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GEO_COL_HEI.py
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import calculateFeatures
import numpy as np # type: ignore
import laspy as lp # type: ignore
import csv
import send_email
import time
from sklearn.ensemble import RandomForestClassifier # type: ignore
from sklearn.model_selection import train_test_split # type: ignore
from sklearn.metrics import classification_report, confusion_matrix, accuracy_score # type: ignore
import matplotlib.pyplot as plt # type: ignore
import pandas as pd #type: ignore
import seaborn as sns #type: ignore
additional_text = "GEO_COL_HEI"
print(f"Classifying data for {additional_text}") #change
start_read = time.time()
# Get current current time
def get_time():
t = time.localtime()
current_time = time.strftime("%H:%M:%S", t)
return current_time
# FILE PATHS
#LAS files
print(f'Reading LAS files... {get_time()}')
classified_pointCloudPath = '../working/training/classified_smaller.las' #change
nonClassified_pointCloudPath = '../working/training/lln_not_classified.las' #change
#create output txt files
outputErrorRF = f'../results_final/{additional_text}/rf_{additional_text}.txt'
# outputErrorSVM = '../results/error_SVM_multi_reduced_gpu_rf.txt'
#create output csv file
importances_path_png = f'../results_final/{additional_text}/rf_importances_{additional_text}.png'
output_path_csv = f'../results_final/{additional_text}/rf_{additional_text}.csv'
output_path_las = f'../results_final/{additional_text}/rf_{additional_text}.las'
#KDE plot variables
hue_order = ['Ground', 'Low Vegetation', 'Medium Vegetation', 'High Vegetation', 'Roof', 'Facade', 'Vehicle']
palette = ['#d4a373','#a3b18a','#588157','#344e41','#c0d0d5','#fefae0','#555555']
#paths for KDE plots
HSV_plot_path = f'../results_final/{additional_text}/HSV_plot_{additional_text}.png'
RGB_plot_path = f'../results_final/{additional_text}/RGB_plot_{additional_text}.png'
heights_plot_path = f'../results_final/{additional_text}/heights_plot_{additional_text}.png'
geomFeatures_plot_path = f'../results_final/{additional_text}/geomFeatures_plot_{additional_text}.png'
# Read LAS data
# send_email.sendUpdate('Script has begun. Reading LAS files...')
classified_pointCloud = lp.read(classified_pointCloudPath)
classified_points_array = np.vstack((classified_pointCloud.x,
classified_pointCloud.y,
classified_pointCloud.z,
classified_pointCloud['normal z'],
classified_pointCloud.classification,
classified_pointCloud.red,
classified_pointCloud.green,
classified_pointCloud.blue)).transpose()
nonClassified_pointCloud = lp.read(nonClassified_pointCloudPath)
nonClassified_points_array = np.vstack((nonClassified_pointCloud.x,
nonClassified_pointCloud.y,
nonClassified_pointCloud.z,
nonClassified_pointCloud['normal z'],
nonClassified_pointCloud.classification,
nonClassified_pointCloud.red,
nonClassified_pointCloud.green,
nonClassified_pointCloud.blue)).transpose()
# Scales and Radii
#Scale tuples (grid, r): [(0.04, 0.2), (0.08, 0.4), (0.16, 0.8)] #accuracy 90%
#Scale tuples (grid, r): [(0.1, 0.5), (0.2, 1.0), (0.4, 2.0)] #accuracy 93% with 50 trees
grid_sizes = [0.1, 0.2, 0.4]
radii = [0.5, 1.0, 2.0]
print(f'Subsampling classified pc... {get_time()}')
# Subsample the data
classified_subsampled_s1 = calculateFeatures.grid_subsampling_with_color(classified_points_array, grid_sizes[0])
classified_subsampled_s2 = calculateFeatures.grid_subsampling_with_color(classified_points_array, grid_sizes[1])
classified_subsampled_s3 = calculateFeatures.grid_subsampling_with_color(classified_points_array, grid_sizes[2])
print(f'Subsampling nonclassified pc... {get_time()}')
