{

 "cells": [

  {

   "cell_type": "markdown",

   "metadata": {},

   "source": [

    "## Save middle slices of scans as images\n",

    "Most image models expect a three-channel input image. Will take the middle three grayscale slices, and dilated middle three slices centered on the middle, and save them as three-channel images."

   ]

  },

  {

   "cell_type": "code",

   "execution_count": 1,

   "metadata": {},

   "outputs": [],

   "source": [

    "import numpy as np\n",

    "from PIL import Image\n",

    "import os"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": 2,

   "metadata": {},

   "outputs": [

    {

     "name": "stdout",

     "output_type": "stream",

     "text": [

      "\u001b[01;34m..\u001b[00m\n",

      "├── \u001b[01;34mdata\u001b[00m\n",

      "│   ├── \u001b[01;34m__MACOSX\u001b[00m\n",

      "│   │   └── \u001b[01;34mMRNet-v1.0\u001b[00m\n",

      "│   │       ├── \u001b[01;34mtrain\u001b[00m\n",

      "│   │       │   ├── \u001b[01;34maxial\u001b[00m\n",

      "│   │       │   ├── \u001b[01;34mcoronal\u001b[00m\n",

      "│   │       │   └── \u001b[01;34msagittal\u001b[00m\n",

      "│   │       └── \u001b[01;34mvalid\u001b[00m\n",

      "│   ├── \u001b[01;34mmid1\u001b[00m\n",

      "│   │   ├── \u001b[01;34mtrain\u001b[00m\n",

      "│   │   │   ├── \u001b[01;34maxial\u001b[00m\n",

      "│   │   │   ├── \u001b[01;34mcoronal\u001b[00m\n",

      "│   │   │   └── \u001b[01;34msagittal\u001b[00m\n",

      "│   │   └── \u001b[01;34mvalid\u001b[00m\n",

      "│   │       ├── \u001b[01;34maxial\u001b[00m\n",

      "│   │       ├── \u001b[01;34mcoronal\u001b[00m\n",

      "│   │       └── \u001b[01;34msagittal\u001b[00m\n",

      "│   ├── \u001b[01;34mMRNet-small\u001b[00m\n",

      "│   │   ├── \u001b[01;34mtrain\u001b[00m\n",

      "│   │   │   ├── \u001b[01;34maxial\u001b[00m\n",

      "│   │   │   ├── \u001b[01;34mcoronal\u001b[00m\n",

      "│   │   │   └── \u001b[01;34msagittal\u001b[00m\n",

      "│   │   └── \u001b[01;34mvalid\u001b[00m\n",

      "│   │       ├── \u001b[01;34maxial\u001b[00m\n",

      "│   │       ├── \u001b[01;34mcoronal\u001b[00m\n",

      "│   │       └── \u001b[01;34msagittal\u001b[00m\n",

      "│   └── \u001b[01;34mMRNet-v1.0\u001b[00m\n",

      "│       ├── \u001b[01;34mtrain\u001b[00m\n",

      "│       │   ├── \u001b[01;34maxial\u001b[00m\n",

      "│       │   ├── \u001b[01;34mcoronal\u001b[00m\n",

      "│       │   └── \u001b[01;34msagittal\u001b[00m\n",

      "│       └── \u001b[01;34mvalid\u001b[00m\n",

      "│           ├── \u001b[01;34maxial\u001b[00m\n",

      "│           ├── \u001b[01;34mcoronal\u001b[00m\n",

      "│           └── \u001b[01;34msagittal\u001b[00m\n",

      "└── \u001b[01;34mmrnet-fastai\u001b[00m\n",

      "    └── \u001b[01;34mexp\u001b[00m\n",

      "\n",

      "37 directories\n"

     ]

    }

   ],

   "source": [

    "! tree -d .."

   ]

  },

  {

   "cell_type": "markdown",

   "metadata": {},

   "source": [

    "Copy data to another directory, for example `/mid3d0`, for middle 3 slices, 0 dilation.\n",

    "Do this for d in (0,1,2) and perhaps greater than 2."

   ]

  },

  {

   "cell_type": "code",

   "execution_count": 3,

   "metadata": {},

   "outputs": [],

   "source": [

    "!cp -r ../data/MRNet-v1.0 ../data/mid3d0"

   ]

  },

  {

   "cell_type": "markdown",

   "metadata": {},

   "source": [

    "Recurse through /train and /valid directories, load the scan file (.npy), select middle 3 slices, possibly with gaps of size dilation between them, and save as 3-channel images. Discard the copied files."

