Python object_detection.utils.np_box_list.BoxList() Examples

def test_concatenate(self):
    boxlist1 = np_box_list.BoxList(
        np.array(
            [[0.25, 0.25, 0.75, 0.75], [0.0, 0.0, 0.5, 0.75]], dtype=
            np.float32))
    boxlist2 = np_box_list.BoxList(
        np.array(
            [[0.5, 0.25, 1.0, 1.0], [0.0, 0.0, 1.0, 1.0]], dtype=np.float32))
    boxlists = [boxlist1, boxlist2]
    boxlist_concatenated = np_box_list_ops.concatenate(boxlists)
    boxlist_concatenated_expected = np_box_list.BoxList(
        np.array(
            [[0.25, 0.25, 0.75, 0.75], [0.0, 0.0, 0.5, 0.75],
             [0.5, 0.25, 1.0, 1.0], [0.0, 0.0, 1.0, 1.0]],
            dtype=np.float32))
    self.assertAllClose(boxlist_concatenated_expected.get(),
                        boxlist_concatenated.get()) 

def test_concatenate(self):
    boxlist1 = np_box_list.BoxList(
        np.array(
            [[0.25, 0.25, 0.75, 0.75], [0.0, 0.0, 0.5, 0.75]], dtype=
            np.float32))
    boxlist2 = np_box_list.BoxList(
        np.array(
            [[0.5, 0.25, 1.0, 1.0], [0.0, 0.0, 1.0, 1.0]], dtype=np.float32))
    boxlists = [boxlist1, boxlist2]
    boxlist_concatenated = np_box_list_ops.concatenate(boxlists)
    boxlist_concatenated_expected = np_box_list.BoxList(
        np.array(
            [[0.25, 0.25, 0.75, 0.75], [0.0, 0.0, 0.5, 0.75],
             [0.5, 0.25, 1.0, 1.0], [0.0, 0.0, 1.0, 1.0]],
            dtype=np.float32))
    self.assertAllClose(boxlist_concatenated_expected.get(),
                        boxlist_concatenated.get()) 

def scale(boxlist, y_scale, x_scale):
  """Scale box coordinates in x and y dimensions.

  Args:
    boxlist: BoxList holding N boxes
    y_scale: float
    x_scale: float

  Returns:
    boxlist: BoxList holding N boxes
  """
  y_min, x_min, y_max, x_max = np.array_split(boxlist.get(), 4, axis=1)
  y_min = y_scale * y_min
  y_max = y_scale * y_max
  x_min = x_scale * x_min
  x_max = x_scale * x_max
  scaled_boxlist = np_box_list.BoxList(np.hstack([y_min, x_min, y_max, x_max]))

  fields = boxlist.get_extra_fields()
  for field in fields:
    extra_field_data = boxlist.get_field(field)
    scaled_boxlist.add_field(field, extra_field_data)

  return scaled_boxlist 

def test_concatenate(self):
    boxlist1 = np_box_list.BoxList(
        np.array(
            [[0.25, 0.25, 0.75, 0.75], [0.0, 0.0, 0.5, 0.75]], dtype=
            np.float32))
    boxlist2 = np_box_list.BoxList(
        np.array(
            [[0.5, 0.25, 1.0, 1.0], [0.0, 0.0, 1.0, 1.0]], dtype=np.float32))
    boxlists = [boxlist1, boxlist2]
    boxlist_concatenated = np_box_list_ops.concatenate(boxlists)
    boxlist_concatenated_expected = np_box_list.BoxList(
        np.array(
            [[0.25, 0.25, 0.75, 0.75], [0.0, 0.0, 0.5, 0.75],
             [0.5, 0.25, 1.0, 1.0], [0.0, 0.0, 1.0, 1.0]],
            dtype=np.float32))
    self.assertAllClose(boxlist_concatenated_expected.get(),
                        boxlist_concatenated.get()) 

def scale(boxlist, y_scale, x_scale):
  """Scale box coordinates in x and y dimensions.

