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65 lines
2.6 KiB
Python
65 lines
2.6 KiB
Python
"""
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Loads a set of annotations corresponding to a dataset and saves an image which
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is the mean spectrogram for each class.
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"""
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import matplotlib.pyplot as plt
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import numpy as np
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import os
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import argparse
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import sys
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import viz_helpers as vz
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sys.path.append(os.path.join('..'))
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import bat_detect.train.train_utils as tu
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import bat_detect.detector.parameters as parameters
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import bat_detect.utils.audio_utils as au
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import bat_detect.train.train_split as ts
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if __name__ == "__main__":
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parser = argparse.ArgumentParser()
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parser.add_argument('audio_path', type=str, help='Input directory for audio')
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parser.add_argument('op_dir', type=str,
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help='Path to where single annotation json file is stored')
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parser.add_argument('--ann_file', type=str,
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help='Path to where single annotation json file is stored')
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parser.add_argument('--uk_split', type=str, default='',
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help='Set as: diff or same')
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parser.add_argument('--file_type', type=str, default='png',
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help='Type of image to save png or pdf')
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args = vars(parser.parse_args())
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if not os.path.isdir(args['op_dir']):
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os.makedirs(args['op_dir'])
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params = parameters.get_params(False)
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params['smooth_spec'] = False
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params['spec_width'] = 48
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params['norm_type'] = 'log' # log, pcen
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params['aud_pad'] = 0.005
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classes_to_ignore = params['classes_to_ignore'] + params['generic_class']
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# load train annotations
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if args['uk_split'] == '':
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print('\nLoading:', args['ann_file'], '\n')
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dataset_name = os.path.basename(args['ann_file']).replace('.json', '')
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datasets = []
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datasets.append(tu.get_blank_dataset_dict(dataset_name, False, args['ann_file'], args['audio_path']))
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else:
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# load uk data - special case
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print('\nLoading:', args['uk_split'], '\n')
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dataset_name = 'uk_' + args['uk_split'] # should be uk_diff, or uk_same
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datasets, _ = ts.get_train_test_data(args['ann_file'], args['audio_path'], args['uk_split'], load_extra=False)
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anns, class_names, _ = tu.load_set_of_anns(datasets, classes_to_ignore, params['events_of_interest'])
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class_names_order = range(len(class_names))
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x_train, y_train = vz.load_data(anns, params, class_names, smooth_spec=params['smooth_spec'], norm_type=params['norm_type'])
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op_file_name = os.path.join(args['op_dir'], dataset_name + '.' + args['file_type'])
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vz.save_summary_image(x_train, y_train, class_names, params, op_file_name, class_names_order)
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print('\nImage saved to:', op_file_name)
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