Add bounding box features to the previous version

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Alireza Adli 2024-08-27 11:57:47 -04:00
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"""
handle_varennes_ds_workflow module
NRCan datalayer has two polygons for each buildings' footprint.
The below workflow has been designed to remove the extra polygons.
Project Developer: Alireza Adli alireza.adli@concordia.ca
"""
# You need to clone mtl_gis_oo project and
# add it as a dependency of this new project
from scrub_layer_class import *
import pandas as pd
# Change the paths by the location of your QGIS installation and datalayers
qgis_path = 'C:/Program Files/QGIS 3.34.1/apps/qgis'
varennes_nrcan_extra_polygons = \
'C:/Users/a_adli/PycharmProjects/varennes_gis_oo/' \
'data/initial_data/endeavor/nrcan_without_centroids/auto_building_2.shp'
# First we duplicate the layer to preserve the main data layer.
duplicated = \
'C:/Users/a_adli/PycharmProjects/varennes_gis_oo/' \
'data/initial_data/endeavor/nrcan_tolerance_7_removed_dups_pro/' \
'nrcan_tolerance_7_removed_dups.shp'
varennes_nrcan = ScrubLayer(
qgis_path, varennes_nrcan_extra_polygons, 'NRCan Varennes')
varennes_nrcan.duplicate_layer(duplicated)
varennes_nrcan_centroids = ScrubLayer(
qgis_path, duplicated, 'NRCan Varennes with Coordinates')
# Then we add coordinates to each polygon so we can remove the
# very similar polygons based on coordinates.
varennes_nrcan_centroids.layer.startEditing()
# Add new fields for the centroid coordinates
varennes_nrcan_centroids.layer.dataProvider().\
addAttributes([QgsField("centroid_x", QVariant.Double),
QgsField("centroid_y", QVariant.Double),
QgsField("min_x", QVariant.Double),
QgsField("min_y", QVariant.Double),
QgsField("max_x", QVariant.Double),
QgsField("max_y", QVariant.Double)]
)
varennes_nrcan_centroids.layer.updateFields()
centroid_x_index = varennes_nrcan_centroids.\
layer.fields().indexFromName("centroid_x")
centroid_y_index = varennes_nrcan_centroids.\
layer.fields().indexFromName("centroid_y")
min_x_index = varennes_nrcan_centroids.\
layer.fields().indexFromName("min_x")
min_y_index = varennes_nrcan_centroids.\
layer.fields().indexFromName("min_y")
max_x_index = varennes_nrcan_centroids.\
layer.fields().indexFromName("max_x")
max_y_index = varennes_nrcan_centroids.\
layer.fields().indexFromName("max_y")
for feature in varennes_nrcan_centroids.layer.getFeatures():
centroid = feature.geometry().centroid().asPoint()
feature.setAttribute(centroid_x_index, centroid.x())
feature.setAttribute(centroid_y_index, centroid.y())
# Calculate bounding box coordinates
bbox = feature.geometry().boundingBox()
feature.setAttribute(min_x_index, bbox.xMinimum())
feature.setAttribute(min_y_index, bbox.yMinimum())
feature.setAttribute(max_x_index, bbox.xMaximum())
feature.setAttribute(max_y_index, bbox.yMaximum())
varennes_nrcan_centroids.layer.updateFeature(feature)
# Commit the changes for adding centroids fields
varennes_nrcan_centroids.layer.commitChanges()
# Pandas is a better option to compare polygons and remove the duplicates
# so we make a dataframe. We just transfer the necessary fields
# to the dataframe.
field_names = \
['feature_id', 'centroid_x', 'centroid_y',
'min_x', 'min_y', 'max_x', 'max_y']
# Get the indices of the specified fields
field_indices = [varennes_nrcan_centroids.layer.fields().indexOf(field)
for field in field_names]
# Extract the attribute values and store them in a list of dictionaries
data = []
for feature in varennes_nrcan_centroids.layer.getFeatures():
attributes = [feature.attributes()[index] for index in field_indices]
data.append(dict(zip(field_names, attributes)))
# Create a DataFrame from the list of dictionaries
varennes_nrcan_centroids_df = pd.DataFrame(data)
varennes_nrcan_centroids_df['ID'] = range(len(varennes_nrcan_centroids_df))
# Removing polygones based on a diifference (tolerance variable)
# between centroid_x of polygons and centroid_y of the polygons
# The tolerance can be changed to one or five
tolerance = 7
counter = 0
centroid_x = varennes_nrcan_centroids_df['centroid_x'].tolist()
centroid_y = varennes_nrcan_centroids_df['centroid_y'].tolist()
min_x = varennes_nrcan_centroids_df['min_x'].tolist()
min_y = varennes_nrcan_centroids_df['min_y'].tolist()
max_x = varennes_nrcan_centroids_df['max_x'].tolist()
max_y = varennes_nrcan_centroids_df['max_y'].tolist()
feature_ids_all = varennes_nrcan_centroids_df['feature_id'].tolist()
duplicated_feature_ids = []
for feature_index in range(len(centroid_x)):
for next_feature_index in range(feature_index + 1, len(centroid_x)):
a_x = centroid_x[feature_index]
b_x = centroid_x[next_feature_index]
subtract_centroid_x = a_x - b_x
a_y = centroid_y[feature_index]
b_y = centroid_y[next_feature_index]
subtract_centroid_y = a_y - b_y
a_min_x = min_x[feature_index]
b_min_x = min_x[next_feature_index]
subtract_min_x = a_min_x - b_min_x
a_min_y = min_y[feature_index]
b_min_y = min_y[next_feature_index]
subtract_min_y = a_min_y - b_min_y
a_max_x = max_x[feature_index]
b_max_x = max_x[next_feature_index]
subtract_max_x = a_max_x - b_max_x
a_max_y = max_y[feature_index]
b_max_y = max_y[next_feature_index]
subtract_max_y = a_max_y - b_max_y
if abs(subtract_centroid_x) < tolerance and \
abs(subtract_centroid_y) < tolerance and \
abs(subtract_min_x) < tolerance and \
abs(subtract_min_y) < tolerance and \
abs(subtract_max_x) < tolerance and \
abs(subtract_max_y) < tolerance:
duplicated_feature_ids.append(feature_ids_all[next_feature_index])
# Removing records based on the duplicated_feature_ids list
varennes_nrcan_centroids.layer.startEditing()
features = varennes_nrcan_centroids.layer.getFeatures()
# Iterate through features in the layer
for feature in features:
if feature['feature_id'] in duplicated_feature_ids:
# Delete the feature
varennes_nrcan_centroids.layer.deleteFeature(feature.id())
# Save changes and stop editing
varennes_nrcan_centroids.layer.commitChanges()