Python preprocessing.preprocess_image() Examples

The following are 11 code examples of preprocessing.preprocess_image(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module preprocessing , or try the search function .
Example #1
Source File: imagenet_input.py    From tpu_models with Apache License 2.0 6 votes vote down vote up 
def build_image_serving_input_fn(image_size):
  """Builds a serving input fn for raw images."""
  def _image_serving_input_fn():
    """Serving input fn for raw images."""
    def _preprocess_image(image_bytes):
      """Preprocess a single raw image."""
      image = preprocessing.preprocess_image(
          image_bytes=image_bytes, is_training=False, image_size=image_size)
      return image

    image_bytes_list = tf.placeholder(
        shape=[None],
        dtype=tf.string,
    )
    images = tf.map_fn(
        _preprocess_image, image_bytes_list, back_prop=False, dtype=tf.float32)
    return tf.estimator.export.ServingInputReceiver(
        images, {'image_bytes': image_bytes_list})
  return _image_serving_input_fn
Example #2
Source File: imagenet_input.py    From tpu_models with Apache License 2.0 6 votes vote down vote up 
def __init__(self,
               is_training,
               use_bfloat16,
               num_cores=8,
               image_size=224,
               transpose_input=False,
               include_background_label=False,
               autoaugment_name=None):
    self.image_preprocessing_fn = preprocessing.preprocess_image
    self.is_training = is_training
    self.use_bfloat16 = use_bfloat16
    self.num_cores = num_cores
    self.transpose_input = transpose_input
    self.image_size = image_size
    self.include_background_label = include_background_label
    self.autoaugment_name = autoaugment_name
Example #3
Source File: imagenet_input.py    From tpu_models with Apache License 2.0 6 votes vote down vote up 
def build_image_serving_input_fn(image_size):
  """Builds a serving input fn for raw images."""
  def _image_serving_input_fn():
    """Serving input fn for raw images."""
    def _preprocess_image(image_bytes):
      """Preprocess a single raw image."""
      image = preprocessing.preprocess_image(
          image_bytes=image_bytes, is_training=False, image_size=image_size)
      return image

    image_bytes_list = tf.placeholder(
        shape=[None],
        dtype=tf.string,
    )
    images = tf.map_fn(
        _preprocess_image, image_bytes_list, back_prop=False, dtype=tf.float32)
    return tf.estimator.export.ServingInputReceiver(
        images, {'image_bytes': image_bytes_list})
  return _image_serving_input_fn
Example #4
Source File: eval_ckpt_main.py    From EfficientNet-PyTorch with Apache License 2.0 6 votes vote down vote up 
def build_dataset(self, filenames, labels, is_training):
    """Build input dataset."""
    filenames = tf.constant(filenames)
    labels = tf.constant(labels)
    dataset = tf.data.Dataset.from_tensor_slices((filenames, labels))

    def _parse_function(filename, label):
      image_string = tf.read_file(filename)
      image_decoded = preprocessing.preprocess_image(
          image_string, is_training, self.image_size)
      image = tf.cast(image_decoded, tf.float32)
      return image, label

    dataset = dataset.map(_parse_function)
    dataset = dataset.batch(self.batch_size)

    iterator = dataset.make_one_shot_iterator()
    images, labels = iterator.get_next()
    return images, labels
Example #5
Source File: eval_ckpt_main.py    From efficientnet-pytorch with MIT License 6 votes vote down vote up 
def build_dataset(self, filenames, labels, is_training):
    """Build input dataset."""
    filenames = tf.constant(filenames)
    labels = tf.constant(labels)
    dataset = tf.data.Dataset.from_tensor_slices((filenames, labels))

    def _parse_function(filename, label):
      image_string = tf.read_file(filename)
      image_decoded = preprocessing.preprocess_image(
          image_string, is_training, image_size=self.image_size)
      image = tf.cast(image_decoded, tf.float32)
      return image, label

    dataset = dataset.map(_parse_function)
    dataset = dataset.batch(self.batch_size)

    iterator = dataset.make_one_shot_iterator()
    images, labels = iterator.get_next()
    return images, labels
Example #6
Source File: imagenet_input.py    From single-path-nas with Apache License 2.0 6 votes vote down vote up 
def image_serving_input_fn():
  """Serving input fn for raw images."""

  def _preprocess_image(image_bytes):
    """Preprocess a single raw image."""
    image = preprocessing.preprocess_image(
        image_bytes=image_bytes, is_training=False)
    return image

  image_bytes_list = tf.placeholder(
      shape=[None],
      dtype=tf.string,
  )
  images = tf.map_fn(
      _preprocess_image, image_bytes_list, back_prop=False, dtype=tf.float32)
  return tf.estimator.export.ServingInputReceiver(
      images, {'image_bytes': image_bytes_list})
Example #7
Source File: imagenet_input.py    From single-path-nas with Apache License 2.0 6 votes vote down vote up 
def build_image_serving_input_fn(image_size):
  """Builds a serving input fn for raw images."""
  def _image_serving_input_fn():
    """Serving input fn for raw images."""
    def _preprocess_image(image_bytes):
      """Preprocess a single raw image."""
      image = preprocessing.preprocess_image(
          image_bytes=image_bytes, is_training=False, image_size=image_size)
      return image

    image_bytes_list = tf.placeholder(
        shape=[None],
        dtype=tf.string,
    )
    images = tf.map_fn(
        _preprocess_image, image_bytes_list, back_prop=False, dtype=tf.float32)
    return tf.estimator.export.ServingInputReceiver(
        images, {'image_bytes': image_bytes_list})
  return _image_serving_input_fn
Example #8
Source File: imagenet_input.py    From single-path-nas with Apache License 2.0 6 votes vote down vote up 
def build_image_serving_input_fn(image_size):
  """Builds a serving input fn for raw images."""
  def _image_serving_input_fn():
    """Serving input fn for raw images."""
    def _preprocess_image(image_bytes):
      """Preprocess a single raw image."""
      image = preprocessing.preprocess_image(
          image_bytes=image_bytes, is_training=False, image_size=image_size)
      return image

    image_bytes_list = tf.placeholder(
        shape=[None],
        dtype=tf.string,
    )
    images = tf.map_fn(
        _preprocess_image, image_bytes_list, back_prop=False, dtype=tf.float32)
    return tf.estimator.export.ServingInputReceiver(
        images, {'image_bytes': image_bytes_list})
  return _image_serving_input_fn
Example #9
Source File: imagenet_input.py    From tpu_models with Apache License 2.0 5 votes vote down vote up 
def __init__(self,
               is_training,
               use_bfloat16,
               num_cores=8,
               image_size=224,
               transpose_input=False):
    self.image_preprocessing_fn = preprocessing.preprocess_image
    self.is_training = is_training
    self.use_bfloat16 = use_bfloat16
    self.num_cores = num_cores
    self.transpose_input = transpose_input
    self.image_size = image_size
Example #10
Source File: imagenet_input.py    From single-path-nas with Apache License 2.0 5 votes vote down vote up 
def __init__(self,
               is_training,
               use_bfloat16,
               num_cores=8,
               image_size=224,
               transpose_input=False):
    self.image_preprocessing_fn = preprocessing.preprocess_image
    self.is_training = is_training
    self.use_bfloat16 = use_bfloat16
    self.num_cores = num_cores
    self.transpose_input = transpose_input
    self.image_size = image_size
Example #11
Source File: imagenet_input.py    From single-path-nas with Apache License 2.0 5 votes vote down vote up 
def __init__(self,
               is_training,
               use_bfloat16,
               num_cores=8,
               image_size=224,
               transpose_input=False):
    self.image_preprocessing_fn = preprocessing.preprocess_image
    self.is_training = is_training
    self.use_bfloat16 = use_bfloat16
    self.num_cores = num_cores
    self.transpose_input = transpose_input
    self.image_size = image_size