# Generate command for available RAM
otb_input_ram = generate_command('-ram ', False, input_ram, False)
command_list.append(otb_input_ram)
# Generate command for output confusion matrix
if len(out_conf_matrix) > 0:
otb_out_conf_matrix = generate_command('-io.confmatout ', True,
out_conf_matrix, False)
command_list.append(otb_out_conf_matrix)
# Generate command for output model file
otb_out_model = generate_command('-io.out ', True, out_model, False)
command_list.append(otb_out_model)
# Generate full command for OTB
otb_write_output = execute_command('otbcli_TrainImagesClassifier ',
command_list, workspace, otb_dir)
# Save command to log
with open(log_file, 'w') as f:
f.write('Train Image Classifier (Bayes) Log')
f.write('\nTimestamp: {}'.format(ts))
f.write('\nInput Images: {}'.format(input_images.split(';')))
f.write('\nTrain/Validation Sample Ratio: {}'.format(
input_train_validation_ratio))
f.write(
'\nBound sample number minimum: {}'.format(input_bound_sample_num_min))
f.write('\nDefault Elevation: {}'.format(input_default_elev))
f.write('\nMaximum Depth of the Tree: {}'.format(input_max_depth_tree))
f.write('\nMinimum Number of Samples in Each Node: {}'.format(
input_min_samples_node))
f.write('\nTermination Criteria for Each Regression Tree: {}'.format(
command_list.append(otb_output_centroid_file)
# Generate command for input mask value
otb_input_mask_value = generate_command('-nodatalabel ', False,
input_mask_value, False)
command_list.append(otb_input_mask_value)
# Generate user seed
otb_input_seed = generate_command('-rand ', False, input_seed, False)
command_list.append(otb_input_seed)
otb_output_file = generate_command('-out ', True, output_file, False)
command_list.append(otb_output_file)
# Generate full command for OTB
otb_write_output = execute_command('otbcli_KMeansClassification ',
command_list, workspace, otb_dir)
# Save command to log
with open(log_file, 'w') as f:
f.write('K Means Classification Log')
f.write('\nTimestamp: {}'.format(ts))
f.write('\nImage Input: {}'.format(input_image))
f.write('\nValidity Mask: {}'.format(input_validity_mask))
f.write('\nTraining Set Size: {}'.format(input_training_set_size))
f.write('\nNumber of classes: {}'.format(input_number_of_classes))
f.write('\nMax Number of Iterations: {}'.format(input_max_num_iterations))
f.write('\nMask Value: {}'.format(input_mask_value))
f.write('\nSampler Type: {}'.format(input_sampler_type))
f.write('\nOutput centroid file: {}'.format(output_centroid_file))
f.write('\nOutput image: {}'.format(output_file))
f.write('\nOTB Command: {}'.format(otb_write_output))
# Generate command for input classified image
otb_input_train_shp = generate_command('-in ', True, input_image, False)
command_list.append(otb_input_train_shp)
# Generate command for truth type
otb_input_truth_type = generate_command('-ref ', False, 'raster', False)
command_list.append(otb_input_truth_type)
# Generate command for input ref image
otb_input_ref_image = generate_command('-ref.raster.in ', True, input_ref_image, False)
command_list.append(otb_input_ref_image)
# Generate command for value for nodata pixels
otb_input_value_for_nodata = generate_command('-ref.raster.nodata ', False, input_value_for_nodata, False)
command_list.append(otb_input_value_for_nodata)
# Generate command for matrix output
otb_output_matrix_output = generate_command('-out ', False, output_matrix_output, False)
command_list.append(otb_output_matrix_output)
# Generate full command for OTB
otb_write_output = execute_command('otbcli_ComputeConfusionMatrix ', command_list, workspace, otb_dir)
# Save command to log
with open(log_file, 'w') as f:
f.write('Compute Confusion Matrix Log')
f.write('\nTimestamp: {}'.format(ts))
f.write('\nInput image: {}'.format(input_image))
f.write('\nOTB Command: {}'.format(otb_write_output))
False)
command_list.append(otb_input_before_image)
# Generate command for input image (after)
otb_input_after_image = generate_command('-in2 ', True, input_after_image,
False)
command_list.append(otb_input_after_image)
# Generate command for ram usage
otb_input_ram = generate_command('-ram ', False, input_ram, False)
command_list.append(otb_input_ram)
# Generate command for output change map
otb_output_change_map = generate_command('-out ', True, output_change_map,
False)
command_list.append(otb_output_change_map)
# Generate full command for OTB
otb_write_output = execute_command('otbcli_MultivariateAlterationDetector ',
command_list, workspace, otb_dir)
# Save command to log
with open(log_file, 'w') as f:
f.write('Multivariate Alteration Detector Log')
f.write('\nTimestamp: {}'.format(ts))
f.write('\nInput Before Image: {}'.format(input_before_image))
f.write('\nInput After Image: {}'.format(input_after_image))
f.write('\nInput RAM Limit: {}'.format(input_ram))
f.write('\nOutput Change Map: {}'.format(output_change_map))
f.write('\nOTB Command : {}'.format(otb_write_output))