https://pan.baidu.com/s/19n-l9hg9v0pfdBAEhS5E3A
提取碼: r7ec

#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Created on Wed Jun 9 12:08:34 2021@author: ledi """import tensorflow as tfdef transform_images(x_train, size):x_train = tf.image.resize(x_train, (size, size))x_train = x_train / 255return 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','./dataLINE_NUMBER = -1 # TODO: use tf.lookup.TextFileIndex.LINE_NUMBERclass_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

輸出結果如下

(<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)>)

總結

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