#!/usr/bin/env python
# Copyright 2016 Google Inc. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
r"""Process the ImageNet Challenge bounding boxes for TensorFlow model training.
Associate the ImageNet 2012 Challenge validation data set with labels.
The raw ImageNet validation data set is expected to reside in JPEG files
located in the following directory structure.
data_dir/ILSVRC2012_val_00000001.JPEG
data_dir/ILSVRC2012_val_00000002.JPEG
...
data_dir/ILSVRC2012_val_00050000.JPEG
This script moves the files into a directory structure like such:
data_dir/n01440764/ILSVRC2012_val_00000293.JPEG
data_dir/n01440764/ILSVRC2012_val_00000543.JPEG
...
where 'n01440764' is the unique synset label associated with
these images.
This directory reorganization requires a mapping from validation image
number (i.e. suffix of the original file) to the associated label. This
is provided in the ImageNet development kit via a Matlab file.
In order to make life easier and divorce ourselves from Matlab, we instead
supply a custom text file that provides this mapping for us.
Sample usage:
./preprocess_imagenet_validation_data.py ILSVRC2012_img_val \
imagenet_2012_validation_synset_labels.txt
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import sys
from six.moves import xrange # pylint: disable=redefined-builtin
if __name__ == '__main__':
if len(sys.argv) < 3: # sys.argv返回腳本本身的名字及給定腳本的參數.
print('Invalid usage\n'
'usage: preprocess_imagenet_validation_data.py '
'<validation data dir> <validation labels file>')
sys.exit(-1) # System.exit(-1)是指所有程序（方法，類等）停止，系統停止運行。
data_dir = sys.argv[1]
validation_labels_file = sys.argv[2]
# Read in the 50000 synsets associated with the validation data set.
# imagenet_2012_validation_synset_labels.txt 這個文件中有50000行類別，有重復，與50000圖片是一一對應的
labels = [l.strip() for l in open(validation_labels_file).readlines()] # strip() 方法用於移除字符串頭尾指定的字符（默認為空格或換行符）。
unique_labels = set(labels) # set() 函數創建一個無序不重復元素集，可進行關系測試，刪除重復數據，還可以計算交集、差集、並集等。
# Make all sub-directories in the validation data dir.
for label in unique_labels:
labeled_data_dir = os.path.join(data_dir, label)
if not os.path.exists(labeled_data_dir):
os.makedirs(labeled_data_dir)
# Move all of the image to the appropriate sub-directory.
for i in xrange(len(labels)): # xrange() 函數用法與 range 完全相同，所不同的是生成的不是一個數組，而是一個生成器。
basename = 'ILSVRC2012_val_000%.5d.JPEG' % (i + 1)
original_filename = os.path.join(data_dir, basename)
if not os.path.exists(original_filename):
#print('Failed to find: ' % original_filename)
continue
#sys.exit(-1)
new_filename = os.path.join(data_dir, labels[i], basename)
os.rename(original_filename, new_filename)
82行的代碼一加進去，就出錯：
TypeError: not all arguments converted during string formatting
過程中還出現了以下錯誤：
Organizing the validation data into sub-directories.
Traceback (most recent call last):
File "F:/datasets/preprocess_imagenet_validation_data.py", line 86, in <module>
os.rename(original_filename, new_filename)
PermissionError: [WinError 32] ▒▒һ▒▒▒▒▒▒▒▒▒▒ʹ▒ô▒▒ļ▒▒▒▒▒▒▒▒▒▒▒▒ʡ▒: 'F:/ILSVRC2012_img_val/ILSVRC2012_val_00032304.JPEG' -> 'F:/ILSVRC2012_img_val/n02109961\\ILSVRC2012_val_00032304.JPEG'
可能是不能夠一次性重命名太多文件，反正我重新運行了
./download_and_convert_imagenet.sh /f/ILSVRC2012_img_val_varified
preprocess_imagenet_validation_data.py這個程序可以繼續重命名文件。