  Args:
    boxlist: BoxList holding N boxes
    y_scale: float
    x_scale: float

  Returns:
    boxlist: BoxList holding N boxes
  """
  y_min, x_min, y_max, x_max = np.array_split(boxlist.get(), 4, axis=1)
  y_min = y_scale * y_min
  y_max = y_scale * y_max
  x_min = x_scale * x_min
  x_max = x_scale * x_max
  scaled_boxlist = np_box_list.BoxList(np.hstack([y_min, x_min, y_max, x_max]))

  fields = boxlist.get_extra_fields()
  for field in fields:
    extra_field_data = boxlist.get_field(field)
    scaled_boxlist.add_field(field, extra_field_data)

  return scaled_boxlist 

def ioa(boxlist1, boxlist2):
  """Computes pairwise intersection-over-area between box collections.

  Intersection-over-area (ioa) between two boxes box1 and box2 is defined as
  their intersection area over box2's area. Note that ioa is not symmetric,
  that is, IOA(box1, box2) != IOA(box2, box1).

  Args:
    boxlist1: BoxList holding N boxes
    boxlist2: BoxList holding M boxes

  Returns:
    a numpy array with shape [N, M] representing pairwise ioa scores.
  """
  return np_box_ops.ioa(boxlist1.get(), boxlist2.get()) 

def setUp(self):
    boxes = np.array([[3.0, 4.0, 6.0, 8.0], [14.0, 14.0, 15.0, 15.0],
                      [0.0, 0.0, 20.0, 20.0]],
                     dtype=float)
    self.boxlist = np_box_list.BoxList(boxes) 

def gather(boxlist, indices, fields=None):
  """Gather boxes from BoxList according to indices and return new BoxList.

  By default, Gather returns boxes corresponding to the input index list, as
  well as all additional fields stored in the boxlist (indexing into the
  first dimension).  However one can optionally only gather from a
  subset of fields.

  Args:
    boxlist: BoxList holding N boxes
    indices: a 1-d numpy array of type int_
    fields: (optional) list of fields to also gather from.  If None (default),
        all fields are gathered from.  Pass an empty fields list to only gather
        the box coordinates.

  Returns:
    subboxlist: a BoxList corresponding to the subset of the input BoxList
        specified by indices

  Raises:
    ValueError: if specified field is not contained in boxlist or if the
        indices are not of type int_
  """
  if indices.size:
    if np.amax(indices) >= boxlist.num_boxes() or np.amin(indices) < 0:
      raise ValueError('indices are out of valid range.')
  subboxlist = np_box_list.BoxList(boxlist.get()[indices, :])
  if fields is None:
    fields = boxlist.get_extra_fields()
  for field in fields:
    extra_field_data = boxlist.get_field(field)
    subboxlist.add_field(field, extra_field_data[indices, ...])
  return subboxlist 

def sort_by_field(boxlist, field, order=SortOrder.DESCEND):
  """Sort boxes and associated fields according to a scalar field.

  A common use case is reordering the boxes according to descending scores.

  Args:
    boxlist: BoxList holding N boxes.
    field: A BoxList field for sorting and reordering the BoxList.
    order: (Optional) 'descend' or 'ascend'. Default is descend.

  Returns:
    sorted_boxlist: A sorted BoxList with the field in the specified order.

  Raises:
    ValueError: if specified field does not exist or is not of single dimension.
    ValueError: if the order is not either descend or ascend.
  """
  if not boxlist.has_field(field):
    raise ValueError('Field ' + field + ' does not exist')
  if len(boxlist.get_field(field).shape) != 1:
    raise ValueError('Field ' + field + 'should be single dimension.')
  if order != SortOrder.DESCEND and order != SortOrder.ASCEND:
    raise ValueError('Invalid sort order')

  field_to_sort = boxlist.get_field(field)
  sorted_indices = np.argsort(field_to_sort)
  if order == SortOrder.DESCEND:
    sorted_indices = sorted_indices[::-1]
  return gather(boxlist, sorted_indices) 

def test_clip_to_window(self):
    boxlist = np_box_list.BoxList(
        np.array(
            [[0.25, 0.25, 0.75, 0.75], [0.0, 0.0, 0.5, 0.75],
             [-0.2, -0.3, 0.7, 1.5]],
            dtype=np.float32))
    boxlist_clipped = np_box_list_ops.clip_to_window(boxlist,
                                                     [0.0, 0.0, 1.0, 1.0])
    expected_boxlist_clipped = np_box_list.BoxList(
        np.array(
            [[0.25, 0.25, 0.75, 0.75], [0.0, 0.0, 0.5, 0.75],
             [0.0, 0.0, 0.7, 1.0]],
            dtype=np.float32))
    self.assertAllClose(expected_boxlist_clipped.get(), boxlist_clipped.get()) 

def _get_overlaps_and_scores_relation_tuples(self, detected_box_tuples,
                                               groundtruth_box_tuples):
    """Computes overlaps and scores between detected and groundtruth tuples.

