_shape = (256, 256, 3)\n",
        "batch_size = 64\n",
        "epochs = 5"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "TlyvgG2QTaF_",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "#x_train, x_val, y_train, y_val= train_test_split(df_sat.values, df_gt.values, test_size=0.3, random_state=42)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "7kopHamAHaDI",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "x_train, x_val, y_train, y_val= train_test_split(split_sat, split_gt, test_size=0.3, random_state=42)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "hktgRSy3CqOH",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "a97d0f19-c9d0-4ddb-e869-beed2325d27a"
      },
      "cell_type": "code",
      "source": [
        "num_train_examples = len(x_train)\n",
        "num_val_examples = len(x_val)\n",
        "print(num_train_examples)"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2606\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "amUwS2K9QIZp",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "f937e82a-3995-4c76-c745-39f7bfab0342"
      },
      "cell_type": "code",
      "source": [
        "\n",
        "from __future__ import division\n",
        "\n",
        "import numpy as np\n",
        "from keras.models import Model\n",
        "from keras.layers import Input, merge, Convolution2D, MaxPooling2D, UpSampling2D, Cropping2D\n",
        "\n",
        "from keras import backend as K\n",
        "\n",
        "import keras\n",
        "import h5py\n",
        "\n",
        "from keras.layers.normalization import BatchNormalization\n",
        "\n",
        "\n",
        "from keras.optimizers import Nadam\n",
        "from keras.callbacks import History\n",
        "import pandas as pd\n",
        "from keras.backend import categorical_crossentropy\n",
        "\n",
        "import datetime\n",
        "import os\n",
        "\n",
        "import random\n",
        "import threading\n",
        "from tensorflow.python.keras import layers\n",
        "from keras.models import model_from_json\n",
        "import tensorflow.contrib as tfcontrib\n",
        "from tensorflow.python.keras import layers\n",
        "from tensorflow.python.keras import losses\n",
        "from tensorflow.python.keras import models\n",
        "from tensorflow.python.keras import backend as K\n",
        "from keras import utils"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "metadata": {
        "id": "PNKPP1t0QJV2",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def conv_block(input_tensor, num_filters):\n",
        "  encoder = layers.Conv2D(num_filters, (3, 3), padding='same')(input_tensor)\n",
        "  encoder = layers.BatchNormalization()(encoder)\n",
        "  encoder = layers.Activation('relu')(encoder)\n",
        "  encoder = layers.Conv2D(num_filters, (3, 3), padding='same')(encoder)\n",
        "  encoder = layers.BatchNormalization()(encoder)\n",
        "  encoder = layers.Activation('relu')(encoder)\n",
        "  return encoder\n",
        "\n",
        "def encoder_block(input_tensor, num_filters):\n",
        "  encoder = conv_block(input_tensor, num_filters)\n",
        "  encoder_pool = layers.MaxPooling2D((2, 2), strides=(2, 2))(encoder)\n",
        "  \n",
        "  return encoder_pool, encoder\n",
        "\n",
        "def decoder_block(input_tensor, concat_tensor, num_filters):\n",
        "  decoder = layers.Conv2DTranspose(num_filters, (2, 2), strides=(2, 2), padding='same')(input_tensor)\n",
        "  decoder = layers.concatenate([concat_tensor, decoder], axis=-1)\n",
        "  decoder = layers.BatchNormalization()(decoder)\n",
        "  decoder = layers.Activation('relu')(decoder)\n",
        "  decoder = layers.Conv2D(num_filters, (3, 3), padding='same')(decoder)\n",
        "  decoder = layers.BatchNormalization()(decoder)\n",
        "  decoder = layers.Activation('relu')(decoder)\n",
        "  decoder = layers.Conv2D(num_filters, (3, 3), padding='same')(decoder)\n",
        "  decoder = layers.BatchNormalization()(decoder)\n",
        "  decoder = layers.Activation('relu')(decoder)\n",
        "  return decoder"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "Cm-bTpMJQL0U",
        "colab_type": "code",
        "outputId": "ef2c4de8-c0dc-4a31-c478-e69783e66bca",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        }
      },
      "cell_type": "code",
      "source": [
        "inputs = layers.Input(shape=img_shape)\n",
        "\n",
        "# 256\n",
        "print(inputs.shape)\n",
        "encoder0_pool, encoder0 = encoder_block(inputs, 32)\n",
        "# 128\n",
        "print(encoder0_pool.shape)\n",
        "encoder1_pool, encoder1 = encoder_block(encoder0_pool, 64)\n",
        "# 64\n",
        "\n",
        "encoder2_pool, encoder2 = encoder_block(encoder1_pool, 128)\n",
        "# 32\n",
        "\n",
        "encoder3_pool, encoder3 = encoder_block(encoder2_pool, 256)\n",
        "# 16\n",
        "\n",
        "encoder4_pool, encoder4 = encoder_block(encoder3_pool, 512)\n",
        "# 8\n",
        "\n",
        "center = conv_block(encoder4_pool, 1024)\n",
        "# center\n",
        "\n",
        "decoder4 = decoder_block(center, encoder4, 512)\n",
        "# 16\n",
        "\n",
        "decoder3 = decoder_block(decoder4, encoder3, 256)\n",
        "# 32\n",
        "\n",
        "decoder2 = decoder_block(decoder3, encoder2, 128)\n",
