本文整理汇总了Python中object_detection.data_decoders.tf_example_decoder.TfExampleDecoder方法的典型用法代码示例。如果您正苦于以下问题：Python tf_example_decoder.TfExampleDecoder方法的具体用法？Python tf_example_decoder.TfExampleDecoder怎么用？Python tf_example_decoder.TfExampleDecoder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.data_decoders.tf_example_decoder的用法示例。
在下文中一共展示了tf_example_decoder.TfExampleDecoder方法的15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: testDecodeJpegImage
# 需要导入模块: from object_detection.data_decoders import tf_example_decoder [as 别名]
# 或者: from object_detection.data_decoders.tf_example_decoder import TfExampleDecoder [as 别名]
def testDecodeJpegImage(self):
image_tensor = np.random.randint(255, size=(4, 5, 3)).astype(np.uint8)
encoded_jpeg = self._EncodeImage(image_tensor)
decoded_jpeg = self._DecodeImage(encoded_jpeg)
example = tf.train.Example(features=tf.train.Features(feature={
'image/encoded': self._BytesFeature(encoded_jpeg),
'image/format': self._BytesFeature('jpeg'),
'image/source_id': self._BytesFeature('image_id'),
})).SerializeToString()
example_decoder = tf_example_decoder.TfExampleDecoder()
tensor_dict = example_decoder.decode(tf.convert_to_tensor(example))
self.assertAllEqual((tensor_dict[fields.InputDataFields.image].
get_shape().as_list()), [None, None, 3])
with self.test_session() as sess:
tensor_dict = sess.run(tensor_dict)
self.assertAllEqual(decoded_jpeg, tensor_dict[fields.InputDataFields.image])
self.assertEqual('image_id', tensor_dict[fields.InputDataFields.source_id]) 示例2: testDecodeImageKeyAndFilename
# 需要导入模块: from object_detection.data_decoders import tf_example_decoder [as 别名]
# 或者: from object_detection.data_decoders.tf_example_decoder import TfExampleDecoder [as 别名]
def testDecodeImageKeyAndFilename(self):
image_tensor = np.random.randint(255, size=(4, 5, 3)).astype(np.uint8)
encoded_jpeg = self._EncodeImage(image_tensor)
example = tf.train.Example(features=tf.train.Features(feature={
'image/encoded': self._BytesFeature(encoded_jpeg),
'image/key/sha256': self._BytesFeature('abc'),
'image/filename': self._BytesFeature('filename')
})).SerializeToString()
example_decoder = tf_example_decoder.TfExampleDecoder()
tensor_dict = example_decoder.decode(tf.convert_to_tensor(example))
with self.test_session() as sess:
tensor_dict = sess.run(tensor_dict)
self.assertEqual('abc', tensor_dict[fields.InputDataFields.key])
self.assertEqual('filename', tensor_dict[fields.InputDataFields.filename]) 示例3: testDecodePngImage
# 需要导入模块: from object_detection.data_decoders import tf_example_decoder [as 别名]
# 或者: from object_detection.data_decoders.tf_example_decoder import TfExampleDecoder [as 别名]
def testDecodePngImage(self):
image_tensor = np.random.randint(255, size=(4, 5, 3)).astype(np.uint8)
encoded_png = self._EncodeImage(image_tensor, encoding_type='png')
decoded_png = self._DecodeImage(encoded_png, encoding_type='png')
example = tf.train.Example(features=tf.train.Features(feature={
'image/encoded': self._BytesFeature(encoded_png),
'image/format': self._BytesFeature('png'),
'image/source_id': self._BytesFeature('image_id')
})).SerializeToString()
example_decoder = tf_example_decoder.TfExampleDecoder()
tensor_dict = example_decoder.decode(tf.convert_to_tensor(example))
self.assertAllEqual((tensor_dict[fields.InputDataFields.image].
get_shape().as_list()), [None, None, 3])
with self.test_session() as sess:
tensor_dict = sess.run(tensor_dict)
self.assertAllEqual(decoded_png, tensor_dict[fields.InputDataFields.image])
self.assertEqual('image_id', tensor_dict[fields.InputDataFields.source_id]) 示例4: testDecodeObjectArea
# 需要导入模块: from object_detection.data_decoders import tf_example_decoder [as 别名]
# 或者: from object_detection.data_decoders.tf_example_decoder import TfExampleDecoder [as 别名]
def testDecodeObjectArea(self):
image_tensor = np.random.randint(255, size=(4, 5, 3)).astype(np.uint8)
encoded_jpeg = self._EncodeImage(image_tensor)
object_area = [100., 174.]
