#
#作者：韦访
#博客：https://blog.youkuaiyun.com/rookie_wei
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#欢迎大家一起学习
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------韦访 20190118
1、概述
想用tensorflow做个疲劳检测，那么，该怎么下手呢？首先，根据常识，疲劳的时候，眼睛总会想闭上吧？还打哈欠吧？那么，先从眼睛入手，那么，要做的工作就是，首先得识别出眼睛的位置，也就是人脸关键点检测，在人脸识别上的教程（https://blog.youkuaiyun.com/rookie_wei/article/details/81676177）中，我们讲过，使用MTCNN可以将人脸检测出来，并且识别出5个关键点（左眼、右眼、鼻子、左嘴角、右嘴角）的位置。检测出眼睛的位置以后，就可以将眼睛的框出来，然后，识别它是开眼还是闭眼。打哈欠的话，是不是也得先识别嘴巴的位置啊？识别出嘴巴以后怎办，后面再说。这一讲，先来对眼睛的开闭进行识别。首先说明一下，我可能并不会给出一个完整的疲劳检测系统的教程，做出来了我可能也不会全部开源，仅供部分参考，作为之前教程的一个回顾，你懂的。
2、下载数据集
下载链接：
http://parnec.nuaa.edu.cn/xtan/data/datasets/dataset_B_Eye_Images.rar
数据集很小，2.5M而已。下载完以后，解压，得到目录如下：
根据文件夹名称，可以猜出应该是将眼睛分为4种状态了，分别是左眼开，左眼闭，右眼开，右眼闭。现在来看看每个文件夹里的内容是否跟我们预想的一样。
果然哈。那么接下来的问题就是，怎么识别？看过我tensorflow系列教程的朋友们应该知道，在第16讲和20讲中，我们使用过slim模型来进行图片分类，如果没看过，出门左转，链接如下：
16讲：https://blog.youkuaiyun.com/rookie_wei/article/details/80639490
20讲：https://blog.youkuaiyun.com/rookie_wei/article/details/80796009
这一讲，其实就是这两讲的一样应用，如果看过这两讲的朋友，就此打住，别往下看了，自己根据这两讲的内容做一下开闭眼的识别。
3、将数据集转成TFRecord格式
slim模型的下载、验证和结构我就不再重复讲了，现在，我们来将数据集转成TFRecord格式。我试过就按数据集的默认分类去识别，即左眼开，左眼闭，右眼开，右眼闭，效果并不好，识别的准确率仅为50%多一点，这肯定不行的。而我们现在的目的是，能识别出开眼和闭眼即可，所以并不需要分的那么详细。因此，现在将closedRightEyes文件夹下的所有图片剪切到closedLeftEyes文件夹，将openRightEyes文件夹下的所有图片剪切到openLeftEyes文件夹。再把两个空的文件夹删除，剩下的如下图所示，
将这个两个文件夹放到slim/images_data/eye_open_and_close文件夹下。
好了，接着就来修改代码了，仿造第20讲的教程做即可，写博客或者笔记的好处就是这样了，随手一拿就可以开干。首先，复制download_and_convert_flowers.py并将文件名改为convert_eye.py。修改后的源码如下，
-
# encoding:utf-8
-
from __future__
import absolute_import
-
from __future__
import division
-
from __future__
import print_function
-
-
import math
-
import os
-
import random
-
import sys
-
-
import tensorflow
as tf
-
-
from datasets
import dataset_utils
-
-
# The URL where the Flowers data can be downloaded.
-
_DATA_URL =
'http://download.tensorflow.org/example_images/flower_photos.tgz'
-
-
# The number of images in the validation set.
-
_NUM_VALIDATION =
350
-
-
# Seed for repeatability.
-
_RANDOM_SEED =
0
-
-
# The number of shards per dataset split.
-
_NUM_SHARDS =
4
-
-
-
class ImageReader(object):
-
"""Helper class that provides TensorFlow image coding utilities."""
-
-
def __init__(self):
-
# Initializes function that decodes RGB JPEG data.
-
self._decode_jpeg_data = tf.placeholder(dtype=tf.string)
-
self._decode_jpeg = tf.image.decode_jpeg(self._decode_jpeg_data, channels=
3)
-
-
def read_image_dims(self, sess, image_data):
-
image = self.decode_jpeg(sess, image_data)
-
return image.shape[
0], image.shape[
1]
-
-
def decode_jpeg(self, sess, image_data):
-
image = sess.run(self._decode_jpeg,
-
feed_dict={self._decode_jpeg_data: image_data})
-
assert len(image.shape) ==
3
-
assert image.shape[
2] ==
3
-
return image
-
-
-
def _get_filenames_and_classes(dataset_dir):
-
"""Returns a list of filenames and inferred class names.
