3D_processing_data.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import SimpleITK as sitk\n",
    "import numpy as np\n",
    "import pandas as pd \n",
    "import cv2\n",
    "import math \n",
    "\n",
    "from os import walk\n",
    "import matplotlib.pylab as plt\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "images_path = '../data/'\n",
    "des_path = '../completed/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "IMG_SIZE = 64\n",
    "PLT_HEIGHT = 4\n",
    "PLT_WIDTH = 8\n",
    "\n",
    "DATA_IMAGES = 1\n",
    "DATA_DEPTH = 32"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_image(filename):\n",
    "    image = sitk.ReadImage(filename)\n",
    "    ct_scan = sitk.GetArrayFromImage(image)\n",
    "\n",
    "    return ct_scan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "888"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "images = []\n",
    "for (dirpath, dirnames, files) in walk(images_path):\n",
    "    for file in files:\n",
    "         if file.endswith(\".mhd\"):\n",
    "            images.append((dirpath+file, os.path.splitext(file)[0]))\n",
    "len(images)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def chunks(l,n):\n",
    "    start = 0\n",
    "    for i in np.arange(0, len(l)-1, n):\n",
    "        end = math.floor(i+n)\n",
    "        yield l[start:end]\n",
    "        start = end"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def mean(l):\n",
    "    return sum(l)/len(l)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def save_slices(file,slices):\n",
    "    np.save(des_path+file, slices)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def normalize_image(image):\n",
    "    \"\"\"From SITK - converting hounds units to grayscale units\"\"\"\n",
    "    maxHU = 400.0\n",
    "    minHU = -1000.0\n",
    "    \n",
    "    image = (image - minHU) / (maxHU - minHU)\n",
    "    image[image > 1] = 1.0\n",
    "    image[image < 0] = 0.0\n",
    "    \n",
    "    return (image * 255).astype(np.uint8);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_slices(file):\n",
    "    return np.load(des_path+file+\".npy\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def draw_slices(slices):\n",
    "    fig = plt.figure(figsize=(50, 50))\n",
    "    \n",
    "    for i,testSlice in enumerate(slices):\n",
    "        subplot = fig.add_subplot(PLT_HEIGHT,PLT_WIDTH,i+1)\n",
    "        subplot.imshow(testSlice)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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    }
   ],
   "source": [
    "for index,image in enumerate(images, start=1):\n",
    "    new_slices = []\n",
    "    slices = load_image(image[0])\n",
    "    \n",
    "    chuck_size = len(slices) / DATA_DEPTH\n",
    "    \n",
    "    for slice_chunk in chunks(slices,chuck_size):\n",
    "        slice_chunk = list(map(mean,zip(*slice_chunk)))\n",
    "        new_slices.append(np.asarray(slice_chunk))\n",
    "        \n",
    "    new_slices = [cv2.resize(image,(IMG_SIZE,IMG_SIZE)) for image in new_slices]\n",
    "    new_slices = [normalize_image(newslice) for newslice in new_slices]   \n",
    "    \n",
    "    print(str(index)+'/'+str(len(images)))\n",
    "    #draw_slices(new_slices)\n",
    "    save_slices(image[1],new_slices)"
   ]
  }
 ],
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