{

 "cells": [

  {

   "cell_type": "code",

   "execution_count": 1,

   "metadata": {},

   "outputs": [

    {

     "name": "stdout",

     "output_type": "stream",

     "text": [

      "Requirement already satisfied: efficientnet in /home/surya/anaconda3/lib/python3.6/site-packages (1.0.0)\n",

      "Requirement already satisfied: scikit-image in /home/surya/anaconda3/lib/python3.6/site-packages (from efficientnet) (0.15.0)\n",

      "Requirement already satisfied: keras-applications<=1.0.8,>=1.0.7 in /home/surya/anaconda3/lib/python3.6/site-packages (from efficientnet) (1.0.8)\n",

      "Requirement already satisfied: imageio>=2.0.1 in /home/surya/anaconda3/lib/python3.6/site-packages (from scikit-image->efficientnet) (2.3.0)\n",

      "Requirement already satisfied: scipy>=0.17.0 in /home/surya/anaconda3/lib/python3.6/site-packages (from scikit-image->efficientnet) (1.1.0)\n",

      "Requirement already satisfied: networkx>=2.0 in /home/surya/anaconda3/lib/python3.6/site-packages (from scikit-image->efficientnet) (2.1)\n",

      "Requirement already satisfied: matplotlib!=3.0.0,>=2.0.0 in /home/surya/anaconda3/lib/python3.6/site-packages (from scikit-image->efficientnet) (2.2.2)\n",

      "Requirement already satisfied: PyWavelets>=0.4.0 in /home/surya/anaconda3/lib/python3.6/site-packages (from scikit-image->efficientnet) (0.5.2)\n",

      "Requirement already satisfied: pillow>=4.3.0 in /home/surya/anaconda3/lib/python3.6/site-packages (from scikit-image->efficientnet) (6.2.0)\n",

      "Requirement already satisfied: h5py in /home/surya/anaconda3/lib/python3.6/site-packages (from keras-applications<=1.0.8,>=1.0.7->efficientnet) (2.7.1)\n",

      "Requirement already satisfied: numpy>=1.9.1 in /home/surya/anaconda3/lib/python3.6/site-packages (from keras-applications<=1.0.8,>=1.0.7->efficientnet) (1.17.3)\n",

      "Requirement already satisfied: decorator>=4.1.0 in /home/surya/anaconda3/lib/python3.6/site-packages (from networkx>=2.0->scikit-image->efficientnet) (4.3.0)\n",

      "Requirement already satisfied: cycler>=0.10 in /home/surya/anaconda3/lib/python3.6/site-packages (from matplotlib!=3.0.0,>=2.0.0->scikit-image->efficientnet) (0.10.0)\n",

      "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /home/surya/anaconda3/lib/python3.6/site-packages (from matplotlib!=3.0.0,>=2.0.0->scikit-image->efficientnet) (2.2.0)\n",

      "Requirement already satisfied: python-dateutil>=2.1 in /home/surya/.local/lib/python3.6/site-packages (from matplotlib!=3.0.0,>=2.0.0->scikit-image->efficientnet) (2.7.3)\n",

      "Requirement already satisfied: pytz in /home/surya/.local/lib/python3.6/site-packages (from matplotlib!=3.0.0,>=2.0.0->scikit-image->efficientnet) (2018.5)\n",

      "Requirement already satisfied: six>=1.10 in /home/surya/.local/lib/python3.6/site-packages (from matplotlib!=3.0.0,>=2.0.0->scikit-image->efficientnet) (1.11.0)\n",

      "Requirement already satisfied: kiwisolver>=1.0.1 in /home/surya/anaconda3/lib/python3.6/site-packages (from matplotlib!=3.0.0,>=2.0.0->scikit-image->efficientnet) (1.0.1)\n",

      "Requirement already satisfied: setuptools in /home/surya/anaconda3/lib/python3.6/site-packages (from kiwisolver>=1.0.1->matplotlib!=3.0.0,>=2.0.0->scikit-image->efficientnet) (39.1.0)\n",

      "\u001b[33mWARNING: You are using pip version 19.1.1, however version 19.3.1 is available.\n",

      "You should consider upgrading via the 'pip install --upgrade pip' command.\u001b[0m\n",

      "Requirement already satisfied: iterative-stratification in /home/surya/anaconda3/lib/python3.6/site-packages (0.1.6)\n",

      "Requirement already satisfied: scikit-learn in /home/surya/anaconda3/lib/python3.6/site-packages (from iterative-stratification) (0.20.3)\n",

      "Requirement already satisfied: numpy in /home/surya/anaconda3/lib/python3.6/site-packages (from iterative-stratification) (1.17.3)\n",

      "Requirement already satisfied: scipy in /home/surya/anaconda3/lib/python3.6/site-packages (from iterative-stratification) (1.1.0)\n",

