Tile-128x128.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We take our 512x512 resized images and produced tiles images of 127x128"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2\n",
    "from PIL import Image\n",
    "import os \n",
    "from glob import glob\n",
    "import pathlib\n",
    "\n",
    "import numpy as np # linear algebra\n",
    "import matplotlib.pylab as plt\n",
    "\n",
    "import random"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tile 512x512 to multiple 128x128\n",
    "\n",
    "Note that this will result in 4x the files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "INPUT_PATH_PNG = '/home/ubuntu/fastai/data/roads/mass_roads_png_512'\n",
    "OUTPUT_PATH_PNG = '/home/ubuntu/fastai/data/roads/mass_roads_png_128'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Set up the destination directories if thet don't already exist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "pathlib.Path(f'{OUTPUT_PATH_PNG}/train/sat').mkdir(parents=True, exist_ok=True) \n",
    "pathlib.Path(f'{OUTPUT_PATH_PNG}/train/map').mkdir(parents=True, exist_ok=True) \n",
    "\n",
    "pathlib.Path(f'{OUTPUT_PATH_PNG}/valid/sat').mkdir(parents=True, exist_ok=True) \n",
    "pathlib.Path(f'{OUTPUT_PATH_PNG}/valid/map').mkdir(parents=True, exist_ok=True) \n",
    "\n",
    "pathlib.Path(f'{OUTPUT_PATH_PNG}/test/sat').mkdir(parents=True, exist_ok=True) \n",
    "pathlib.Path(f'{OUTPUT_PATH_PNG}/test/map').mkdir(parents=True, exist_ok=True) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Define the constants for the cropping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Input image size\n",
    "image_x, image_y, image_layers = (512,512,3)\n",
    "\n",
    "# Titled image size, and offset (should be zero overlap)\n",
    "tile_size = (128,128)\n",
    "offset = (128,128)\n",
    "\n",
    "# Set the minimum number of white pixels in a mask to be approx. \n",
    "# 5 percent of 128*128 (16,384)\n",
    "threshold = 128*128*.05"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Get the filenames only from one of the datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_ids(path, data_set):\n",
    "    ''' Look in the \"sat\" directory and get the filenames from here. The\n",
    "    same filenames appear in the \"map\" directory - no need to do it as well.'''\n",
    "    sat_dir = '{}/{}/sat/'.format(path, data_set)\n",
    "    file_dir = '{}*.png'.format(sat_dir)\n",
    "    files = glob(file_dir)\n",
    "    ids = [s[len(sat_dir):-4] for s in files]\n",
    "    return ids"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['22978885_15', '25679230_15', '22678870_15', '17128885_15', '23428960_15']"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_ids(INPUT_PATH_PNG, 'train')[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['24479170_15', '17878735_15', '18178780_15', '10978735_15', '22978990_15']"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_ids(INPUT_PATH_PNG, 'valid')[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tile the training data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Get the full path file names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def crop_image_cordinates(tile_size, offset, image_x, image_y):\n",
    "    for i in range(0, image_x, tile_size[0]):\n",
    "        for j in range(0, image_y, tile_size[1]):\n",
    "            tl_x = i\n",
    "            tl_y = j\n",
    "            br_x = i + tile_size[0]\n",
    "            br_y = j + tile_size[1]\n",
    "            yield (tl_x, tl_y, br_x, br_y)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(128, 128) (128, 128) 512 512\n",
      "(0, 0, 128, 128)\n",
      "(0, 128, 128, 256)\n",
      "(0, 256, 128, 384)\n",
      "(0, 384, 128, 512)\n",
      "(128, 0, 256, 128)\n",
      "(128, 128, 256, 256)\n",
      "(128, 256, 256, 384)\n",
      "(128, 384, 256, 512)\n",
      "(256, 0, 384, 128)\n",
      "(256, 128, 384, 256)\n",
      "(256, 256, 384, 384)\n",
      "(256, 384, 384, 512)\n",
      "(384, 0, 512, 128)\n",
      "(384, 128, 512, 256)\n",
      "(384, 256, 512, 384)\n",
      "(384, 384, 512, 512)\n"
     ]
    }
   ],
