code: #!/usr/bin/env python3
  # -*- coding: utf-8 -*-
  """
  Created on Mon May  6 14:05:30 2019
  
  @author: avanetten
  """
  
  import osmnx_funcs
  import numpy as np
  from osgeo import gdal, ogr, osr
  import scipy.spatial
  import geopandas as gpd
  import rasterio as rio
  import affine as af
  import shapely
  import time
  import os
  import sys
  import cv2
  import skimage
  import subprocess
  import matplotlib.pyplot as plt
  from math import sqrt, radians, cos, sin, asin
  # import logging
  
  # add apls path and import apls_tools
  path_apls_src = os.path.dirname(os.path.realpath(__file__))
  sys.path.append(path_apls_src)
  
  
  ###############################################################################
  def pixelToGeoCoord(xPix, yPix, inputRaster, sourceSR='', geomTransform='',
                      targetSR=''):
      '''From spacenet geotools'''
      # If you want to gauruntee lon lat output, specify TargetSR  otherwise, geocoords will be in image geo reference
      # targetSR = osr.SpatialReference()
      # targetSR.ImportFromEPSG(4326)
      # Transform can be performed at the polygon level instead of pixel level
  
      if targetSR == '':
          performReprojection = False
          targetSR = osr.SpatialReference()
          targetSR.ImportFromEPSG(4326)
      else:
          performReprojection = True
  
      if geomTransform == '':
          srcRaster = gdal.Open(inputRaster)
          geomTransform = srcRaster.GetGeoTransform()
  
          source_sr = osr.SpatialReference()
          source_sr.ImportFromWkt(srcRaster.GetProjectionRef())
  
      geom = ogr.Geometry(ogr.wkbPoint)
      xOrigin = geomTransform[0]
      yOrigin = geomTransform[3]
      pixelWidth = geomTransform[1]
      pixelHeight = geomTransform[5]
  
      xCoord = (xPix * pixelWidth) + xOrigin
      yCoord = (yPix * pixelHeight) + yOrigin
      geom.AddPoint(xCoord, yCoord)
  
      if performReprojection:
          if sourceSR == '':
              srcRaster = gdal.Open(inputRaster)
              sourceSR = osr.SpatialReference()
              sourceSR.ImportFromWkt(srcRaster.GetProjectionRef())
          coord_trans = osr.CoordinateTransformation(sourceSR, targetSR)
          geom.Transform(coord_trans)
  
      return (geom.GetX(), geom.GetY())
  
  
  ###############################################################################
  def nodes_near_point(x, y, kdtree, kd_idx_dic, x_coord='x', y_coord='y',
                       n_neighbors=-1,
                       radius_m=150,
                       verbose=False):
      """
      Get nodes near the given point.
  
      Notes
      -----
      if n_neighbors < 0, query based on distance,
      else just return n nearest neighbors
  
      Arguments
      ---------
      x : float
          x coordinate of point
      y: float
          y coordinate of point
      kdtree : scipy.spatial.kdtree
          kdtree of nondes in graph
      kd_idx_dic : dict
          Dictionary mapping kdtree entry to node name
      x_coord : str
          Name of x_coordinate, can be 'x' or 'lon'. Defaults to ``'x'``.
      y_coord : str
          Name of y_coordinate, can be 'y' or 'lat'. Defaults to ``'y'``.
      n_neighbors : int
          Neareast number of neighbors to return. If < 0, ignore.
          Defaults to ``-1``.
      radius_meters : float
          Radius to search for nearest neighbors
      Returns
      -------
      kd_idx_dic, kdtree, arr : tuple
          kd_idx_dic maps kdtree entry to node name
          kdree is the actual kdtree
          arr is the numpy array of node positions
      """
  
      point = [x, y]
  
      # query kd tree for nodes of interest
      if n_neighbors > 0:
          node_names, idxs_refine, dists_m_refine = _query_kd_nearest(
              kdtree, kd_idx_dic, point, n_neighbors=n_neighbors)
      else:
          node_names, idxs_refine, dists_m_refine = _query_kd_ball(
              kdtree, kd_idx_dic, point, radius_m)
  
      if verbose:
          print(("subgraph node_names:", node_names))
  
      # get subgraph
      # G_sub = G_.subgraph(node_names)
  
      return node_names, dists_m_refine  # G_sub
  
  
  ###############################################################################
  def _nodes_near_origin(G_, node, kdtree, kd_idx_dic,
                         x_coord='x', y_coord='y', radius_m=150, verbose=False):
      '''Get nodes a given radius from the desired node.  G_ should be the 
      maximally simplified graph'''
  
      # get node coordinates
      n_props = G_.node[node]
      x0, y0 = n_props[x_coord], n_props[y_coord]
      point = [x0, y0]
  
      # query kd tree for nodes of interest
      node_names, idxs_refine, dists_m_refine = _query_kd_ball(
          kdtree, kd_idx_dic, point, radius_m)
      if verbose:
          print(("subgraph node_names:", node_names))
  
      # get subgraph
      # G_sub = G_.subgraph(node_names)
  
      return node_names, dists_m_refine  # G_sub
  
  
  ###############################################################################
  def G_to_kdtree(G_, x_coord='x', y_coord='y', verbose=False):
      """
      Create kd tree from node positions.
  
      Notes
      -----
      (x, y) = (lon, lat)
      kd_idx_dic maps kdtree entry to node name:
          kd_idx_dic[i] = n (n in G.nodes())
      x_coord can be in utm (meters), or longitude
  
      Arguments
      ---------
      G_ : networkx graph
          Input networkx graph, with nodes assumed to have a dictioary of
          properties that includes position
      x_coord : str
          Name of x_coordinate, can be 'x' or 'lon'. Defaults to ``'x'``.
      y_coord : str
          Name of y_coordinate, can be 'y' or 'lat'. Defaults to ``'y'``.
  
      Returns
      -------
      kd_idx_dic, kdtree, arr : tuple
          kd_idx_dic maps kdtree entry to node name
          kdree is the actual kdtree
          arr is the numpy array of node positions
      """
  
      nrows = len(G_.nodes())
      ncols = 2
      kd_idx_dic = {}
      arr = np.zeros((nrows, ncols))
      # populate node array
      t1 = time.time()
      for i, n in enumerate(G_.nodes()):
          n_props = G_.nodes[n]
          if x_coord == 'lon':
              lat, lon = n_props['lat'], n_props['lon']
              x, y = lon, lat
          else:
              x, y = n_props[x_coord], n_props[y_coord]
  
          arr[i] = [x, y]
          kd_idx_dic[i] = n
  
      # now create kdtree from numpy array
      kdtree = scipy.spatial.KDTree(arr)
      if verbose:
          print("Time to create k-d tree:", time.time() - t1, "seconds")
      return kd_idx_dic, kdtree, arr
  
  
  ###############################################################################
  def _query_kd_nearest(kdtree, kd_idx_dic, point, n_neighbors=10,
                        distance_upper_bound=10000, keep_point=True):
      '''
      Query the kd-tree for neighbors
      Return nearest node names, distances, nearest node indexes
      If not keep_point, remove the origin point from the list
      '''
  
      dists_m, idxs = kdtree.query(point, k=n_neighbors,
                                   distance_upper_bound=distance_upper_bound)
  
      idxs_refine = list(np.asarray(idxs))
      # print("apls_utils.query_kd_neareast - idxs_refilne:", idxs_refine)
      # print("apls_utils.query_kd_neareast - dists_m_refilne:", dists_m)
      dists_m_refine = list(dists_m)
      node_names = [kd_idx_dic[i] for i in idxs_refine]
  
      return node_names, idxs_refine, dists_m_refine
  
  
  ###############################################################################
  def _query_kd_ball(kdtree, kd_idx_dic, point, r_meters, keep_point=True):
      '''
      Query the kd-tree for neighbors within a distance r of the point
      Return nearest node names, distances, nearest node indexes
      if not keep_point, remove the origin point from the list
      '''
  
      dists_m, idxs = kdtree.query(point, k=500, distance_upper_bound=r_meters)
      # keep only points within distance and greaater than 0?
      if not keep_point:
          f0 = np.where((dists_m <= r_meters) & (dists_m > 0))
      else:
          f0 = np.where((dists_m <= r_meters))
      idxs_refine = list(np.asarray(idxs)[f0])
      dists_m_refine = list(dists_m[f0])
      node_names = [kd_idx_dic[i] for i in idxs_refine]
  
      return node_names, idxs_refine, dists_m_refine
  
  
  ###############################################################################
  def _get_graph_extent(G_):
      '''min and max x and y'''
      xall = [G_.node[n]['x'] for n in G_.nodes()]
      yall = [G_.node[n]['y'] for n in G_.nodes()]
      xmin, xmax = np.min(xall), np.max(xall)
      ymin, ymax = np.min(yall), np.max(yall)
      dx, dy = xmax-xmin, ymax-ymin
      return xmin, xmax, ymin, ymax, dx, dy
  
  
  ###############################################################################
  def _latlon2pixel(lat, lon, input_raster='', targetsr='', geom_transform=''):
      '''
      Convert latitude, longitude coords to pixexl coords.
      From spacenet geotools
      '''
  
      sourcesr = osr.SpatialReference()
      sourcesr.ImportFromEPSG(4326)
  
      geom = ogr.Geometry(ogr.wkbPoint)
      # geom.AddPoint(lon, lat)
      geom.AddPoint(lat, lon)
  
      if targetsr == '':
          src_raster = gdal.Open(input_raster)
          targetsr = osr.SpatialReference()
          targetsr.ImportFromWkt(src_raster.GetProjectionRef())
      coord_trans = osr.CoordinateTransformation(sourcesr, targetsr)
      if geom_transform == '':
          src_raster = gdal.Open(input_raster)
          transform = src_raster.GetGeoTransform()
      else:
          transform = geom_transform
  
      x_origin = transform[0]
      # print(x_origin)
      y_origin = transform[3]
      # print(y_origin)
      pixel_width = transform[1]
      # print(pixel_width)
      pixel_height = transform[5]
      # print(pixel_height)
      geom.Transform(coord_trans)
      # print(geom.GetPoint())
      x_pix = (geom.GetPoint()[0] - x_origin) / pixel_width
      y_pix = (geom.GetPoint()[1] - y_origin) / pixel_height
  
      return (x_pix, y_pix)
  
  
  ###############################################################################
  def _wmp2pixel(x, y, input_raster='', targetsr='', geom_transform=''):
      '''
      Convert wmp coords to pixexl coords.
      '''
  
      sourcesr = osr.SpatialReference()
      sourcesr.ImportFromEPSG(3857)
  
      geom = ogr.Geometry(ogr.wkbPoint)
      geom.AddPoint(x, y)
  
      if targetsr == '':
          src_raster = gdal.Open(input_raster)
          targetsr = osr.SpatialReference()
          targetsr.ImportFromWkt(src_raster.GetProjectionRef())
      coord_trans = osr.CoordinateTransformation(sourcesr, targetsr)
      if geom_transform == '':
          src_raster = gdal.Open(input_raster)
          transform = src_raster.GetGeoTransform()
      else:
          transform = geom_transform
  
      x_origin = transform[0]
      # print(x_origin)
      y_origin = transform[3]
      # print(y_origin)
      pixel_width = transform[1]
      # print(pixel_width)
      pixel_height = transform[5]
      # print(pixel_height)
      geom.Transform(coord_trans)
      # print(geom.GetPoint())
      x_pix = (geom.GetPoint()[0] - x_origin) / pixel_width
      y_pix = (geom.GetPoint()[1] - y_origin) / pixel_height
  
      return (x_pix, y_pix)
  
  
  ###############################################################################
  def _set_pix_coords(G_, im_test_file=''):
      '''Get pixel coords.  Update G_ and get control_points, and graph_coords'''
  
      if len(G_.nodes()) == 0:
          return G_, [], []
  
      control_points, cp_x, cp_y = [], [], []
      for n in G_.nodes():
          u_x, u_y = G_.nodes[n]['x'], G_.nodes[n]['y']
          control_points.append([n, u_x, u_y])
          lat, lon = G_.nodes[n]['lat'], G_.nodes[n]['lon']
          if len(im_test_file) > 0:
              pix_x, pix_y = _latlon2pixel(lat, lon, input_raster=im_test_file)
          else:
              print("set_pix_coords(): oops, no image file")
              pix_x, pix_y = 0, 0
          # update G_
          G_.nodes[n]['pix_col'] = pix_x
          G_.nodes[n]['pix_row'] = pix_y
          G_.nodes[n]['x_pix'] = pix_x
          G_.nodes[n]['y_pix'] = pix_y
          # add to arrays
          cp_x.append(pix_x)
          cp_y.append(pix_y)
      # get line segements in pixel coords
      seg_endpoints = []
      for (u, v) in G_.edges():
          ux, uy = G_.nodes[u]['pix_col'], G_.nodes[u]['pix_row']
          vx, vy = G_.nodes[v]['pix_col'], G_.nodes[v]['pix_row']
          seg_endpoints.append([(ux, uy), (vx, vy)])
      gt_graph_coords = (cp_x, cp_y, seg_endpoints)
  
      return G_, control_points, gt_graph_coords
  
  
  ###############################################################################
  def convertTo8Bit(rasterImageName, outputRaster,
                    outputPixType='Byte',
                    outputFormat='GTiff',
                    rescale_type='rescale',
                    percentiles=[2, 98]):
      '''
      This does a relatively poor job of converting to 8bit, as opening in qgis
      the images look very different.
      rescale_type = [clip, rescale]
          if resceale, each band is rescaled to its own min and max
          if clip, scaling is done sctricly between 0 65535
      '''
  
      srcRaster = gdal.Open(rasterImageName)
      nbands = srcRaster.RasterCount
      if nbands == 3:
          cmd = ['gdal_translate', '-ot', outputPixType, '-of', outputFormat,
                 '-co', '"PHOTOMETRIC=rgb"']
      else:
          cmd = ['gdal_translate', '-ot', outputPixType, '-of', outputFormat]
  
      for bandId in range(srcRaster.RasterCount):
          bandId = bandId+1
          band = srcRaster.GetRasterBand(bandId)
          if rescale_type == 'rescale':
              bmin = band.GetMinimum()
              bmax = band.GetMaximum()
              # if not exist minimum and maximum values
              if bmin is None or bmax is None:
                  (bmin, bmax) = band.ComputeRasterMinMax(1)
              # else, rescale
              band_arr_tmp = band.ReadAsArray()
              bmin = np.percentile(band_arr_tmp.flatten(), percentiles[0])
              bmax = np.percentile(band_arr_tmp.flatten(), percentiles[1])
  
          else:
              bmin, bmax = 0, 65535
  
          cmd.append('-scale_{}'.format(bandId))
          cmd.append('{}'.format(bmin))
          cmd.append('{}'.format(bmax))
          cmd.append('{}'.format(0))
          cmd.append('{}'.format(255))
  
      cmd.append(rasterImageName)
      cmd.append(outputRaster)
      print(cmd)
      subprocess.call(cmd)
  
      return
  
  
  ###############################################################################
  # def edit_node_props(props, new):
  #    pass
  
  
  ###############################################################################
  def create_buffer_geopandas(inGDF, buffer_distance_meters=2,
                              buffer_cap_style=1, dissolve_by='class',
                              projectToUTM=True, verbose=False):
      """
      Create a buffer around the lines of the geojson
  
      Arguments
      ---------
      inGDF : geodataframe
          Geodataframe from a SpaceNet geojson.
      buffer_distance_meters : float
          Width of buffer around geojson lines.  Formally, this is the distance
          to each geometric object.  Optional.  Defaults to ``2``.
      buffer_cap_style : int
          Cap_style of buffer, see: (https://shapely.readthedocs.io/en/stable/manual.html#constructive-methods)
          Defaults to ``1`` (round).
      dissolve_by : str
          Method for differentiating rows in geodataframe, and creating unique
          mask values.  Defaults to ``'class'``.
      projectToUTM : bool
          Switch to project gdf to UTM coordinates. Defaults to ``True``.
      verbose : bool
          Switch to print relevant values.  Defaults to ``False``.
  
      Returns
      -------
      gdf_buffer : geopandas dataframe
          Dataframe created from geojson
  
      """
  
      # inGDF = gpd.read_file(geoJsonFileName)
      if len(inGDF) == 0:
          return []
  
      # if we want a geojson instead of gdf for input
      # try:
      #    inGDF = gpd.read_file(geoJsonFileName)
      # except:
      #    return []
  
      # Transform gdf Roadlines into UTM so that Buffer makes sense
      if projectToUTM:
          tmpGDF = osmnx_funcs.project_gdf(inGDF, inGDF.crs)
      else:
          tmpGDF = inGDF
  
      if verbose:
          print("inGDF.columns:", tmpGDF.columns)
      gdf_utm_buffer = tmpGDF.copy()
  
      # perform Buffer to produce polygons from Line Segments
      gdf_utm_buffer['geometry'] = tmpGDF.buffer(buffer_distance_meters,
                                                 cap_style=buffer_cap_style)
  
      gdf_utm_dissolve = gdf_utm_buffer.dissolve(by=dissolve_by)
      gdf_utm_dissolve.crs = gdf_utm_buffer.crs
      if projectToUTM:
          gdf_buffer = gdf_utm_dissolve.to_crs(inGDF.crs)
      else:
          gdf_buffer = gdf_utm_dissolve
      if verbose:
          print("gdf_buffer['geometry'].values[0]:",
                gdf_buffer['geometry'].values[0])
  
      # add the dissolve_by column back into final gdf, since it's now the index
      gdf_buffer[dissolve_by] = gdf_buffer.index.values
  
      return gdf_buffer
  
  
  ###############################################################################
  def _get_road_buffer(geoJson, im_vis_file, output_raster,
                       buffer_meters=2, burnValue=1,
                       # max_mask_val=1,
                       buffer_cap_style=6,
                       useSpacenetLabels=False,
                       plot_file='', figsize=(11, 3), fontsize=6,
                       dpi=800, show_plot=False,
                       valid_road_types=set([]), verbose=False):
      '''
      Wrapper around create_buffer_geopandas(), with plots
      Get buffer around roads defined by geojson and image files
      valid_road_types serves as a filter of valid types (no filter if len==0)
      https://wiki.openstreetmap.org/wiki/Key:highway
      valid_road_types = set(['motorway', 'trunk', 'primary', 'secondary',
                              'tertiary',
                              'motorway_link', 'trunk_link', 'primary_link',
                              'secondary_link', 'tertiary_link',
                              'unclassified', 'residential', 'service' ])
      '''
  
      # get buffer
  
      # filter out roads of the wrong type
      try:
          inGDF_raw = gpd.read_file(geoJson)
      except:
          mask_gray = np.zeros(cv2.imread(im_vis_file, 0).shape)
          cv2.imwrite(output_raster, mask_gray)
          return [], []
  
      if useSpacenetLabels:
          inGDF = inGDF_raw
          # use try/except to handle empty label files
          try:
              inGDF['type'] = inGDF['road_type'].values
              inGDF['class'] = 'highway'
              inGDF['highway'] = 'highway'
          except:
              pass
  
      else:
          # filter out roads of the wrong type
          if (len(valid_road_types) > 0) and (len(inGDF_raw) > 0):
              if 'highway' in inGDF_raw.columns:
                  inGDF = inGDF_raw[inGDF_raw['highway'].isin(valid_road_types)]
                  # set type tag
                  inGDF['type'] = inGDF['highway'].values
                  inGDF['class'] = 'highway'
              else:
                  inGDF = inGDF_raw[inGDF_raw['type'].isin(valid_road_types)]
                  # set highway tag
                  inGDF['highway'] = inGDF['type'].values
  
              if verbose:
                  print("gdf.type:", inGDF['type'])
                  if len(inGDF) != len(inGDF_raw):
                      print("len(inGDF), len(inGDF_raw)",
                            len(inGDF), len(inGDF_raw))
                      print("gdf['type']:", inGDF['type'])
          else:
              inGDF = inGDF_raw
              try:
                  inGDF['type'] = inGDF['highway'].values
                  inGDF['class'] = 'highway'
              except:
                  pass
  
      gdf_buffer = create_buffer_geopandas(inGDF,
                                           buffer_distance_meters=buffer_meters,
                                           buffer_cap_style=buffer_cap_style,
                                           dissolve_by='class',
                                           projectToUTM=True)
  
      # make sure gdf is not null
      if len(gdf_buffer) == 0:
          mask_gray = np.zeros(cv2.imread(im_vis_file, 0).shape)
          cv2.imwrite(output_raster, mask_gray)
      # create label image
      else:
          gdf_to_array(gdf_buffer, im_vis_file, output_raster,
                       burnValue=burnValue)
      # load mask
      mask_gray = cv2.imread(output_raster, 0)
      # mask_gray = np.clip(mask_gray, 0, max_mask_val)
  
      if plot_file:
  
          fig, (ax0, ax1, ax2, ax3) = plt.subplots(1, 4, figsize=figsize)
  
          # road lines
          try:
              gdfRoadLines = gpd.read_file(geoJson)
              gdfRoadLines.plot(ax=ax0, marker='o', color='red')
          except:
              ax0.imshow(mask_gray)
          ax0.axis('off')
          ax0.set_aspect('equal')
          ax0.set_title('Unfiltered Roads from GeoJson', fontsize=fontsize)
  
          # first show raw image
          im_vis = cv2.imread(im_vis_file, 1)
          img_mpl = cv2.cvtColor(im_vis, cv2.COLOR_BGR2RGB)
          ax1.imshow(img_mpl)
          ax1.axis('off')
          ax1.set_title('Raw Image', fontsize=fontsize)
  
          # plot mask
          ax2.imshow(mask_gray)
          ax2.axis('off')
          ax2.set_title('Roads Mask (' + str(np.round(buffer_meters))
                        + ' meter buffer)', fontsize=fontsize)
  
          # plot combined
          ax3.imshow(img_mpl)
          # overlay mask
          # set zeros to nan
          z = mask_gray.astype(float)
          z[z == 0] = np.nan
          # change palette to orange
          palette = plt.cm.gray
          palette.set_over('orange', 1.0)
          ax3.imshow(z, cmap=palette, alpha=0.4,
                     norm=matplotlib.colors.Normalize(vmin=0.5, vmax=0.9, clip=False))
          ax3.set_title('Raw Image + Buffered Roads', fontsize=fontsize)
          ax3.axis('off')
  
          #plt.axes().set_aspect('equal', 'datalim')
  
          # plt.tight_layout()
          plt.savefig(plot_file, dpi=dpi)
          if not show_plot:
              plt.close()
  
      return mask_gray, gdf_buffer
  
  
  ##############################################################################
  def gdf_to_array(gdf, im_file, output_raster, burnValue=150,
                   mask_burn_val_key='', compress=True, NoData_value=0,
                   verbose=False):
      """
      Create buffer around geojson for desired geojson feature, save as mask
  
      Notes
      -----
      https://gis.stackexchange.com/questions/260736/how-to-burn-a-different-value-for-each-polygon-in-a-json-file-using-gdal-rasteri/260737
  
  
      Arguments
      ---------
      image_path : gdf
          Input geojson
      im_file : str
          Path to image file corresponding to gdf.
      output_raster : str
          Output path of saved mask (should end in .tif).
      burnValue : int
          Value to burn to mask. Superceded by mask_burn_val_key.
          Defaults to ``150``.
      mask_burn_val_key : str
          Column name in gdf to use for mask burning. Supercedes burnValue.
          Defaults to ``''`` (in which case burnValue is used).
      compress : bool
          Switch to compress output raster. Defaults to ``True``.
      NoData_value : int
          Value to assign array if no data exists. If this value is <0
          (e.g. -9999), a null value will show in the image. Defaults to ``0``.
      verbose : bool
          Switch to print relevant values.  Defaults to ``False``.
  
      Returns
      -------
      None
      """
  
      gdata = gdal.Open(im_file)
  
      # set target info
      if compress:
          target_ds = gdal.GetDriverByName('GTiff').Create(output_raster,
                                                           gdata.RasterXSize,
                                                           gdata.RasterYSize, 1,
                                                           gdal.GDT_Byte,
                                                           ['COMPRESS=LZW'])
      else:
          target_ds = gdal.GetDriverByName('GTiff').Create(output_raster,
                                                           gdata.RasterXSize,
                                                           gdata.RasterYSize, 1,
                                                           gdal.GDT_Byte)
  
      target_ds.SetGeoTransform(gdata.GetGeoTransform())
      if verbose:
          print("gdata.GetGeoTransform():", gdata.GetGeoTransform())
  
      # set raster info
      raster_srs = osr.SpatialReference()
      raster_srs.ImportFromWkt(gdata.GetProjectionRef())
      target_ds.SetProjection(raster_srs.ExportToWkt())
      if verbose:
          print("target_ds:", target_ds)
  
      band = target_ds.GetRasterBand(1)
      band.SetNoDataValue(NoData_value)
  
      outdriver = ogr.GetDriverByName('MEMORY')
      outDataSource = outdriver.CreateDataSource('memData')
      tmp = outdriver.Open('memData', 1)
      outLayer = outDataSource.CreateLayer("states_extent", raster_srs,
                                           geom_type=ogr.wkbMultiPolygon)
      # burn
      burnField = "burn"
      idField = ogr.FieldDefn(burnField, ogr.OFTInteger)
      outLayer.CreateField(idField)
      featureDefn = outLayer.GetLayerDefn()
      for j, geomShape in enumerate(gdf['geometry'].values):
          if verbose:
              print(j, "geomshape:", geomShape)
          outFeature = ogr.Feature(featureDefn)
          outFeature.SetGeometry(ogr.CreateGeometryFromWkt(geomShape.wkt))
          if len(mask_burn_val_key) > 0:
              burnVal = int(gdf[mask_burn_val_key].values[j])
              if verbose:
                  print("burnVal:", burnVal)
          else:
              burnVal = burnValue
          outFeature.SetField(burnField, burnVal)
          outLayer.CreateFeature(outFeature)
          # if verbose:
          #     print ("outFeature:", outFeature)
          outFeature = 0
  
      if len(mask_burn_val_key) > 0:
          gdal.RasterizeLayer(target_ds, [1], outLayer,
                              options=["ATTRIBUTE=%s" % burnField])
      else:
          gdal.RasterizeLayer(target_ds, [1], outLayer, burn_values=[burnVal])
  
      outLayer = 0
      outDatSource = 0
      tmp = 0
      return
  
  
  ###############################################################################
  def geojson_to_arr(image_path, geojson_path, mask_path_out_gray,
                     buffer_distance_meters=2, buffer_cap_style=1,
                     dissolve_by='speed_mph', mask_burn_val_key='burnValue',
                     min_burn_val=0, max_burn_val=255,
                     verbose=False):
      """
      Create buffer around geojson for desired geojson feature, save as mask
  
      Arguments
      ---------
      image_path : str
          Path to input image corresponding to the geojson file.
      geojson_path : str
          Path to geojson file.
      mask_path_out_gray : str
          Output path of saved mask (should end in .tif).
      buffer_distance_meters : float
          Width of buffer around geojson lines.  Formally, this is the distance
          to each geometric object.  Optional.  Defaults to ``2``.
      buffer_cap_style : int
          Cap_style of buffer, see: (https://shapely.readthedocs.io/en/stable/manual.html#constructive-methods)
          Defaults to ``1`` (round).
      dissolve_by : str
          Method for differentiating rows in geodataframe, and creating unique
          mask values.  Defaults to ``'speed_m/s'``.
      mask_burn_value : str
          Column to name burn value in geodataframe. Defaults to ``'burnValue'``.
      min_burn_val : int
          Minimum value to burn to mask. Rescale all values linearly with this
          minimum value.  If <= 0, ignore.  Defaultst to ``0``.
      max_burn_val : int
          Maximum value to burn to mask. Rescale all values linearly with this
          maxiumum value.  If <= 0, ignore.  Defaultst to ``256``.
      verbose : bool
          Switch to print relevant values.  Defaults to ``False``.
  
      Returns
      -------
      gdf_buffer : geopandas dataframe
          Dataframe created from geojson
      """
  
      # get gdf_buffer
      try:
          inGDF = gpd.read_file(geojson_path)
      except TypeError:
          print("Empty mask for path:", geojson_path)
          # create emty mask
          h, w = cv2.imread(image_path, 0).shape[:2]
          mask_gray = np.zeros((h, w)).astype(np.uint8)
          skimage.io.imsave(mask_path_out_gray, mask_gray)
          # cv2.imwrite(mask_path_out, mask_gray)
          return []
  
      gdf_buffer = create_buffer_geopandas(
          inGDF, buffer_distance_meters=buffer_distance_meters,
          buffer_cap_style=buffer_cap_style, dissolve_by=dissolve_by,
          projectToUTM=False, verbose=verbose)
  
      if verbose:
          print("gdf_buffer.columns:", gdf_buffer.columns)
          print("gdf_buffer:", gdf_buffer)
  
      # set burn values
      burn_vals_raw = gdf_buffer[dissolve_by].values.astype(float)
      if verbose:
          print("burn_vals_raw:", burn_vals_raw)
      if (max_burn_val > 0) and (min_burn_val >= 0):
          scale_mult = (max_burn_val - min_burn_val) / np.max(burn_vals_raw)
          # scale_mult = max_burn_val / np.max(burn_vals_raw)
          burn_vals = min_burn_val + scale_mult * burn_vals_raw
      else:
          burn_vals = burn_vals_raw
      if verbose:
          print("np.unique burn_vals:", np.sort(np.unique(burn_vals)))
      gdf_buffer[mask_burn_val_key] = burn_vals
  
      # create mask
      gdf_to_array(gdf_buffer, image_path, mask_path_out_gray,
                   mask_burn_val_key=mask_burn_val_key,
                   verbose=verbose)
  
      return gdf_buffer
  
  
  ###############################################################################
  def _create_speed_arr(image_path, geojson_path, mask_path_out_gray,
                        bin_conversion_func, mask_burn_val_key='burnValue',
                        buffer_distance_meters=2, buffer_cap_style=1,
                        dissolve_by='speed_m/s', bin_conversion_key='speed_mph',
                        verbose=False):
      '''
      Similar to create_arr_from_geojson()
      Create buffer around geojson for speeds, use bin_conversion_func to
      assign values to the mask
      '''
  
      # get gdf_buffer
      try:
          inGDF = gpd.read_file(geojson_path)
      except:
          print("Empty mask for path:", geojson_path)
          # create emty mask
          h, w = cv2.imread(image_path, 0).shape[:2]
          mask_gray = np.zeros((h, w)).astype(np.uint8)
          skimage.io.imsave(mask_path_out_gray, mask_gray)
          # cv2.imwrite(mask_path_out, mask_gray)
          return []
  
      gdf_buffer = create_buffer_geopandas(
          inGDF, buffer_distance_meters=buffer_distance_meters,
          buffer_cap_style=buffer_cap_style, dissolve_by=dissolve_by,
          projectToUTM=True, verbose=verbose)
  
      # set burn values
      speed_arr = gdf_buffer[bin_conversion_key].values
      burnVals = [bin_conversion_func(s) for s in speed_arr]
      gdf_buffer[mask_burn_val_key] = burnVals
  
      # create mask
      gdf_to_array(gdf_buffer, image_path, mask_path_out_gray,
                   mask_burn_val_key=mask_burn_val_key, verbose=verbose)
  
      return gdf_buffer
  
  
  ###############################################################################
  def create_speed_gdf_v0(image_path, geojson_path, mask_path_out_gray,
                          bin_conversion_func, mask_burn_val_key='burnValue',
                          buffer_distance_meters=2, buffer_cap_style=1,
                          dissolve_by='speed_m/s', bin_conversion_key='speed_mph',
                          verbose=False):
      '''
      Create buffer around geojson for speeds, use bin_conversion_func to
      assign values to the mask
      '''
  
      # get gdf_buffer
      try:
          inGDF = gpd.read_file(geojson_path)
      except:
          print("Empty mask for path:", geojson_path)
          # create emty mask
          h, w = cv2.imread(image_path, 0).shape[:2]
          mask_gray = np.zeros((h, w)).astype(np.uint8)
          skimage.io.imsave(mask_path_out_gray, mask_gray)
          # cv2.imwrite(mask_path_out, mask_gray)
          return []
  
      # project
      projGDF = osmnx_funcs.project_gdf(inGDF)
      if verbose:
          print("inGDF.columns:", inGDF.columns)
  
      gdf_utm_buffer = projGDF.copy()
      # perform Buffer to produce polygons from Line Segments
      gdf_utm_buffer['geometry'] = gdf_utm_buffer.buffer(buffer_distance_meters,
                                                         buffer_cap_style)
      gdf_utm_dissolve = gdf_utm_buffer.dissolve(by=dissolve_by)
      gdf_utm_dissolve.crs = gdf_utm_buffer.crs
      gdf_buffer = gdf_utm_dissolve.to_crs(inGDF.crs)
      if verbose:
          print("gdf_buffer['geometry'].values[0]:",
                gdf_buffer['geometry'].values[0])
  
      # set burn values
      speed_arr = gdf_buffer[bin_conversion_key].values
      burnVals = [bin_conversion_func(s) for s in speed_arr]
      gdf_buffer[mask_burn_val_key] = burnVals
  
      # create mask
      gdf_to_array(gdf_buffer, image_path, mask_path_out_gray,
                   mask_burn_val_key=mask_burn_val_key, verbose=verbose)
  
      return gdf_buffer
  
  
  ###############################################################################
  def convert_array_to_multichannel(in_arr, n_channels=7, burnValue=255,
                                    append_total_band=False, verbose=False):
      '''Take input array with multiple values, and make each value a unique
      channel.  Assume a zero value is background, while value of 1 is the 
      first channel, 2 the second channel, etc.'''
  
      h, w = in_arr.shape[:2]
      # scikit image wants it in this format by default
      out_arr = np.zeros((n_channels, h, w), dtype=np.uint8)
      #out_arr = np.zeros((h,w,n_channels), dtype=np.uint8)
  
      for band in range(n_channels):
          val = band + 1
          band_out = np.zeros((h, w), dtype=np.uint8)
          if verbose:
              print("band:", band)
          band_arr_bool = np.where(in_arr == val)
          band_out[band_arr_bool] = burnValue
          out_arr[band, :, :] = band_out
          #out_arr[:,:,band] = band_out
  
      if append_total_band:
          tot_band = np.zeros((h, w), dtype=np.uint8)
          band_arr_bool = np.where(in_arr > 0)
          tot_band[band_arr_bool] = burnValue
          tot_band = tot_band.reshape(1, h, w)
          out_arr = np.concatenate((out_arr, tot_band), axis=0).astype(np.uint8)
  
      if verbose:
          print("out_arr.shape:", out_arr.shape)
      return out_arr
  
  
  # Helper Functions
  ###############################################################################
  def CreateMultiBandGeoTiff(OutPath, Array):
      '''
      Author: Jake Shermeyer
      Array has shape:
          Channels, Y, X?
      '''
      driver = gdal.GetDriverByName('GTiff')
      DataSet = driver.Create(OutPath, Array.shape[2], Array.shape[1],
                              Array.shape[0], gdal.GDT_Byte,
                              ['COMPRESS=LZW'])
      for i, image in enumerate(Array, 1):
          DataSet.GetRasterBand(i).WriteArray(image)
      del DataSet
  
      return OutPath
  
  
  ###############################################################################
  def geomGeo2geomPixel(geom, affineObject=[], input_raster='',
                        gdal_geomTransform=[]):
      '''spacenet utilities v3 geotools.py'''
      # This function transforms a shapely geometry in geospatial coordinates into pixel coordinates
      # geom must be shapely geometry
      # affineObject = rasterio.open(input_raster).affine
      # gdal_geomTransform = gdal.Open(input_raster).GetGeoTransform()
      # input_raster is path to raster to gather georectifcation information
      if not affineObject:
          if input_raster != '':
              affineObject = rio.open(input_raster).transform
          elif gdal_geomTransform != []:
              affineObject = af.Affine.from_gdal(gdal_geomTransform)
          else:
              return geom
  
      affineObjectInv = ~affineObject
  
      geomTransform = shapely.affinity.affine_transform(geom,
                                                        [affineObjectInv.a,
                                                         affineObjectInv.b,
                                                         affineObjectInv.d,
                                                         affineObjectInv.e,
                                                         affineObjectInv.xoff,
                                                         affineObjectInv.yoff]
                                                        )
  
      return geomTransform
  
  
  ###############################################################################
  def geomPixel2geomGeo(geom, affineObject=[], input_raster='', gdal_geomTransform=[]):
      '''spacenet utilities v3 geotools.py'''
      # This function transforms a shapely geometry in pixel coordinates into geospatial coordinates
      # geom must be shapely geometry
      # affineObject = rasterio.open(input_raster).affine
      # gdal_geomTransform = gdal.Open(input_raster).GetGeoTransform()
      # input_raster is path to raster to gather georectifcation information
      if not affineObject:
          if input_raster != '':
              affineObject = rio.open(input_raster).transform
          elif gdal_geomTransform != []:
              affineObject = af.Affine.from_gdal(gdal_geomTransform)
          else:
              return geom
  
      geomTransform = shapely.affinity.affine_transform(geom,
                                                        [affineObject.a,
                                                         affineObject.b,
                                                         affineObject.d,
                                                         affineObject.e,
                                                         affineObject.xoff,
                                                         affineObject.yoff]
                                                        )
  
      return geomTransform
  
  
  ###############################################################################
  # Haversine formula example in Python
  # Author: Wayne Dyck
  # def distance_haversine(lat1, lon1, lat2, lon2, earth_radius_km=6371):
  #    #lat1, lon1 = origin
  #    #lat2, lon2 = destination
  #
  #    dlat = math.radians(lat2-lat1)
  #    dlon = math.radians(lon2-lon1)
  #    a = math.sin(dlat/2) * math.sin(dlat/2) + math.cos(math.radians(lat1)) \
  #        * math.cos(math.radians(lat2)) * math.sin(dlon/2) * math.sin(dlon/2)
  #    c = 2 * math.atan2(math.sqrt(a), math.sqrt(1-a))
  #    d = earth_radius_km * c
  #
  #    return d
  
  
  ###############################################################################
  def _haversine(lon1, lat1, lon2, lat2):
      """
      Calculate the great circle distance between two points in m
      on the earth (specified in decimal degrees)
      http://stackoverflow.com/questions/15736995/how-can-i-
          quickly-estimate-the-distance-between-two-latitude-longitude-points
      """
      # convert decimal degrees to radians
      lon1, lat1, lon2, lat2 = list(map(radians, [lon1, lat1, lon2, lat2]))
      # haversine formula
      dlon = lon2 - lon1
      dlat = lat2 - lat1
      a = sin(dlat/2)**2 + cos(lat1) * cos(lat2) * sin(dlon/2)**2
      c = 2 * asin(sqrt(a))
      km = 6367 * c
      m = 1000. * km
      return m
  
  ###############################################################################
  
  
  def get_gsd(im_test_file):
      '''return gsd in meters'''
      srcImage = gdal.Open(im_test_file)
      geoTrans = srcImage.GetGeoTransform()
      ulX = geoTrans[0]
      ulY = geoTrans[3]
      # xDist = geoTrans[1]
      yDist = geoTrans[5]
      # rtnX = geoTrans[2]
      # rtnY = geoTrans[4]
  
      # get haversine distance
      # dx = _haversine(ulX, ulY, ulX+xDist, ulY) #haversine(lon1, lat1, lon2, lat2)
      # haversine(lon1, lat1, lon2, lat2)
      dy = _haversine(ulX, ulY, ulX, ulY+yDist)
  
      return dy  # dx
  
  
  ###############################################################################
  def get_extent(srcFileImage):
      gdata = gdal.Open(srcFileImage)
      geo = gdata.GetGeoTransform()
      # data = gdata.ReadAsArray()
  
      xres = geo[1]
      yres = geo[5]
      # xmin = geo[0]
      # xmax = geo[0] + (xres * gdata.RasterXSize)
      # ymin = geo[3] + (yres * gdata.RasterYSize)
      # ymax = geo[3]
      xmin = geo[0] + xres * 0.5
      xmax = geo[0] + (xres * gdata.RasterXSize) - xres * 0.5
      ymin = geo[3] + (yres * gdata.RasterYSize) + yres * 0.5
      ymax = geo[3] - yres * 0.5
  
      return xmin, ymin, xmax, ymax
  
  
  ###############################################################################
  def get_pixel_dist_from_meters(im_test_file, len_meters):
      '''For the input image, we want a buffer or other distance in meters,
      this function determines the pixel distance by calculating the GSD'''
      gsd = get_gsd(im_test_file)
      pix_width = max(1, np.rint(len_meters/gsd))
  
      return gsd, pix_width
  
  
  ###############################################################################
  def get_unique(seq, idfun=None):
      '''https://www.peterbe.com/plog/uniqifiers-benchmark'''
      # order preserving
      if idfun is None:
          def idfun(x): return x
      seen = {}
      result = []
      for item in seq:
          marker = idfun(item)
          # in old Python versions:
          # if seen.has_key(marker)
          # but in new ones:
          if marker in seen:
              continue
          seen[marker] = 1
          result.append(item)
      return result
  
  
  ###############################################################################
  def _get_node_positions(G_, x_coord='x', y_coord='y'):
      '''Get position array for all nodes'''
      nrows = len(G_.nodes())
      ncols = 2
      arr = np.zeros((nrows, ncols))
      # populate node array
      for i, n in enumerate(G_.nodes()):
          n_props = G_.node[n]
          x, y = n_props[x_coord], n_props[y_coord]
          arr[i] = [x, y]
      return arr
  
apis: numpy.rint numpy.zeros matplotlib.pyplot.savefig numpy.concatenate numpy.asarray matplotlib.pyplot.subplots numpy.max numpy.min matplotlib.pyplot.close numpy.round numpy.where numpy.unique