step4_post_processing.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import tifffile as tif\n",
    "import cv2\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from importlib import reload\n",
    "import os, gc, glob\n",
    "\n",
    "from shapely import wkt\n",
    "from skimage import measure, morphology\n",
    "\n",
    "import utils\n",
    "import global_vars"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Outline\n",
    "* load prediction masks\n",
    "* threshold the masks \n",
    "    * find decent thresholds for each class, using train data and labels\n",
    "* generate wkt\n",
    "    * turn masks into polygons\n",
    "    * turn pollygons into wkt\n",
    "* write each wkt to a file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "im_names = glob.glob(os.path.join(global_vars.DATA_DIR, 'bin_masks', 'train_16x16','*.tif'))\n",
    "label_list = list()\n",
    "train_pred_list = list()\n",
    "\n",
    "for name in im_names:\n",
    "    prediction = tif.imread(name)\n",
    "    train_pred_list.append(prediction[:,:,1:])\n",
    "    shape = prediction.shape[:2]\n",
    "    label_list.append(utils.load_all_lab(name.split('/')[-1].split('.')[0], 835))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "25"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(train_pred_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "thresholds = list()\n",
    "\n",
    "for i in range(8):\n",
    "    best_threshold = utils.find_thresh(train_pred_list, label_list,i)\n",
    "    thresholds.append(best_threshold)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#this method is based on solution posted in forum for differentiating river from standing water. \n",
    "def get_standing_river(im_mask, water_ind = -2):\n",
    "    empty = np.zeros(im_mask.shape[:2])\n",
    "    if im_mask[:,:,water_ind].sum() > 50000:\n",
    "        return im_mask[:,:,-2], empty\n",
    "    else:\n",
    "        return empty, im_mask[:,:,-2]\n",
    "\n",
    "def remove_large(train_mask, limit):\n",
    "    tl = measure.regionprops(measure.label(train_mask))\n",
    "    for i in tl:\n",
    "        if i.area > limit:\n",
    "            c = i.coords\n",
    "            for k in range(c.shape[0]):\n",
    "                train_mask[c[k,0], c[k,1]] = 0\n",
    "    return train_mask\n",
    "\n",
    "def make_car_truck(vehicles):\n",
    "    car = remove_large(vehicles.copy(),200)\n",
    "    car = morphology.remove_small_objects(car, 12)\n",
    "    \n",
    "    truck = remove_large(vehicles.copy(),800)\n",
    "    truck = morphology.remove_small_objects(truck, 30)\n",
    "    return car, truck"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.5, 0.25, 0.6, 0.25, 0.4, 0.7, 0.7, 0.1]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "thresholds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/media/d/ssd2/dstl/bin_masks/test_16x16/6100_0_0.tif\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/d/anaconda3/envs/tf/lib/python3.5/site-packages/skimage/morphology/misc.py:122: UserWarning: Only one label was provided to `remove_small_objects`. Did you mean to use a boolean array?\n",
      "  warn(\"Only one label was provided to `remove_small_objects`. \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
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      "/media/d/ssd2/dstl/bin_masks/test_16x16/6150_2_0.tif\n",
      "/media/d/ssd2/dstl/bin_masks/test_16x16/6010_2_2.tif\n",
      "/media/d/ssd2/dstl/bin_masks/test_16x16/6150_0_2.tif\n",
      "/media/d/ssd2/dstl/bin_masks/test_16x16/6170_2_1.tif\n",
