+++ /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