-
Args:
-
dataset_dir: A directory containing a set of subdirectories representing
-
class names. Each subdirectory should contain PNG or JPG encoded images.
-
Returns:
-
A list of image file paths, relative to `dataset_dir` and the list of
-
subdirectories, representing class names.
-
"""
-
# 将flower_photos改为eye_photos
-
flower_root = os.path.join(dataset_dir,
'eye_open_and_close')
-
directories = []
-
class_names = []
-
for filename
in os.listdir(flower_root):
-
path = os.path.join(flower_root, filename)
-
if os.path.isdir(path):
-
directories.append(path)
-
class_names.append(filename)
-
-
photo_filenames = []
-
for directory
in directories:
-
for filename
in os.listdir(directory):
-
path = os.path.join(directory, filename)
-
photo_filenames.append(path)
-
-
return photo_filenames, sorted(class_names)
-
-
-
def _get_dataset_filename(dataset_dir, split_name, shard_id):
-
# 修改文件名，将flowersg改为eye
-
output_filename =
'eye_%s_%05d-of-%05d.tfrecord' % (
-
split_name, shard_id, _NUM_SHARDS)
-
return os.path.join(dataset_dir, output_filename)
-
-
-
def _convert_dataset(split_name, filenames, class_names_to_ids, dataset_dir):
-
"""Converts the given filenames to a TFRecord dataset.
-
Args:
-
split_name: The name of the dataset, either 'train' or 'validation'.
-
filenames: A list of absolute paths to png or jpg images.
-
class_names_to_ids: A dictionary from class names (strings) to ids
-
(integers).
-
dataset_dir: The directory where the converted datasets are stored.
-
"""
-
assert split_name
in [
'train',
'validation']
-
-
num_per_shard = int(math.ceil(len(filenames) / float(_NUM_SHARDS)))
-
-
with tf.Graph().as_default():
-
image_reader = ImageReader()
-
-
with tf.Session(
'')
as sess:
-
-
for shard_id
in range(_NUM_SHARDS):
-
output_filename = _get_dataset_filename(
-
dataset_dir, split_name, shard_id)
-
-
with tf.python_io.TFRecordWriter(output_filename)
as tfrecord_writer:
-
start_ndx = shard_id * num_per_shard
-
end_ndx = min((shard_id +
1) * num_per_shard, len(filenames))
-
for i
in range(start_ndx, end_ndx):
-
sys.stdout.write(
'\r>> Converting image %d/%d shard %d' % (
-
i +
1, len(filenames), shard_id))
-
sys.stdout.flush()
-
-
# Read the filename:
-
image_data = tf.gfile.FastGFile(filenames[i],
'rb').read()
-
height, width = image_reader.read_image_dims(sess, image_data)
-
-
class_name = os.path.basename(os.path.dirname(filenames[i]))
-
class_id = class_names_to_ids[class_name]
-
-
example = dataset_utils.image_to_tfexample(
-
image_data,
b'jpg', height, width, class_id)
-
tfrecord_writer.write(example.SerializeToString())
-
-
sys.stdout.write(
'\n')
-
sys.stdout.flush()
-
-
-
def _clean_up_temporary_files(dataset_dir):
-
"""Removes temporary files used to create the dataset.
-
Args:
-
dataset_dir: The directory where the temporary files are stored.
-
"""
-
filename = _DATA_URL.split(
'/')[
-1]
-
filepath = os.path.join(dataset_dir, filename)
-
tf.gfile.Remove(filepath)
-
-
# 将flower_photos改为eye_photos
-
tmp_dir = os.path.join(dataset_dir,
'eye_photos')
-
tf.gfile.DeleteRecursively(tmp_dir)
-
-
-
def _dataset_exists(dataset_dir):
-
for split_name
in [
'train',
'validation']:
-
for shard_id
in range(_NUM_SHARDS):
-
output_filename = _get_dataset_filename(
-
dataset_dir, split_name, shard_id)
-
if
not tf.gfile.Exists(output_filename):
-
return
False
-
return
True
-
-
-
def run(dataset_dir):
-
"""Runs the download and conversion operation.