example = tf.train.Example(features=tf.train.Features(feature={
'image/encoded': self._BytesFeature(encoded_jpeg),
'image/format': self._BytesFeature('jpeg'),
'image/object/area': self._FloatFeature(object_area),
})).SerializeToString()
example_decoder = tf_example_decoder.TfExampleDecoder()
tensor_dict = example_decoder.decode(tf.convert_to_tensor(example))
self.assertAllEqual((tensor_dict[fields.InputDataFields.groundtruth_area].
get_shape().as_list()), [None])
with self.test_session() as sess:
tensor_dict = sess.run(tensor_dict)
self.assertAllEqual(object_area,
tensor_dict[fields.InputDataFields.groundtruth_area]) 示例5: testDecodeObjectIsCrowd
# 需要导入模块: from object_detection.data_decoders import tf_example_decoder [as 别名]
# 或者: from object_detection.data_decoders.tf_example_decoder import TfExampleDecoder [as 别名]
def testDecodeObjectIsCrowd(self):
image_tensor = np.random.randint(255, size=(4, 5, 3)).astype(np.uint8)
encoded_jpeg = self._EncodeImage(image_tensor)
object_is_crowd = [0, 1]
example = tf.train.Example(features=tf.train.Features(feature={
'image/encoded': self._BytesFeature(encoded_jpeg),
'image/format': self._BytesFeature('jpeg'),
'image/object/is_crowd': self._Int64Feature(object_is_crowd),
})).SerializeToString()
example_decoder = tf_example_decoder.TfExampleDecoder()
tensor_dict = example_decoder.decode(tf.convert_to_tensor(example))
self.assertAllEqual((tensor_dict[
fields.InputDataFields.groundtruth_is_crowd].get_shape().as_list()),
[None])
with self.test_session() as sess:
tensor_dict = sess.run(tensor_dict)
self.assertAllEqual([bool(item) for item in object_is_crowd],
tensor_dict[
fields.InputDataFields.groundtruth_is_crowd]) 示例6: testDecodeObjectDifficult
# 需要导入模块: from object_detection.data_decoders import tf_example_decoder [as 别名]
# 或者: from object_detection.data_decoders.tf_example_decoder import TfExampleDecoder [as 别名]
def testDecodeObjectDifficult(self):
image_tensor = np.random.randint(255, size=(4, 5, 3)).astype(np.uint8)
encoded_jpeg = self._EncodeImage(image_tensor)
object_difficult = [0, 1]
example = tf.train.Example(features=tf.train.Features(feature={
'image/encoded': self._BytesFeature(encoded_jpeg),
'image/format': self._BytesFeature('jpeg'),
'image/object/difficult': self._Int64Feature(object_difficult),
})).SerializeToString()
example_decoder = tf_example_decoder.TfExampleDecoder()
tensor_dict = example_decoder.decode(tf.convert_to_tensor(example))
self.assertAllEqual((tensor_dict[
fields.InputDataFields.groundtruth_difficult].get_shape().as_list()),
[None])
with self.test_session() as sess:
tensor_dict = sess.run(tensor_dict)
self.assertAllEqual([bool(item) for item in object_difficult],
tensor_dict[
fields.InputDataFields.groundtruth_difficult]) 示例7: testDecodeJpegImage
# 需要导入模块: from object_detection.data_decoders import tf_example_decoder [as 别名]
# 或者: from object_detection.data_decoders.tf_example_decoder import TfExampleDecoder [as 别名]
def testDecodeJpegImage(self):
image_tensor = np.random.randint(255, size=(4, 5, 3)).astype(np.uint8)
encoded_jpeg = self._EncodeImage(image_tensor)
decoded_jpeg = self._DecodeImage(encoded_jpeg)
example = tf.train.Example(features=tf.train.Features(feature={
'image/encoded': self._BytesFeature(encoded_jpeg),
'image/format': self._BytesFeature('jpeg'),
'image/source_id': self._BytesFeature('image_id'),
})).SerializeToString()
example_decoder = tf_example_decoder.TfExampleDecoder()
tensor_dict = example_decoder.Decode(tf.convert_to_tensor(example))
self.assertAllEqual((tensor_dict[fields.InputDataFields.image].