    Both detections and groundtruth boxes have the same class tuples.

    Args:
      detected_box_tuples: A numpy array of structures with shape [N,],
          representing N tuples, each tuple containing the same number of named
          bounding boxes.
          Each box is of the format [y_min, x_min, y_max, x_max]
      groundtruth_box_tuples: A float numpy array of structures with the shape
          [M,], representing M tuples, each tuple containing the same number
          of named bounding boxes.
          Each box is of the format [y_min, x_min, y_max, x_max]

    Returns:
      result_iou: A float numpy array of size
        [num_detected_tuples, num_gt_box_tuples].
    """

    result_iou = np.ones(
        (detected_box_tuples.shape[0], groundtruth_box_tuples.shape[0]),
        dtype=float)
    for field in detected_box_tuples.dtype.fields:
      detected_boxlist_field = np_box_list.BoxList(detected_box_tuples[field])
      gt_boxlist_field = np_box_list.BoxList(groundtruth_box_tuples[field])
      iou_field = np_box_list_ops.iou(detected_boxlist_field, gt_boxlist_field)
      result_iou = np.minimum(iou_field, result_iou)
    return result_iou 

def _get_overlaps_and_scores_box_mode(
      self,
      detected_boxes,
      detected_scores,
      groundtruth_boxes,
      groundtruth_is_group_of_list):
    """Computes overlaps and scores between detected and groudntruth boxes.

    Args:
      detected_boxes: A numpy array of shape [N, 4] representing detected box
          coordinates
      detected_scores: A 1-d numpy array of length N representing classification
          score
      groundtruth_boxes: A numpy array of shape [M, 4] representing ground truth
          box coordinates
      groundtruth_is_group_of_list: A boolean numpy array of length M denoting
          whether a ground truth box has group-of tag. If a groundtruth box
          is group-of box, every detection matching this box is ignored.

    Returns:
      iou: A float numpy array of size [num_detected_boxes, num_gt_boxes]. If
          gt_non_group_of_boxlist.num_boxes() == 0 it will be None.
      ioa: A float numpy array of size [num_detected_boxes, num_gt_boxes]. If
          gt_group_of_boxlist.num_boxes() == 0 it will be None.
      scores: The score of the detected boxlist.
      num_boxes: Number of non-maximum suppressed detected boxes.
    """
    detected_boxlist = np_box_list.BoxList(detected_boxes)
    detected_boxlist.add_field('scores', detected_scores)
    detected_boxlist = np_box_list_ops.non_max_suppression(
        detected_boxlist, self.nms_max_output_boxes, self.nms_iou_threshold)
    gt_non_group_of_boxlist = np_box_list.BoxList(
        groundtruth_boxes[~groundtruth_is_group_of_list])
    gt_group_of_boxlist = np_box_list.BoxList(
        groundtruth_boxes[groundtruth_is_group_of_list])
    iou = np_box_list_ops.iou(detected_boxlist, gt_non_group_of_boxlist)
    ioa = np.transpose(
        np_box_list_ops.ioa(gt_group_of_boxlist, detected_boxlist))
    scores = detected_boxlist.get_field('scores')
    num_boxes = detected_boxlist.num_boxes()
    return iou, ioa, scores, num_boxes 

def setUp(self):
    boxes1 = np.array([[4.0, 3.0, 7.0, 5.0], [5.0, 6.0, 10.0, 7.0]],
                      dtype=float)
    boxes2 = np.array([[3.0, 4.0, 6.0, 8.0], [14.0, 14.0, 15.0, 15.0],
                       [0.0, 0.0, 20.0, 20.0]],
                      dtype=float)
    self.boxlist1 = np_box_list.BoxList(boxes1)
    self.boxlist2 = np_box_list.BoxList(boxes2) 

