(image, height, width,
                     labels, bboxes,
                     is_training=False,
                     fast_mode=True)
Pre-process one image for training or evaluation.

  Args:
    image: 3-D Tensor [height, width, channels] with the image.
    height: integer, image expected height.
    width: integer, image expected width.
    is_training: Boolean. If true it would transform an image for train,
      otherwise it would transform it for evaluation.
    bbox: 3-D float Tensor of bounding boxes arranged [1, num_boxes, coords]
      where each coordinate is [0, 1) and the coordinates are arranged as
      [ymin, xmin, ymax, xmax].
    fast_mode: Optional boolean, if True avoids slower transformations.

  Returns:
    3-D float Tensor containing an appropriately scaled image

  Raises:
    ValueError: if user does not provide bounding box
Pre-process one image for training or evaluation.
Pre - process one image for training or evaluation .
def preprocess_image(image, height, width,
                     labels, bboxes,
                     is_training=False,
                     fast_mode=True):
  """Pre-process one image for training or evaluation.

  Args:
    image: 3-D Tensor [height, width, channels] with the image.
    height: integer, image expected height.
    width: integer, image expected width.
    is_training: Boolean. If true it would transform an image for train,
      otherwise it would transform it for evaluation.
    bbox: 3-D float Tensor of bounding boxes arranged [1, num_boxes, coords]
      where each coordinate is [0, 1) and the coordinates are arranged as
      [ymin, xmin, ymax, xmax].
    fast_mode: Optional boolean, if True avoids slower transformations.

  Returns:
    3-D float Tensor containing an appropriately scaled image

  Raises:
    ValueError: if user does not provide bounding box
  """
  if is_training:
    return preprocess_for_train(image, height, width, labels, bboxes, fast_mode)
  else:
    return preprocess_for_eval(image, height, width)
def preprocess_image ( image , height , width , labels , bboxes , is_training = False , fast_mode = True ) : if is_training : return preprocess_for_train ( image , height , width , labels , bboxes , fast_mode ) else : return preprocess_for_eval ( image , height , width )