{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "_cell_guid": "f3e15541-bd2a-6c04-b8cd-33f1388438e5"
      },
      "source": [
        "just a pixel jaccard calculation helper"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "_cell_guid": "143f3545-b518-e372-bafe-804fa70be569"
      },
      "outputs": [],
      "source": [
        "# This Python 3 environment comes with many helpful analytics libraries installed\n",
        "# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\n",
        "# For example, here's several helpful packages to load in \n",
        "\n",
        "import numpy as np # linear algebra\n",
        "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
        "\n",
        "# Input data files are available in the \"../input/\" directory.\n",
        "# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory\n",
        "\n",
        "from subprocess import check_output\n",
        "#print(check_output([\"ls\", \"../input\"]).decode(\"utf8\"))\n",
        "\n",
        "# Any results you write to the current directory are saved as output."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "_cell_guid": "f462a754-c4a5-2944-5d4b-c41f39dbc928"
      },
      "outputs": [],
      "source": [
        "from shapely.geometry import MultiPolygon, Polygon\n",
        "import shapely.wkt\n",
        "import shapely.affinity\n",
        "import cv2, os, sys, csv\n",
        "csv.field_size_limit(sys.maxsize)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "_cell_guid": "59d26b3b-ac01-dd83-57ec-0c2b51588ab1"
      },
      "outputs": [],
      "source": [
        "\n",
        "\n",
        "GS = pd.read_csv(os.path.join('..','input', 'grid_sizes.csv'), names=['ImageId', 'Xmax', 'Ymin'], skiprows=1)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "_cell_guid": "7fbe7748-96aa-b2fc-31d9-9df1cf74c354"
      },
      "outputs": [],
      "source": [
        "\n",
        "smooth = 1e-12\n",
        "\n",
        "# helper function to compute jaccard based on masks not polygons\n",
        "def compute_jaccard_from_masks( pred, target):\n",
        "    # expect to have pred shape as (n_cls, width, height) and target as well\n",
        "    assert(len(pred.shape) == 3)\n",
        "    assert(pred.shape == target.shape)\n",
        "    \n",
        "    # expect to have pred and target type set to bool\n",
        "    assert(pred.dtype == np.bool)\n",
        "    assert(target.dtype == np.bool)\n",
        "    \n",
        "    matching_pixels = np.sum(pred * target, axis=(1,2)).astype(np.float64)\n",
        "    false_positive = np.sum( pred * (1. - target), axis = (1,2)).astype(np.float64)\n",
        "    false_negative = np.sum( (1 - pred) * target, axis = (1,2)).astype(np.float64)\n",
        "    \n",
        "    \n",
        "    jaccard = matching_pixels / ((matching_pixels + false_positive  + false_negative ) + smooth)\n",
        "    \n",
        "    return jaccard\n",
        "    "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "_cell_guid": "a8e3ea37-635a-ed62-e994-1e268bfca2cf"
      },
      "outputs": [],
      "source": [
        "# simple test of the function\n",
        "fake_target = np.ones((10,2,3), dtype=np.bool)\n",
        "fake_pred = np.zeros((10,2,3), dtype=np.bool)\n",
        "fake_pred[0,1,:] = True\n",
        "fake_pred[8] = True\n",
        "\n",
        "jaccard_by_class = compute_jaccard_from_masks(fake_pred, fake_target)\n",
        "for cls, score in enumerate(jaccard_by_class):\n",
        "    print('score for class {} is {}'.format(cls+1, score))\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "_cell_guid": "b8228d77-b10d-fa8e-4451-3d4a379e7e31"
      },
      "outputs": [],
      "source": [
        "def get_scalers(im_size, x_max, y_min):\n",
        "    # __author__ = Konstantin Lopuhin\n",
        "    # https://www.kaggle.com/lopuhin/dstl-satellite-imagery-feature-detection/full-pipeline-demo-poly-pixels-ml-poly\n",
        "    h, w = im_size  # they are flipped so that mask_for_polygons works correctly\n",
        "    h, w = float(h), float(w)\n",
        "    w_ = 1.0 * w * (w / (w + 1))\n",
        "    h_ = 1.0 * h * (h / (h + 1))\n",
        "    return w_ / x_max, h_ / y_min\n",
        "\n",
        "\n",
        "\n",
        "def mask_for_polygons(polygons, im_size):\n",
        "    # based from \n",
        "    # __author__ = Konstantin Lopuhin\n",
        "    # https://www.kaggle.com/lopuhin/dstl-satellite-imagery-feature-detection/full-pipeline-demo-poly-pixels-ml-poly\n",
        "    img_mask = np.zeros(im_size, np.uint8)\n",
        "    if not polygons:\n",
        "        return img_mask\n",
        "    int_coords = lambda x: np.array(x).round().astype(np.int32)\n",
        "    exteriors = [int_coords(poly.exterior.coords) for poly in polygons]\n",
        "    interiors = [int_coords(pi.coords) for poly in polygons\n",
        "                 for pi in poly.interiors]\n",
        "    cv2.fillPoly(img_mask, exteriors, 1)\n",
        "    cv2.fillPoly(img_mask, interiors, 0)\n",
        "    return (img_mask > 0).astype(np.bool)\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "def scale_polygons(mpolygon, x_scaler, y_scaler):\n",
        "    return shapely.affinity.scale(mpolygon, xfact=x_scaler, yfact=y_scaler,\n",
        "                                                      origin=(0, 0, 0))\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "_cell_guid": "dcb9f363-a6ed-3810-0f40-71773991a4ba"
      },
      "outputs": [],
      "source": [
        "\n",
        "def get_scalers_for_image(imageid, mask_size):\n",
        "    x_max = GS.loc[GS['ImageId'] == imageid, 'Xmax'].as_matrix()[0]\n",
        "    y_min = GS.loc[GS['ImageId'] == imageid, 'Ymin'].as_matrix()[0]\n",
        "    return get_scalers(mask_size, x_max, y_min)\n",
        "\n",
        "\n",
        "def scale_wkt_to_mask(mpolygon, x_scaler, y_scaler):\n",
        "    return shapely.affinity.scale(mpolygon, xfact=x_scaler, yfact=y_scaler,\n",
        "                                                      origin=(0, 0, 0))\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "_cell_guid": "c697616f-e538-8d23-4121-415755f70e25"
      },
      "outputs": [],
      "source": [
        "if not os.path.isfile('fake_pred.csv'):\n",
        "    with open('fake_pred.csv','w') as fp:\n",
        "        writer = csv.writer(fp)\n",
        "        writer.writerow(['ImageId','ClassType','MultipolygonWKT'])\n",
        "        writer.writerow(['6010_1_2','1','GEOMETRYCOLLECTION EMPTY'])\n",
        "        writer.writerow(['6010_1_2','2','GEOMETRYCOLLECTION EMPTY'])\n",
        "        writer.writerow(['6010_1_2','3','GEOMETRYCOLLECTION EMPTY'])\n",
        "        writer.writerow(['6010_1_2','4','GEOMETRYCOLLECTION EMPTY'])\n",
        "        writer.writerow(['6010_1_2','5',\"MULTIPOLYGON (((0.0091608969965358 -0.0020822525025185, 0.0091365903712502 -0.0021146611017795, 0.0091527947881073 -0.0021335661180151, 0.0091608969965358 -0.0020822525025185)), ((0.0091581962603930 -0.0019715231217102, 0.0090717727038219 -0.0019796252715254, 0.0090582690231076 -0.0020147345873915, 0.0090798749122504 -0.0020336396036271, 0.0091581962603930 -0.0019958295711559, 0.0091581962603930 -0.0019715231217102)), ((0.0090123565086792 -0.0016717435785460, 0.0089799476749651 -0.0017635679431188, 0.0090447653423934 -0.0017797722427493, 0.0090609697592505 -0.0017149550442273, 0.0090960793291075 -0.0016987507445968, 0.0090825756483932 -0.0016825464449663, 0.0090285609255363 -0.0016987507445968, 0.0090123565086792 -0.0016717435785460)), ((0.0090852763845361 -0.0018769980405323, 0.0090015535641078 -0.0018932023401628, 0.0090582690231076 -0.0019067059231882, 0.0090852763845361 -0.0018769980405323)), ((0.0090636704953933 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        "        writer.writerow(['6010_1_2','6',\"MULTIPOLYGON (((0.0091284881628216 -0.0087665261000975, 0.0090339623978220 -0.0088016354159636, 0.0091257874266788 -0.0088016354159636, 0.0091284881628216 -0.0087665261000975)), ((0.0089124292713939 -0.0088016354159636, 0.0090285609255363 -0.0088016354159636, 0.0090204587171078 -0.0087071103347857, 0.0090906778568218 -0.0086963074683654, 0.0089097285352511 -0.0086963074683654, 0.0089124292713939 -0.0088016354159636), (0.0089745462026794 -0.0087611246668874, 0.0089907506195365 -0.0087692268167026, 0.0089799476749651 -0.0087854311163331, 0.0089745462026794 -0.0087611246668874)), ((0.0091419918435359 -0.0084802501399588, 0.0091284881628216 -0.0085855780875570, 0.0091500940519644 -0.0085558702049011, 0.0091419918435359 -0.0084802501399588)), ((0.0057957797625489 -0.0005590483372521, 0.0058470937492630 -0.0005914569365130, 0.0058281885962630 -0.0005563476206470, 0.0057957797625489 -0.0005590483372521)), ((0.0045912514428393 -0.0003781003247115, 0.0045912514428393 -0.0004834282723098, 0.0046371639572677 -0.0004834282723098, 0.0046128573319821 -0.0004186110737878, 0.0046965801524103 -0.0004645232560742, 0.0046857772078389 -0.0003916039077370, 0.0045912514428393 -0.0003781003247115)))\"])\n",
        "        writer.writerow(['6010_1_2','7','GEOMETRYCOLLECTION EMPTY'])\n",
        "        writer.writerow(['6010_1_2','8','GEOMETRYCOLLECTION EMPTY'])\n",
        "        writer.writerow(['6010_1_2','9','GEOMETRYCOLLECTION EMPTY'])\n",
        "        writer.writerow(['6010_1_2','10','GEOMETRYCOLLECTION EMPTY'])\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "_cell_guid": "daa19ec0-b7e3-d6d0-d31a-20b03d576a31"
      },
      "outputs": [],
      "source": [
        "train_pred_fn = os.path.join('..','input', 'train_wkt_v4.csv')\n",
        "\n",
        "test_size = (3000,3000)\n",
        "num_classes = 10\n",
        "\n",
        "\n",
        "with open('fake_pred.csv','r') as fp:\n",
        "    pred_reader = csv.reader(fp)\n",
        "    header = next(pred_reader)\n",
        "    with open(train_pred_fn,'r') as tp:\n",
        "        train_reader = csv.reader(tp)\n",
        "        header = next(train_reader)\n",
        "        \n",
        "        old_imageid = ''\n",
        "        \n",
        "        # here only expect 1 imageid from pred_reader\n",
        "        for row_p in pred_reader:\n",
        "#            print('searching for {}'.format(row_p[:2]))\n",
        "            found = False\n",
        "            while not found:\n",
        "                row_t = next(train_reader)\n",
        "                if (row_p[:2] == row_t[:2]):\n",
        "                    found = True\n",
        "            imageid = row_p[0]\n",
        "            clsid = int(row_p[1])\n",
        "            if old_imageid != imageid:\n",
        "                old_imageid = imageid\n",
        "                pred = np.empty( (num_classes,) + test_size, dtype=np.bool)\n",
        "                target = np.empty( (num_classes,) + test_size, dtype=np.bool)\n",
        "                x_scaler, y_scaler = get_scalers_for_image(imageid, test_size)\n",
        "\n",
        "            pred_mp = shapely.wkt.loads(row_p[2])\n",
        "            target_mp = shapely.wkt.loads(row_t[2])\n",
        "            \n",
        "            pred_mp_scaled = scale_wkt_to_mask(pred_mp, x_scaler, y_scaler)\n",
        "            target_mp_scaled = scale_wkt_to_mask(target_mp, x_scaler, y_scaler)\n",
        "\n",
        "            pred[(clsid - 1)] = mask_for_polygons(pred_mp_scaled, test_size)\n",
        "            target[(clsid - 1)] = mask_for_polygons(pred_mp_scaled, test_size)\n",
        "            \n",
        "            if clsid == 10:\n",
        "                # compute jaccard\n",
        "                jaccard_by_class = compute_jaccard_from_masks(pred, target)\n",
        "                for cls, score in enumerate(jaccard_by_class):\n",
        "                    print('score for {} class {} is {}'.format(imageid, cls+1, score))\n",
        "                \n",
        "            \n",
        "\n",
        "\n"
      ]
    },
    {
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      "execution_count": null,
      "metadata": {
        "_cell_guid": "a8457d9d-55cf-d8be-b8c0-f03f736c5062"
      },
      "outputs": [],
      "source": ""
    }
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