def test_ioa(self):
    boxlist1 = np_box_list.BoxList(
        np.array(
            [[0.25, 0.25, 0.75, 0.75], [0.0, 0.0, 0.5, 0.75]], dtype=
            np.float32))
    boxlist2 = np_box_list.BoxList(
        np.array(
            [[0.5, 0.25, 1.0, 1.0], [0.0, 0.0, 1.0, 1.0]], dtype=np.float32))
    ioa21 = np_box_list_ops.ioa(boxlist2, boxlist1)
    expected_ioa21 = np.array([[0.5, 0.0],
                               [1.0, 1.0]],
                              dtype=np.float32)
    self.assertAllClose(ioa21, expected_ioa21) 

def test_change_coordinate_frame(self):
    boxlist = np_box_list.BoxList(
        np.array(
            [[0.25, 0.25, 0.75, 0.75], [0.0, 0.0, 0.5, 0.75]], dtype=
            np.float32))
    boxlist_coord = np_box_list_ops.change_coordinate_frame(
        boxlist, np.array([0, 0, 0.5, 0.5], dtype=np.float32))
    expected_boxlist_coord = np_box_list.BoxList(
        np.array([[0.5, 0.5, 1.5, 1.5], [0, 0, 1.0, 1.5]], dtype=np.float32))
    self.assertAllClose(boxlist_coord.get(), expected_boxlist_coord.get()) 

def test_filter_scores_greater_than(self):
    boxlist = np_box_list.BoxList(
        np.array(
            [[0.25, 0.25, 0.75, 0.75], [0.0, 0.0, 0.5, 0.75]], dtype=
            np.float32))
    boxlist.add_field('scores', np.array([0.8, 0.2], np.float32))
    boxlist_greater = np_box_list_ops.filter_scores_greater_than(boxlist, 0.5)

    expected_boxlist_greater = np_box_list.BoxList(
        np.array([[0.25, 0.25, 0.75, 0.75]], dtype=np.float32))

    self.assertAllClose(boxlist_greater.get(), expected_boxlist_greater.get()) 

def clip_to_window(boxlist, window):
  """Clip bounding boxes to a window.

  This op clips input bounding boxes (represented by bounding box
  corners) to a window, optionally filtering out boxes that do not
  overlap at all with the window.

  Args:
    boxlist: BoxList holding M_in boxes
    window: a numpy array of shape [4] representing the
            [y_min, x_min, y_max, x_max] window to which the op
            should clip boxes.

  Returns:
    a BoxList holding M_out boxes where M_out <= M_in
  """
  y_min, x_min, y_max, x_max = np.array_split(boxlist.get(), 4, axis=1)
  win_y_min = window[0]
  win_x_min = window[1]
  win_y_max = window[2]
  win_x_max = window[3]
  y_min_clipped = np.fmax(np.fmin(y_min, win_y_max), win_y_min)
  y_max_clipped = np.fmax(np.fmin(y_max, win_y_max), win_y_min)
  x_min_clipped = np.fmax(np.fmin(x_min, win_x_max), win_x_min)
  x_max_clipped = np.fmax(np.fmin(x_max, win_x_max), win_x_min)
  clipped = np_box_list.BoxList(
      np.hstack([y_min_clipped, x_min_clipped, y_max_clipped, x_max_clipped]))
  clipped = _copy_extra_fields(clipped, boxlist)
  areas = area(clipped)
  nonzero_area_indices = np.reshape(np.nonzero(np.greater(areas, 0.0)),
                                    [-1]).astype(np.int32)
  return gather(clipped, nonzero_area_indices) 

def intersection(boxlist1, boxlist2):
  """Compute pairwise intersection areas between boxes.

  Args:
    boxlist1: BoxList holding N boxes
    boxlist2: BoxList holding M boxes

  Returns:
    a numpy array with shape [N*M] representing pairwise intersection area
  """
  return np_box_ops.intersection(boxlist1.get(), boxlist2.get()) 

def area(boxlist):
  """Computes area of boxes.

