data .py

import os
import urllib
import hashlib
import argparse
import numpy as np
import pandas as pd
from skimage import io
import multiprocessing



example_dirname = os.path.abspath(os.path.dirname(__file__))
caffe_dirname = os.path.abspath(os.path.join(example_dirname, '../..'))



def image(args_tuple):
   
    try:
        url, filename = args_tuple
        if not os.path.exists(filename):
            urllib.urlretrieve(url, filename)
        with open(filename) as f:
            assert hashlib.sha1(f.read()).hexdigest() != MISSING_IMAGE_SHA1
        test_read_image = io.imread(filename)
        return True
    except KeyboardInterrupt:
        raise Exception()  
    except:
        return False


if __name__ == '__main__':
    parser = argparse.ArgumentParser(
        description=)
    parser.add_argument(
        '-s', '--seed', type=int, default=0,
        help="random seed")
    parser.add_argument(
        '-i', '--images', type=int, default=-1,
        help="",
    )
    parser.add_argument(
        '-w', '--workers', type=int, default=-1,
        help=" -x uses (all - x) cores [-1 default]."
    )
    parser.add_argument(
        '-l', '--labels', type=int, default=0,
        help=""
    )

    args = parser.parse_args()
    np.random.seed(args.seed)

   
    csv_filename = os.path.join(example_dirname, '')
    df = pd.read_csv(csv_filename, index_col=0, compression='gzip')
    df = df.iloc[np.random.permutation(df.shape[0])]
    if args.labels > 0:
        df = df.loc[df['label'] < args.labels]
    if args.images > 0 and args.images < df.shape[0]:
        df = df.iloc[:args.images]


    if training_dirname is None:
        training_dirname = os.path.join(caffe_dirname, '')
    images_dirname = os.path.join(training_dirname, 'images')
    if not os.path.exists(images_dirname):
        os.makedirs(images_dirname)
    df['image_filename'] = [
        os.path.join(images_dirname, _.split('/')[-1]) for _ in df['']
    ]

   
    num_workers = args.workers
    if num_workers <= 0:
        num_workers = multiprocessing.cpu_count() + num_workers
    print(''.format(
        df.shape[0], num_workers))
    pool = multiprocessing.Pool(processes=num_workers)
    map_args = zip(df[''], df['image_filename'])
    results = pool.map(download_image, map_args)


    df = df[results]
    for split in ['']:
        split_df = df[df['_split'] == split]
        filename = os.path.join(training_dirname, '{}.txt'.format(split))
        split_df[['image_filename', 'label']].to_csv(
            filename, sep=' ', header=None, index=None)