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InventoryGenerator now works with updated FacadeParser
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@bacetiner
bacetiner committed Dec 29, 2023
1 parent 8a8e3cb commit 114d5df
Showing 1 changed file with 29 additions and 44 deletions.
73 changes: 29 additions & 44 deletions brails/InventoryGenerator.py
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Original file line number Diff line number Diff line change
Expand Up @@ -41,7 +41,7 @@
# Satish Rao
#
# Last updated:
# 05-06-2023
# 12-29-2023

import random
import sys
Expand Down Expand Up @@ -265,7 +265,7 @@ def write_to_dataframe(df,predictions,column,imtype='street_images'):
'garageExists')
self.inventory['garageExists'] = self.inventory['garageExists'].astype(dtype="boolean")

elif attribute=='numstories' and 'storyModel' not in locals():
elif attribute=='numstories':
# Initialize the floor detector object:
storyModel = NFloorDetector()

Expand Down Expand Up @@ -325,28 +325,13 @@ def write_to_dataframe(df,predictions,column,imtype='street_images'):
'satellite_images')

elif attribute in ['buildingheight','roofeaveheight','roofpitch']:
if 'storyModel' not in locals():
# Initialize the floor detector object:
storyModel = NFloorDetector()

# Call the floor detector to determine number of floors of
# buildings in each image:
storyModel.predict(imstreet)

# Write the results to the inventory DataFrame:
self.inventory = write_to_dataframe(self.inventory,
[storyModel.system_dict['infer']['images'],
storyModel.system_dict['infer']['predictions']],
'NumberOfStories')
self.inventory['NumberOfStories'] = self.inventory['NumberOfStories'].astype(dtype='Int64')

if 'facadeParserModel' not in locals():
if 'facadeParserModel' not in locals():
# Initialize the facade parser object:
facadeParserModel = FacadeParser()

# Call the facade parser to determine the requested
# attribute for each building:
facadeParserModel.predict(image_handler,storyModel)
facadeParserModel.predict(image_handler)

self.inventory = write_to_dataframe(self.inventory,
[facadeParserModel.predictions['image'].to_list(),
Expand Down Expand Up @@ -383,35 +368,35 @@ def write_to_dataframe(df,predictions,column,imtype='street_images'):
# Merge the DataFrame of predicted attributes with the DataFrame of
# incomplete inventory and print the resulting table to the output file
# titled IncompleteInventory.csv:
dfout2merge['fp_as_string'] = dfout2merge['Footprint'].apply(lambda x: "".join(str(x)))
# dfout2merge['fp_as_string'] = dfout2merge['Footprint'].apply(lambda x: "".join(str(x)))

dfout_incomp = self.incompleteInventory.copy(deep=True)
dfout_incomp['fp_as_string'] = dfout_incomp['Footprint'].apply(lambda x: "".join(str(x)))
# dfout_incomp = self.incompleteInventory.copy(deep=True)
# dfout_incomp['fp_as_string'] = dfout_incomp['Footprint'].apply(lambda x: "".join(str(x)))

dfout_incomp = pd.merge(left=dfout_incomp,
right=dfout2merge.drop(columns=['Footprint'], errors='ignore'),
how='left', left_on=['fp_as_string','PlanArea'],
right_on=['fp_as_string','PlanArea'],
sort=False)
# dfout_incomp = pd.merge(left=dfout_incomp,
# right=dfout2merge.drop(columns=['Footprint'], errors='ignore'),
# how='left', left_on=['fp_as_string','PlanArea'],
# right_on=['fp_as_string','PlanArea'],
# sort=False)

dfout_incomp = dfout2merge.append(dfout_incomp[dfout_incomp.roofshape.isnull()])
dfout_incomp = dfout_incomp.reset_index(drop=True).drop(columns=['fp_as_string'], errors='ignore')
# dfout_incomp = dfout2merge.append(dfout_incomp[dfout_incomp.roofshape.isnull()])
# dfout_incomp = dfout_incomp.reset_index(drop=True).drop(columns=['fp_as_string'], errors='ignore')

self.incompleteInventory = dfout_incomp.copy(deep=True)
# self.incompleteInventory = dfout_incomp.copy(deep=True)

dfout_incomp4print = dfout_incomp.copy(deep=True)
for index, row in dfout_incomp.iterrows():
dfout_incomp4print.loc[index, 'Footprint'] = ('{"type":"Feature","geometry":' +
'{"type":"Polygon","coordinates":[' +
f"""{row['Footprint']}""" +
']},"properties":{}}')
centroid = Polygon(row['Footprint']).centroid
dfout_incomp4print.loc[index, 'Latitude'] = centroid.y
dfout_incomp4print.loc[index, 'Longitude'] = centroid.x
# dfout_incomp4print = dfout_incomp.copy(deep=True)
# for index, row in dfout_incomp.iterrows():
# dfout_incomp4print.loc[index, 'Footprint'] = ('{"type":"Feature","geometry":' +
# '{"type":"Polygon","coordinates":[' +
# f"""{row['Footprint']}""" +
# ']},"properties":{}}')
# centroid = Polygon(row['Footprint']).centroid
# dfout_incomp4print.loc[index, 'Latitude'] = centroid.y
# dfout_incomp4print.loc[index, 'Longitude'] = centroid.x

cols = [col for col in dfout_incomp4print.columns if col!='Footprint']
new_cols = ['Latitude','Longitude'] + cols[:-2] + ['Footprint']
dfout_incomp4print = dfout_incomp4print[new_cols]
# cols = [col for col in dfout_incomp4print.columns if col!='Footprint']
# new_cols = ['Latitude','Longitude'] + cols[:-2] + ['Footprint']
# dfout_incomp4print = dfout_incomp4print[new_cols]

dfout_incomp4print.to_csv('IncompleteInventory.csv', index=True, index_label='id', na_rep='NA')
print('Incomplete inventory data available in IncompleteInventory.csv')
# dfout_incomp4print.to_csv('IncompleteInventory.csv', index=True, index_label='id', na_rep='NA')
# print('Incomplete inventory data available in IncompleteInventory.csv')

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