https://pan.baidu.com/s/19n-l9hg9v0pfdBAEhS5E3A 提取码: r7ec
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Jun 9 12:08:34 2021
@author: ledi
"""
import tensorflow as tf
def transform_images(x_train, size):
x_train = tf.image.resize(x_train, (size, size))
x_train = x_train / 255
return x_train
# https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/using_your_own_dataset.md#conversion-script-outline-conversion-script-outline
# Commented out fields are not required in our project
IMAGE_FEATURE_MAP = {
# 'image/width': tf.io.FixedLenFeature([], tf.int64),
# 'image/height': tf.io.FixedLenFeature([], tf.int64),
# 'image/filename': tf.io.FixedLenFeature([], tf.string),
# 'image/source_id': tf.io.FixedLenFeature([], tf.string),
# 'image/key/sha256': tf.io.FixedLenFeature([], tf.string),
'image/encoded': tf.io.FixedLenFeature([], tf.string),
# 'image/format': tf.io.FixedLenFeature([], tf.string),
'image/object/bbox/xmin': tf.io.VarLenFeature(tf.float32),
'image/object/bbox/ymin': tf.io.VarLenFeature(tf.float32),
'image/object/bbox/xmax': tf.io.VarLenFeature(tf.float32),
'image/object/bbox/ymax': tf.io.VarLenFeature(tf.float32),
'image/object/class/text': tf.io.VarLenFeature(tf.string),
# 'image/object/class/label': tf.io.VarLenFeature(tf.int64),
# 'image/object/difficult': tf.io.VarLenFeature(tf.int64),
# 'image/object/truncated': tf.io.VarLenFeature(tf.int64),
# 'image/object/view': tf.io.VarLenFeature(tf.string),
}
def parse_tfrecord(tfrecord, class_table, size):
x = tf.io.parse_single_example(tfrecord, IMAGE_FEATURE_MAP)
x_train = tf.image.decode_jpeg(x['image/encoded'], channels=3)
x_train = tf.image.resize(x_train, (size, size))
print( x_train)
class_text = tf.sparse.to_dense(
x['image/object/class/text'], default_value='')
labels = tf.cast(class_table.lookup(class_text), tf.float32)
y_train = tf.stack([tf.sparse.to_dense(x['image/object/bbox/xmin']),
tf.sparse.to_dense(x['image/object/bbox/ymin']),
tf.sparse.to_dense(x['image/object/bbox/xmax']),
tf.sparse.to_dense(x['image/object/bbox/ymax']),
labels], axis=1)
# print('FLAGS.yolo_max_boxes=',FLAGS.yolo_max_boxes)
paddings = [[0, 100 - tf.shape(y_train)[0]], [0, 0]]
# paddings = [[0, FLAGS.yolo_max_boxes - tf.shape(y_train)[0]], [0, 0]]
y_train = tf.pad(y_train, paddings)
return x_train, y_train
"""
count=0
for k in files:
if count<10:
print(k)
count+=1
"""
def load_tfrecord_dataset(file_pattern, class_file, size=416):
#file_pattern, class_file, size='./data/voc2012_train.tfrecord','./data
LINE_NUMBER = -1 # TODO: use tf.lookup.TextFileIndex.LINE_NUMBER
class_table = tf.lookup.StaticHashTable(tf.lookup.TextFileInitializer(
class_file, tf.string, 0, tf.int64, LINE_NUMBER, delimiter="\n"), -1)
files = tf.data.Dataset.list_files(file_pattern)
dataset = files.flat_map(tf.data.TFRecordDataset)
return dataset.map(lambda x: parse_tfrecord(x, class_table, size))
train_dataset = load_tfrecord_dataset(
'./data/voc2012_train.tfrecord','./data/voc2012.names', 416)
count=0
for k in train_dataset:
if count<3:
print(k)
count+=1
输出结果如下
(<tf.Tensor: shape=(416, 416, 3), dtype=float32, numpy=
array([[[255. , 255. , 255. ],
[255. , 255. , 255. ],
[255. , 255. , 255. ],
...,
[201.51099 , 204.51099 , 247.51099 ],
[202.67535 , 205.67535 , 248.67535 ],
[202.96875 , 205.96875 , 248.96875 ]],
[[255. , 255. , 255. ],
[255. , 255. , 255. ],
[255. , 255. , 255. ],
...,
[202.375 , 205.375 , 248.375 ],
[202.30965 , 205.30965 , 248.17892 ],
[202.19696 , 205.19696 , 248.00946 ]],
[[255. , 255. , 255. ],
[255. , 255. , 255. ],
[255. , 255. , 255. ],
...,
[205.84375 , 209. , 251.21875 ],
[205.36447 , 208.52072 , 249.56303 ],
[204.39767 , 207.55392 , 248.08517 ]],
...,
[ 79.83946 , 75.82988 , 70.3127 ],
[ 75.77214 , 72.77214 , 65.72856 ],
[ 80.510895, 77.510895, 70.448395]]], dtype=float32)>, <tf.Tensor: shape=(100, 5), dtype=float32, numpy=
array([[ 0.106 , 0.19683258, 0.942 , 0.95022625, 12. ],
[ 0.316 , 0.09954751, 0.578 , 0.37782806, 14. ],
[ 0. , 0. , 0. , 0. , 0. ],
[ 0. , 0. , 0. , 0. , 0. ],
[ 0. , 0. , 0. , 0. , 0. ],
[ 0. , 0. , 0. , 0. , 0. ],
[ 0. , 0. , 0. , 0. , 0. ],
....
[ 0. , 0. , 0. , 0. , 0. ],
[ 0. , 0. , 0. , 0. , 0. ],
[ 0. , 0. , 0. , 0. , 0. ],
[ 0. , 0. , 0. , 0. , 0. ],
[ 0. , 0. , 0. , 0. , 0. ]],
dtype=float32)>)
