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Python preprocessor.py方法代码示例

本文整理汇总了Python中object_detection.core.preprocessor.py方法的典型用法代码示例。如果您正苦于以下问题:Python preprocessor.py方法的具体用法?Python preprocessor.py怎么用?Python preprocessor.py使用的例子?那么, 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object_detection.core.preprocessor的用法示例。


在下文中一共展示了preprocessor.py方法的8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: _create_input_queue

# 需要导入模块: from object_detection.core import preprocessor [as 别名]
# 或者: from object_detection.core.preprocessor import py [as 别名]
def _create_input_queue(batch_size_per_clone, create_tensor_dict_fn,
                        batch_queue_capacity, num_batch_queue_threads,
                        prefetch_queue_capacity, data_augmentation_options):
  """Sets up reader, prefetcher and returns input queue.

  Args:
    batch_size_per_clone: batch size to use per clone.
    create_tensor_dict_fn: function to create tensor dictionary.
    batch_queue_capacity: maximum number of elements to store within a queue.
    num_batch_queue_threads: number of threads to use for batching.
    prefetch_queue_capacity: maximum capacity of the queue used to prefetch
                             assembled batches.
    data_augmentation_options: a list of tuples, where each tuple contains a
      data augmentation function and a dictionary containing arguments and their
      values (see preprocessor.py).

  Returns:
    input queue: a batcher.BatchQueue object holding enqueued tensor_dicts
      (which hold images, boxes and targets).  To get a batch of tensor_dicts,
      call input_queue.Dequeue().
  """
  tensor_dict = create_tensor_dict_fn()

  tensor_dict[fields.InputDataFields.image] = tf.expand_dims(
      tensor_dict[fields.InputDataFields.image], 0)

  images = tensor_dict[fields.InputDataFields.image]
  float_images = tf.to_float(images)
  tensor_dict[fields.InputDataFields.image] = float_images

  if data_augmentation_options:
    tensor_dict = preprocessor.preprocess(tensor_dict,
                                          data_augmentation_options)

  input_queue = batcher.BatchQueue(
      tensor_dict,
      batch_size=batch_size_per_clone,
      batch_queue_capacity=batch_queue_capacity,
      num_batch_queue_threads=num_batch_queue_threads,
      prefetch_queue_capacity=prefetch_queue_capacity)
  return input_queue 
开发者ID:ringringyi,项目名称:DOTA_models,代码行数:43,代码来源:trainer.py

示例2: create_input_queue

# 需要导入模块: from object_detection.core import preprocessor [as 别名]
# 或者: from object_detection.core.preprocessor import py [as 别名]
def create_input_queue(batch_size_per_clone, create_tensor_dict_fn,
                       batch_queue_capacity, num_batch_queue_threads,
                       prefetch_queue_capacity, data_augmentation_options):
  """Sets up reader, prefetcher and returns input queue.

  Args:
    batch_size_per_clone: batch size to use per clone.
    create_tensor_dict_fn: function to create tensor dictionary.
    batch_queue_capacity: maximum number of elements to store within a queue.
    num_batch_queue_threads: number of threads to use for batching.
    prefetch_queue_capacity: maximum capacity of the queue used to prefetch
                             assembled batches.
    data_augmentation_options: a list of tuples, where each tuple contains a
      data augmentation function and a dictionary containing arguments and their
      values (see preprocessor.py).

  Returns:
    input queue: a batcher.BatchQueue object holding enqueued tensor_dicts
      (which hold images, boxes and targets).  To get a batch of tensor_dicts,
      call input_queue.Dequeue().
  """
  tensor_dict = create_tensor_dict_fn()

  tensor_dict[fields.InputDataFields.image] = tf.expand_dims(
      tensor_dict[fields.InputDataFields.image], 0)

  images = tensor_dict[fields.InputDataFields.image]
  float_images = tf.to_float(images)
  tensor_dict[fields.InputDataFields.image] = float_images