-
Args:
-
dataset_dir: The dataset directory where the dataset is stored.
-
"""
-
if
not tf.gfile.Exists(dataset_dir):
-
tf.gfile.MakeDirs(dataset_dir)
-
-
if _dataset_exists(dataset_dir):
-
print(
'Dataset files already exist. Exiting without re-creating them.')
-
return
-
-
# 因为我们不需要下载，所以这行注释掉
-
# dataset_utils.download_and_uncompress_tarball(_DATA_URL, dataset_dir)
-
photo_filenames, class_names = _get_filenames_and_classes(dataset_dir)
-
class_names_to_ids = dict(zip(class_names, range(len(class_names))))
-
-
# Divide into train and test:
-
random.seed(_RANDOM_SEED)
-
random.shuffle(photo_filenames)
-
training_filenames = photo_filenames[_NUM_VALIDATION:]
-
validation_filenames = photo_filenames[:_NUM_VALIDATION]
-
-
# First, convert the training and validation sets.
-
_convert_dataset(
'train', training_filenames, class_names_to_ids,
-
dataset_dir)
-
_convert_dataset(
'validation', validation_filenames, class_names_to_ids,
-
dataset_dir)
-
-
# Finally, write the labels file:
-
labels_to_class_names = dict(zip(range(len(class_names)), class_names))
-
dataset_utils.write_label_file(labels_to_class_names, dataset_dir)
-
-
# 将这行注释掉，要不然转换完以后，原始数据会被删除
-
# _clean_up_temporary_files(dataset_dir)
-
print(
'\nFinished converting the Flowers dataset!')
再修改download_and_convert_data.py，添加
from datasets import convert_eye
再在
elif FLAGS.dataset_name == 'mnist':
download_and_convert_mnist.run(FLAGS.dataset_dir)
后添加
elif FLAGS.dataset_name == 'eye':
convert_eye.run(FLAGS.dataset_dir)
如下图所示，
然后运行命令，
python download_and_convert_data.py --dataset_name=eye --dataset_dir=images_data/eye_open_and_close
运行结果，
Instructions for updating:
Use tf.gfile.GFile.
>> Converting image 143/4498 shard 0Traceback (most recent call last):
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/client/session.py", line 1335, in _do_call
return fn(*args)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/client/session.py", line 1320, in _run_fn
options, feed_dict, fetch_list, target_list, run_metadata)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/client/session.py", line 1408, in _call_tf_sessionrun
run_metadata)
tensorflow.python.framework.errors_impl.InvalidArgumentError: Expected image (JPEG, PNG, or GIF), got unknown format starting with '\320\317\021\340\241\261\032\341\000\000\000\000\000\000\000\000'
[[{{node DecodeJpeg}}]]
报错，看这句，
tensorflow.python.framework.errors_impl.InvalidArgumentError: Expected image (JPEG, PNG, or GIF), got unknown format starting with
应该首先想到的是，我们数据集里是不是有不是图片的文件？
果然就看到了如下图这个文件，
删掉即可，注意，两个文件夹下都有这个文件啊。
再运行上面的命令，运行结果，
这就对了，当然，这个打印还是打印Flowsers数据集的，如果你有强迫症，也可以把它改了。去images_data/eye_open_and_close/文件夹下看看有没有TFRecord文件生成，
你也可以用第20讲的代码显示一张图片以验证这个TFRecord是否正确，我这里就不验证了。
4、定义datasets文件
继续修改代码，将datasets/flowers.py复制并重命名为eye.py ,将
_FILE_PATTERN = 'flowers_%s_*.tfrecord'
改为
_FILE_PATTERN = 'eye_%s_*.tfrecord'
将
SPLITS_TO_SIZES = {'train': 3320, 'validation': 350}
改为
SPLITS_TO_SIZES = {'train': 4496, 'validation': 350}
其中，train代表训练的图片张数，validation代表验证使用的图片张数。完整代码如下，
-
# Copyright 2016 The TensorFlow Authors. 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.
-
# ==============================================================================
-
"""Provides data for the flowers dataset.