        "# 64\n",
        "\n",
        "decoder1 = decoder_block(decoder2, encoder1, 64)\n",
        "# 128\n",
        "\n",
        "decoder0 = decoder_block(decoder1, encoder0, 32)\n",
        "\n",
        "#encoder4_pool, encoder4 = encoder_block(encoder3_pool, 512)\n",
        "outputs = layers.Conv2D(3, (1, 1), activation='sigmoid')(decoder0)\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(?, 256, 256, 3)\n",
            "(?, 128, 128, 32)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "dpt32_BYQQU0",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "model = models.Model(inputs=[inputs], outputs=[outputs])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "zZ2caAOnQUjf",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def dice_coeff(y_true, y_pred):\n",
        "    smooth = 1.\n",
        "    # Flatten\n",
        "    y_true_f = tf.reshape(y_true, [-1])\n",
        "    y_pred_f = tf.reshape(y_pred, [-1])\n",
        "    intersection = tf.reduce_sum(y_true_f * y_pred_f)\n",
        "    score = (2. * intersection + smooth) / (tf.reduce_sum(y_true_f) + tf.reduce_sum(y_pred_f) + smooth)\n",
        "    return score"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "mzHFoFCGQVMy",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def dice_loss(y_true, y_pred):\n",
        "    loss = 1 - dice_coeff(y_true, y_pred)\n",
        "    return loss"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "eyeXN4UbQW5T",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def bce_dice_loss(y_true, y_pred):\n",
        "    loss = losses.categorical_crossentropy(y_true, y_pred) + dice_loss(y_true, y_pred)\n",
        "    return loss\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "8-FOrDMEQY1F",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def jaccard_coef(y_true, y_pred):\n",
        "    intersection = K.sum(y_true * y_pred, axis=[0, -1, -2])\n",
        "    sum_ = K.sum(y_true + y_pred, axis=[0, -1, -2])\n",
        "\n",
        "    jac = (intersection + smooth) / (sum_ - intersection + smooth)\n",
        "\n",
        "    return K.mean(jac)\n",
        "\n",
        "\n",
        "def jaccard_coef_int(y_true, y_pred):\n",
        "    y_pred_pos = K.round(K.clip(y_pred, 0, 1))\n",
        "\n",
        "    intersection = K.sum(y_true * y_pred_pos, axis=[0, -1, -2])\n",
        "    sum_ = K.sum(y_true + y_pred_pos, axis=[0, -1, -2])\n",
        "\n",
        "    jac = (intersection + smooth) / (sum_ - intersection + smooth)\n",
        "\n",
        "    return K.mean(jac)\n",
        "\n",
        "\n",
        "def jaccard_coef_loss(y_true, y_pred):\n",
        "    return -K.log(jaccard_coef(y_true, y_pred)) + categorical_crossentropy(y_pred, y_true)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "No0IieuVQace",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "model.compile(optimizer='adam', loss=bce_dice_loss, metrics=[dice_loss])\n",
        "\n",
        "#model.summary()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "jxzHwDh8QceP",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "save_model_path = 'drive/inter_iit/model/weights.hdf5'\n",
        "cp = tf.keras.callbacks.ModelCheckpoint(filepath=save_model_path, monitor='val_dice_loss', save_best_only=True, verbose=1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "MuPG3bEUVYYR",
        "colab_type": "code",
        "outputId": "0284145c-8ea4-4fac-c014-0779940afc6e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "print(x_val.shape)"
      ],
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(1117, 256, 256, 3)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "qhFzjBmEzHjH",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "print(x_train[2])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "64qHY5b7CTz-",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "im_rows = 256 \n",
        "im_cols = 256\n",
        "x_test=x_val\n",
        "y_test=y_val\n",
        "\n",
        "im_shape = (im_rows, im_cols, 3)\n",
        "\n",
        "x_train = x_train.reshape(x_train.shape[0], *im_shape)\n",
        "y_train = y_train.reshape(y_train.shape[0], *im_shape)\n",
        "x_test = x_test.reshape(x_test.shape[0], *im_shape)\n",
        "y_test = x_test.reshape(y_test.shape[0], *im_shape)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "gjFWTXNEWeaq",
        "colab_type": "code",
        "outputId": "ea87b14b-680f-4efa-9623-9c7e0a066adc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "\n",
        "print(x_train.shape)"
      ],
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(2606, 256, 256, 3)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "f-TQv6IrgiiI",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 287
        },
        "outputId": "e710d4fc-efc3-403b-d041-3bad82791ab7"
      },
      "cell_type": "code",
      "source": [
        "\n",
        "plt.imshow(x_train[1])"
      ],
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7fd00a289f28>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 68
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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kgEi8a4d+8hCAVBLnR1FRIFEioKQe5a04/p/Nak1wDm/dKGo7y5iTPx3+ugXEvxQxjCwI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\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fd00a3832e8>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "TVVcO8yEFpTx",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "'''parse function to be used. This function is needed to do \n",
