{ "cells": [ { "cell_type": "markdown", "metadata": { "_cell_guid": "2b650242-a8be-3c9e-4983-159c9f65b24d" }, "source": [ "This script draws selected class polygons on a image produced from sixteen m band image. I used [Small Vehicles][1] as a template. To get closer look for each polygon uncommend and edit the commented part of the script.\n", "\n", " [1]: https://www.kaggle.com/aamaia/dstl-satellite-imagery-feature-detection/small-vehicles" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "_cell_guid": "b369332f-a783-8f5f-8893-078f859365e8" }, "outputs": [], "source": [ "\n", "\"\"\"\n", "This Scritp Display Traning Polygons on Satalite \u0130mages for any Class\n", "\"\"\"\n", "\n", "\n", "\n", "#Import Libraries\n", "import os,cv2,tifffile\n", "import pandas as pd\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from shapely import wkt, affinity\n", "\n", "\n", "\n", "\"\"\"\n", "Classes \n", " 'Buildings' :1,\n", " 'Structures ' :2,\n", " 'Road' :3,\n", " 'Track' :4,\n", " 'Trees' :5,\n", " 'Crops' :6,\n", " 'Waterway' :7,\n", " 'StandingWater' :8,\n", " 'VehicleLarge' :9,\n", " 'VehicleSmall' :10,\n", "\"\"\"\n", "\n", "#Select a class and image id to be drawn\n", "Class = 1\n", "ImageID = '6120_2_2' #'6120_2_2', '6100_1_3', '6140_3_1','6110_3_1','6100_2_3','6140_1_2','6120_2_0','6100_2_2','6110_1_2','6070_2_3','6110_4_0','6090_2_0','6060_2_3'\n", "#---------------------------------------\n", "\n", "#Define Functions\n", "def stretch2(band, lower_percent=2, higher_percent=98):\n", " a = 0 #np.min(band)\n", " b = 255 #np.max(band)\n", " c = np.percentile(band, lower_percent)\n", " d = np.percentile(band, higher_percent) \n", " out = a + (band - c) * (b - a) / (d - c) \n", " out[outb] = b\n", " return out\n", "def adjust_contrast(x): \n", " for i in range(3):\n", " x[:,:,i] = stretch2(x[:,:,i])\n", " return x.astype(np.uint8) \n", " \n", "W = 3396\n", "H = 3348\n", "def truth_polys(image_id, class_id, W, H):\n", " x = pd.read_csv('../input/train_wkt_v4.csv')\n", " rows = x.loc[(x.ImageId==image_id) & (x.ClassType==class_id), 'MultipolygonWKT']\n", " mp = wkt.loads(rows.values[0])\n", " grid_sizes = pd.read_csv('../input/grid_sizes.csv', names=['ImageId', 'Xmax', 'Ymin'], skiprows=1)\n", " xmax, ymin = grid_sizes[grid_sizes.ImageId == ImageID].iloc[0,1:].astype(float) \n", " W_ = W * (W/(W+1.))\n", " H_ = H * (H/(H+1.))\n", " x_scaler = W_ / xmax\n", " y_scaler = H_ / ymin\n", " return affinity.scale(mp, xfact = x_scaler, yfact= y_scaler, origin=(0,0,0)) \n", "#----------------------------------------------------------------------------------------- \n", "\n", "\n", "#Read threeband image\n", "rgbfile=os.path.join('..', 'input', 'three_band', '{}.tif'.format(ImageID)) \n", "rgb = tifffile.imread(rgbfile)\n", "rgb = np.rollaxis(rgb, 0, 3) \n", "\n", "#Read 16band m image\n", "mfile = os.path.join('..', 'input', 'sixteen_band', '{}_M.tif'.format(ImageID))\n", "img_m = tifffile.imread(mfile) \n", "img_m = np.rollaxis(img_m, 0, 3)\n", "img_m = cv2.resize(img_m, tuple(reversed(rgb.shape[:2]))) \n", " \n", "#Turn m image into rgb color \n", "x = np.zeros_like(rgb)\n", "x[:,:,0] = img_m[:,:,4]\n", "x[:,:,1] = img_m[:,:,2]\n", "x[:,:,2] = img_m[:,:,1]\n", "x = adjust_contrast(x).copy()\n", "\n", "H=len(x); W=len(x[0])\n", "#Read Polygons\n", "polys = truth_polys(ImageID, Class,W,H)\n", "\n", "#Add polygons to the x image\n", "#patches=[]\n", "for poly_id, poly in enumerate(polys):\n", " x1,y1,x2,y2 = [int(pb) for pb in poly.bounds]\n", " xys=np.array(list(poly.exterior.coords)) \n", " xys = xys.reshape((-1,1,2))\n", " xys=np.around(xys, decimals=0)\n", " xys=xys.astype(int)\n", " cv2.polylines(x,[xys],True,(255,0,0),3)\n", " #patches.append(np.hstack([x[y1-PADDING:y2+PADDING, x1-PADDING:x2+PADDING,:]]))\n", "\n", "##To focus on each element\n", "#PADDING = 10\n", "#W = 3396\n", "#H = 3348\n", "#patches = []\n", "#titles = [] \n", "#for poly_id, poly in enumerate(polys):\n", "# x1,y1,x2,y2 = [int(pb) for pb in poly.bounds]\n", "# cv2.rectangle(x, (x1,y1), (x2,y2), (255,0,0), 1)\n", "# cv2.rectangle(img_p, (x1,y1), (x2,y2), (255,0,0), 1)\n", "# patches.append(np.hstack([x[y1-PADDING:y2+PADDING, x1-PADDING:x2+PADDING,:], img_p[y1-PADDING:y2+PADDING, x1-PADDING:x2+PADDING,:]]))\n", "# titles.append(\"ImageID: {} -- poly_id: {}\".format(ImageID, poly_id))\n", "#\n", "\n", "\n", "#----------------------------------------------------------------------------------------- \n", "#Plot\n", "fig, ax = plt.subplots(figsize=(30,30))\n", "ax.imshow(x)\n", "plt.savefig(ImageID+\".png\")\n", "\n", "#fig, ax = plt.subplots(1, 1, figsize=(10,10))\n", "#for i in range(10):\n", "# ax.imshow(patches[i]) \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 }