  Args:
    boxlist: BoxList holding N boxes

  Returns:
    a numpy array with shape [N*1] representing box areas
  """
  y_min, x_min, y_max, x_max = boxlist.get_coordinates()
  return (y_max - y_min) * (x_max - x_min) 

def test_multiclass_nms(self):
    boxlist = np_box_list.BoxList(
        np.array(
            [[0.2, 0.4, 0.8, 0.8], [0.4, 0.2, 0.8, 0.8], [0.6, 0.0, 1.0, 1.0]],
            dtype=np.float32))
    scores = np.array([[-0.2, 0.1, 0.5, -0.4, 0.3],
                       [0.7, -0.7, 0.6, 0.2, -0.9],
                       [0.4, 0.34, -0.9, 0.2, 0.31]],
                      dtype=np.float32)
    boxlist.add_field('scores', scores)
    boxlist_clean = np_box_list_ops.multi_class_non_max_suppression(
        boxlist, score_thresh=0.25, iou_thresh=0.1, max_output_size=3)

    scores_clean = boxlist_clean.get_field('scores')
    classes_clean = boxlist_clean.get_field('classes')
    boxes = boxlist_clean.get()
    expected_scores = np.array([0.7, 0.6, 0.34, 0.31])
    expected_classes = np.array([0, 2, 1, 4])
    expected_boxes = np.array([[0.4, 0.2, 0.8, 0.8],
                               [0.4, 0.2, 0.8, 0.8],
                               [0.6, 0.0, 1.0, 1.0],
                               [0.6, 0.0, 1.0, 1.0]],
                              dtype=np.float32)
    self.assertAllClose(scores_clean, expected_scores)
    self.assertAllClose(classes_clean, expected_classes)
    self.assertAllClose(boxes, expected_boxes) 

def test_different_iou_threshold(self):
    boxes = np.array([[0, 0, 20, 100], [0, 0, 20, 80], [200, 200, 210, 300],
                      [200, 200, 210, 250]],
                     dtype=float)
    boxlist = np_box_list.BoxList(boxes)
    boxlist.add_field('scores', np.array([0.9, 0.8, 0.7, 0.6]))
    max_output_size = 4

    iou_threshold = .4
    expected_boxes = np.array([[0, 0, 20, 100],
                               [200, 200, 210, 300],],
                              dtype=float)
    nms_boxlist = np_box_list_ops.non_max_suppression(
        boxlist, max_output_size, iou_threshold)
    self.assertAllClose(nms_boxlist.get(), expected_boxes)

    iou_threshold = .5
    expected_boxes = np.array([[0, 0, 20, 100], [200, 200, 210, 300],
                               [200, 200, 210, 250]],
                              dtype=float)
    nms_boxlist = np_box_list_ops.non_max_suppression(
        boxlist, max_output_size, iou_threshold)
    self.assertAllClose(nms_boxlist.get(), expected_boxes)

    iou_threshold = .8
    expected_boxes = np.array([[0, 0, 20, 100], [0, 0, 20, 80],
                               [200, 200, 210, 300], [200, 200, 210, 250]],
                              dtype=float)
    nms_boxlist = np_box_list_ops.non_max_suppression(
        boxlist, max_output_size, iou_threshold)
    self.assertAllClose(nms_boxlist.get(), expected_boxes) 

def test_select_from_ten_indentical_boxes(self):
    boxes = np.array(10 * [[0, 0, 1, 1]], dtype=float)
    boxlist = np_box_list.BoxList(boxes)
    boxlist.add_field('scores', np.array(10 * [0.8]))
    iou_threshold = .5
    max_output_size = 3
    expected_boxes = np.array([[0, 0, 1, 1]], dtype=float)
    nms_boxlist = np_box_list_ops.non_max_suppression(
        boxlist, max_output_size, iou_threshold)
    self.assertAllClose(nms_boxlist.get(), expected_boxes) 

def test_select_at_most_two_from_three_clusters(self):
    boxlist = np_box_list.BoxList(self._boxes)
    boxlist.add_field('scores',
                      np.array([.9, .75, .6, .95, .5, .3], dtype=float))
    max_output_size = 2
    iou_threshold = 0.5