      "\u001b[33mWARNING: You are using pip version 19.1.1, however version 19.3.1 is available.\n",

      "You should consider upgrading via the 'pip install --upgrade pip' command.\u001b[0m\n",

      "Requirement already satisfied: gdown in /home/surya/anaconda3/lib/python3.6/site-packages (3.8.1)\n",

      "Requirement already satisfied: six in /home/surya/.local/lib/python3.6/site-packages (from gdown) (1.11.0)\n",

      "Requirement already satisfied: filelock in /home/surya/anaconda3/lib/python3.6/site-packages (from gdown) (3.0.4)\n",

      "Requirement already satisfied: requests in /home/surya/anaconda3/lib/python3.6/site-packages (from gdown) (2.18.4)\n",

      "Requirement already satisfied: tqdm in /home/surya/anaconda3/lib/python3.6/site-packages (from gdown) (4.28.1)\n",

      "Requirement already satisfied: chardet<3.1.0,>=3.0.2 in /home/surya/anaconda3/lib/python3.6/site-packages (from requests->gdown) (3.0.4)\n",

      "Requirement already satisfied: idna<2.7,>=2.5 in /home/surya/anaconda3/lib/python3.6/site-packages (from requests->gdown) (2.6)\n",

      "Requirement already satisfied: urllib3<1.23,>=1.21.1 in /home/surya/anaconda3/lib/python3.6/site-packages (from requests->gdown) (1.22)\n",

      "Requirement already satisfied: certifi>=2017.4.17 in /home/surya/anaconda3/lib/python3.6/site-packages (from requests->gdown) (2018.4.16)\n",

      "\u001b[33mWARNING: You are using pip version 19.1.1, however version 19.3.1 is available.\n",

      "You should consider upgrading via the 'pip install --upgrade pip' command.\u001b[0m\n"

     ]

    }

   ],

   "source": [

    "!pip install efficientnet\n",

    "!pip install iterative-stratification\n",

    "!pip install gdown"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": 2,

   "metadata": {},

   "outputs": [],

   "source": [

    "import os\n",

    "if not os.path.isfile('model_effnet_bo_087.h5'):\n",

    "    !gdown https://drive.google.com/uc?id=1FXF1HymYbRf3OlThMTXAa74TRup3AhD_"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": 3,

   "metadata": {},

   "outputs": [

    {

     "name": "stderr",

     "output_type": "stream",

     "text": [

      "/home/surya/anaconda3/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:523: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",

      "  _np_qint8 = np.dtype([(\"qint8\", np.int8, 1)])\n",

      "/home/surya/anaconda3/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:524: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",

      "  _np_quint8 = np.dtype([(\"quint8\", np.uint8, 1)])\n",

      "/home/surya/anaconda3/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:525: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",

      "  _np_qint16 = np.dtype([(\"qint16\", np.int16, 1)])\n",

      "/home/surya/anaconda3/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:526: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",

      "  _np_quint16 = np.dtype([(\"quint16\", np.uint16, 1)])\n",

      "/home/surya/anaconda3/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:527: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",

      "  _np_qint32 = np.dtype([(\"qint32\", np.int32, 1)])\n",

      "/home/surya/anaconda3/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:532: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",

      "  np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\n",

      "/home/surya/anaconda3/lib/python3.6/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",

      "  from ._conv import register_converters as _register_converters\n",

      "Using TensorFlow backend.\n"

     ]

    }

   ],

   "source": [

    "import numpy as np\n",

    "import pandas as pd\n",

    "import pydicom\n",

    "import cv2\n",

    "import tensorflow as tf\n",

    "import multiprocessing\n",

    "from math import ceil, floor\n",

    "import keras\n",

    "import keras.backend as K\n",

    "from keras.callbacks import Callback, ModelCheckpoint\n",

    "from keras.layers import Dense, Flatten, Dropout\n",

    "from keras.models import Model, load_model\n",

    "from keras.utils import Sequence\n",

    "from keras.losses import binary_crossentropy\n",

    "from keras.optimizers import Adam\n",

    "import efficientnet.keras as efn \n",

    "from iterstrat.ml_stratifiers import MultilabelStratifiedShuffleSplit\n",

    "import matplotlib.pyplot as plt"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": 4,

   "metadata": {},

   "outputs": [],

   "source": [

    "def correct_dcm(dcm):\n",

    "    x = dcm.pixel_array + 1000\n",

    "    px_mode = 4096\n",

    "    x[x>=px_mode] = x[x>=px_mode] - px_mode\n",

    "    dcm.PixelData = x.tobytes()\n",

    "    dcm.RescaleIntercept = -1000\n",

    "\n",

    "def window_image(dcm, window_center, window_width):    \n",

    "    if (dcm.BitsStored == 12) and (dcm.PixelRepresentation == 0) and (int(dcm.RescaleIntercept) > -100):\n",

