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from skimage import io |
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import numpy as np |
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from batchgenerators.utilities.file_and_folder_operations import * |
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import argparse |
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def extract_boundaries(sample_path): |
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zone_orig = io.imread(sample_path) |
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zone_pad = np.pad(zone_orig, ((1,1),(1,1)), 'reflect') |
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zone_b = np.pad(zone_orig, ((2,0),(1,1)), "reflect") |
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zone_t = np.pad(zone_orig, ((0,2),(1,1)), "reflect") |
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zone_r = np.pad(zone_orig, ((1,1),(0,2)), "reflect") |
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zone_l = np.pad(zone_orig, ((1,1),(2,0)), "reflect") |
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boundaries = np.zeros_like(zone_pad) |
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isboundary = np.logical_or.reduce((zone_pad != zone_b, zone_pad != zone_t, zone_pad != zone_r, zone_pad != zone_l)) |
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boundaries[np.logical_and(zone_pad == 127, isboundary)] = 255 |
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return boundaries[1:-1, 1:-1] |
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if __name__ == '__main__': |
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parser = argparse.ArgumentParser() |
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parser.add_argument("-data_path", |
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help="percentage of the dataset used for training validation and test") |
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args = parser.parse_args() |
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data_path = args.data_path |
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output_path = join(data_path, 'boundaries') |
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train_data_path = join(data_path, 'zones', 'train') |
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test_data_path = join(data_path,'zones', 'test') |
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train_output_path = join(output_path, 'train') |
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test_output_path = join(output_path, 'test') |
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maybe_mkdir_p(output_path) |
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maybe_mkdir_p(train_output_path) |
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maybe_mkdir_p(test_output_path) |
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for train_file in os.listdir(train_data_path): |
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print(train_file) |
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sample_path = join(train_data_path, train_file) |
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boundary = extract_boundaries(sample_path) |
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output_file = train_file[:-len('zones.png')] + 'boundary.png' |
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output_path = join(train_output_path, output_file) |
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io.imsave(output_path, boundary) |
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for test_file in os.listdir(test_data_path): |
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print(test_file) |
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sample_path = join(test_data_path, test_file) |
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boundary = extract_boundaries(sample_path) |
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output_file = test_file[:-len('zones.png')]+ 'boundary.png' |
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output_path = join(test_output_path, output_file) |
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io.imsave(output_path, boundary) |