  include_instance_masks = (fields.InputDataFields.groundtruth_instance_masks
                            in tensor_dict)
  include_keypoints = (fields.InputDataFields.groundtruth_keypoints
                       in tensor_dict)
  include_multiclass_scores = (fields.InputDataFields.multiclass_scores
                               in tensor_dict)
  if data_augmentation_options:
    tensor_dict = preprocessor.preprocess(
        tensor_dict, data_augmentation_options,
        func_arg_map=preprocessor.get_default_func_arg_map(
            include_multiclass_scores=include_multiclass_scores,
            include_instance_masks=include_instance_masks,
            include_keypoints=include_keypoints))

  input_queue = batcher.BatchQueue(
      tensor_dict,
      batch_size=batch_size_per_clone,
      batch_queue_capacity=batch_queue_capacity,
      num_batch_queue_threads=num_batch_queue_threads,
      prefetch_queue_capacity=prefetch_queue_capacity)
  return input_queue 
开发者ID:ahmetozlu,项目名称:vehicle_counting_tensorflow,代码行数:53,代码来源:trainer.py

示例3: create_input_queue

# 需要导入模块: from object_detection.core import preprocessor [as 别名]
# 或者: from object_detection.core.preprocessor import py [as 别名]
def create_input_queue(batch_size_per_clone, create_tensor_dict_fn,
                       batch_queue_capacity, num_batch_queue_threads,
                       prefetch_queue_capacity, data_augmentation_options):
  """Sets up reader, prefetcher and returns input queue.

  Args:
    batch_size_per_clone: batch size to use per clone.
    create_tensor_dict_fn: function to create tensor dictionary.
    batch_queue_capacity: maximum number of elements to store within a queue.
    num_batch_queue_threads: number of threads to use for batching.
    prefetch_queue_capacity: maximum capacity of the queue used to prefetch
                             assembled batches.
    data_augmentation_options: a list of tuples, where each tuple contains a
      data augmentation function and a dictionary containing arguments and their
      values (see preprocessor.py).

  Returns:
    input queue: a batcher.BatchQueue object holding enqueued tensor_dicts
      (which hold images, boxes and targets).  To get a batch of tensor_dicts,
      call input_queue.Dequeue().
  """
  tensor_dict = create_tensor_dict_fn()

  tensor_dict[fields.InputDataFields.image] = tf.expand_dims(
      tensor_dict[fields.InputDataFields.image], 0)

  images = tensor_dict[fields.InputDataFields.image]
  float_images = tf.to_float(images)
  tensor_dict[fields.InputDataFields.image] = float_images

  include_instance_masks = (fields.InputDataFields.groundtruth_instance_masks
                            in tensor_dict)
  include_keypoints = (fields.InputDataFields.groundtruth_keypoints
                       in tensor_dict)
  if data_augmentation_options:
    tensor_dict = preprocessor.preprocess(
        tensor_dict, data_augmentation_options,
        func_arg_map=preprocessor.get_default_func_arg_map(
            include_instance_masks=include_instance_masks,
            include_keypoints=include_keypoints))

  input_queue = batcher.BatchQueue(
      tensor_dict,
      batch_size=batch_size_per_clone,
      batch_queue_capacity=batch_queue_capacity,
      num_batch_queue_threads=num_batch_queue_threads,
      prefetch_queue_capacity=prefetch_queue_capacity)
  return input_queue 
开发者ID:cagbal,项目名称:ros_people_object_detection_tensorflow,代码行数:50,代码来源:trainer.py

示例4: create_input_queue

# 需要导入模块: from object_detection.core import preprocessor [as 别名]
# 或者: from object_detection.core.preprocessor import py [as 别名]
def create_input_queue(batch_size_per_clone, create_tensor_dict_fn,
                       batch_queue_capacity, num_batch_queue_threads,
                       prefetch_queue_capacity, data_augmentation_options):
    """Sets up reader, prefetcher and returns input queue.

    Args:
      batch_size_per_clone: batch size to use per clone.
      create_tensor_dict_fn: function to create tensor dictionary.
      batch_queue_capacity: maximum number of elements to store within a queue.
      num_batch_queue_threads: number of threads to use for batching.
      prefetch_queue_capacity: maximum capacity of the queue used to prefetch
                               assembled batches.
      data_augmentation_options: a list of tuples, where each tuple contains a
        data augmentation function and a dictionary containing arguments and their
        values (see preprocessor.py).

    Returns:
      input queue: a batcher.BatchQueue object holding enqueued tensor_dicts
        (which hold images, boxes and targets).  To get a batch of tensor_dicts,
        call input_queue.Dequeue().
    """
    tensor_dict = create_tensor_dict_fn()

    tensor_dict[fields.InputDataFields.image] = tf.expand_dims(
        tensor_dict[fields.InputDataFields.image], 0)

    images = tensor_dict[fields.InputDataFields.image]
    float_images = tf.to_float(images)
    tensor_dict[fields.InputDataFields.image] = float_images

    include_instance_masks = (fields.InputDataFields.groundtruth_instance_masks
                              in tensor_dict)
    include_keypoints = (fields.InputDataFields.groundtruth_keypoints
                         in tensor_dict)
    if data_augmentation_options:
        tensor_dict = preprocessor.preprocess(
            tensor_dict, data_augmentation_options,
            func_arg_map=preprocessor.get_default_func_arg_map(
                include_instance_masks=include_instance_masks,
                include_keypoints=include_keypoints))

    input_queue = batcher.BatchQueue(
        tensor_dict,
        batch_size=batch_size_per_clone,
        batch_queue_capacity=batch_queue_capacity,
        num_batch_queue_threads=num_batch_queue_threads,
        prefetch_queue_capacity=prefetch_queue_capacity)
    return input_queue 
开发者ID:scorelab,项目名称:Elphas,代码行数:50,代码来源:trainer.py

示例5: create_input_queue

# 需要导入模块: from object_detection.core import preprocessor [as 别名]
# 或者: from object_detection.core.preprocessor import py [as 别名]
def create_input_queue(batch_size_per_clone, create_tensor_dict_fn,
                       batch_queue_capacity, num_batch_queue_threads,
                       prefetch_queue_capacity, data_augmentation_options):
  """Sets up reader, prefetcher and returns input queue.

  Args:
    batch_size_per_clone: batch size to use per clone.
    create_tensor_dict_fn: function to create tensor dictionary.
    batch_queue_capacity: maximum number of elements to store within a queue.
    num_batch_queue_threads: number of threads to use for batching.
    prefetch_queue_capacity: maximum capacity of the queue used to prefetch
                             assembled batches.
    data_augmentation_options: a list of tuples, where each tuple contains a
      data augmentation function and a dictionary containing arguments and their
      values (see preprocessor.py).

  Returns:
    input queue: a batcher.BatchQueue object holding enqueued tensor_dicts
      (which hold images, boxes and targets).  To get a batch of tensor_dicts,
      call input_queue.Dequeue().
  """
  tensor_dict = create_tensor_dict_fn()

  tensor_dict[fields.InputDataFields.image] = tf.expand_dims(
      tensor_dict[fields.InputDataFields.image], 0)

  images = tensor_dict[fields.InputDataFields.image]
  float_images = tf.to_float(images)
  tensor_dict[fields.InputDataFields.image] = float_images

  include_instance_masks = (fields.InputDataFields.groundtruth_instance_masks
                            in tensor_dict)
  include_keypoints = (fields.InputDataFields.groundtruth_keypoints
                       in tensor_dict)
  include_multiclass_scores = (fields.InputDataFields.multiclass_scores
                               in tensor_dict)
  if data_augmentation_options:
    tensor_dict = preprocessor.preprocess(
        tensor_dict, data_augmentation_options,
        func_arg_map=preprocessor.get_default_func_arg_map(
            include_label_weights=True,
            include_multiclass_scores=include_multiclass_scores,
            include_instance_masks=include_instance_masks,
            include_keypoints=include_keypoints))

  input_queue = batcher.BatchQueue(
      tensor_dict,
      batch_size=batch_size_per_clone,
      batch_queue_capacity=batch_queue_capacity,
      num_batch_queue_threads=num_batch_queue_threads,
      prefetch_queue_capacity=prefetch_queue_capacity)
  return input_queue 
开发者ID:IBM,项目名称:MAX-Object-Detector,代码行数:54,代码来源:trainer.py

