def MockCallBacks(self):
df = pd.read_csv('data/CrossValidationData.csv')
df.columns = ['x', 'y', 'z', 'id', 'is_building', 'label']
deleteIndex = df[df['is_building'] == 0]['id']
labels = df[df['is_building'] == 1][['id', 'label']]
# The prune model
sd = Methods()
df = sd.splitCallBack(labels, 'lidar_sf__1')
sd.hullCallBack(df, 'lidar_sf__1')
sd.deleteCallBack(df, 'lidar_sf__1')
sd.deleteCallBack(deleteIndex, 'lidar_sf__1')
def MockCallBacks(self):
df = pd.read_csv('data/CrossValidationData.csv');
df.columns = ['x','y','z','id', 'is_building', 'label']
deleteIndex = df[df['is_building']==0]['id'];
labels = df[df['is_building']==1][['id','label']];
# The prune model
sd = Methods()
df = sd.splitCallBack(labels, 'lidar_sf__1')
sd.hullCallBack(df, 'lidar_sf__1');
sd.deleteCallBack(df, 'lidar_sf__1');
sd.deleteCallBack(deleteIndex, 'lidar_sf__1')
def __init__(self,
database='building',
User='******',
host='localhost',
projection=4326,
transform_to=None):
# If the dataset is not in metric units, transform projection
self.transform_to = transform_to
# file uploads
self.upload = pgDataLoader(database=database,
User=User,
host=host,
projection=projection)
# methods to cal to server
self.methods = Methods(database=database,
User=User,
host=host,
projection=projection)
# Reset storage tables
self.methods.tableSetUp(transform_to=transform_to)
lambda x: len(x) <= second_layer_screen or len(x) >= 10000)
not_blds.reset_index(inplace=True)
# Recluster buildings in only x-y to negate the effects of funny-shaped rooves.
db = DBSCAN(eps=eps, min_samples=min_samples).fit(blds[['x',
'y']].as_matrix())
blds = pd.DataFrame([blds.id, db.labels_]).T
blds.columns = ['id', 'label']
blds['id'] = blds['id'].astype(int)
# Is as building: id, label
# Not a building: id
return blds, not_blds['id']
if __name__ == '__main__':
import os
from pgMethods import Methods
m = Methods(projection=32610)
df = m.pg_df('SELECT x, y, z, id FROM lidar_nyc_small')
print 'df loaded'
#df = pd.read_csv('../tests/data/CrossValidationData.csv')
blds, not_blds = dbscan(df)
m.df_pg(blds, 'temp12345678')
m.pg_post(
'DROP TABLE IF EXISTS checkcheck; CREATE TABLE checkcheck AS SELECT a.* FROM z_20130805_usgsnyc14_18TWL835075 a RIGHT JOIN temp12345678 b ON a.id=b.id;'
)
m.pg_post('DROP TABLE temp12345678;')
# Recluster buildings in only x-y to negate the effects of funny-shaped rooves.
db = DBSCAN(eps=eps, min_samples=min_samples).fit(blds[['x', 'y']].as_matrix())
blds = pd.DataFrame([blds.id, db.labels_]).T
blds.columns = ['id', 'label'];
blds['id'] = blds['id'].astype(int)
# Is as building: id, label
# Not a building: id
return blds, not_blds['id']
if __name__ == '__main__':
import os
from pgMethods import Methods
m = Methods(projection=32610);
df = m.pg_df('SELECT x, y, z, id FROM lidar_nyc_small')
print 'df loaded'
#df = pd.read_csv('../tests/data/CrossValidationData.csv')
blds, not_blds = dbscan(df)
m.df_pg(blds, 'temp12345678');
m.pg_post('DROP TABLE IF EXISTS checkcheck; CREATE TABLE checkcheck AS SELECT a.* FROM z_20130805_usgsnyc14_18TWL835075 a RIGHT JOIN temp12345678 b ON a.id=b.id;')
m.pg_post('DROP TABLE temp12345678;')
def MockPartition(self, split=6):
sd = Methods(projection=32610)
sd.partionSpace('lidar_sf', split=split)
def MockPartition(self, split=6):
sd = Methods(projection=32610)
sd.partionSpace('lidar_sf', split=split)
class Building:
def __init__(self,
database='building',
User='******',
host='localhost',
projection=4326,
transform_to=None):
# If the dataset is not in metric units, transform projection
self.transform_to = transform_to
# file uploads
self.upload = pgDataLoader(database=database,
User=User,
host=host,
projection=projection)
# methods to cal to server
self.methods = Methods(database=database,
User=User,
host=host,
projection=projection)
# Reset storage tables
self.methods.tableSetUp(transform_to=transform_to)
def screen(self, file_path, hull=True, load_lidar=False, split=10):
# Create table name from file path
lidar_name = "z_" + basename(file_path).split('.')[0]
if load_lidar:
# Load the data to server
self.upload.lidar_to_server(file_path,
lidar_name,
transform_to=self.transform_to)
# Split the bounding box into equal children
self.methods.partionSpace(lidar_name,
split=split,
transform_to=self.transform_to)
# Get a dataframe of bounding box id's
bbox_ids = self.methods.pg_df('SELECT id FROM %s_bbox' % lidar_name)
for index, row in bbox_ids.T.iteritems():
# Get the id of the child bounding box
child_id = row.id
print child_id
# Get the points intersecting this bounding box
pts = self.methods.partionPoints(child_id)
# Get the points of buildings and not buildings
blds, not_blds = dbscan(pts, zboost=1.4)
pts = None
# Save some memory
if hull:
# Split points into interior points and boundary points.
# Save the boundary points to an overflow table.
inside_points = self.methods.splitCallBack(blds, child_id)
blds = None
# Save some memory
# Calculate the outline of the interior buildings and save geometries.
self.methods.hullCallBack(inside_points, child_id)
inside_points = None
else:
self.methods.loadpointsCallBack(blds, child_id)
blds = None
