{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "_cell_guid": "baa9a008-482d-dedf-ec30-86d571680925" }, "outputs": [], "source": [ "%matplotlib inline\n", "import pandas as pd\n", "import numpy as np\n", "\n", "from shapely.wkt import loads as wkt_loads\n", "from matplotlib.patches import Polygon, Patch\n", "\n", "# decartes package makes plotting with holes much easier\n", "from descartes.patch import PolygonPatch\n", "\n", "import matplotlib.pyplot as plt\n", "import tifffile as tiff\n", "\n", "import pylab\n", "# turn interactive mode on so that plots immediately\n", "# See: http://stackoverflow.com/questions/2130913/no-plot-window-in-matplotlib\n", "# pylab.ion()\n", "\n", "inDir = '../input'\n", "\n", "# Give short names, sensible colors and zorders to object types\n", "CLASSES = {\n", " 1 : 'Bldg',\n", " 2 : 'Struct',\n", " 3 : 'Road',\n", " 4 : 'Track',\n", " 5 : 'Trees',\n", " 6 : 'Crops',\n", " 7 : 'Fast H20',\n", " 8 : 'Slow H20',\n", " 9 : 'Truck',\n", " 10 : 'Car',\n", " }\n", "COLORS = {\n", " 1 : '0.7',\n", " 2 : '0.4',\n", " 3 : '#b35806',\n", " 4 : '#dfc27d',\n", " 5 : '#1b7837',\n", " 6 : '#a6dba0',\n", " 7 : '#74add1',\n", " 8 : '#4575b4',\n", " 9 : '#f46d43',\n", " 10: '#d73027',\n", " }\n", "ZORDER = {\n", " 1 : 5,\n", " 2 : 5,\n", " 3 : 4,\n", " 4 : 1,\n", " 5 : 3,\n", " 6 : 2,\n", " 7 : 7,\n", " 8 : 8,\n", " 9 : 9,\n", " 10: 10,\n", " }\n", "\n", "# read the training data from train_wkt_v4.csv\n", "df = pd.read_csv(inDir + '/train_wkt_v4.csv')\n", "print(df.head())\n", "\n", "# grid size will also be needed later..\n", "gs = pd.read_csv(inDir + '/grid_sizes.csv', names=['ImageId', 'Xmax', 'Ymin'], skiprows=1)\n", "print(gs.head())\n", "\n", "# imageIds in a DataFrame\n", "allImageIds = gs.ImageId.unique()\n", "trainImageIds = df.ImageId.unique()\n", "\n", "\n", "def get_image_names(imageId):\n", " '''\n", " Get the names of the tiff files\n", " '''\n", " d = {'3': '{}/three_band/{}.tif'.format(inDir, imageId),\n", " 'A': '{}/sixteen_band/{}_A.tif'.format(inDir, imageId),\n", " 'M': '{}/sixteen_band/{}_M.tif'.format(inDir, imageId),\n", " 'P': '{}/sixteen_band/{}_P.tif'.format(inDir, imageId),\n", " }\n", " return d\n", "\n", "\n", "def get_images(imageId, img_key = None):\n", " '''\n", " Load images correspoding to imageId\n", "\n", " Parameters\n", " ----------\n", " imageId : str\n", " imageId as used in grid_size.csv\n", " img_key : {None, '3', 'A', 'M', 'P'}, optional\n", " Specify this to load single image\n", " None loads all images and returns in a dict\n", " '3' loads image from three_band/\n", " 'A' loads '_A' image from sixteen_band/\n", " 'M' loads '_M' image from sixteen_band/\n", " 'P' loads '_P' image from sixteen_band/\n", "\n", " Returns\n", " -------\n", " images : dict\n", " A dict of image data from TIFF files as numpy array\n", " '''\n", " img_names = get_image_names(imageId)\n", " images = dict()\n", " if img_key is None:\n", " for k in img_names.keys():\n", " images[k] = tiff.imread(img_names[k])\n", " else:\n", " images[img_key] = tiff.imread(img_names[img_key])\n", " return images\n", "\n", "\n", "def get_size(imageId):\n", " \"\"\"\n", " Get the grid size of the image\n", "\n", " Parameters\n", " ----------\n", " imageId : str\n", " imageId as used in grid_size.csv\n", " \"\"\"\n", " xmax, ymin = gs[gs.ImageId == imageId].iloc[0,1:].astype(float)\n", " W, H = get_images(imageId, '3')['3'].shape[1:]\n", " return (xmax, ymin, W, H)\n", "\n", "\n", "def is_training_image(imageId):\n", " '''\n", " Returns\n", " -------\n", " is_training_image : bool\n", " True if imageId belongs to training data\n", " '''\n", " return any(trainImageIds == imageId)\n", "\n", "\n", "def plot_polygons(fig, ax, polygonsList):\n", " '''\n", " Plot descrates.PolygonPatch from list of polygons objs for each CLASS\n", " '''\n", " legend_patches = []\n", " for cType in