{ "cells": [ { "cell_type": "markdown", "metadata": { "_cell_guid": "a2c5f3be-61e9-1471-961c-6578f05b9fdc" }, "source": [ "This is a quick demonstration of how to load a TIF, display it, and then overlay polygons of one of the feature types, `POOR_DIRT_CART_TRACK`. \n", "\n", "I used tifffile to load the image because it's easier to install than GDAL, and the files don't have headers anyway so there's no advantage to GDAL. I used matplotlib to handle polygons, although I should mention that some of my other code uses openCV for some polygon operations." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "_cell_guid": "45757e81-8479-7383-4ae2-9b50d5234b96" }, "outputs": [], "source": [ "import os\n", "import numpy as np\n", "import tifffile\n", "import json\n", "from PIL import Image\n", "import matplotlib\n", "import matplotlib.pyplot as plt\n", "import pandas as pd\n", "from matplotlib.collections import PatchCollection\n", "\n", "%matplotlib inline\n", "\n", "data_dir = '../input'\n", "grid_name = '6010_4_2'\n", "\n", "# Load grid CSV\n", "grid_sizes = pd.read_csv(os.path.join(data_dir, 'grid_sizes.csv'), index_col=0)\n", "grid_sizes.ix[grid_name]\n", " \n", "# Load JSON of image overlays\n", "sh_fname = os.path.join(data_dir, 'train_geojson_v3/%s/002_TR_L4_POOR_DIRT_CART_TRACK.geojson'%grid_name)\n", "with open(sh_fname, 'r') as f:\n", " sh_json = json.load(f)\n", " \n", "# Load the tif file\n", "im_fname = os.path.join(data_dir, 'three_band','%s.tif'%grid_name)\n", "tif_data = tifffile.imread(im_fname).transpose([1,2,0])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "_cell_guid": "b593d958-47d4-396d-0fb4-10d03284e54b" }, "outputs": [], "source": [ "def scale_coords(tif_data, grid_name, point):\n", " \"\"\"Scale the coordinates of a polygon into the image coordinates for a grid cell\"\"\"\n", " w,h,_ = tif_data.shape\n", " Xmax, Ymin = grid_sizes.ix[grid_name][['Xmax', 'Ymin']]\n", " x,y = point[:,0], point[:,1]\n", "\n", " wp = float(w**2)/(w+1)\n", " xp = x/Xmax*wp\n", "\n", " hp = float(h**2)/(h+1)\n", " yp = y/Ymin*hp\n", "\n", " return np.concatenate([xp[:,None],yp[:,None]], axis=1)\n", "\n", "def scale_percentile(matrix):\n", " \"\"\"Fixes the pixel value range to 2%-98% original distribution of values\"\"\"\n", " orig_shape = matrix.shape\n", " matrix = np.reshape(matrix, [matrix.shape[0]*matrix.shape[1], 3]).astype(float)\n", " \n", " # Get 2nd and 98th percentile\n", " mins = np.percentile(matrix, 1, axis=0)\n", " maxs = np.percentile(matrix, 99, axis=0) - mins\n", " \n", " matrix = (matrix - mins[None,:])/maxs[None,:]\n", " matrix = np.reshape(matrix, orig_shape)\n", " matrix = matrix.clip(0,1)\n", " return matrix" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "_cell_guid": "df46e4ec-830d-4056-b2a3-d7d2cdf236d7" }, "outputs": [], "source": [ "# Show the image with the values scaled from 2-98 percentile to make them visible\n", "fixed_im = scale_percentile(tif_data)\n", "plt.imshow(fixed_im)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "_cell_guid": "78115b1c-4629-cbe4-eb87-d80cb8b077af" }, "outputs": [], "source": [ "# Load JSON of image overlays, and convert it into image coordinates\n", "def load_overlays(tile_name):\n", " \"\"\"Get all of the polygon overlays for a tile.\n", " Returns a dict: {LABEL: POLYGON}\"\"\"\n", " dirname = os.path.join(data_dir, 'train_geojson_v3/%s/'%tile_name)\n", " fnames = [os.path.join(dirname, fname) for fname in \n", " os.listdir(dirname) \n", " if fname.endswith('.geojson') and not fname.startswith('Grid')]\n", " \n", " overlays = dict()\n", " for fname in fnames:\n", " with open(fname, 'r') as f:\n", " sh_json = json.load(f)\n", " label = sh_json['features'][0]['properties']['LABEL']\n", " print(label)\n", " \n", " polygons = []\n", " for sh in sh_json['features']:\n", " pts = scale_coords(tif_data, grid_name, np.array(sh['geometry']['coordinates'][0])).squeeze()\n", " \n", " # Remove badly formatted polygons\n", " if not ((len(pts.shape)==2) and (pts.shape[1]==2) and (pts.shape[0] > 2)):\n", " continue\n", " polygons.append(pts)\n", " \n", " overlays[label] = polygons\n", "\n", " return overlays\n", " \n", "overlays_poly = load_overlays(grid_name)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "_cell_guid": "ea638582-fd7b-eca7-401c-ccb2095635a1" }, "outputs": [], "source": [ "# Put polygons on top of the image\n", "\n", "fig, ax = plt.subplots(figsize=(6,6))\n", "patches = []\n", "for pts in overlays_poly['POOR_DIRT_CART_TRACK']:\n", " \n", " poly = matplotlib.patches.Polygon(pts)\n", " patches.append(poly)\n", "\n", "p = PatchCollection(patches, cmap=matplotlib.cm.jet, alpha=0.4)\n", "\n", "colors = 100*np.random.rand(len(patches))\n", "p.set_array(np.array(colors))\n", "\n", "ax.imshow(fixed_im)\n", "ax.add_collection(p)\n", "\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "_cell_guid": "4ceddf90-25c1-3238-cbd1-e9489c72814d" }, "outputs": [], "source": "" } ], "metadata": { "_change_revision": 0, "_is_fork": false, "kernelspec": { "display_name": "Python 3", "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": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }