VOC07+12合并训练用于图像分类
该程序可以用于图像分类，比如Voc2012的数据集，首先将数据集中标签和图片按照类别分为20类，然后在使用该程序将分好类的文件，转换为tfrecord格式，用于训练图像分类。具体程序如下：
# -*- coding: utf-8 -*-
"""
Created on Sat Mar 9 13:22:18 2019
import os
import sys
import random
import numpy as np
import tensorflow as tf
import xml.etree.ElementTree as ET
import six
from six.moves import cPickle
sys.path.append(r".....\datasets")
sys.path.append(r".....\utils")
#import label_map_util
#import dataset_utils
DIRECTORY_ANNOTATIONS = 'Annotations/'
DIRECTORY_IMAGES = 'JPEGImages/'
RANDOM_SEED = 4242
SAMPLES_PER_FILES = 2000
VOC_LABELS = {
'aeroplane': (1, 'Vehicle'),
'bicycle': (2, 'Vehicle'),
'bird': (3, 'Animal'),
'boat': (4, 'Vehicle'),
'bottle': (5, 'Indoor'),
'bus': (6, 'Vehicle'),
'car': (7, 'Vehicle'),
'cat': (8, 'Animal'),
'chair': (9, 'Indoor'),
'cow': (10, 'Animal'),
'diningtable': (11, 'Indoor'),
'dog': (12, 'Animal'),
'horse': (13, 'Animal'),
'motorbike': (14, 'Vehicle'),
'person': (15, 'Person'),
'pottedplant': (16, 'Indoor'),
'sheep': (17, 'Animal'),
'sofa': (18, 'Indoor'),
'train': (19, 'Vehicle'),
'tvmonitor': (20, 'Indoor'),
}
def int64_feature(values):
"""Returns a TF-Feature of int64s.
Args:
values: A scalar or list of values.
Returns:
a TF-Feature.
"""
if not isinstance(values, (tuple, list)):
values = [values]
return tf.train.Feature(int64_list=tf.train.Int64List(value=values))
def float_feature(value):
"""Wrapper for inserting float features into Example proto.
"""
if not isinstance(value, list):
value = [value]
return tf.train.Feature(float_list=tf.train.FloatList(value=value))
def bytes_feature(value):
"""Wrapper for inserting bytes features into Example proto.
"""
if not isinstance(value, list):
value = [value]
return tf.train.Feature(bytes_list=tf.train.BytesList(value=value))
def _bytes_feature(value):
"""Wrapper for inserting bytes features into Example proto."""
if six.PY3 and isinstance(value, six.text_type):
value = six.binary_type(value, encoding='utf-8')
return tf.train.Feature(bytes_list=tf.train.BytesList(value=[value]))
SPLIT_MAP = ['train', 'val', 'trainval','test']
"""
Process a image and annotation file.
Args:
filename: string, path to an image file e.g., '/path/to/example.JPG'.
coder: instance of ImageCoder to provide TensorFlow image coding utils.
Returns:
image_buffer: string, JPEG encoding of RGB image.
height: integer, image height in pixels.
width: integer, image width in pixels.
读取一个样本图片及对应信息
"""
def _process_image(directory, name):
# Read the image file.
filename = os.path.join(directory, DIRECTORY_IMAGES, name + '.jpg')
image_data = tf.gfile.FastGFile(filename, 'rb').read()
# Read the XML annotation file.
filename = os.path.join(directory, DIRECTORY_ANNOTATIONS, name + '.xml')
tree = ET.parse(filename)
root = tree.getroot()
# Image shape.#
size = root.find('size')
shape = [int(size.find('height').text), int(size.find('width').text), int(size.find('depth').text)]
# Find annotations.
# 获取每个object的信息
bboxes = []
labels = []
labels_text = []
poses = []
i =1
for obj in root.findall('object'):
#计算总公共有几个object,若等于1，则直接标识；否则，判断标识是否相同，如果相同，则不变
label = obj.find('name').text
label_id = int(int(VOC_LABELS[label][0]))
labels.append(int(VOC_LABELS[label][0])) #添加标识
labels_text.append(label.encode('ascii'))#添加accii
bbox = obj.find('bndbox')
bboxes.append((float(bbox.find('ymin').text) / shape[0],
float(bbox.find('xmin').text) / shape[1],
float(bbox.find('ymax').text) / shape[0],
float(bbox.find('xmax').text) / shape[1]
))
pose = obj.find('pose').text
poses.append(pose.encode('ascii'))
return image_data, shape, bboxes, labels, labels_text, label_id, poses, name_num
"""
Build an Example proto for an image example.