   "source": [
    "print(tile_size, offset, image_x, image_y)\n",
    "for tpl in crop_image_cordinates(tile_size, offset, image_x, image_y):\n",
    "    print(tpl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def crop_image(data_set, fn, tile_size, offset, threshold=800):\n",
    "    map_file = '{}/{}/map/{}.png'.format(INPUT_PATH_PNG, data_set, fn)\n",
    "    sat_file = '{}/{}/sat/{}.png'.format(INPUT_PATH_PNG, data_set, fn)\n",
    "    \n",
    "    map_img = Image.open(map_file)\n",
    "    sat_img = Image.open(sat_file)\n",
    "\n",
    "    counter = 0\n",
    "    \n",
    "    for tpl in crop_image_cordinates(tile_size, offset, image_x, image_y):\n",
    "        map_img_crop = map_img.crop((tpl[0], tpl[1], tpl[2], tpl[3]))\n",
    "            \n",
    "        white_pixels = sum(map_img_crop.point(lambda x: 255 if x else 0).convert(\"L\").point(bool).getdata())\n",
    "        if white_pixels > threshold:\n",
    "            sat_img_crop = sat_img.crop((tpl[0], tpl[1], tpl[2], tpl[3]))\n",
    "            \n",
    "            map_output_file = '{}/{}/map/{}_{}.png'.format(OUTPUT_PATH_PNG, data_set, fn, counter)\n",
    "            sat_output_file = '{}/{}/sat/{}_{}.png'.format(OUTPUT_PATH_PNG, data_set, fn, counter)\n",
    "            \n",
    "            map_img_crop.save(map_output_file)\n",
    "            sat_img_crop.save(sat_output_file)\n",
    "\n",
    "            counter += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_ids = get_ids(INPUT_PATH_PNG, 'train')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "for fn in train_ids:\n",
    "    crop_image('train', fn, tile_size, offset, threshold)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Check the size of the training set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training files initial: 1108\n",
      "Training files discarded: 10714(60.44)\n",
      "Training files retained: 7014\n"
     ]
    }
   ],
   "source": [
    "# Number of files discarded, and as percentage\n",
    "nif = !ls ~/fastai/data/roads/mass_roads_png_512/train/map | wc -l\n",
    "nof = !ls ~/fastai/data/roads/mass_roads_png_128/train/map | wc -l\n",
    "\n",
    "lost = int(nif[0])*16-int(nof[0])\n",
    "percent = round(lost / (int(nif[0]) * 16) * 100, 2)\n",
    "\n",
    "print('Training files initial: {}'.format(int(nif[0])))\n",
    "print('Training files discarded: {}({})'.format(lost, percent))\n",
    "print('Training files retained: {}'.format(int(nof[0])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['23579050_15_2',\n",
       " '19978630_15_4',\n",
       " '23728825_15_2',\n",
       " '22679050_15_13',\n",
       " '23279170_15_14']"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_ids = get_ids(OUTPUT_PATH_PNG, 'train')\n",
    "train_ids[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tile the validation data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "valid_ids = get_ids(INPUT_PATH_PNG, 'valid')\n",
    "for fn in valid_ids:\n",
    "    crop_image('valid', fn, tile_size, offset, threshold)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Check the size of the validation set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation files initial: 63\n",
      "Validation files discarded: 548(54.37)\n",
      "Validation files retained: 460\n"
     ]
    }
   ],
   "source": [
    "# Number of files discarded, and as percentage\n",
    "nif = !ls ~/fastai/data/roads/mass_roads_png_512/valid/map | wc -l\n",
    "nof = !ls ~/fastai/data/roads/mass_roads_png_128/valid/map | wc -l\n",
    "\n",
    "lost = int(nif[0])*16-int(nof[0])\n",
    "percent = round(lost / (int(nif[0]) * 16) * 100, 2)\n",
    "\n",
    "print('Validation files initial: {}'.format(int(nif[0])))\n",
    "print('Validation files discarded: {}({})'.format(lost, percent))\n",
    "print('Validation files retained: {}'.format(int(nof[0])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "valid_ids = get_ids(OUTPUT_PATH_PNG, 'valid')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tile the test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "valid_ids = get_ids(INPUT_PATH_PNG, 'test')\n",
    "for fn in valid_ids:\n",
    "    crop_image('test', fn, tile_size, offset, threshold)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Check the size of the test sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test files initial: 49\n",
      "Test files discarded: 460(58.67)\n",
      "Test files retained: 324\n"
     ]
    }
   ],
   "source": [
    "# Number of files discarded, and as percentage\n",
    "nif = !ls ~/fastai/data/roads/mass_roads_png_512/test/map | wc -l\n",
    "nof = !ls ~/fastai/data/roads/mass_roads_png_128/test/map | wc -l\n",
    "\n",
    "lost = int(nif[0])*16-int(nof[0])\n",
    "percent = round(lost / (int(nif[0]) * 16) * 100, 2)\n",
    "\n",
    "print('Test files initial: {}'.format(int(nif[0])))\n",
    "print('Test files discarded: {}({})'.format(lost, percent))\n",
    "print('Test files retained: {}'.format(int(nof[0])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "valid_ids = get_ids(OUTPUT_PATH_PNG, 'test')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Display a random image from the training set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "def display_image(fn, data_set, tile_size):\n",
    "    plt.figure(figsize=(20, 20))\n",
    "\n",
    "    map_output_file = '{}/{}/map/{}.png'.format(OUTPUT_PATH_PNG, data_set, fn)\n",
    "    sat_output_file = '{}/{}/sat/{}.png'.format(OUTPUT_PATH_PNG, data_set, fn)\n",
    " \n",
    "    mask_img = Image.open(map_output_file)\n",
    "    mask = np.asarray(mask_img)\n",
    "    \n",
    "    sat_img = Image.open(sat_output_file)\n",
    "    img = np.asarray(sat_img)\n",
    "\n",
    "    img_masked = cv2.bitwise_and(img, img, mask=mask)\n",
    "\n",
    "    print(\"Image shape: {} | image type: {} | mask shape: {} | mask type: {}\".format(img.shape, img.dtype, mask.shape, mask.dtype) )\n",
    "\n",
    "    plt.subplot(131)\n",
    "    plt.imshow(img)\n",
    "    plt.subplot(132)\n",
    "    plt.imshow(mask)\n",
    "    plt.subplot(133)\n",
    "    plt.imshow(img_masked)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Image shape: (128, 128, 3) | image type: uint8 | mask shape: (128, 128) | mask type: uint8\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f = random.choice(train_ids)\n",
    "\n",
    "display_image(f, 'train', tile_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  0,   0,   0, ...,   0,   0,   0],\n",
       "       [  0,   0,   0, ...,   0,   0,   0],\n",
       "       [  0,   0,   0, ...,   0,   0,   0],\n",
       "       ...,\n",
       "       [  0,   0,   0, ...,   0, 255, 255],\n",
       "       [  0,   0,   0, ...,   0, 255, 255],\n",
       "       [  0,   0,   0, ...,   0,   0, 255]], dtype=uint8)"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#f = '23579050_15_2'\n",
    "map_output_file = f'{OUTPUT_PATH_PNG}/train/map/{f}.png'\n",
    "mask_img = Image.open(map_output_file)\n",
    "np.asarray(mask_img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1935"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.count_nonzero(np.asarray(mask_img))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['21028945_15_7',\n",
       " '22228915_15_8',\n",
       " '23579035_15_10',\n",
       " '25529260_15_4',\n",
       " '17878930_15_1',\n",
       " '22678870_15_8',\n",
       " '22229005_15_13',\n",
       " '10978750_15_2',\n",
       " '17728855_15_2',\n",
       " '24478840_15_3',\n",
       " '22828915_15_12',\n",
       " '22378960_15_10',\n",
       " '23428930_15_14',\n",
       " '23278945_15_10',\n",
       " '17578930_15_15',\n",
       " '22229020_15_7']"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rndf = [random.choice(train_ids) for i in range(0,16)]\n",
    "rndf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig, axes = plt.subplots(4, 4, figsize=(10, 10))\n",
    "for i,ax in enumerate(axes.flat):\n",
    "    ax = show_img(ims[i], ax=ax)\n",
    "    show_img(im_masks[i][...,0], ax=ax, alpha=0.5)\n",
    "plt.tight_layout(pad=0.1)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [default]",
   "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": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}