get_shape().as_list()), [None, None, 3])
with self.test_session() as sess:
tensor_dict = sess.run(tensor_dict)
self.assertAllEqual(decoded_jpeg, tensor_dict[fields.InputDataFields.image])
self.assertEqual('image_id', tensor_dict[fields.InputDataFields.source_id]) 示例8: testDecodeImageKeyAndFilename
# 需要导入模块: from object_detection.data_decoders import tf_example_decoder [as 别名]
# 或者: from object_detection.data_decoders.tf_example_decoder import TfExampleDecoder [as 别名]
def testDecodeImageKeyAndFilename(self):
image_tensor = np.random.randint(255, size=(4, 5, 3)).astype(np.uint8)
encoded_jpeg = self._EncodeImage(image_tensor)
example = tf.train.Example(features=tf.train.Features(feature={
'image/encoded': self._BytesFeature(encoded_jpeg),
'image/key/sha256': self._BytesFeature('abc'),
'image/filename': self._BytesFeature('filename')
})).SerializeToString()
example_decoder = tf_example_decoder.TfExampleDecoder()
tensor_dict = example_decoder.Decode(tf.convert_to_tensor(example))
with self.test_session() as sess:
tensor_dict = sess.run(tensor_dict)
self.assertEqual('abc', tensor_dict[fields.InputDataFields.key])
self.assertEqual('filename', tensor_dict[fields.InputDataFields.filename]) 示例9: testDecodePngImage
# 需要导入模块: from object_detection.data_decoders import tf_example_decoder [as 别名]
# 或者: from object_detection.data_decoders.tf_example_decoder import TfExampleDecoder [as 别名]
def testDecodePngImage(self):
image_tensor = np.random.randint(255, size=(4, 5, 3)).astype(np.uint8)
encoded_png = self._EncodeImage(image_tensor, encoding_type='png')
decoded_png = self._DecodeImage(encoded_png, encoding_type='png')
example = tf.train.Example(features=tf.train.Features(feature={
'image/encoded': self._BytesFeature(encoded_png),
'image/format': self._BytesFeature('png'),
'image/source_id': self._BytesFeature('image_id')
})).SerializeToString()
example_decoder = tf_example_decoder.TfExampleDecoder()
tensor_dict = example_decoder.Decode(tf.convert_to_tensor(example))
self.assertAllEqual((tensor_dict[fields.InputDataFields.image].
get_shape().as_list()), [None, None, 3])
with self.test_session() as sess:
tensor_dict = sess.run(tensor_dict)
self.assertAllEqual(decoded_png, tensor_dict[fields.InputDataFields.image])
self.assertEqual('image_id', tensor_dict[fields.InputDataFields.source_id]) 示例10: testDecodeObjectArea
# 需要导入模块: from object_detection.data_decoders import tf_example_decoder [as 别名]
# 或者: from object_detection.data_decoders.tf_example_decoder import TfExampleDecoder [as 别名]
def testDecodeObjectArea(self):
image_tensor = np.random.randint(255, size=(4, 5, 3)).astype(np.uint8)
encoded_jpeg = self._EncodeImage(image_tensor)
object_area = [100., 174.]
example = tf.train.Example(features=tf.train.Features(feature={
'image/encoded': self._BytesFeature(encoded_jpeg),
'image/format': self._BytesFeature('jpeg'),
'image/object/area': self._FloatFeature(object_area),
})).SerializeToString()
example_decoder = tf_example_decoder.TfExampleDecoder()
tensor_dict = example_decoder.Decode(tf.convert_to_tensor(example))
self.assertAllEqual((tensor_dict[fields.InputDataFields.groundtruth_area].
get_shape().as_list()), [None])
with self.test_session() as sess:
tensor_dict = sess.run(tensor_dict)
self.assertAllEqual(object_area,
tensor_dict[fields.InputDataFields.groundtruth_area]) 示例11: testDecodeObjectIsCrowd
# 需要导入模块: from object_detection.data_decoders import tf_example_decoder [as 别名]
# 或者: from object_detection.data_decoders.tf_example_decoder import TfExampleDecoder [as 别名]
def testDecodeObjectIsCrowd(self):
image_tensor = np.random.randint(255, size=(4, 5, 3)).astype(np.uint8)
encoded_jpeg = self._EncodeImage(image_tensor)
object_is_crowd = [0, 1]
example = tf.train.Example(features=tf.train.Features(feature={
'image/encoded': self._BytesFeature(encoded_jpeg),
'image/format': self._BytesFeature('jpeg'),
'image/object/is_crowd': self._Int64Feature(object_is_crowd),
})).SerializeToString()
example_decoder = tf_example_decoder.TfExampleDecoder()
tensor_dict = example_decoder.Decode(tf.convert_to_tensor(example))
self.assertAllEqual((tensor_dict[
fields.InputDataFields.groundtruth_is_crowd].get_shape().as_list()),