   ]

  },

  {

   "cell_type": "code",

   "execution_count": 7,

   "metadata": {},

   "outputs": [

    {

     "name": "stdout",

     "output_type": "stream",

     "text": [

      "-----------------------\n",

      "Creating png files centered on middle slices with dilations of size 2\n",

      "Processing 1130 .npy files in ../data/mid3d2/train/axial\n",

      "===============================\n",

      "Processing 1130 .npy files in ../data/mid3d2/train/coronal\n",

      "===============================\n",

      "Processing 1130 .npy files in ../data/mid3d2/train/sagittal\n",

      "===============================\n",

      "Processing 120 .npy files in ../data/mid3d2/valid/axial\n",

      "===============================\n",

      "Processing 120 .npy files in ../data/mid3d2/valid/coronal\n",

      "===============================\n",

      "Processing 120 .npy files in ../data/mid3d2/valid/sagittal\n",

      "===============================\n"

     ]

    }

   ],

   "source": [

    "dilations = [0,1,2]\n",

    "for d in dilations:\n",

    "    print('-----------------------')\n",

    "    print('Creating png files centered on middle slices with dilations of size {}'.format(d))\n",

    "    for ds in ('train','valid'):\n",

    "        for p in ('axial','coronal','sagittal'):\n",

    "            dirpath = '../data/mid3d{}/{}/{}'.format(d, ds, p)\n",

    "            npy_files = [f for f in os.listdir(dirpath) if f[-4:]=='.npy']\n",

    "            print('Processing {} .npy files in {}'.format(len(npy_files), dirpath))\n",

    "\n",

    "            for npyf in npy_files:\n",

    "                npyfilepath = dirpath + '/' + npyf\n",

    "                #print('Converting {}'.format(npyfilepath))\n",

    "                # load the array\n",

    "                scanarray = np.load(npyfilepath)\n",

    "                #print('Number of slices for this scan: {}'.format(scanarray.shape[0]))\n",

    "                # get the middle slice index\n",

    "                midslice = scanarray.shape[0]//2\n",

    "                # get indices for three slices centered on midslice, skipping dilation d images between the slices\n",

    "                slice_indices = range(midslice-(1+d),midslice+(1+d+1),1+d)\n",

    "                slices3  = scanarray[slice_indices,:,:]\n",

    "                # put channels last for PIL\n",

    "                slices3  = np.moveaxis(slices3, 0, -1)\n",

    "                # convert to rgb\n",

    "                im = Image.fromarray(slices3)\n",

    "                # save file\n",

    "                # replace .npy with .png\n",

    "                pngf = npyf[:-4] + '.png'\n",

    "                pngfilepath = dirpath + '/' + pngf\n",

    "                im.save(pngfilepath)\n",

    "                # remove corresponding .npy file\n",

    "                os.remove(npyfilepath)\n",

    "\n",

    "            print('===============================')        "

   ]

  },

  {

   "cell_type": "markdown",

   "metadata": {},

   "source": [

    "## Take just middle slice from each scan"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": 11,

   "metadata": {},

   "outputs": [

    {

     "name": "stdout",

     "output_type": "stream",

     "text": [

      "Found 1130 .npy files in ../data/mid1/train/axial\n",

      "===============================\n",

      "===============================\n",

      "Found 1130 .npy files in ../data/mid1/train/coronal\n",

      "===============================\n",

      "===============================\n",

      "Found 1130 .npy files in ../data/mid1/train/sagittal\n",

      "===============================\n",

      "===============================\n",

      "Found 120 .npy files in ../data/mid1/valid/axial\n",

      "===============================\n",

      "===============================\n",

      "Found 120 .npy files in ../data/mid1/valid/coronal\n",

      "===============================\n",

      "===============================\n",

      "Found 120 .npy files in ../data/mid1/valid/sagittal\n",

      "===============================\n",

      "===============================\n"

     ]

    }

   ],

   "source": [

    "dilation = ''\n",

    "for d in ('train','valid'):\n",

    "    for p in ('axial','coronal','sagittal'):\n",

    "        dirpath = '../data/mid1{}/{}/{}'.format(dilation, d, p)\n",

    "        npy_files = [f for f in os.listdir(dirpath) if f[-4:]=='.npy']\n",

    "        print('Processing {} .npy files in {}'.format(len(npy_files), dirpath))\n",

    "\n",

    "        for f in npy_files:\n",

    "            filepath = dirpath + '/' + f\n",

    "            #print('Converting {}'.format(filepath))\n",

    "            # load the array\n",

    "            scanarray = np.load(filepath)\n",

    "            # calculate interpolation factor(s)\n",

    "            #print('Number of slices for this scan: {}'.format(scanarray.shape[0]))\n",

    "            midslice = scanarray.shape[0]//2\n",

    "            im = Image.fromarray(scanarray[midslice,:,:], 'L').convert('RGB')\n",

    "            # replace .npy with .png\n",

    "            newf = f[:-4] + '.png'\n",

    "            newfilepath = dirpath + '/' + newf\n",

    "            im.save(newfilepath)\n",

    "            # remove corresponding .npy file\n",

    "            os.remove(filepath)\n",

    "        \n",

    "        print('===============================')        "

   ]

  },

  {

   "cell_type": "code",

   "execution_count": null,

   "metadata": {},

   "outputs": [],

   "source": []

  }

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