Patrick Hagerty hagerty

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHE

	#Generator
	with tf.device(gpu):
	x8 = tf.placeholder(tf.float32, shape=[None, FLAGS.ws, FLAGS.ws, 8]) # 8-band input
	x3 = tf.placeholder(tf.float32, shape=[None, scale * FLAGS.ws, scale * FLAGS.ws, 3]) # 3-band ipnput
	label_distance = tf.placeholder(tf.float32, shape=[None, FLAGS.ws, FLAGS.ws, 1]) # distance transform as a label

	for i in range(layers):
	alpha[i] = tf.Variable(0.9, name='alpha_' + str(i))
	beta[i] = tf.maximum( 0.0 , tf.minimum ( 1.0 , alpha[i] ), name='beta_'+str(i))
	bi[i] = tf.Variable(tf.constant(0.0,shape=[FLAGS.filters]), name='bi_'+str(i))

	import json
	from shapely.geometry import Polygon, shape, Point
	import gdal
	import numpy as np
	import sys
	fn = sys.argv[1]
	path = sys.argv[2]

	def Pixel2World ( geoMatrix, i , j ):
	ulX = geoMatrix[0]

	with tf.Session(config=tf.ConfigProto(log_device_placement=True)) as sess:
	sess.run(ginit)
	global_step = 0
	for i in range(layers):
	for step in range(FLAGS.max_layer_steps):
	global_step += 1
	random_input8, random_input3, random_label = GetRandomInput( labels, im_raw8, im_raw3, FLAGS.batch_size, FLAGS.ws, scale)
	sess.run(label_optimizer[i],feed_dict={x8: random_input8, x3: random_input3, label_distance: random_label})
	for step in range(5*FLAGS.max_layer_steps):
	global_step += 1

	def CreateGeoJSON ( fn, cluster, geom, proj ):
	memdrv = gdal.GetDriverByName ( 'MEM')
	src_ds = memdrv.Create('',cluster.shape[1],cluster.shape[0],1)
	src_ds.SetGeoTransform(geom)
	src_ds.SetProjection(proj)
	band = src_ds.GetRasterBand(1)
	band.WriteArray(cluster)
	dst_layername = "BuildingID"
	drv = ogr.GetDriverByName("geojson")

	with tf.device(gpu):
	# Generator
	x8 = tf.placeholder(tf.float32, shape=[None, FLAGS.ws, FLAGS.ws, 8])
	x3 = tf.placeholder(tf.float32, shape=[None, scale * FLAGS.ws, scale * FLAGS.ws, 3])
	label_distance = tf.placeholder(tf.float32, shape=[None, FLAGS.ws, FLAGS.ws, 1])
	for i in range(layers):
	alpha[i] = tf.Variable(0.9, name='alpha_' + str(i))
	beta[i] = tf.maximum( 0.0 , tf.minimum ( 1.0 , alpha[i] ), name='beta_'+str(i))
	bi[i] = tf.Variable(tf.constant(0.0,shape=[FLAGS.filters]), name='bi_'+str(i))
	bo[i] = tf.Variable(tf.constant(0.0,shape=[FLAGS.filters]), name='bo_'+str(i))

	def GrowCluster ( intensity, cluster, mask, k ):
	newcluster = cluster
	#find boundary of current cluster
	indexes = np.nonzero( np.logical_and(mask, (np.logical_not(newcluster))))
	while indexes :
	growingboundary = 0 * cluster
	for kk in range ( len (indexes[0] ) ):
	i = indexes[0][kk]
	j = indexes[1][kk]
	isConnected = False


	def FixGeoJSON( fn ):
	buf_dist = 0.0
	dst_layername = "BuildingID"
	drv = ogr.GetDriverByName("geojson")
	dst_ds = drv.Open ( './geojson.full/' + fn + dst_layername + ".geojson")
	dst_layer = dst_ds.GetLayer(0)
	if os.path.exists('./geojson.full/buffer' + fn + dst_layername + ".geojson"):
	drv.DeleteDataSource('./geojson.full/buffer' + fn + dst_layername + ".geojson")
	adst_ds = drv.CreateDataSource ( './geojson.full/buffer' + fn + dst_layername + ".geojson")

	# The NN
	with tf.device(gpu):
	# Input is has numberOfBands for the pre-processed image and numberOfBands for the original image
	xy = tf.placeholder(tf.float32, shape=[None, FLAGS.ws, FLAGS.ws, 2*numberOfBands])
	with tf.name_scope("split") as scope:
	x = tf.slice(xy, [0,0,0,0], [-1,-1,-1,numberOfBands]) # low res image
	y = tf.slice(xy, [0,0,0,numberOfBands], [-1,-1,-1,-1]) # high res image

	with tf.name_scope("initial_costs") as scope:
	# used as a measure of improvement not for optimization