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from skimage import io |
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import cv2 |
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import matplotlib.pyplot as plt |
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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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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_dilated_5') |
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train_data_path = join(data_path, 'boundaries', 'train') |
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test_data_path = join(data_path,'boundaries', '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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kernel = np.ones((5, 5), 'uint8') |
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for train_file in os.listdir(train_data_path): |
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print(train_file) |
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file_path = join(train_data_path, train_file) |
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front = io.imread(file_path) |
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boundary_dil = cv2.dilate(front, kernel) |
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output_file_path = join(train_output_path, train_file) |
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io.imsave(output_file_path, boundary_dil) |
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for test_file in os.listdir(test_data_path): |
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print(test_file) |
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file_path = join(test_data_path, test_file) |
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front = io.imread(file_path) |
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boundary_dil = cv2.dilate(front, kernel) |
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output_file_path = join(test_output_path, test_file) |
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io.imsave(output_file_path, boundary_dil) |