nonClassified_subsampled_s1 = calculateFeatures.grid_subsampling_with_color(nonClassified_points_array, grid_sizes[0])
nonClassified_subsampled_s2 = calculateFeatures.grid_subsampling_with_color(nonClassified_points_array, grid_sizes[1])
nonClassified_subsampled_s3 = calculateFeatures.grid_subsampling_with_color(nonClassified_points_array, grid_sizes[2])
print(f'Calculating geometric features for classified pc... {get_time()}')
# Calculate geometric features for both
classified_features_s1 = calculateFeatures.calculateGeometricFeatures(classified_subsampled_s1, radii[0])
classified_features_s2 = calculateFeatures.calculateGeometricFeatures(classified_subsampled_s2, radii[1])
classified_features_s3 = calculateFeatures.calculateGeometricFeatures(classified_subsampled_s3, radii[2])
print(f'Calculating geometric features for nonclassified pc... {get_time()}')
nonClassified_features_s1 = calculateFeatures.calculateGeometricFeatures(nonClassified_subsampled_s1, radii[0])
nonClassified_features_s2 = calculateFeatures.calculateGeometricFeatures(nonClassified_subsampled_s2, radii[1])
nonClassified_features_s3 = calculateFeatures.calculateGeometricFeatures(nonClassified_subsampled_s3, radii[2])
print(f'Concatenating features... {get_time()}')
# Concatenate the features for classified
classified_Z = np.concatenate([classified_features_s1.get('Z'),classified_features_s2.get('Z'),classified_features_s3.get('Z')])
classified_Z_scaled = np.concatenate([classified_features_s1.get('Z_scaled'),classified_features_s2.get('Z_scaled'),classified_features_s3.get('Z_scaled')])
classified_omnivariance = np.concatenate([classified_features_s1.get('omnivariance'),classified_features_s2.get('omnivariance'),classified_features_s3.get('omnivariance')])
classified_eigenentropy = np.concatenate([classified_features_s1.get('eigenentropy'),classified_features_s2.get('eigenentropy'),classified_features_s3.get('eigenentropy')])
classified_anisotropy = np.concatenate([classified_features_s1.get('anisotropy'),classified_features_s2.get('anisotropy'),classified_features_s3.get('anisotropy')])
classified_linearity = np.concatenate([classified_features_s1.get('linearity'),classified_features_s2.get('linearity'),classified_features_s3.get('linearity')])
classified_planarity = np.concatenate([classified_features_s1.get('planarity'),classified_features_s2.get('planarity'),classified_features_s3.get('planarity')])
classified_curvature = np.concatenate([classified_features_s1.get('curvature'),classified_features_s2.get('curvature'),classified_features_s3.get('curvature')])
classified_sphericity = np.concatenate([classified_features_s1.get('sphericity'),classified_features_s2.get('sphericity'),classified_features_s3.get('sphericity')])
classified_verticality = np.concatenate([classified_features_s1.get('verticality'),classified_features_s2.get('verticality'),classified_features_s3.get('verticality')])
classified_height_range = np.concatenate([classified_features_s1.get('height_range'),classified_features_s2.get('height_range'),classified_features_s3.get('height_range')])
classified_height_avg = np.concatenate([classified_features_s1.get('height_avg'),classified_features_s2.get('height_avg'),classified_features_s3.get('height_avg')])
classified_height_below = np.concatenate([classified_features_s1.get('height_below'),classified_features_s2.get('height_below'),classified_features_s3.get('height_below')])
classified_height_above = np.concatenate([classified_features_s1.get('height_above'),classified_features_s2.get('height_above'),classified_features_s3.get('height_above')])
classified_neighbor_H = np.concatenate([classified_features_s1.get('neighbor_H'),classified_features_s2.get('neighbor_H'),classified_features_s3.get('neighbor_H')])
classified_neighbor_S = np.concatenate([classified_features_s1.get('neighbor_S'),classified_features_s2.get('neighbor_S'),classified_features_s3.get('neighbor_S')])
classified_neighbor_V = np.concatenate([classified_features_s1.get('neighbor_V'),classified_features_s2.get('neighbor_V'),classified_features_s3.get('neighbor_V')])
classified_H_values = np.concatenate([classified_features_s1.get('H'),classified_features_s2.get('H'),classified_features_s3.get('H')])
classified_S_values = np.concatenate([classified_features_s1.get('S'),classified_features_s2.get('S'),classified_features_s3.get('S')])
classified_V_values = np.concatenate([classified_features_s1.get('V'),classified_features_s2.get('V'),classified_features_s3.get('V')])
# Concatenate the features for nonclassified
nonClassified_X = np.concatenate([nonClassified_features_s1.get('X'),nonClassified_features_s2.get('X'),nonClassified_features_s3.get('X')])
nonClassified_Y = np.concatenate([nonClassified_features_s1.get('Y'),nonClassified_features_s2.get('Y'),nonClassified_features_s3.get('Y')])
nonClassified_Z = np.concatenate([nonClassified_features_s1.get('Z'),nonClassified_features_s2.get('Z'),nonClassified_features_s3.get('Z')])
nonClassified_Z_scaled = np.concatenate([nonClassified_features_s1.get('Z_scaled'),nonClassified_features_s2.get('Z_scaled'),nonClassified_features_s3.get('Z_scaled')])
nonClassified_red = np.concatenate([nonClassified_features_s1.get('red'),nonClassified_features_s2.get('red'),nonClassified_features_s3.get('red')])
nonClassified_green = np.concatenate([nonClassified_features_s1.get('green'),nonClassified_features_s2.get('green'),nonClassified_features_s3.get('green')])
nonClassified_blue = np.concatenate([nonClassified_features_s1.get('blue'),nonClassified_features_s2.get('blue'),nonClassified_features_s3.get('blue')])
nonClassified_omnivariance = np.concatenate([nonClassified_features_s1.get('omnivariance'),nonClassified_features_s2.get('omnivariance'),nonClassified_features_s3.get('omnivariance')])
nonClassified_eigenentropy = np.concatenate([nonClassified_features_s1.get('eigenentropy'),nonClassified_features_s2.get('eigenentropy'),nonClassified_features_s3.get('eigenentropy')])
nonClassified_anisotropy = np.concatenate([nonClassified_features_s1.get('anisotropy'),nonClassified_features_s2.get('anisotropy'),nonClassified_features_s3.get('anisotropy')])
nonClassified_linearity = np.concatenate([nonClassified_features_s1.get('linearity'),nonClassified_features_s2.get('linearity'),nonClassified_features_s3.get('linearity')])
nonClassified_planarity = np.concatenate([nonClassified_features_s1.get('planarity'),nonClassified_features_s2.get('planarity'),nonClassified_features_s3.get('planarity')])
nonClassified_curvature = np.concatenate([nonClassified_features_s1.get('curvature'),nonClassified_features_s2.get('curvature'),nonClassified_features_s3.get('curvature')])
nonClassified_sphericity = np.concatenate([nonClassified_features_s1.get('sphericity'),nonClassified_features_s2.get('sphericity'),nonClassified_features_s3.get('sphericity')])
nonClassified_verticality = np.concatenate([nonClassified_features_s1.get('verticality'),nonClassified_features_s2.get('verticality'),nonClassified_features_s3.get('verticality')])
nonClassified_height_range = np.concatenate([nonClassified_features_s1.get('height_range'),nonClassified_features_s2.get('height_range'),nonClassified_features_s3.get('height_range')])
nonClassified_height_avg = np.concatenate([nonClassified_features_s1.get('height_avg'),nonClassified_features_s2.get('height_avg'),nonClassified_features_s3.get('height_avg')])
nonClassified_height_below = np.concatenate([nonClassified_features_s1.get('height_below'),nonClassified_features_s2.get('height_below'),nonClassified_features_s3.get('height_below')])
nonClassified_height_above = np.concatenate([nonClassified_features_s1.get('height_above'),nonClassified_features_s2.get('height_above'),nonClassified_features_s3.get('height_above')])
nonClassified_neighbor_H = np.concatenate([nonClassified_features_s1.get('neighbor_H'),nonClassified_features_s2.get('neighbor_H'),nonClassified_features_s3.get('neighbor_H')])
nonClassified_neighbor_S = np.concatenate([nonClassified_features_s1.get('neighbor_S'),nonClassified_features_s2.get('neighbor_S'),nonClassified_features_s3.get('neighbor_S')])
nonClassified_neighbor_V = np.concatenate([nonClassified_features_s1.get('neighbor_V'),nonClassified_features_s2.get('neighbor_V'),nonClassified_features_s3.get('neighbor_V')])
nonClassified_H_values = np.concatenate([nonClassified_features_s1.get('H'),nonClassified_features_s2.get('H'),nonClassified_features_s3.get('H')])
nonClassified_S_values = np.concatenate([nonClassified_features_s1.get('S'),nonClassified_features_s2.get('S'),nonClassified_features_s3.get('S')])
nonClassified_V_values = np.concatenate([nonClassified_features_s1.get('V'),nonClassified_features_s2.get('V'),nonClassified_features_s3.get('V')])
#Stack features for classification
print(f'Stacking features... {get_time()}')
classified_features = np.vstack((
classified_omnivariance,
classified_eigenentropy,
classified_anisotropy,
classified_linearity,
classified_planarity,
classified_curvature,
classified_sphericity,
classified_verticality,
classified_height_range,
classified_height_avg,
classified_height_below,
classified_height_above,
classified_neighbor_H,
classified_neighbor_S,
classified_neighbor_V,
classified_H_values,
classified_S_values,
classified_V_values
)).transpose()
nonClassified_features = np.vstack((
nonClassified_omnivariance,
nonClassified_eigenentropy,
nonClassified_anisotropy,
nonClassified_linearity,
nonClassified_planarity,
nonClassified_curvature,
nonClassified_sphericity,
nonClassified_verticality,
nonClassified_height_range,
nonClassified_height_avg,
nonClassified_height_below,
nonClassified_height_above,
nonClassified_neighbor_H,
nonClassified_neighbor_S,
nonClassified_neighbor_V,
nonClassified_H_values,
nonClassified_S_values,
nonClassified_V_values
)).transpose()
features = [
'Omnivariance',
'Eigenentropy',
'Anisotropy',
"Linearity",
"Planarity",
"Curvature",
"Sphericity",
"Verticality",
"Height range",
"Height mean",
"Height below",
"Height above",
"Neighbor H",
"Neighbor S",
"Neighbor V",
"Hue",
"Saturation",
"Value"
]
# Labels
labels = np.concatenate([classified_features_s1.get('classification'),classified_features_s2.get('classification'),classified_features_s3.get('classification')])
times, predictions_RF = calculateFeatures.classifyPointCloud(additional_text,
classified_features,
nonClassified_features,
features,
labels,
outputErrorRF,
importances_path_png)
fieldnames = ["model", "trainingTime", "predictingTime"]
# Open the CSV file in append mode
with open("../working/times/times.csv", "a", newline='') as csvfile:
# Create a DictWriter object, passing the file object and the fieldnames
writer = csv.DictWriter(csvfile, fieldnames=fieldnames)
# Check if the file is empty to write the header
csvfile.seek(0, 2) # Move the cursor to the end of the file
if csvfile.tell() == 0:
# Write the header only if the file is empty
writer.writeheader()
# Write the dictionary to the CSV file
writer.writerow(times)
# predictions_SVM = svm_model.predict(nonClassified_features)
result_output_array= np.vstack((nonClassified_X,
nonClassified_Y,
nonClassified_Z,
predictions_RF
)).T
print(f'Saving CSV file... {get_time()}')
#write to txt file in case las write didnt work
#np.savetxt(output_path_csv,result_output_array, delimiter=',',header='X,Y,Z,RF,GBT',comments='')
try:
print(f'Saving classified points as LAS... {get_time()}')
calculateFeatures.saveNP_as_LAS(result_output_array, # Array with X,Y,Z values
nonClassified_pointCloud, # Reference pc with headers
output_path_las, # output path
predictions_RF, # RF values
geom=True,
geomList=[nonClassified_omnivariance,
nonClassified_eigenentropy,
nonClassified_anisotropy,
nonClassified_linearity,
nonClassified_planarity,
nonClassified_curvature,
nonClassified_sphericity,
nonClassified_verticality])
except Exception as e:
print(e)
send_email.sendNotification('Error in saving classified points as LAS')
#stack color information
nonClassified_colors = np.vstack((nonClassified_red,nonClassified_green,nonClassified_blue)).T
# extended list for housing parameters that were not used for classification
extended_features = features.copy()
#add labels for the extended parameters
extended_features.append('Red')
extended_features.append('Green')
extended_features.append('Blue')
extended_features.append('classification')
try:
# array that includes the gch and predictions for plotting
full_value_array = np.vstack((nonClassified_features.T,nonClassified_colors.T, predictions_RF)).T
df = pd.DataFrame(full_value_array, columns=extended_features)
# convert numeric labels to semantic labels
semantic_labels = {
2.0: 'Ground',
3.0: 'Low Vegetation',
4.0: 'Medium Vegetation',
5.0: 'High Vegetation',
6.0: 'Roof',
7.0: 'Facade',
12.0: 'Vehicle'
}
df['classification'] = df['classification'].map(semantic_labels)
# plot HSV side to side
fig1, axs1 = plt.subplots(1, 3, figsize=(15, 5))
#plot kernel density estimate
sns.kdeplot(data=df, #data
x="Hue", #value to plot
hue="classification", #color by classification
hue_order=hue_order, #order of classification
palette=palette, #color palette
multiple= 'stack', #stacked KDE
legend=False, #no legend
lw=0.5, #line width
ax=axs1[0]) #plot on first subplot
#axs1[0].set_ylim(0, 4.5) #set y-axis limits
sns.despine(ax=axs1[0])
sns.kdeplot(data=df,
x="Saturation",
hue="classification",
hue_order=hue_order,
palette=palette,
multiple='stack',ax=axs1[1],legend=False,lw=0.5).set_ylabel('')
#axs1[1].set_ylim(0, 4.5)
#axs1[1].set_yticks([])
#sns.despine(ax=axs1[1],left=True)
sns.despine(ax=axs1[1])
sns.kdeplot(data=df,
x="Value",
hue="classification",
hue_order=hue_order,
palette=palette,
multiple='stack',ax=axs1[2],lw=0.5).set_ylabel('')
#axs1[2].set_ylim(0, 4.2)
#axs1[2].set_yticks([])
#sns.despine(ax=axs1[2],left=True)
sns.despine(ax=axs1[2])
# Display the figure
fig1.tight_layout()
fig1.savefig(HSV_plot_path)
# plot RGB side to side
fig4, axs4 = plt.subplots(1, 3, figsize=(15, 5))
#plot kernel density estimate
sns.kdeplot(data=df, #data
x="Red", #value to plot
hue="classification", #color by classification
hue_order=hue_order, #order of classification
palette=palette, #color palette
multiple= 'stack', #stacked KDE
legend=False, #no legend
lw=0.5, #line width
ax=axs4[0]) #plot on first subplot
#axs1[0].set_ylim(0, 4.5) #set y-axis limits
sns.despine(ax=axs4[0])
sns.kdeplot(data=df,
x="Green",
hue="classification",
hue_order=hue_order,
palette=palette,
multiple='stack',ax=axs4[1],legend=False,lw=0.5).set_ylabel('')
#axs1[1].set_ylim(0, 4.5)
#axs1[1].set_yticks([])
