本文整理汇总了Python中object_detection.dataset_tools.oid_tfrecord_creation.open_sharded_output_tfrecords方法的典型用法代码示例。如果您正苦于以下问题：Python oid_tfrecord_creation.open_sharded_output_tfrecords方法的具体用法？Python oid_tfrecord_creation.open_sharded_output_tfrecords怎么用？Python oid_tfrecord_creation.open_sharded_output_tfrecords使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类object_detection.dataset_tools.oid_tfrecord_creation的用法示例。
在下文中一共展示了oid_tfrecord_creation.open_sharded_output_tfrecords方法的3个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: main
# 需要导入模块: from object_detection.dataset_tools import oid_tfrecord_creation [as 别名]
# 或者: from object_detection.dataset_tools.oid_tfrecord_creation import open_sharded_output_tfrecords [as 别名]
def main(_):
tf.logging.set_verbosity(tf.logging.INFO)
required_flags = [
'input_annotations_csv', 'input_images_directory', 'input_label_map',
'output_tf_record_path_prefix'
]
for flag_name in required_flags:
if not getattr(FLAGS, flag_name):
raise ValueError('Flag --{} is required'.format(flag_name))
label_map = label_map_util.get_label_map_dict(FLAGS.input_label_map)
all_annotations = pd.read_csv(FLAGS.input_annotations_csv)
all_images = tf.gfile.Glob(
os.path.join(FLAGS.input_images_directory, '*.jpg'))
all_image_ids = [os.path.splitext(os.path.basename(v))[0] for v in all_images]
all_image_ids = pd.DataFrame({'ImageID': all_image_ids})
all_annotations = pd.concat([all_annotations, all_image_ids])
tf.logging.log(tf.logging.INFO, 'Found %d images...', len(all_image_ids))
with contextlib2.ExitStack() as tf_record_close_stack:
output_tfrecords = oid_tfrecord_creation.open_sharded_output_tfrecords(
tf_record_close_stack, FLAGS.output_tf_record_path_prefix,
FLAGS.num_shards)
for counter, image_data in enumerate(all_annotations.groupby('ImageID')):
tf.logging.log_every_n(tf.logging.INFO, 'Processed %d images...', 1000,
counter)
image_id, image_annotations = image_data
# In OID image file names are formed by appending ".jpg" to the image ID.
image_path = os.path.join(FLAGS.input_images_directory, image_id + '.jpg')
with tf.gfile.Open(image_path) as image_file:
encoded_image = image_file.read()
tf_example = oid_tfrecord_creation.tf_example_from_annotations_data_frame(
image_annotations, label_map, encoded_image)
if tf_example:
shard_idx = int(image_id, 16) % FLAGS.num_shards
output_tfrecords[shard_idx].write(tf_example.SerializeToString()) 示例2: test_sharded_tfrecord_writes
# 需要导入模块: from object_detection.dataset_tools import oid_tfrecord_creation [as 别名]
# 或者: from object_detection.dataset_tools.oid_tfrecord_creation import open_sharded_output_tfrecords [as 别名]
def test_sharded_tfrecord_writes(self):
with contextlib2.ExitStack() as tf_record_close_stack:
output_tfrecords = oid_tfrecord_creation.open_sharded_output_tfrecords(
tf_record_close_stack,
os.path.join(tf.test.get_temp_dir(), 'test.tfrec'), 10)
for idx in range(10):
output_tfrecords[idx].write('test_{}'.format(idx))
for idx in range(10):
tf_record_path = '{}-{:05d}-of-00010'.format(
os.path.join(tf.test.get_temp_dir(), 'test.tfrec'), idx)
records = list(tf.python_io.tf_record_iterator(tf_record_path))
self.assertAllEqual(records, ['test_{}'.format(idx)]) 示例3: main
# 需要导入模块: from object_detection.dataset_tools import oid_tfrecord_creation [as 别名]
# 或者: from object_detection.dataset_tools.oid_tfrecord_creation import open_sharded_output_tfrecords [as 别名]
def main(_):
tf.logging.set_verbosity(tf.logging.INFO)
required_flags = [
'input_annotations_csv', 'input_images_directory', 'input_label_map',
'output_tf_record_path_prefix'
]
for flag_name in required_flags:
if not getattr(FLAGS, flag_name):
raise ValueError('Flag --{} is required'.format(flag_name))
label_map = label_map_util.get_label_map_dict(FLAGS.input_label_map)
all_annotations = pd.read_csv(FLAGS.input_annotations_csv)
all_images = tf.gfile.Glob(
os.path.join(FLAGS.input_images_directory, '*.jpg'))
all_image_ids = [os.path.splitext(os.path.basename(v))[0] for v in all_images]
all_image_ids = pd.DataFrame({'ImageID': all_image_ids})
all_annotations = pd.concat([all_annotations, all_image_ids])
tf.logging.log(tf.logging.INFO, 'Found %d images...', len(all_image_ids))
with contextlib2.ExitStack() as tf_record_close_stack:
output_tfrecords = oid_tfrecord_creation.open_sharded_output_tfrecords(
tf_record_close_stack, FLAGS.output_tf_record_path_prefix,
FLAGS.num_shards)
for counter, image_data in enumerate(all_annotations.groupby('ImageID')):
tf.logging.log_every_n(tf.logging.INFO, 'Processed %d images...', 1000,
counter)
image_id, image_annotations = image_data
# In OID image file names are formed by appending ".jpg" to the image ID.
image_path = os.path.join(FLAGS.input_images_directory, image_id + '.jpg')
with tf.gfile.Open(image_path) as image_file:
encoded_image = image_file.read()
tf_example = oid_tfrecord_creation.tf_example_from_annotations_data_frame(
image_annotations, label_map, encoded_image)
if tf_example:
shard_idx = long(image_id, 16) % FLAGS.num_shards
output_tfrecords[shard_idx].write(tf_example.SerializeToString())