示例6: create_input_queue

# 需要导入模块: from object_detection.core import preprocessor [as 别名]
# 或者: from object_detection.core.preprocessor import py [as 别名]
def create_input_queue(batch_size_per_clone, create_tensor_dict_fn,
                       batch_queue_capacity, num_batch_queue_threads,
                       prefetch_queue_capacity, data_augmentation_options):
  """Sets up reader, prefetcher and returns input queue.

  Args:
    batch_size_per_clone: batch size to use per clone.
    create_tensor_dict_fn: function to create tensor dictionary.
    batch_queue_capacity: maximum number of elements to store within a queue.
    num_batch_queue_threads: number of threads to use for batching.
    prefetch_queue_capacity: maximum capacity of the queue used to prefetch
                             assembled batches.
    data_augmentation_options: a list of tuples, where each tuple contains a
      data augmentation function and a dictionary containing arguments and their
      values (see preprocessor.py).

  Returns:
    input queue: a batcher.BatchQueue object holding enqueued tensor_dicts
      (which hold images, boxes and targets).  To get a batch of tensor_dicts,
      call input_queue.Dequeue().
  """
  tensor_dict = create_tensor_dict_fn()

  tensor_dict[fields.InputDataFields.image] = tf.expand_dims(
      tensor_dict[fields.InputDataFields.image], 0)

  images = tensor_dict[fields.InputDataFields.image]
  float_images = tf.cast(images, dtype=tf.float32)
  tensor_dict[fields.InputDataFields.image] = float_images

  include_instance_masks = (fields.InputDataFields.groundtruth_instance_masks
                            in tensor_dict)
  include_keypoints = (fields.InputDataFields.groundtruth_keypoints
                       in tensor_dict)
  include_multiclass_scores = (fields.InputDataFields.multiclass_scores
                               in tensor_dict)
  if data_augmentation_options:
    tensor_dict = preprocessor.preprocess(
        tensor_dict, data_augmentation_options,
        func_arg_map=preprocessor.get_default_func_arg_map(
            include_label_weights=True,
            include_multiclass_scores=include_multiclass_scores,
            include_instance_masks=include_instance_masks,
            include_keypoints=include_keypoints))

  input_queue = batcher.BatchQueue(
      tensor_dict,
      batch_size=batch_size_per_clone,
      batch_queue_capacity=batch_queue_capacity,
      num_batch_queue_threads=num_batch_queue_threads,
      prefetch_queue_capacity=prefetch_queue_capacity)
  return input_queue 
开发者ID:tensorflow,项目名称:models,代码行数:54,代码来源:trainer.py

示例7: _create_input_queue

# 需要导入模块: from object_detection.core import preprocessor [as 别名]
# 或者: from object_detection.core.preprocessor import py [as 别名]
def _create_input_queue(batch_size_per_clone, create_tensor_dict_fn,
                        batch_queue_capacity, num_batch_queue_threads,
                        prefetch_queue_capacity, data_augmentation_options):
  """Sets up reader, prefetcher and returns input queue.

  Args:
    batch_size_per_clone: batch size to use per clone.
    create_tensor_dict_fn: function to create tensor dictionary.
    batch_queue_capacity: maximum number of elements to store within a queue.
    num_batch_queue_threads: number of threads to use for batching.
    prefetch_queue_capacity: maximum capacity of the queue used to prefetch
                             assembled batches.
    data_augmentation_options: a list of tuples, where each tuple contains a
      data augmentation function and a dictionary containing arguments and their
      values (see preprocessor.py).