        "the preprocessing of data like reshaping ,\n",
        "converting to tensors from numpy arrays ,one-hot encoding ,etc.'''\n",
        "def _parse_(x,y):\n",
        "    x = tf.reshape(x,(784,)) #reshape to 784 as expected.\n",
        "    x = tf.cast(x,tf.float32) #cast to float32 as the weights are float32.\n",
        "    # y = tf.one_hot(y,10) #should be one hot encoded but tensorflow is throwing error .idky??\n",
        "    y = tf.cast(y,tf.int32) #cast to tensor of int32\n",
        "    return (dict({'image':x}),y) #return tuple of dict of feature name with value and label."
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "j6u-LxrLFpcS",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def train_input_fn(x_train,y_train,batch_size=64):\n",
        "    ##Here we are using dataset API.\n",
        "    '''\n",
        "    take the data from tensor_slices i.e. an array of datapoints in simple words.\n",
        "    '''\n",
        "    dataset = tf.data.Dataset.from_tensor_slices((x_train,y_train)) \n",
        "    \n",
        "    \n",
        "    dataset = dataset.map(lambda x,y:_parse_(x,y)).shuffle(buffer_size=128) \\\n",
        "                .batch(batch_size).make_one_shot_iterator()\n",
        "    \n",
        "    return dataset.get_next()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "g9ns2e52FpqC",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "_4xNZN0fQhUA",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "epochs=5\n",
        "batch_size=64"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "PH2gX7_YF7HA",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 324
        },
        "outputId": "cfb57601-664a-480c-e506-292d9ae34c15"
      },
      "cell_type": "code",
      "source": [
        "history=model.fit(input_fn=tf.estimator.inputs.numpy_input_fn(dict({'image':x_train}),\n",
        "                                                        np.array(y_train,np.int32),shuffle=True),steps=10)"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "error",
          "ename": "TypeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-31-d8a3298e486f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m history=model.fit(input_fn=tf.estimator.inputs.numpy_input_fn(dict({'image':x_train}),\n\u001b[0;32m----> 2\u001b[0;31m                                                         np.array(y_train,np.int32),shuffle=True),steps=10)\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, max_queue_size, workers, use_multiprocessing, **kwargs)\u001b[0m\n\u001b[1;32m   1511\u001b[0m       \u001b[0mepochs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'nb_epoch'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1512\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1513\u001b[0;31m       \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Unrecognized keyword arguments: '\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1514\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1515\u001b[0m     \u001b[0;31m# Validate and standardize user data.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mTypeError\u001b[0m: Unrecognized keyword arguments: {'input_fn': <function numpy_input_fn.<locals>.input_fn at 0x7fd00e9dfe18>, 'steps': 10}"
          ]
        }
      ]
    },
    {
      "metadata": {
        "id": "FrU3nTE_F7cS",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 376
        },
        "outputId": "5d3bac6f-a068-4c02-d245-8a6226410bf0"
      },
      "cell_type": "code",
      "source": [
        "import time\n",
        "t1 = time.time()\n",
        "model.fit(input_fn=lambda:train_input_fn(x_train,y_train,64),steps=150)\n",
        "t2= time.time()\n",
        "print('---------{}'.format(t2 - t1))\n",
        "\n"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "error",
          "ename": "TypeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-35-901c2ff5637e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mtime\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mt1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput_fn\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mlambda\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mtrain_input_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx_train\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my_train\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m64\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0msteps\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m150\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m \u001b[0mt2\u001b[0m\u001b[0;34m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'---------{}'\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt2\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mt1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, max_queue_size, workers, use_multiprocessing, **kwargs)\u001b[0m\n\u001b[1;32m   1511\u001b[0m       \u001b[0mepochs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'nb_epoch'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1512\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1513\u001b[0;31m       \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Unrecognized keyword arguments: '\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1514\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1515\u001b[0m     \u001b[0;31m# Validate and standardize user data.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mTypeError\u001b[0m: Unrecognized keyword arguments: {'input_fn': <function <lambda> at 0x7fd00e166f28>, 'steps': 150}"