    expected_boxes = np.array([[0, 10, 1, 11], [0, 0, 1, 1]], dtype=float)
    nms_boxlist = np_box_list_ops.non_max_suppression(
        boxlist, max_output_size, iou_threshold)
    self.assertAllClose(nms_boxlist.get(), expected_boxes) 

def test_select_from_three_clusters(self):
    boxlist = np_box_list.BoxList(self._boxes)
    boxlist.add_field('scores',
                      np.array([.9, .75, .6, .95, .2, .3], dtype=float))
    max_output_size = 3
    iou_threshold = 0.5

    expected_boxes = np.array([[0, 10, 1, 11], [0, 0, 1, 1], [0, 100, 1, 101]],
                              dtype=float)
    nms_boxlist = np_box_list_ops.non_max_suppression(
        boxlist, max_output_size, iou_threshold)
    self.assertAllClose(nms_boxlist.get(), expected_boxes) 

def test_nms_disabled_max_output_size_equals_three(self):
    boxlist = np_box_list.BoxList(self._boxes)
    boxlist.add_field('scores',
                      np.array([.9, .75, .6, .95, .2, .3], dtype=float))
    max_output_size = 3
    iou_threshold = 1.  # No NMS

    expected_boxes = np.array([[0, 10, 1, 11], [0, 0, 1, 1], [0, 0.1, 1, 1.1]],
                              dtype=float)
    nms_boxlist = np_box_list_ops.non_max_suppression(
        boxlist, max_output_size, iou_threshold)
    self.assertAllClose(nms_boxlist.get(), expected_boxes) 

def test_with_no_scores_field(self):
    boxlist = np_box_list.BoxList(self._boxes)
    max_output_size = 3
    iou_threshold = 0.5

    with self.assertRaises(ValueError):
      np_box_list_ops.non_max_suppression(
          boxlist, max_output_size, iou_threshold) 

def setUp(self):
    self._boxes = np.array([[0, 0, 1, 1],
                            [0, 0.1, 1, 1.1],
                            [0, -0.1, 1, 0.9],
                            [0, 10, 1, 11],
                            [0, 10.1, 1, 11.1],
                            [0, 100, 1, 101]],
                           dtype=float)
    self._boxlist = np_box_list.BoxList(self._boxes) 

def setUp(self):
    boxes = np.array([[3.0, 4.0, 6.0, 8.0], [14.0, 14.0, 15.0, 15.0],
                      [0.0, 0.0, 20.0, 20.0]],
                     dtype=float)
    self.boxlist = np_box_list.BoxList(boxes)
    self.boxlist.add_field('scores', np.array([0.5, 0.7, 0.9], dtype=float))
    self.boxlist.add_field('labels',
                           np.array([[0, 0, 0, 1, 0], [0, 1, 0, 0, 0],
                                     [0, 0, 0, 0, 1]],
                                    dtype=int)) 

def test_filter_scores_greater_than(self):
    boxlist = np_box_list.BoxList(
        np.array(
            [[0.25, 0.25, 0.75, 0.75], [0.0, 0.0, 0.5, 0.75]], dtype=
            np.float32))
    boxlist.add_field('scores', np.array([0.8, 0.2], np.float32))
    boxlist_greater = np_box_list_ops.filter_scores_greater_than(boxlist, 0.5)

    expected_boxlist_greater = np_box_list.BoxList(
        np.array([[0.25, 0.25, 0.75, 0.75]], dtype=np.float32))

    self.assertAllClose(boxlist_greater.get(), expected_boxlist_greater.get()) 

def test_change_coordinate_frame(self):
    boxlist = np_box_list.BoxList(
        np.array(
            [[0.25, 0.25, 0.75, 0.75], [0.0, 0.0, 0.5, 0.75]], dtype=
            np.float32))
    boxlist_coord = np_box_list_ops.change_coordinate_frame(
        boxlist, np.array([0, 0, 0.5, 0.5], dtype=np.float32))
    expected_boxlist_coord = np_box_list.BoxList(
        np.array([[0.5, 0.5, 1.5, 1.5], [0, 0, 1.0, 1.5]], dtype=np.float32))
    self.assertAllClose(boxlist_coord.get(), expected_boxlist_coord.get())