[None])
with self.test_session() as sess:
tensor_dict = sess.run(tensor_dict)
self.assertAllEqual([bool(item) for item in object_is_crowd],
tensor_dict[
fields.InputDataFields.groundtruth_is_crowd]) 示例12: testDecodeObjectDifficult
# 需要导入模块: from object_detection.data_decoders import tf_example_decoder [as 别名]
# 或者: from object_detection.data_decoders.tf_example_decoder import TfExampleDecoder [as 别名]
def testDecodeObjectDifficult(self):
image_tensor = np.random.randint(255, size=(4, 5, 3)).astype(np.uint8)
encoded_jpeg = self._EncodeImage(image_tensor)
object_difficult = [0, 1]
example = tf.train.Example(features=tf.train.Features(feature={
'image/encoded': self._BytesFeature(encoded_jpeg),
'image/format': self._BytesFeature('jpeg'),
'image/object/difficult': self._Int64Feature(object_difficult),
})).SerializeToString()
example_decoder = tf_example_decoder.TfExampleDecoder()
tensor_dict = example_decoder.Decode(tf.convert_to_tensor(example))
self.assertAllEqual((tensor_dict[
fields.InputDataFields.groundtruth_difficult].get_shape().as_list()),
[None])
with self.test_session() as sess:
tensor_dict = sess.run(tensor_dict)
self.assertAllEqual([bool(item) for item in object_difficult],
tensor_dict[
fields.InputDataFields.groundtruth_difficult]) 示例13: _tf_example_input_placeholder
# 需要导入模块: from object_detection.data_decoders import tf_example_decoder [as 别名]
# 或者: from object_detection.data_decoders.tf_example_decoder import TfExampleDecoder [as 别名]
def _tf_example_input_placeholder():
"""Returns input that accepts a batch of strings with tf examples.
Returns:
a tuple of input placeholder and the output decoded images.
"""
batch_tf_example_placeholder = tf.placeholder(
tf.string, shape=[None], name='tf_example')
def decode(tf_example_string_tensor):
tensor_dict = tf_example_decoder.TfExampleDecoder().decode(
tf_example_string_tensor)
image_tensor = tensor_dict[fields.InputDataFields.image]
return image_tensor
return (batch_tf_example_placeholder,
shape_utils.static_or_dynamic_map_fn(
decode,
elems=batch_tf_example_placeholder,
dtype=tf.uint8,
parallel_iterations=32,
back_prop=False)) 示例14: testDecodeImageKeyAndFilename
# 需要导入模块: from object_detection.data_decoders import tf_example_decoder [as 别名]
# 或者: from object_detection.data_decoders.tf_example_decoder import TfExampleDecoder [as 别名]
def testDecodeImageKeyAndFilename(self):
image_tensor = np.random.randint(256, size=(4, 5, 3)).astype(np.uint8)
encoded_jpeg = self._EncodeImage(image_tensor)
example = tf.train.Example(
features=tf.train.Features(
feature={
'image/encoded': dataset_util.bytes_feature(encoded_jpeg),
'image/key/sha256': dataset_util.bytes_feature('abc'),
'image/filename': dataset_util.bytes_feature('filename')
})).SerializeToString()
example_decoder = tf_example_decoder.TfExampleDecoder()
tensor_dict = example_decoder.decode(tf.convert_to_tensor(example))
with self.test_session() as sess:
tensor_dict = sess.run(tensor_dict)
self.assertEqual('abc', tensor_dict[fields.InputDataFields.key])
self.assertEqual('filename', tensor_dict[fields.InputDataFields.filename]) 示例15: testDecodeObjectArea
# 需要导入模块: from object_detection.data_decoders import tf_example_decoder [as 别名]
# 或者: from object_detection.data_decoders.tf_example_decoder import TfExampleDecoder [as 别名]
def testDecodeObjectArea(self):
image_tensor = np.random.randint(256, size=(4, 5, 3)).astype(np.uint8)
encoded_jpeg = self._EncodeImage(image_tensor)
object_area = [100., 174.]
example = tf.train.Example(
features=tf.train.Features(
feature={
'image/encoded':
dataset_util.bytes_feature(encoded_jpeg),
'image/format':
dataset_util.bytes_feature('jpeg'),
'image/object/area':
dataset_util.float_list_feature(object_area),
})).SerializeToString()
example_decoder = tf_example_decoder.TfExampleDecoder()
tensor_dict = example_decoder.decode(tf.convert_to_tensor(example))
self.assertAllEqual((tensor_dict[fields.InputDataFields.groundtruth_area]
.get_shape().as_list()), [2])
with self.test_session() as sess:
tensor_dict = sess.run(tensor_dict)
self.assertAllEqual(object_area,
tensor_dict[fields.InputDataFields.groundtruth_area])