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# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Tests for object_detection.core.box_list."""
import tensorflow as tf
from object_detection.core import box_list
class BoxListTest(tf.test.TestCase):
"""Tests for BoxList class."""
def test_num_boxes(self):
data = tf.constant([[0, 0, 1, 1], [1, 1, 2, 3], [3, 4, 5, 5]], tf.float32)
expected_num_boxes = 3
boxes = box_list.BoxList(data)
with self.test_session() as sess:
num_boxes_output = sess.run(boxes.num_boxes())
self.assertEquals(num_boxes_output, expected_num_boxes)
def test_get_correct_center_coordinates_and_sizes(self):
boxes = [[10.0, 10.0, 20.0, 15.0], [0.2, 0.1, 0.5, 0.4]]
boxes = box_list.BoxList(tf.constant(boxes))
centers_sizes = boxes.get_center_coordinates_and_sizes()
expected_centers_sizes = [[15, 0.35], [12.5, 0.25], [10, 0.3], [5, 0.3]]
with self.test_session() as sess:
centers_sizes_out = sess.run(centers_sizes)
self.assertAllClose(centers_sizes_out, expected_centers_sizes)
def test_create_box_list_with_dynamic_shape(self):
data = tf.constant([[0, 0, 1, 1], [1, 1, 2, 3], [3, 4, 5, 5]], tf.float32)
indices = tf.reshape(tf.where(tf.greater([1, 0, 1], 0)), [-1])
data = tf.gather(data, indices)
assert data.get_shape().as_list() == [None, 4]
expected_num_boxes = 2
boxes = box_list.BoxList(data)
with self.test_session() as sess:
num_boxes_output = sess.run(boxes.num_boxes())
self.assertEquals(num_boxes_output, expected_num_boxes)
def test_transpose_coordinates(self):
boxes = [[10.0, 10.0, 20.0, 15.0], [0.2, 0.1, 0.5, 0.4]]
boxes = box_list.BoxList(tf.constant(boxes))
boxes.transpose_coordinates()
expected_corners = [[10.0, 10.0, 15.0, 20.0], [0.1, 0.2, 0.4, 0.5]]
with self.test_session() as sess:
corners_out = sess.run(boxes.get())
self.assertAllClose(corners_out, expected_corners)
def test_box_list_invalid_inputs(self):
data0 = tf.constant([[[0, 0, 1, 1], [3, 4, 5, 5]]], tf.float32)
data1 = tf.constant([[0, 0, 1], [1, 1, 2], [3, 4, 5]], tf.float32)
data2 = tf.constant([[0, 0, 1], [1, 1, 2], [3, 4, 5]], tf.int32)
with self.assertRaises(ValueError):
_ = box_list.BoxList(data0)
with self.assertRaises(ValueError):
_ = box_list.BoxList(data1)
with self.assertRaises(ValueError):
_ = box_list.BoxList(data2)
def test_num_boxes_static(self):
box_corners = [[10.0, 10.0, 20.0, 15.0], [0.2, 0.1, 0.5, 0.4]]
boxes = box_list.BoxList(tf.constant(box_corners))
self.assertEquals(boxes.num_boxes_static(), 2)
self.assertEquals(type(boxes.num_boxes_static()), int)
def test_num_boxes_static_for_uninferrable_shape(self):
placeholder = tf.placeholder(tf.float32, shape=[None, 4])
boxes = box_list.BoxList(placeholder)
self.assertEquals(boxes.num_boxes_static(), None)
def test_as_tensor_dict(self):
boxlist = box_list.BoxList(
tf.constant([[0.1, 0.1, 0.4, 0.4], [0.1, 0.1, 0.5, 0.5]], tf.float32))
boxlist.add_field('classes', tf.constant([0, 1]))
boxlist.add_field('scores', tf.constant([0.75, 0.2]))
tensor_dict = boxlist.as_tensor_dict()
expected_boxes = [[0.1, 0.1, 0.4, 0.4], [0.1, 0.1, 0.5, 0.5]]
expected_classes = [0, 1]
expected_scores = [0.75, 0.2]
with self.test_session() as sess:
tensor_dict_out = sess.run(tensor_dict)
self.assertAllEqual(3, len(tensor_dict_out))
self.assertAllClose(expected_boxes, tensor_dict_out['boxes'])
self.assertAllEqual(expected_classes, tensor_dict_out['classes'])
self.assertAllClose(expected_scores, tensor_dict_out['scores'])
def test_as_tensor_dict_with_features(self):
boxlist = box_list.BoxList(
tf.constant([[0.1, 0.1, 0.4, 0.4], [0.1, 0.1, 0.5, 0.5]], tf.float32))
boxlist.add_field('classes', tf.constant([0, 1]))
boxlist.add_field('scores', tf.constant([0.75, 0.2]))
tensor_dict = boxlist.as_tensor_dict(['boxes', 'classes', 'scores'])
expected_boxes = [[0.1, 0.1, 0.4, 0.4], [0.1, 0.1, 0.5, 0.5]]
expected_classes = [0, 1]
expected_scores = [0.75, 0.2]
with self.test_session() as sess:
tensor_dict_out = sess.run(tensor_dict)
self.assertAllEqual(3, len(tensor_dict_out))
self.assertAllClose(expected_boxes, tensor_dict_out['boxes'])
self.assertAllEqual(expected_classes, tensor_dict_out['classes'])
self.assertAllClose(expected_scores, tensor_dict_out['scores'])
def test_as_tensor_dict_missing_field(self):
boxlist = box_list.BoxList(
tf.constant([[0.1, 0.1, 0.4, 0.4], [0.1, 0.1, 0.5, 0.5]], tf.float32))
boxlist.add_field('classes', tf.constant([0, 1]))
boxlist.add_field('scores', tf.constant([0.75, 0.2]))
with self.assertRaises(ValueError):
boxlist.as_tensor_dict(['foo', 'bar'])
if __name__ == '__main__':
tf.test.main()
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