          ]
        }
      ]
    },
    {
      "metadata": {
        "id": "jODtIq22IOXk",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "t1 = time.time()\n",
        "model.train(input_fn=lambda:train_input_fn(x_train,y_train,64),steps=251)\n",
        "t2= time.time()\n",
        "print('---------{}'.format(t2 - t1))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "Wfjp1KGQQi6L",
        "colab_type": "code",
        "outputId": "56aa73f7-a38c-4aa5-a216-c2f0bd041d00",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        }
      },
      "cell_type": "code",
      "source": [
        "history = model.fit((x_train/255,y_train/255 ),\n",
        "                   steps_per_epoch=int(np.ceil(num_train_examples / float(batch_size))),\n",
        "                   epochs=epochs,\n",
        "                   validation_data=(x_val/255,y_val/255),\n",
        "                   validation_steps=int(np.ceil(num_val_examples / float(batch_size))),\n",
        "                   callbacks=[cp])"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-1-d08c3bca77e5>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m history = model.fit((x_train/255,y_train/255 ),\n\u001b[0m\u001b[1;32m      2\u001b[0m                    \u001b[0msteps_per_epoch\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mceil\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnum_train_examples\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0mfloat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch_size\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m                    \u001b[0mepochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mepochs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m                    \u001b[0mvalidation_data\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx_val\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0;36m255\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my_val\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0;36m255\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m                    \u001b[0mvalidation_steps\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mceil\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnum_val_examples\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0mfloat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch_size\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mNameError\u001b[0m: name 'model' is not defined"
          ]
        }
      ]
    },
    {
      "metadata": {
        "id": "su-fSUJLQltA",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "dice = history.history['dice_loss']\n",
        "val_dice = history.history['val_dice_loss']\n",
        "\n",
        "loss = history.history['loss']\n",
        "val_loss = history.history['val_loss']\n",
        "\n",
        "epochs_range = range(epochs)\n",
        "\n",
        "plt.figure(figsize=(16, 8))\n",
        "plt.subplot(1, 2, 1)\n",
        "plt.plot(epochs_range, dice, label='Training Dice Loss')\n",
        "plt.plot(epochs_range, val_dice, label='Validation Dice Loss')\n",
        "plt.legend(loc='upper right')\n",
        "plt.title('Training and Validation Dice Loss')\n",
        "\n",
        "plt.subplot(1, 2, 2)\n",
        "plt.plot(epochs_range, loss, label='Training Loss')\n",
        "plt.plot(epochs_range, val_loss, label='Validation Loss')\n",
        "plt.legend(loc='upper right')\n",
        "plt.title('Training and Validation Loss')\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "xvxfV1ZmQr9t",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Alternatively, load the weights directly: model.load_weights(save_model_path)\n",
        "model = models.load_model(save_model_path, custom_objects={'bce_dice_loss': bce_dice_loss,\n",
        "                                                           'dice_loss': dice_loss})"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "QLWicDNYQtWA",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "\n",
        "# Let's visualize some of the outputs \n",
        "data_aug_iter = val_ds.make_one_shot_iterator()\n",
        "next_element = data_aug_iter.get_next()\n",
        "\n",
        "# Running next element in our graph will produce a batch of images\n",
        "plt.figure(figsize=(10, 20))\n",
        "for i in range(5):\n",
        "  batch_of_imgs, label = tf.keras.backend.get_session().run(next_element)\n",
        "  img = batch_of_imgs[0]\n",
        "  predicted_label = model.predict(batch_of_imgs)[0]\n",
        "\n",
        "  plt.subplot(5, 3, 3 * i + 1)\n",
        "  plt.imshow(img)\n",
        "  plt.title(\"Input image\")\n",
        "  \n",
        "  plt.subplot(5, 3, 3 * i + 2)\n",
        "  plt.imshow(label[0, :, :, :])\n",
        "  plt.title(\"Actual Mask\")\n",
        "  plt.subplot(5, 3, 3 * i + 3)\n",
        "  plt.imshow(predicted_label[:, :, :])\n",
        "  plt.title(\"Predicted Mask\")\n",
        "plt.suptitle(\"Examples of Input Image, Label, and Prediction\")\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}