  Returns:
    input queue: a batcher.BatchQueue object holding enqueued tensor_dicts
      (which hold images, boxes and targets).  To get a batch of tensor_dicts,
      call input_queue.Dequeue().
  """
  tensor_dict = create_tensor_dict_fn()

  tensor_dict[fields.InputDataFields.image] = tf.expand_dims(
      tensor_dict[fields.InputDataFields.image], 0)

  images = tensor_dict[fields.InputDataFields.image]
  float_images = tf.to_float(images)
  tensor_dict[fields.InputDataFields.image] = float_images

  next_images = tensor_dict.get(fields.InputDataFields.next_image)
  if next_images is not None:
    next_float_images = tf.to_float(next_images)
    tensor_dict[fields.InputDataFields.next_image] = next_float_images

  if data_augmentation_options:
    # TODO handle next_image, depth and flow to re-enable augmentations
    tensor_dict = preprocessor.preprocess(tensor_dict,
                                          data_augmentation_options)

  input_queue = batcher.BatchQueue(
      tensor_dict,
      batch_size=batch_size_per_clone,
      batch_queue_capacity=batch_queue_capacity,
      num_batch_queue_threads=num_batch_queue_threads,
      prefetch_queue_capacity=prefetch_queue_capacity)
  return input_queue 
开发者ID:simonmeister,项目名称:motion-rcnn,代码行数:49,代码来源:trainer.py

示例8: _create_input_queue

# 需要导入模块: from object_detection.core import preprocessor [as 别名]
# 或者: from object_detection.core.preprocessor import py [as 别名]
def _create_input_queue(batch_size_per_clone, create_tensor_dict_fn,
                        batch_queue_capacity, num_batch_queue_threads,
                        prefetch_queue_capacity, data_augmentation_options,
                        ignore_options=None, mtl_window=False, mtl_edgemask=False):
  """Sets up reader, prefetcher and returns input queue.

  Args:
    batch_size_per_clone: batch size to use per clone.
    create_tensor_dict_fn: function to create tensor dictionary.
    batch_queue_capacity: maximum number of elements to store within a queue.
    num_batch_queue_threads: number of threads to use for batching.
    prefetch_queue_capacity: maximum capacity of the queue used to prefetch
                             assembled batches.
    data_augmentation_options: a list of tuples, where each tuple contains a
      data augmentation function and a dictionary containing arguments and their
      values (see preprocessor.py).
    ignore_options: exception condition of training loss

  Returns:
    input queue: a batcher.BatchQueue object holding enqueued tensor_dicts
      (which hold images, boxes and targets).  To get a batch of tensor_dicts,
      call input_queue.Dequeue().
  """
  tensor_dict = create_tensor_dict_fn()

  tensor_dict[fields.InputDataFields.image] = tf.expand_dims(
      tensor_dict[fields.InputDataFields.image], 0)

  images = tensor_dict[fields.InputDataFields.image]
  float_images = tf.to_float(images)
  tensor_dict[fields.InputDataFields.image] = float_images

  preprocessor.make_ignore_list(tensor_dict, ignore_options)

  if mtl_window:
    for option in data_augmentation_options:
      if 'random_horizontal_flip' in option[0].func_name:
        option[1][fields.InputDataFields.window_boxes] = tensor_dict[fields.InputDataFields.window_boxes]

  if mtl_edgemask:
    for option in data_augmentation_options:
      if 'random_horizontal_flip' in option[0].func_name:
        option[1][fields.InputDataFields.groundtruth_edgemask_masks] = tensor_dict[fields.InputDataFields.groundtruth_edgemask_masks]

  if data_augmentation_options:
    tensor_dict = preprocessor.preprocess(tensor_dict, data_augmentation_options,
                                          mtl_window=mtl_window, mtl_edgemask=mtl_edgemask)

  input_queue = batcher.BatchQueue(
      tensor_dict,
      batch_size=batch_size_per_clone,
      batch_queue_capacity=batch_queue_capacity,
      num_batch_queue_threads=num_batch_queue_threads,
      prefetch_queue_capacity=prefetch_queue_capacity)
  return input_queue 
开发者ID:wonheeML,项目名称:mtl-ssl,代码行数:57,代码来源:trainer.py


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