class_docstr: Convenience wrapper for BoxList with masks.
  
  BoxMaskList extends the np_box_list.BoxList to contain masks as well.
  In particular, its constructor receives both boxes and masks. Note that the
  masks correspond to the full image.
class_docstr_tok: ["Convenience","wrapper","for","BoxList","with","masks",".","BoxMaskList","extends","the","np_box_list.BoxList","to","contain","masks","as","well",".","In","particular",",","its","constructor","receives","both","boxes","and","masks",".","Note","that","the","masks","correspond","to","the","full","image","."]
docstr: Constructs box collection.
  
  Args:
    box_data: a numpy array of shape [N, 4] representing box coordinates
    mask_data: a numpy array of shape [N, height, width] representing masks
      with values are in {0,1}. The masks correspond to the full
      image. The height and the width will be equal to image height and width.
  
  Raises:
    ValueError: if bbox data is not a numpy array
    ValueError: if invalid dimensions for bbox data
    ValueError: if mask data is not a numpy array
    ValueError: if invalid dimension for mask data
docstr_tok: ["Constructs","box","collection",".","Args",":","box_data",":","a","numpy","array","of","shape","[","N",",","4","]","representing","box","coordinates","mask_data",":","a","numpy","array","of","shape","[","N",",","height",",","width","]","representing","masks","with","values","are","in","{","0,1","}",".","The","masks","correspond","to","the","full","image",".","The","height","and","the","width","will","be","equal","to","image","height","and","width",".","Raises",":","ValueError",":","if","bbox","data","is","not","a","numpy","array","ValueError",":","if","invalid","dimensions","for","bbox","data","ValueError",":","if","mask","data","is","not","a","numpy","array","ValueError",":","if","invalid","dimension","for","mask","data"]
code: def __init__(self, box_data, mask_data):
      """Constructs box collection.
  
      Args:
        box_data: a numpy array of shape [N, 4] representing box coordinates
        mask_data: a numpy array of shape [N, height, width] representing masks
          with values are in {0,1}. The masks correspond to the full
          image. The height and the width will be equal to image height and width.
  
      Raises:
        ValueError: if bbox data is not a numpy array
        ValueError: if invalid dimensions for bbox data
        ValueError: if mask data is not a numpy array
        ValueError: if invalid dimension for mask data
      """
      super(BoxMaskList, self).__init__(box_data)
      if not isinstance(mask_data, np.ndarray):
          raise ValueError("Mask data must be a numpy array.")
      if len(mask_data.shape) != 3:
          raise ValueError("Invalid dimensions for mask data.")
      if mask_data.dtype != np.uint8:
          raise ValueError(
              "Invalid data type for mask data: uint8 is required."
          )
      if mask_data.shape[0] != box_data.shape[0]:
          raise ValueError(
              "There should be the same number of boxes and masks."
          )
      self.data["masks"] = mask_data
  
imports: from __future__ import absolute_import, division, print_function, unicode_literals
  import numpy
  from .None import np_box_list
label_desc: Use image node_id 1 to create a new BoxMaskList object from inherited base classes: np_box_list with example: obj = BoxMaskList(box_data, mask_data)