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reduced_3scalesRF.py
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import calculateFeatures
import numpy as np
import laspy as lp
import sys
import send_email
import time
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report, confusion_matrix, accuracy_score
import matplotlib.pyplot as plt
additional_texts = ["COL_ONLY","HEI_ONLY","COL_HEI","GEO_ONLY","GEO_COL","ROOF_FAC"] #change
#additional_text = additional_texts[0]
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_area.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
output_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'
# 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_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_avg",
# "height_below",
# "height_above",
"neighbor_H",
"neighbor_S",
"neighbor_V",
"H_values",
"S_values",
"V_values"
]
# Labels
labels = np.concatenate([classified_features_s1.get('classification'),classified_features_s2.get('classification'),classified_features_s3.get('classification')])
#-------#
send_email.sendUpdate(f'Classification has begun for {additional_text}')
# Train a classifier
X_train, X_test, y_train, y_test = train_test_split(classified_features, labels, test_size=0.2, random_state=42)
# Machine learning models
rf_model = RandomForestClassifier(n_estimators=50)
# Train the models
print(f'Training models... {get_time()}')
rf_model.fit(X_train, y_train)
#svm_model.fit(X_train, y_train)
# Evaluate model
send_email.sendUpdate('Training done. Evaluating models...')
print(f'Evaluating models... {get_time()}')
y_pred_rf = rf_model.predict(X_test)
#y_pred_svm = svm_model.predict(X_test)
#Predict the non-classified area
# RF model report
print(f'Writing RF classification performance to file... {get_time()}')
report_RF = classification_report(y_test, y_pred_rf)
matrix_RF = confusion_matrix(y_test, y_pred_rf)
accuracy_RF = accuracy_score(y_test, y_pred_rf)
importances = rf_model.feature_importances_
#Get accuracy results and write to file
with open(outputErrorRF, 'w') as f:
f.write('Classification Report for Random Forests:\n')
f.write(report_RF)
f.write('\nConfusion Matrix:\n')
f.write(str(matrix_RF))
f.write(f'\nAccuracy: {accuracy_RF * 100:.2f}%')
f.write('\nFeature ranking:\n')
for f_index in range(len(features)):
f.write(f"{features[f_index]}: {importances[f_index]}\n")
print(f'Predicting non-classified pc... {get_time()}')
predictions_RF = rf_model.predict(nonClassified_features)
# predictions_SVM = svm_model.predict(nonClassified_features)
result_output_array= np.vstack((nonClassified_X,
nonClassified_Y,
nonClassified_Z,
predictions_RF,
nonClassified_verticality #place holder second ML values
)).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
nonClassified_verticality) #place holder second ML values
except Exception as e:
print(e)
send_email.sendNotification('Error in saving classified points as LAS')
#save plot of importances
try:
#create dictionary
combined_dict = {features[i]: importances[i] for i in range(len(features))}
#sort the values
sorted_dict = dict(sorted(combined_dict.items(), key=lambda item: item[1]))
plt.clf()
plt.figure(figsize=(10, 6))
plt.bar(sorted_dict.keys(), sorted_dict.values())
plt.style.use('fast')
plt.xlabel('Features')
plt.ylabel('Importance')
plt.title('Importance of features')
plt.xticks(rotation=45, ha='right')
plt.savefig(output_path_png, bbox_inches='tight')
except:
print('chart was not saved')
done_time = time.time()
eval_time = round((done_time - start_read)/3600,2)
print(f"Evaluated time for {additional_text}: {eval_time} hours")