def _generate(self, feature_map_shape_list, im_height, im_width):
"""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.
im_width: the width of the image to generate the grid for.
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 not integers.
"""
if not isinstance(im_height, int) or not isinstance(im_width, int):
raise ValueError(
'MultiscaleGridAnchorGenerator currently requires '
'input image shape to be statically defined.')
anchor_grid_list = []
for feat_shape, grid_info in zip(feature_map_shape_list,
self._anchor_grid_info):
# TODO(rathodv) check the feature_map_shape_list is consistent with
# self._anchor_grid_info
level = grid_info['level']
stride = 2**level
scales, aspect_ratios, base_anchor_size, anchor_stride = grid_info[
'info']
feat_h = feat_shape[0]
feat_w = feat_shape[1]
anchor_offset = [0, 0]
if im_height % 2.0**level == 0:
anchor_offset[0] = stride / 2.0
if im_width % 2.0**level == 0:
anchor_offset[1] = stride / 2.0
ag = grid_anchor_generator.GridAnchorGenerator(
scales,
aspect_ratios,
base_anchor_size=base_anchor_size,
anchor_stride=anchor_stride,
anchor_offset=anchor_offset)
(anchor_grid, ) = ag.generate(feature_map_shape_list=[(feat_h,
feat_w)])
if self._normalize_coordinates:
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
def test_to_normalized_coordinates_already_normalized(self):
coordinates = tf.constant([[0, 0, 1, 1],
[0.25, 0.25, 0.75, 0.75]], tf.float32)
img = tf.ones((128, 100, 100, 3))
boxlist = box_list.BoxList(coordinates)
normalized_boxlist = box_list_ops.to_normalized_coordinates(
boxlist, tf.shape(img)[1], tf.shape(img)[2])
with self.test_session() as sess:
with self.assertRaisesOpError('assertion failed'):
sess.run(normalized_boxlist.get())
def test_to_normalized_coordinates(self):
coordinates = tf.constant([[0, 0, 100, 100],
[25, 25, 75, 75]], tf.float32)
img = tf.ones((128, 100, 100, 3))
boxlist = box_list.BoxList(coordinates)
normalized_boxlist = box_list_ops.to_normalized_coordinates(
boxlist, tf.shape(img)[1], tf.shape(img)[2])
expected_boxes = [[0, 0, 1, 1],
[0.25, 0.25, 0.75, 0.75]]
with self.test_session() as sess:
normalized_boxes = sess.run(normalized_boxlist.get())
self.assertAllClose(normalized_boxes, expected_boxes)
def test_convert_to_absolute_and_back(self):
coordinates = np.random.uniform(size=(100, 4))
coordinates = np.sort(coordinates)
coordinates[99, :] = [0, 0, 1, 1]
img = tf.ones((128, 202, 202, 3))
boxlist = box_list.BoxList(tf.constant(coordinates, tf.float32))
boxlist = box_list_ops.to_absolute_coordinates(boxlist,
tf.shape(img)[1],
tf.shape(img)[2])
boxlist = box_list_ops.to_normalized_coordinates(boxlist,
tf.shape(img)[1],
tf.shape(img)[2])
with self.test_session() as sess:
out = sess.run(boxlist.get())
self.assertAllClose(out, coordinates)