import os
import time
import shutil
import wget
import zipfile
import argparse
import numpy as np
import tensorflow as tf
from pathlib2 import Path
def check_dir(path, check=False):
if check:
assert os.path.exists(path), '{} does not exist!'.format(path)
else:
if not os.path.exists(path):
os.makedirs(path)
return Path(path).resolve(strict=False)
def download(source, target, force_clear=False):
if force_clear and os.path.exists(target):
print('Removing {}...'.format(target))
shutil.rmtree(target)
check_dir(target)
targt_file = str(Path(target).joinpath('data.zip'))
if os.path.exists(targt_file) and not force_clear:
print('data already exists, skipping download')
return
if source.startswith('http'):
print("Downloading from {} to {}".format(source, target))
wget.download(source, targt_file)
print("Done!")
else:
print("Copying from {} to {}".format(source, target))
shutil.copyfile(source, targt_file)
print('Unzipping {}'.format(targt_file))
zipr = zipfile.ZipFile(targt_file)
zipr.extractall(target)
zipr.close()
def process_image(path, image_size=160):
img_raw = tf.io.read_file(path)
img_tensor = tf.image.decode_jpeg(img_raw, channels=3)
img_final = tf.image.resize(img_tensor, [image_size, image_size]) / 255
return img_final
def walk_images(base_path, image_size=160):
images = []
print('Scanning {}'.format(base_path))
# find subdirectories in base path
# (they should be the labels)
labels = []
for (_, dirs, _) in os.walk(base_path):
print('Found {}'.format(dirs))
labels = dirs
break
for d in labels:
path = os.path.join(base_path, d)
print('Processing {}'.format(path))
# only care about files in directory
for item in os.listdir(path):
if not item.lower().endswith('.jpg'):
print('skipping {}'.format(item))
continue
image = os.path.join(path, item)
try:
img = process_image(image, image_size)
assert img.shape[2] == 3, "Invalid channel count"
# write out good images
images.append(image)
except Exception as e:
print('{}\n{}\n'.format(e, image))
return images
if __name__ == "__main__":
parser = argparse.ArgumentParser(description='data cleaning for binary image task')
parser.add_argument('-b', '--base_path', help='directory to base data', default='../../data')
parser.add_argument('-d', '--data', help='directory to training data', default='train')
parser.add_argument('-t', '--target', help='target file to hold good data', default='train.txt')
parser.add_argument('-i', '--img_size', help='target image size to verify', default=160, type=int)
parser.add_argument('-z', '--zipfile', help='source data zip file', default='../../tacodata.zip')
parser.add_argument('-f', '--force', help='force clear all data', default=False, action='store_true')
args = parser.parse_args()
print(args)
print('Using TensorFlow v.{}'.format(tf.__version__))
base_path = Path(args.base_path).resolve(strict=False)
print('Base Path: {}'.format(base_path))
data_path = base_path.joinpath(args.data).resolve(strict=False)
print('Train Path: {}'.format(data_path))
target_path = Path(base_path).resolve(strict=False).joinpath(args.target)
print('Train File: {}'.format(target_path))
zip_path = args.zipfile
print('Acquiring data...')
download(str(zip_path), str(base_path), args.force)
if os.path.exists(str(target_path)):
print('dataset text file already exists, skipping check')
else:
print('Testing images...')
images = walk_images(str(data_path), args.img_size)
# save file
print('writing dataset to {}'.format(target_path))
with open(str(target_path), 'w+') as f:
f.write('\n'.join(images))
# python data.py -z https://centeotl.blob.core.windows.net/public/tacodata.zip -t train.txt
