主要涉及兩方面：
- mat文件的讀取
- 構(gòu)建自己的tfrecord數(shù)據(jù)集
python讀取.mat文件
import scipy.io as sio
mat = sio.loadmat(matdir)
SynthText數(shù)據(jù)集
這一節(jié)講gt.mat中的數(shù)據(jù)揭朝，可略過不讀
下載地址：http://www.robots.ox.ac.uk/~vgg/data/scenetext/
包含858,750張圖片
gt.mat中包含imnames,txt,globals,charBB,hearder,version,wordBB等
其中：
mat['imnames'][0] 放圖片相對地址
mat['wordBB'][0] 放bbox的位置信息诬留，張量的維度是24圖片中包含的word數(shù)量，實際操作中一定要小心超出它的值圖片大小范圍
mat['txt'][0] 放每張圖片中包含的文本字符串杯矩。注意，它將在相同區(qū)域呈現(xiàn)相同字體，顏色币砂，變形等的組合在一起;因此可能與wordBB中對應(yīng)圖片中word數(shù)量不一致。
比如：
>>mat['imnames'][0][0]
array(['8/ballet_106_0.jpg'],
dtype='<U18')
對應(yīng)下圖
ballet_106_0.jpg
>>mat['txt'][0][0]
array(['Lines:\nI lost\nKevin ', 'will ',
'line\nand ', 'and\nthe ',
'(and ', 'the\nout ',
'you ', "don't\n pkg "],
dtype='<U20')
>>mat['wordBB'][0][0].shape
(2,4,15)
因此我們對mat['txt']中的數(shù)據(jù)要經(jīng)過strip()去掉空格琉挖，re.split()分割后在進行轉(zhuǎn)tfrecord操作,以對mat['txt'][0][0]的處理為例
for val in mat['txt'][0][0]:
v = [x.encode('ascii') for x in re.split("[ \n]", val.strip()) if x]
str.extend(v)
代碼
import numpy as np
import scipy.io as sio
import os
import re
import Image
import tensorflow as tf
import sys
def arr2list(x):
""'confirm every member is in [0.0,1.0]
convert np.array to a list
"""
x[x>1] = 1
x[x<0] = 0
return list(x)
def int64_feature(value):
"""Wrapper for inserting int64 features into Example proto.
"""
if not isinstance(value, list):
value = [value]
return tf.train.Feature(int64_list=tf.train.Int64List(value=value))
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 _convert_to_example(image_name, labels, labels_text, bboxes, shape,
difficult=0, truncated=0):
"""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 text-string;
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
"""
image_data = tf.gfile.FastGFile(image_name, 'rb').read()
xmin = bboxes[0]
ymin = bboxes[1]
xmax = bboxes[2]
ymax = bboxes[3]
image_format = b'JPEG'
example = tf.train.Example(features=tf.train.Features(feature={
'image/height': int64_feature(shape[1]),
'image/width': int64_feature(shape[0]),
'image/channels': int64_feature(shape[2]),
'image/shape': int64_feature(shape),
'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/bbox/label': int64_feature(labels),
'image/object/bbox/label_text': bytes_feature(labels_text),
'image/object/bbox/difficult': int64_feature(difficult),
'image/object/bbox/truncated': int64_feature(truncated),
'image/format': bytes_feature(image_format),
'image/encoded': bytes_feature(image_data)}))
return example
mat_dir = 'gt.mat'
txt_dir = "info.txt"
# get gt.mat
mat = sio.loadmat(mat_dir)
print('load gt.mat')
input("continue")
# get imformation
imnames = mat['imnames'][0]
txt = mat['txt'][0]
wordBB = mat['wordBB'][0]
# set TFrecord dir set
tf_dir_set = set()
tf_writer = None
tf_dirs = "ttfrecords"
total_count = 0
i = 0
need = set()
while i < imnames.size:
try:
image_dir = imnames[i][0]
tfrecord_name = os.path.split(image_dir)[0]
if not tfrecord_name in tf_dir_set:
# 新開一個tfwriter
if tf_writer is not None:
tf_writer.close()
tf_name = ("%s/synthText_%s.tfrecords" % (tf_dirs, tfrecord_name))
tf_writer = tf.python_io.TFRecordWriter(tf_name)
tf_dir_set.add(tfrecord_name)
img_size = Image.open(image_dir).size
shape = [img_size[0], img_size[1], 3]
if len(wordBB[i][0].shape) >1:
minx = np.amin(wordBB[i][0], axis=0) / img_size[0]
miny = np.amin(wordBB[i][1], axis=0) / img_size[1]
maxx = np.amax(wordBB[i][0], axis=0) / img_size[0]
maxy = np.amax(wordBB[i][1], axis=0) / img_size[1]
else:
minx = [np.amin(wordBB[i][0]) / img_size[0]]
miny = [np.amin(wordBB[i][1]) / img_size[1]]
maxx = [np.amax(wordBB[i][0]) / img_size[0]]
maxy = [np.amax(wordBB[i][1]) / img_size[1]]
#檢查是否有>1的情況启泣，并轉(zhuǎn)為list
minx = arr2list(minx)
miny = arr2list(miny)
maxy = arr2list(maxy)
maxx = arr2list(maxx)
bboxes = [minx, miny, maxx, maxy]
str = []
for val in txt[i]:
v = [x.encode('ascii') for x in re.split("[ \n]", val.strip()) if x]
str.extend(v)
labels = [1] * len(str)
example = _convert_to_example(image_dir, labels, str, bboxes, shape)
tf_writer.write(example.SerializeToString())
sys.stdout.write('\r>> Converting image %d/%d' % (i + 1, imnames.size))
sys.stdout.flush()
i = i + 1
total_count += 1
except Exception as e:
print(e)
choose = input("continue?Y/N")
if choose == "Y":
i = i+1
else:
sys.exit()
print("Converting image competely! totally %d records" % (total_count))