polygonsList:\n", " print('{} : {} \\tcount = {}'.format(cType, CLASSES[cType], len(polygonsList[cType])))\n", " legend_patches.append(Patch(color=COLORS[cType],\n", " label='{} ({})'.format(CLASSES[cType], len(polygonsList[cType]))))\n", " for polygon in polygonsList[cType]:\n", " mpl_poly = PolygonPatch(polygon,\n", " color=COLORS[cType],\n", " lw=0,\n", " alpha=0.7,\n", " zorder=ZORDER[cType])\n", " ax.add_patch(mpl_poly)\n", " # ax.relim()\n", " ax.autoscale_view()\n", " ax.set_title('Objects')\n", " ax.set_xticks([])\n", " ax.set_yticks([])\n", " return legend_patches\n", "\n", "def plot_image(fig, ax, imageId, img_key, selected_channels=None):\n", " '''\n", " Plot get_images(imageId)[image_key] on axis/fig\n", " Optional: select which channels of the image are used (used for sixteen_band/ images)\n", " Parameters\n", " ----------\n", " img_key : str, {'3', 'P', 'N', 'A'}\n", " See get_images for description.\n", " '''\n", " images = get_images(imageId, img_key)\n", " img = images[img_key]\n", " title_suffix = ''\n", " if selected_channels is not None:\n", " img = img[selected_channels]\n", " title_suffix = ' (' + ','.join([ repr(i) for i in selected_channels ]) + ')'\n", " if len(img.shape) == 2:\n", " new_img = np.zeros((3, img.shape[0], img.shape[1]))\n", " new_img[0] = img\n", " new_img[1] = img\n", " new_img[2] = img\n", " img = new_img\n", " \n", " tiff.imshow(img, figure=fig, subplot=ax)\n", " ax.set_title(imageId + ' - ' + img_key + title_suffix)\n", " ax.set_xlabel(img.shape[-2])\n", " ax.set_ylabel(img.shape[-1])\n", " ax.set_xticks([])\n", " ax.set_yticks([])\n", "\n", "\n", "\n", "def visualize_image(imageId, plot_all=True):\n", " ''' \n", " Plot all images and object-polygons\n", " \n", " Parameters\n", " ----------\n", " imageId : str\n", " imageId as used in grid_size.csv\n", " plot_all : bool, True by default\n", " If True, plots all images (from three_band/ and sixteen_band/) as subplots.\n", " Otherwise, only plots Polygons.\n", " ''' \n", " df_image = df[df.ImageId == imageId]\n", " xmax, ymin, W, H = get_size(imageId)\n", " \n", " if plot_all:\n", " fig, axArr = plt.subplots(figsize=(10, 10), nrows=3, ncols=3)\n", " ax = axArr[0][0]\n", " else:\n", " fig, axArr = plt.subplots(figsize=(10, 10))\n", " ax = axArr\n", " if is_training_image(imageId):\n", " print('ImageId : {}'.format(imageId))\n", " polygonsList = {}\n", " for cType in CLASSES.keys():\n", " polygonsList[cType] = wkt_loads(df_image[df_image.ClassType == cType].MultipolygonWKT.values[0])\n", " legend_patches = plot_polygons(fig, ax, polygonsList)\n", " ax.set_xlim(0, xmax)\n", " ax.set_ylim(ymin, 0)\n", " ax.set_xlabel(xmax)\n", " ax.set_ylabel(ymin)\n", " if plot_all:\n", " plot_image(fig, axArr[0][1], imageId, '3')\n", " plot_image(fig, axArr[0][2], imageId, 'P')\n", " plot_image(fig, axArr[1][0], imageId, 'A', [0, 3, 6])\n", " plot_image(fig, axArr[1][1], imageId, 'A', [1, 4, 7])\n", " plot_image(fig, axArr[1][2], imageId, 'A', [2, 5, 0])\n", " plot_image(fig, axArr[2][0], imageId, 'M', [0, 3, 6])\n", " plot_image(fig, axArr[2][1], imageId, 'M', [1, 4, 7])\n", " plot_image(fig, axArr[2][2], imageId, 'M', [2, 5, 0])\n", "\n", " if is_training_image(imageId):\n", " ax.legend(handles=legend_patches,\n", " # loc='upper center',\n", " bbox_to_anchor=(0.9, 1),\n", " bbox_transform=plt.gcf().transFigure,\n", " ncol=5,\n", " fontsize='x-small',\n", " title='Objects-' + imageId,\n", " # mode=\"expand\",\n", " framealpha=0.3)\n", " return (fig, axArr, ax)\n", "\n", "# Loop over few training images and save to files\n", "i = 1\n", "for imageId in trainImageIds:\n", " fig, axArr, ax = visualize_image(imageId, plot_all=False)\n", " #plt.savefig('Objects--' + imageId + '.png')\n", " #plt.clf()\n", " plt.show()\n", " i = i+1\n", " if(i>2):\n", " break\n" ] } ], "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 }