    "        correct_dcm(dcm)\n",

    "    img = dcm.pixel_array * dcm.RescaleSlope + dcm.RescaleIntercept\n",

    "    \n",

    "    # Resize\n",

    "    img = cv2.resize(img, SHAPE[:2], interpolation = cv2.INTER_LINEAR)\n",

    "   \n",

    "    img_min = window_center - window_width // 2\n",

    "    img_max = window_center + window_width // 2\n",

    "    img = np.clip(img, img_min, img_max)\n",

    "    return img\n",

    "\n",

    "def bsb_window(dcm):\n",

    "    brain_img = window_image(dcm, 40, 80)\n",

    "    subdural_img = window_image(dcm, 80, 200)\n",

    "    soft_img = window_image(dcm, 40, 380)\n",

    "    \n",

    "    brain_img = (brain_img - 0) / 80\n",

    "    subdural_img = (subdural_img - (-20)) / 200\n",

    "    soft_img = (soft_img - (-150)) / 380\n",

    "    bsb_img = np.array([brain_img, subdural_img, soft_img]).transpose(1,2,0)\n",

    "    return bsb_img\n",

    "\n",

    "def _read(path, SHAPE):\n",

    "    dcm = pydicom.dcmread(path)\n",

    "    try:\n",

    "        img = bsb_window(dcm)\n",

    "    except:\n",

    "        img = np.zeros(SHAPE)\n",

    "    return img"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": 5,

   "metadata": {},

   "outputs": [],

   "source": [

    "base_model =  efn.EfficientNetB0(weights = 'imagenet', include_top = False, \\\n",

    "                                 pooling = 'avg', input_shape = (256, 256, 3))\n",

    "\n",

    "x = base_model.output\n",

    "x = Dropout(0.125)(x)\n",

    "output_layer = Dense(6, activation = 'sigmoid')(x)\n",

    "model = Model(inputs=base_model.input, outputs=output_layer)\n",

    "model.compile(optimizer = Adam(lr = 0.0001), \n",

    "              loss = 'binary_crossentropy',\n",

    "              metrics = ['acc'])"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": 6,

   "metadata": {},

   "outputs": [],

   "source": [

    "model.load_weights('model_effnet_bo_087.h5')"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": 7,

   "metadata": {},

   "outputs": [

    {

     "data": {

      "text/plain": [

       "(256, 256)"

      ]

     },

     "execution_count": 7,

     "metadata": {},

     "output_type": "execute_result"

    }

   ],

   "source": [

    "img_data = _read('ID_000012eaf.dcm', (256, 256))\n",

    "img_data.shape"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": 14,

   "metadata": {},

   "outputs": [

    {

     "data": {

      "text/plain": [

       "<matplotlib.image.AxesImage at 0x7f1265f52588>"

      ]

     },

     "execution_count": 14,

     "metadata": {},

     "output_type": "execute_result"

    },

    {

     "data": {

      "image/png": "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\n",

      "text/plain": [

       "<Figure size 432x288 with 1 Axes>"

      ]

     },

     "metadata": {},

     "output_type": "display_data"

    }

   ],

   "source": [

    "plt.imshow(img_data)"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": 35,

   "metadata": {},

   "outputs": [

    {

     "data": {

      "text/plain": [

       "(1, 256, 256, 3)"

      ]

     },

     "execution_count": 35,

     "metadata": {},

     "output_type": "execute_result"

    }

   ],

   "source": [

    "X = np.empty((1, 256,256, 3))\n",

    "X[0] = img_data\n",

    "X.shape"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": 36,

   "metadata": {},

   "outputs": [

    {

     "data": {

      "text/plain": [

       "array([[0.02540314, 0.00152788, 0.0010443 , 0.0023855 , 0.00326541,\n",

       "        0.00998502]], dtype=float32)"

      ]

     },

     "execution_count": 36,

     "metadata": {},

     "output_type": "execute_result"

    }

   ],

   "source": [

    "preds = model.predict(X)\n",

    "preds"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": 37,

   "metadata": {},

   "outputs": [

    {

     "data": {

      "text/plain": [

       "(1, 6)"

      ]

     },

     "execution_count": 37,

     "metadata": {},

     "output_type": "execute_result"

    }

   ],

   "source": [

    "preds.shape"

   ]

  }

 ],

 "metadata": {

  "kernelspec": {

   "display_name": "Python 3",

   "language": "python",

   "name": "python3"

  },

  "language_info": {

   "codemirror_mode": {

    "name": "ipython",

    "version": 3

   },

   "file_extension": ".py",

   "mimetype": "text/x-python",

   "name": "python",

   "nbconvert_exporter": "python",

   "pygments_lexer": "ipython3",

   "version": "3.6.5"

  }

 },

 "nbformat": 4,

 "nbformat_minor": 1

}