      "/media/d/ssd2/dstl/bin_masks/test_16x16/6130_1_1.tif\n",
      "/media/d/ssd2/dstl/bin_masks/test_16x16/6070_0_1.tif\n",
      "/media/d/ssd2/dstl/bin_masks/test_16x16/6180_2_3.tif\n",
      "/media/d/ssd2/dstl/bin_masks/test_16x16/6150_0_0.tif\n",
      "/media/d/ssd2/dstl/bin_masks/test_16x16/6110_1_0.tif\n",
      "/media/d/ssd2/dstl/bin_masks/test_16x16/6040_3_0.tif\n",
      "/media/d/ssd2/dstl/bin_masks/test_16x16/6020_1_1.tif\n",
      "/media/d/ssd2/dstl/bin_masks/test_16x16/6160_2_3.tif\n",
      "/media/d/ssd2/dstl/bin_masks/test_16x16/6090_0_2.tif\n",
      "/media/d/ssd2/dstl/bin_masks/test_16x16/6140_2_1.tif\n",
      "/media/d/ssd2/dstl/bin_masks/test_16x16/6080_2_4.tif\n",
      "/media/d/ssd2/dstl/bin_masks/test_16x16/6050_2_4.tif\n",
      "/media/d/ssd2/dstl/bin_masks/test_16x16/6090_2_3.tif\n",
      "/media/d/ssd2/dstl/bin_masks/test_16x16/6100_2_0.tif\n",
      "/media/d/ssd2/dstl/bin_masks/test_16x16/6130_0_0.tif\n",
      "/media/d/ssd2/dstl/bin_masks/test_16x16/6090_2_4.tif\n",
      "/media/d/ssd2/dstl/bin_masks/test_16x16/6180_0_2.tif\n",
      "/media/d/ssd2/dstl/bin_masks/test_16x16/6030_4_2.tif\n",
      "/media/d/ssd2/dstl/bin_masks/test_16x16/6080_0_1.tif\n",
      "/media/d/ssd2/dstl/bin_masks/test_16x16/6180_3_2.tif\n",
      "/media/d/ssd2/dstl/bin_masks/test_16x16/6050_3_0.tif\n",
      "/media/d/ssd2/dstl/bin_masks/test_16x16/6030_3_2.tif\n",
      "/media/d/ssd2/dstl/bin_masks/test_16x16/6100_0_1.tif\n",
      "/media/d/ssd2/dstl/bin_masks/test_16x16/6050_1_4.tif\n",
      "/media/d/ssd2/dstl/bin_masks/test_16x16/6030_4_4.tif\n",
      "/media/d/ssd2/dstl/bin_masks/test_16x16/6060_0_0.tif\n",
      "/media/d/ssd2/dstl/bin_masks/test_16x16/6080_4_4.tif\n",
      "/media/d/ssd2/dstl/bin_masks/test_16x16/6170_0_2.tif\n",
      "/media/d/ssd2/dstl/bin_masks/test_16x16/6150_2_1.tif\n",
      "/media/d/ssd2/dstl/bin_masks/test_16x16/6180_3_3.tif\n",
      "/media/d/ssd2/dstl/bin_masks/test_16x16/6030_0_3.tif\n",
      "/media/d/ssd2/dstl/bin_masks/test_16x16/6120_0_1.tif\n",
      "/media/d/ssd2/dstl/bin_masks/test_16x16/6140_2_4.tif\n",
      "/media/d/ssd2/dstl/bin_masks/test_16x16/6180_2_1.tif\n",
      "/media/d/ssd2/dstl/bin_masks/test_16x16/6040_4_0.tif\n",
      "/media/d/ssd2/dstl/bin_masks/test_16x16/6170_4_3.tif\n"
     ]
    }
   ],
   "source": [
    "files = glob.glob(os.path.join(global_vars.DATA_DIR, 'bin_masks', 'test_16x16','*.tif'))\n",
    "\n",
    "for file in files:\n",
    "    preds = tif.imread(file)\n",
    "    new_preds = np.zeros(list(preds.shape[:2])+[8])\n",
    "    for i in range(8):\n",
    "        new_preds[:,:,i] = preds[:,:,i+1] > thresholds[i]\n",
    "        \n",
    "    new_preds = new_preds.astype(np.uint8)\n",
    "    preds = new_preds.copy()\n",
    "    \n",
    "    new_preds = np.zeros((list(preds.shape[:2])+[10]), dtype=bool)\n",
    "    new_preds[:,:,:6] = preds[:,:,:6]\n",
    "    river, water = get_standing_river(preds.copy())\n",
    "    new_preds[:,:,6] = river\n",
    "    new_preds[:,:,7] = water\n",
    "    \n",
    "    car, truck = make_car_truck(preds[:,:,-1])\n",
    "    \n",
    "    new_preds[:,:,8] = car\n",
    "    new_preds[:,:,9] = truck\n",
    "    \n",
    "    pred_df = utils.simple_to_wkt(new_preds, file[-12:-4], list(range(1,11)))\n",
    "    tmp_path = os.path.join(global_vars.DATA_DIR,'submissions','subm16x16',file[-12:-4]+ '_wkt.csv') \n",
    "    pred_df.to_csv(tmp_path)\n",
    "    print(file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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