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‎Create_Masks.ipynb

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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Here, we want to create mask-files\n",
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"# from the geojson-files"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# GeoJSON --> .png"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Plot and save the given coordinates in GeoJSON files."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"import matplotlib.pyplot as plt\n",
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"import os\n",
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"import json\n",
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"import geoio"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"ResimPATH = 'TRAINING TIF-FILE PATH/Train/AOI_2_Vegas_Train/RGB-PanSharpen' \n",
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"# All the pictures from the given path\n",
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"ResimAdlari = os.listdir(ResimPATH)\n",
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"\n",
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"GeoJSONPATH = 'TRAINING GEOJSON-FILE PATH/Train/AOI_2_Vegas_Train/geojson/buildings'\n",
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"# All the geojson-files from the given path\n",
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"GeoAdlari = os.listdir(GeoJSONPATH)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"scrolled": false
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},
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"outputs": [],
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"source": [
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"# DosyaNumarasi takes the number from 0 to how big your training dataset\n",
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"for DosyaNumarasi in range(len(GeoAdlari)):\n",
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"\n",
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" # data holds the info for the each GeoJSON-file\n",
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" with open(GeoJSONPATH+'/'+GeoAdlari[DosyaNumarasi]) as f:\n",
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" data = json.load(f)\n",
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" \n",
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" # RGBTIFResmi holds the info for each TIF-file\n",
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" RGBTIFResmi = geoio.GeoImage(ResimPATH+'/'+ResimAdlari[DosyaNumarasi])\n",
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"\n",
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" cokgenler = [] # Hold the coordinates for each building in the picture.\n",
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" # (Outside for loop is for each picture, and here, cokgenler\n",
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" # will hold the coordinates for each building in one picture.)\n",
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" types = [] # Holds the type of the buldings (MultiPolygon - Partial Building - Point)\n",
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" # We are not interested in the points.\n",
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" \n",
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" # Create the pane size of 650x650 to put the figures from geojson-file. Otherwise,\n",
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" # the buildings may be flipped or they may saved one by one \n",
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" arkaPlan = np.zeros([650,650])\n",
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" plt.imshow(arkaPlan)\n",
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"\n",
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" try:\n",
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" # We do not know how many buildings the picture includes.\n",
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" # So, we just give very big number to make sure that we utilized\n",
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" # all the buildings in one picture.\n",
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" # In short, bina keeps what order the building is.\n",
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" for bina in range(2000):\n",
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" tip = str(data['features'][bina]['geometry']['type'])\n",
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" types.append(tip) # Append all the type of the buildings\n",
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"\n",
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" # If type is point, do not do anything\n",
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" if tip == ('Point'):\n",
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" pass\n",
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" \n",
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" # If type is MultiPolygon, cokgenler will hold the coordinates\n",
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" elif tip == ('MultiPolygon'):\n",
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" kucukBinalar = (data['features'][bina]['geometry']['coordinates'])\n",
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" for b in range(len(kucukBinalar)): \n",
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" cokgenler.append(kucukBinalar[b])\n",
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" \n",
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" # For the rest of the types, cokgenler will hold the coordinates again\n",
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" else:\n",
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" cokgenler.append(data['features'][bina]['geometry']['coordinates'])\n",
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"\n",
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" except IndexError:\n",
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" # If we utilized all the buildings in the given picture,\n",
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" # lest create mask for each one.\n",
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" \n",
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" # cokgenBina holds the each building's coordinates\n",
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" for cokgenBina in cokgenler: \n",
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"\n",
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" # binaNoktalari holds the individual edge coordinates for each building.\n",
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" for binaNoktalari in cokgenBina:\n",
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" \n",
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" # To hold the edge coordinates (in pixel form)\n",
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" doldurX = []\n",
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" doldurY = []\n",
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"\n",
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" # noktas holds x and y for each edge coordinate\n",
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" for noktas in binaNoktalari:\n",
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" \n",
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" # Convert Latitude&Longitude to the pixels\n",
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" xPixel, yPixel = RGBTIFResmi.proj_to_raster(noktas[0], noktas[1])\n",
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" \n",
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" # The pixels may be 650 which defaces the masks.\n",
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" xPixel = 649 if xPixel > 649 else xPixel\n",
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" yPixel = 649 if yPixel > 649 else yPixel\n",
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" \n",
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" # Keep x and y in pixel form\n",
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" doldurX.append(xPixel)\n",
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" doldurY.append(yPixel)\n",
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" \n",
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" # To paint between given pixel values\n",
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" plt.fill_between(doldurX, doldurY, facecolor='red')\n",
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" \n",
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" # To remove white area around matplotlib figure\n",
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" fig = plt.figure(1)\n",
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" extent = plt.gca().get_window_extent().transformed(fig.dpi_scale_trans.inverted())\n",
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"\n",
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" # Adjust the DPI for 650x650\n",
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" # and save the figure\n",
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" # While saving, you should put them in order; 0 to ...\n",
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" fig.savefig('Where to save the figure '+str(DosyaNumarasi)+'.png', bbox_inches=extent, dpi=215.24)\n",
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" \n",
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" # Close the figure after an image is done.\n",
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" plt.close()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.6.7"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}

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