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| import os | |
| import pandas as pd | |
| from skimage import io | |
| import numpy as np | |
| import json | |
| import tqdm | |
| # R, G, B | |
| class GetImgMeanStd: | |
| def __init__(self, data_file): | |
| assert os.path.exists(data_file), 'train.csv dose not exist!' | |
| self.data_info = pd.read_csv(data_file, index_col=0) | |
| self.save_path_mean_std_info = 'generate_dep_info' | |
| self.mean = None | |
| self.std = None | |
| def get_img_mean_std(self): | |
| means = [] | |
| stds = [] | |
| bar = tqdm.tqdm(total=len(self.data_info)) | |
| for row in self.data_info.iterrows(): | |
| bar.update(1) | |
| img_name = row[1]['img'] | |
| # print(img_name) | |
| img = io.imread(img_name) | |
| img = img / 255. | |
| assert img is not None, img_name + 'is not valid' | |
| # height*width*channels, axis=0 is the first dim | |
| mean = np.mean(np.mean(img, axis=0), axis=0) | |
| means.append(mean) | |
| std = np.std(np.std(img, axis=0), axis=0) | |
| stds.append(std) | |
| bar.close() | |
| self.mean = np.mean(np.array(means), axis=0).tolist() | |
| self.std = np.mean(np.array(stds), axis=0).tolist() | |
| return {'mean': self.mean, 'std': self.std} | |
| def write_mean_std_information(self): | |
| info = self.get_img_mean_std() | |
| writer = os.path.join(self.save_path_mean_std_info, 'mean_std_info_test.json') | |
| with open(writer, 'w') as f_writer: | |
| json.dump(info, f_writer) | |
| print('\'PRIOR_MEAN\': %s\n\'PRIOR_STD\': %s\n' % (info['mean'], info['std'])) | |
| if __name__ == '__main__': | |
| data_file = r'generate_dep_info/train_data.csv' | |
| getImgMeanStd = GetImgMeanStd(data_file) | |
| getImgMeanStd.write_mean_std_information() | |