-
-
The dataset scripts used to create the dataset can be found at:
-
tensorflow/models/research/slim/datasets/download_and_convert_flowers.py
-
"""
-
-
from __future__
import absolute_import
-
from __future__
import division
-
from __future__
import print_function
-
-
import os
-
import tensorflow
as tf
-
-
from datasets
import dataset_utils
-
-
slim = tf.contrib.slim
-
-
_FILE_PATTERN =
'eye_%s_*.tfrecord'
-
-
SPLITS_TO_SIZES = {
'train':
4496,
'validation':
350}
-
-
_NUM_CLASSES =
2
-
-
_ITEMS_TO_DESCRIPTIONS = {
-
'image':
'A color image of varying size.',
-
'label':
'A single integer between 0 and 4',
-
}
-
-
-
def get_split(split_name, dataset_dir, file_pattern=None, reader=None):
-
"""Gets a dataset tuple with instructions for reading flowers.
-
-
Args:
-
split_name: A train/validation split name.
-
dataset_dir: The base directory of the dataset sources.
-
file_pattern: The file pattern to use when matching the dataset sources.
-
It is assumed that the pattern contains a '%s' string so that the split
-
name can be inserted.
-
reader: The TensorFlow reader type.
-
-
Returns:
-
A `Dataset` namedtuple.
-
-
Raises:
-
ValueError: if `split_name` is not a valid train/validation split.
-
"""
-
if split_name
not
in SPLITS_TO_SIZES:
-
raise ValueError(
'split name %s was not recognized.' % split_name)
-
-
if
not file_pattern:
-
file_pattern = _FILE_PATTERN
-
file_pattern = os.path.join(dataset_dir, file_pattern % split_name)
-
-
# Allowing None in the signature so that dataset_factory can use the default.
-
if reader
is
None:
-
reader = tf.TFRecordReader
-
-
keys_to_features = {
-
'image/encoded': tf.FixedLenFeature((), tf.string, default_value=
''),
-
'image/format': tf.FixedLenFeature((), tf.string, default_value=
'png'),
-
'image/class/label': tf.FixedLenFeature(
-
[], tf.int64, default_value=tf.zeros([], dtype=tf.int64)),
-
}
-
-
items_to_handlers = {
-
'image': slim.tfexample_decoder.Image(),
-
'label': slim.tfexample_decoder.Tensor(
'image/class/label'),
-
}
-
-
decoder = slim.tfexample_decoder.TFExampleDecoder(
-
keys_to_features, items_to_handlers)
-
-
labels_to_names =
None
-
if dataset_utils.has_labels(dataset_dir):
-
labels_to_names = dataset_utils.read_label_file(dataset_dir)
-
-
return slim.dataset.Dataset(
-
data_sources=file_pattern,
-
reader=reader,
-
decoder=decoder,
-
num_samples=SPLITS_TO_SIZES[split_name],
-
items_to_descriptions=_ITEMS_TO_DESCRIPTIONS,
-
num_classes=_NUM_CLASSES,
-
labels_to_names=labels_to_names)
5、开始训练
接着，修改datasets/dataset_factory.py文件，
将
from datasets import cifar10
from datasets import flowers
from datasets import imagenet
from datasets import mnist
datasets_map = {
'cifar10': cifar10,
'flowers': flowers,
'imagenet': imagenet,
'mnist': mnist,
}
改成
from datasets import cifar10
from datasets import flowers
from datasets import imagenet
from datasets import mnist
from datasets import eye
datasets_map = {
'cifar10': cifar10,
'flowers': flowers,
'imagenet': imagenet,
'mnist': mnist,
'eye': eye,
}
然后，运行以下代码进行训练，
python train_image_classifier.py --train_dir=saver/inv3_eye_open_and_close --dataset_name=eye --dataset_split_name=train --dataset_dir=images_data/eye_open_and_close --model_name=inception_v3 --learning_rate_decay_type=fixed --save_interval_secs=60 --save_summaries_secs=60 --log_every_n_steps=10 --optimizer=rmsprop --learning_rate=0.0001
运行结果，
OK，跑起来就行了。
6、测试准确率
训练到感觉loss不怎么下降的时候，测试一下它的准确率，命令如下，
python eval_image_classifier.py --checkpoint_path=saver/inv3_eye_open_and_close/ --eval_dir=saver/inv3_eye_open_and_close/ --dataset_name=eye --dataset_split_name=validation --dataset_dir=images_data/eye_open_and_close --model_name=inception_v3 --batch_size=64
在测试集上的准确率为93.49%，在精度不是要求特别高的情况下还是可以了。
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