Python grid_anchor_generator.tile_anchors方法代码示例

# 需要导入模块: from object_detection.anchor_generators import grid_anchor_generator [as 别名]
# 或者: from object_detection.anchor_generators.grid_anchor_generator import tile_anchors [as 别名]
def _generate(self, feature_map_shape_list, im_height=1, im_width=1):
    """Generates a collection of bounding boxes to be used as anchors.

    Currently we require the input image shape to be statically defined.  That
    is, im_height and im_width should be integers rather than tensors.

    Args:
      feature_map_shape_list: list of pairs of convnet layer resolutions in the
        format [(height_0, width_0), (height_1, width_1), ...]. For example,
        setting feature_map_shape_list=[(8, 8), (7, 7)] asks for anchors that
        correspond to an 8x8 layer followed by a 7x7 layer.
      im_height: the height of the image to generate the grid for. If both
        im_height and im_width are 1, anchors can only be generated in
        absolute coordinates.
      im_width: the width of the image to generate the grid for. If both
        im_height and im_width are 1, anchors can only be generated in
        absolute coordinates.

    Returns:
      boxes_list: a list of BoxLists each holding anchor boxes corresponding to
        the input feature map shapes.
    Raises:
      ValueError: if im_height and im_width are 1, but normalized coordinates
        were requested.
    """
    anchor_grid_list = []
    for (feat_shape, base_sizes, aspect_ratios, anchor_stride, anchor_offset
        ) in zip(feature_map_shape_list, self._base_sizes, self._aspect_ratios,
                 self._anchor_strides, self._anchor_offsets):
      anchor_grid = grid_anchor_generator.tile_anchors(
          feat_shape[0],
          feat_shape[1],
          tf.cast(tf.convert_to_tensor(base_sizes), dtype=tf.float32),
          tf.cast(tf.convert_to_tensor(aspect_ratios), dtype=tf.float32),
          tf.constant([1.0, 1.0]),
          tf.cast(tf.convert_to_tensor(anchor_stride), dtype=tf.float32),
          tf.cast(tf.convert_to_tensor(anchor_offset), dtype=tf.float32))
      num_anchors = anchor_grid.num_boxes_static()
      if num_anchors is None:
        num_anchors = anchor_grid.num_boxes()
      anchor_indices = tf.zeros([num_anchors])
      anchor_grid.add_field('feature_map_index', anchor_indices)
      if self._normalize_coordinates:
        if im_height == 1 or im_width == 1:
          raise ValueError(
              'Normalized coordinates were requested upon construction of the '
              'FlexibleGridAnchorGenerator, but a subsequent call to '
              'generate did not supply dimension information.')
        anchor_grid = box_list_ops.to_normalized_coordinates(
            anchor_grid, im_height, im_width, check_range=False)
      anchor_grid_list.append(anchor_grid)

    return anchor_grid_list