{

 "cells": [

  {

   "cell_type": "code",

   "execution_count": 12,

   "metadata": {},

   "outputs": [],

   "source": [

    "# David Ouyang 10/2/2019\n",

    "\n",

    "# Notebook which iterates through a folder, including subfolders, \n",

    "# and convert DICOM files to AVI files of a defined size (natively 112 x 112)\n",

    "\n",

    "import re\n",

    "import os, os.path\n",

    "from os.path import splitext\n",

    "import pydicom as dicom\n",

    "import numpy as np\n",

    "from pydicom.uid import UID, generate_uid\n",

    "import shutil\n",

    "from multiprocessing import dummy as multiprocessing\n",

    "import time\n",

    "import subprocess\n",

    "import datetime\n",

    "from datetime import date\n",

    "import sys\n",

    "import cv2\n",

    "#from scipy.misc import imread\n",

    "import matplotlib.pyplot as plt\n",

    "import sys\n",

    "from shutil import copy\n",

    "import math\n",

    "\n",

    "destinationFolder = \"Output Folder Name\"\n"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": 10,

   "metadata": {},

   "outputs": [

    {

     "name": "stdout",

     "output_type": "stream",

     "text": [

      "Requirement already satisfied: pillow in c:\\programdata\\anaconda3\\lib\\site-packages (6.2.0)\n",

      "Requirement already satisfied: scipy in c:\\programdata\\anaconda3\\lib\\site-packages (1.3.1)\n"

     ]

    }

   ],

   "source": [

    "# Dependencies you might need to run code\n",

    "# Commonly missing\n",

    "\n",

    "#!pip install pydicom\n",

    "#!pip install opencv-python\n"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": 2,

   "metadata": {},

   "outputs": [],

   "source": [

    "def mask(output):\n",

    "    dimension = output.shape[0]\n",

    "    \n",

    "    # Mask pixels outside of scanning sector\n",

    "    m1, m2 = np.meshgrid(np.arange(dimension), np.arange(dimension))\n",

    "    \n",

    "\n",

    "    mask = ((m1+m2)>int(dimension/2) + int(dimension/10)) \n",

    "    mask *=  ((m1-m2)<int(dimension/2) + int(dimension/10))\n",

    "    mask = np.reshape(mask, (dimension, dimension)).astype(np.int8)\n",

    "    maskedImage = cv2.bitwise_and(output, output, mask = mask)\n",

    "    \n",

    "    #print(maskedImage.shape)\n",

    "    \n",

    "    return maskedImage\n",

    "\n"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": 3,

   "metadata": {},

   "outputs": [],

   "source": [

    "def makeVideo(fileToProcess, destinationFolder):\n",

    "    try:\n",

    "        fileName = fileToProcess.split('\\\\')[-1] #\\\\ if windows, / if on mac or sherlock\n",

    "                                                 #hex(abs(hash(fileToProcess.split('/')[-1]))).upper()\n",

    "\n",

    "        if not os.path.isdir(os.path.join(destinationFolder,fileName)):\n",

    "\n",

    "            dataset = dicom.dcmread(fileToProcess, force=True)\n",

    "            testarray = dataset.pixel_array\n",

    "\n",

    "            frame0 = testarray[0]\n",

    "            mean = np.mean(frame0, axis=1)\n",

    "            mean = np.mean(mean, axis=1)\n",

    "            yCrop = np.where(mean<1)[0][0]\n",

    "            testarray = testarray[:, yCrop:, :, :]\n",

    "\n",

    "            bias = int(np.abs(testarray.shape[2] - testarray.shape[1])/2)\n",

    "            if bias>0:\n",

    "                if testarray.shape[1] < testarray.shape[2]:\n",

    "                    testarray = testarray[:, :, bias:-bias, :]\n",

    "                else:\n",

    "                    testarray = testarray[:, bias:-bias, :, :]\n",

    "\n",

    "\n",

    "            print(testarray.shape)\n",

    "            frames,height,width,channels = testarray.shape\n",

    "\n",

    "            fps = 30\n",

    "\n",

    "            try:\n",

    "                fps = dataset[(0x18, 0x40)].value\n",

    "            except:\n",

    "                print(\"couldn't find frame rate, default to 30\")\n",

    "\n",

    "            fourcc = cv2.VideoWriter_fourcc('M','J','P','G')\n",

    "            video_filename = os.path.join(destinationFolder, fileName + '.avi')\n",

    "            out = cv2.VideoWriter(video_filename, fourcc, fps, cropSize)\n",

    "\n",

    "\n",

    "            for i in range(frames):\n",

    "\n",

    "                outputA = testarray[i,:,:,0]\n",

    "                smallOutput = outputA[int(height/10):(height - int(height/10)), int(height/10):(height - int(height/10))]\n",

    "\n",

    "                # Resize image\n",

    "                output = cv2.resize(smallOutput, cropSize, interpolation = cv2.INTER_CUBIC)\n",

    "\n",

    "                finaloutput = mask(output)\n",

    "\n",

    "\n",

    "                finaloutput = cv2.merge([finaloutput,finaloutput,finaloutput])\n",

    "                out.write(finaloutput)\n",

    "\n",

    "            out.release()\n",

    "\n",

    "        else:\n",

    "            print(fileName,\"hasAlreadyBeenProcessed\")\n",

    "    except:\n",

    "        print(\"something filed, not sure what, have to debug\", fileName)\n",

    "    return 0"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": null,

   "metadata": {},

   "outputs": [],

   "source": [

    "AllA4cNames = \"Input Folder Name\"\n",

    "\n",

    "count = 0\n",

    "    \n",

    "cropSize = (112,112)\n",

    "subfolders = os.listdir(AllA4cNames)\n",

    "\n",

    "\n",

    "for folder in subfolders:\n",

    "    print(folder)\n",

    "\n",

    "    for content in os.listdir(os.path.join(AllA4cNames, folder)):\n",

    "        for subcontent in os.listdir(os.path.join(AllA4cNames, folder, content)):\n",

    "            count += 1\n",

    "            \n",

    "\n",

    "            VideoPath = os.path.join(AllA4cNames, folder, content, subcontent)\n",

    "\n",

    "            print(count, folder, content, subcontent)\n",

    "\n",

    "            if not os.path.exists(os.path.join(destinationFolder,subcontent + \".avi\")):\n",

    "                makeVideo(VideoPath, destinationFolder)\n",

    "            else:\n",

    "                print(\"Already did this file\", VideoPath)\n",

    "\n",

    "\n",

    "print(len(AllA4cFilenames))"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": null,

   "metadata": {},

   "outputs": [],

   "source": []

  }

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