Args:
image_data: string, JPEG encoding of RGB image;
labels: list of integers, identifier for the ground truth;
labels_text: list of strings, human-readable labels;
bboxes: list of bounding boxes; each box is a list of integers;
specifying [xmin, ymin, xmax, ymax]. All boxes are assumed to belong
to the same label as the image label.
shape: 3 integers, image shapes in pixels.
Returns:
Example proto
将一个图片及对应信息按格式转换成训练时可读取的一个样本
"""
def _convert_to_example(image_data, labels, labels_text, bboxes, shape, label_id, poses, name):#
xmin = []
ymin = []
xmax = []
ymax = []
for b in bboxes:
assert len(b) == 4
# pylint: disable=expression-not-assigned
[l.append(point) for l, point in zip([ymin, xmin, ymax, xmax], b)]
# pylint: enable=expression-not-
image_format = b'JPEG'
colorspace = 'RGB'
channels = 3
example = tf.train.Example(features=tf.train.Features(feature={
'image/height': int64_feature(shape[0]),
'image/width': int64_feature(shape[1]),
'image/colorspace': _bytes_feature(colorspace),
'image/channels': int64_feature(channels),
'image/label': int64_feature(label_id),
'image/object/bbox/xmin': float_feature(xmin),
'image/object/bbox/xmax': float_feature(xmax),
'image/object/bbox/ymin': float_feature(ymin),
'image/object/bbox/ymax': float_feature(ymax),
'image/object/class/label': int64_feature(labels),
'image/object/bbox/label_text': bytes_feature(labels_text),
'image/object/view': bytes_feature(poses),
'image/format': bytes_feature(image_format),
'image/filename': _bytes_feature(name),
'image/encoded': bytes_feature(image_data)}))
#print()
#print(example)
return example
"""
Loads data from image and annotations files and add them to a TFRecord.
Args:
dataset_dir: Dataset directory;
name: Image name to add to the TFRecord;
tfrecord_writer: The TFRecord writer to use for writing.
"""
def _add_to_tfrecord(dataset_dir, filename, label_map_path, tfrecord_writer):
image_data, shape, bboxes, labels, labels_text, label_id, poses ,name = \
_process_image(dataset_dir, filename)
example = _convert_to_example(image_data,
labels,
labels_text,
bboxes,
shape, label_id, poses, name)
tfrecord_writer.write(example.SerializeToString())
#split为main文件夹中train或者是trainval，亦或者是其他的名称；具体情况看自己如何使用
def run(voc_root, year, split, output_dir, out_name, label_map_path, shuffling=True):
# 如果output_dir不存在则创建
if not tf.gfile.Exists(output_dir):
tf.gfile.MakeDirs(output_dir)
# VOCdevkit/VOC2012/ImageSets/Main/train.txt
# 中存放有所有20个类别的训练样本名称，共5717个
split_file_path = os.path.join(voc_root,'VOC%s'%year,'ImageSets','Main','%s.txt'%split)
print ('>> ', split_file_path)
with open(split_file_path) as f:
filenames = f.readlines()
# shuffling == Ture时，打乱顺序
if shuffling:
random.seed(RANDOM_SEED)
random.shuffle(filenames)
# Process dataset files.
i = 0
fidx = 0
dataset_dir = os.path.join(voc_root, 'VOC%s'%year)
while i < len(filenames):
# Open new TFRecord file.
tf_filename = '%s/VOC0712_%s%02d.tfrecord' % (output_dir, out_name, fidx)#
with tf.python_io.TFRecordWriter(tf_filename) as tfrecord_writer:
j = 0
while i < len(filenames) and j < SAMPLES_PER_FILES:
sys.stdout.write('\r>> Converting image %d/%d' % (i+1, len(filenames)))
sys.stdout.flush()
filename = filenames[i].strip()
_add_to_tfrecord(dataset_dir, filename, label_map_path, tfrecord_writer)
i += 1
j += 1
fidx += 1
# Finally, write the labels file:
abels_to_class_names = dict(zip(range(len(VOC_LABELS)), VOC_LABELS))
dataset_utils.write_label_file(labels_to_class_names, output_dir)
print('\n>> Finished converting the Pascal VOC dataset!')
dataset_dir="......../VOCdevkit/"
output_dir="........./VOC0712/"
name="trainval" #
out_name='train'#train
label_map_path = 'labels'
def main(_):
run(dataset_dir, 1207, name, output_dir, out_name, label_map_path)
if __name__ == '__main__':
tf.app.run()