--- /dev/null
+# -*- coding: utf-8 -*-
+
+from __future__ import absolute_import
+from __future__ import print_function
+from __future__ import division
+
+import numpy as np
+import tensorflow as tf
+import os
+from data.io import image_preprocess
+from libs.configs import cfgs
+
+def read_single_example_and_decode(filename_queue):
+
+ # tfrecord_options = tf.python_io.TFRecordOptions(tf.python_io.TFRecordCompressionType.ZLIB)
+
+ # reader = tf.TFRecordReader(options=tfrecord_options)
+ reader = tf.TFRecordReader()
+ _, serialized_example = reader.read(filename_queue)
+
+ features = tf.parse_single_example(
+ serialized=serialized_example,
+ features={
+ 'img_name': tf.FixedLenFeature([], tf.string),
+ 'img_height': tf.FixedLenFeature([], tf.int64),
+ 'img_width': tf.FixedLenFeature([], tf.int64),
+ 'img': tf.FixedLenFeature([], tf.string),
+ 'gtboxes_and_label': tf.FixedLenFeature([], tf.string),
+ 'num_objects': tf.FixedLenFeature([], tf.int64)
+ }
+ )
+ img_name = features['img_name']
+ img_height = tf.cast(features['img_height'], tf.int32)
+ img_width = tf.cast(features['img_width'], tf.int32)
+ img = tf.decode_raw(features['img'], tf.uint8)
+
+ img = tf.reshape(img, shape=[img_height, img_width, 3])
+
+ gtboxes_and_label = tf.decode_raw(features['gtboxes_and_label'], tf.int32)
+ gtboxes_and_label = tf.reshape(gtboxes_and_label, [-1, 5])
+
+ num_objects = tf.cast(features['num_objects'], tf.int32)
+ return img_name, img, gtboxes_and_label, num_objects
+
+
+def read_and_prepocess_single_img(filename_queue, shortside_len, is_training):
+
+ img_name, img, gtboxes_and_label, num_objects = read_single_example_and_decode(filename_queue)
+
+ img = tf.cast(img, tf.float32)
+
+ if is_training:
+ img, gtboxes_and_label = image_preprocess.short_side_resize(img_tensor=img, gtboxes_and_label=gtboxes_and_label,
+ target_shortside_len=shortside_len,
+ length_limitation=cfgs.IMG_MAX_LENGTH)
+ img, gtboxes_and_label = image_preprocess.random_flip_left_right(img_tensor=img,
+ gtboxes_and_label=gtboxes_and_label)
+
+ else:
+ img, gtboxes_and_label = image_preprocess.short_side_resize(img_tensor=img, gtboxes_and_label=gtboxes_and_label,
+ target_shortside_len=shortside_len,
+ length_limitation=cfgs.IMG_MAX_LENGTH)
+ img = img - tf.constant([[cfgs.PIXEL_MEAN]]) # sub pixel mean at last
+ return img_name, img, gtboxes_and_label, num_objects
+
+
+def next_batch(dataset_name, batch_size, shortside_len, is_training):
+ '''
+ :return:
+ img_name_batch: shape(1, 1)
+ img_batch: shape:(1, new_imgH, new_imgW, C)
+ gtboxes_and_label_batch: shape(1, Num_Of_objects, 5] .each row is [x1, y1, x2, y2, label]
+ '''
+ assert batch_size == 1, "we only support batch_size is 1.We may support large batch_size in the future"
+
+ if dataset_name not in ['ship', 'spacenet', 'pascal', 'coco','pcb']:
+ raise ValueError('dataSet name must be in pascal, coco spacenet and ship')
+
+ if is_training:
+ pattern = os.path.join('../data/tfrecord', dataset_name + '_train*')
+ else:
+ pattern = os.path.join('../data/tfrecord', dataset_name + '_test*')
+
+ print('tfrecord path is -->', os.path.abspath(pattern))
+
+ filename_tensorlist = tf.train.match_filenames_once(pattern)
+
+ filename_queue = tf.train.string_input_producer(filename_tensorlist)
+
+ img_name, img, gtboxes_and_label, num_obs = read_and_prepocess_single_img(filename_queue, shortside_len,
+ is_training=is_training)
+ img_name_batch, img_batch, gtboxes_and_label_batch, num_obs_batch = \
+ tf.train.batch(
+ [img_name, img, gtboxes_and_label, num_obs],
+ batch_size=batch_size,
+ capacity=1,
+ num_threads=1,
+ dynamic_pad=True)
+ return img_name_batch, img_batch, gtboxes_and_label_batch, num_obs_batch