#sns.despine(ax=axs1[1],left=True)
sns.despine(ax=axs4[1])
sns.kdeplot(data=df,
x="Blue",
hue="classification",
hue_order=hue_order,
palette=palette,
multiple='stack',ax=axs4[2],lw=0.5).set_ylabel('')
#axs1[2].set_ylim(0, 4.2)
#axs1[2].set_yticks([])
#sns.despine(ax=axs1[2],left=True)
sns.despine(ax=axs4[2])
# Display the figure
fig4.tight_layout()
fig4.savefig(RGB_plot_path)
# plot heights
fig2, axs2 = plt.subplots(1, 3, figsize=(15, 5))
ylimit = 10.5
sns.kdeplot(data=df,
x="Height mean",
hue="classification",
hue_order=hue_order,
palette=palette,
multiple='stack',ax=axs2[0],legend=False,lw=0.5)
#axs2[0].set_ylim(0, ylimit)
sns.despine(ax=axs2[0])
sns.kdeplot(data=df,
x="Height below",
hue="classification",
hue_order=hue_order,
palette=palette,
multiple='stack',ax=axs2[1],legend=False,lw=0.5).set_ylabel('')
#axs2[1].set_ylim(0, ylimit)
#axs2[1].set_yticks([])
#sns.despine(ax=axs2[1],left=True)
sns.despine(ax=axs2[1])
sns.kdeplot(data=df,
x="Height above",
hue="classification",
hue_order=hue_order,
palette=palette,
multiple='stack',ax=axs2[2],lw=0.5).set_ylabel('')
sns.move_legend(axs2[2], 2)
#axs2[2].set_ylim(0, ylimit)
#axs2[2].set_yticks([])
#sns.despine(ax=axs2[2],left=True)
sns.despine(ax=axs2[2])
# Display the figure
fig2.tight_layout()
fig2.savefig(heights_plot_path)
# plot geometric features
fig3, axs3 = plt.subplots(4, 2, figsize=(15, 10))
ylimit = 10.5
sns.kdeplot(data=df,x="Omnivariance",
hue="classification",
hue_order=hue_order,
palette=palette,
multiple='stack',ax=axs3[0][0],legend=False,lw=0.5).set_ylabel('')
#axs[0][0].set_ylim(0, ylimit)
#axs[0][0].set_yticks([])
sns.despine(ax=axs3[0][0])
sns.kdeplot(data=df,
x="Eigenentropy",
hue="classification",
hue_order=hue_order,
palette=palette,
multiple='stack',ax=axs3[0][1],legend=False,lw=0.5).set_ylabel('')
#axs[0][1].set_ylim(0, ylimit)
#axs[0][1].set_yticks([])
sns.despine(ax=axs3[0][1])
sns.kdeplot(data=df,
x="Anisotropy",
hue="classification",
hue_order=hue_order,
palette=palette,
multiple='stack',ax=axs3[1][0],legend=False,lw=0.5).set_ylabel('')
#axs[1][0].set_ylim(0, ylimit)
#axs[1][0].set_yticks([])
sns.despine(ax=axs3[1][0])
sns.kdeplot(data=df,
x="Linearity",
hue="classification",
hue_order=hue_order,
palette=palette,
multiple='stack',ax=axs3[1][1],legend=False,lw=0.5).set_ylabel('')
#axs[1][1].set_ylim(0, ylimit)
#axs[1][1].set_yticks([])
sns.despine(ax=axs3[1][1])
sns.kdeplot(data=df,
x="Planarity",
hue="classification",
hue_order=hue_order,
palette=palette,
multiple='stack',ax=axs3[2][0],legend=False,lw=0.5).set_ylabel('')
#axs[2][0].set_ylim(0, ylimit)
#axs[2][0].set_yticks([])
sns.despine(ax=axs3[2][0])
sns.kdeplot(data=df,
x="Curvature",
hue="classification",
hue_order=hue_order,
palette=palette,
multiple='stack',ax=axs3[2][1],lw=0.5).set_ylabel('')
#axs3[2][1].set_ylim(0, ylimit)
#axs[2][1].set_yticks([])
sns.despine(ax=axs3[2][1])
sns.kdeplot(data=df,
x="Sphericity",
hue="classification",
hue_order=hue_order,
palette=palette,
multiple='stack',ax=axs3[3][0],legend=False,lw=0.5).set_ylabel('')
#axs[3][0].set_ylim(0, ylimit)
#axs[3][0].set_yticks([])
sns.despine(ax=axs3[3][0])
sns.kdeplot(data=df,
x="Verticality",
hue="classification",
hue_order=hue_order,
palette=palette,
multiple='stack',ax=axs3[3][1],legend=False,lw=0.5).set_ylabel('')
#axs[3][1].set_ylim(0, ylimit)
#axs[3][1].set_yticks([])
sns.despine(ax=axs3[3][1])
# Display the figure
fig3.tight_layout()
fig3.savefig(geomFeatures_plot_path)
except Exception as e:
print('Density charts were not saved')
print(e)
done_time = time.time()
eval_time = round((done_time - start_read)/3600,2)
print(f"Evaluated time for {additional_text}: {eval_time} hours")