research/object_detection/utils/np_box_mask_list_test.py
# 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.utils.np_box_mask_list_test."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
import tensorflow.compat.v1 as tf
from object_detection.utils import np_box_mask_list
class BoxMaskListTest(tf.test.TestCase):
def test_invalid_box_mask_data(self):
with self.assertRaises(ValueError):
np_box_mask_list.BoxMaskList(
box_data=[0, 0, 1, 1],
mask_data=np.zeros([1, 3, 3], dtype=np.uint8))
with self.assertRaises(ValueError):
np_box_mask_list.BoxMaskList(
box_data=np.array([[0, 0, 1, 1]], dtype=int),
mask_data=np.zeros([1, 3, 3], dtype=np.uint8))
with self.assertRaises(ValueError):
np_box_mask_list.BoxMaskList(
box_data=np.array([0, 1, 1, 3, 4], dtype=float),
mask_data=np.zeros([1, 3, 3], dtype=np.uint8))
with self.assertRaises(ValueError):
np_box_mask_list.BoxMaskList(
box_data=np.array([[0, 1, 1, 3], [3, 1, 1, 5]], dtype=float),
mask_data=np.zeros([2, 3, 3], dtype=np.uint8))
with self.assertRaises(ValueError):
np_box_mask_list.BoxMaskList(
box_data=np.array([[0, 1, 1, 3], [1, 1, 1, 5]], dtype=float),
mask_data=np.zeros([3, 5, 5], dtype=np.uint8))
with self.assertRaises(ValueError):
np_box_mask_list.BoxMaskList(
box_data=np.array([[0, 1, 1, 3], [1, 1, 1, 5]], dtype=float),
mask_data=np.zeros([2, 5], dtype=np.uint8))
with self.assertRaises(ValueError):
np_box_mask_list.BoxMaskList(
box_data=np.array([[0, 1, 1, 3], [1, 1, 1, 5]], dtype=float),
mask_data=np.zeros([2, 5, 5, 5], dtype=np.uint8))
with self.assertRaises(ValueError):
np_box_mask_list.BoxMaskList(
box_data=np.array([[0, 1, 1, 3], [1, 1, 1, 5]], dtype=float),
mask_data=np.zeros([2, 5, 5], dtype=np.int32))
def test_has_field_with_existed_field(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)
box_mask_list = np_box_mask_list.BoxMaskList(
box_data=boxes, mask_data=np.zeros([3, 5, 5], dtype=np.uint8))
self.assertTrue(box_mask_list.has_field('boxes'))
self.assertTrue(box_mask_list.has_field('masks'))
def test_has_field_with_nonexisted_field(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)
box_mask_list = np_box_mask_list.BoxMaskList(
box_data=boxes, mask_data=np.zeros([3, 3, 3], dtype=np.uint8))
self.assertFalse(box_mask_list.has_field('scores'))
def test_get_field_with_existed_field(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)
masks = np.zeros([3, 3, 3], dtype=np.uint8)
box_mask_list = np_box_mask_list.BoxMaskList(
box_data=boxes, mask_data=masks)
self.assertTrue(np.allclose(box_mask_list.get_field('boxes'), boxes))
self.assertTrue(np.allclose(box_mask_list.get_field('masks'), masks))
def test_get_field_with_nonexited_field(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)
masks = np.zeros([3, 3, 3], dtype=np.uint8)
box_mask_list = np_box_mask_list.BoxMaskList(
box_data=boxes, mask_data=masks)
with self.assertRaises(ValueError):
box_mask_list.get_field('scores')
class AddExtraFieldTest(tf.test.TestCase):
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)
masks = np.zeros([3, 3, 3], dtype=np.uint8)
self.box_mask_list = np_box_mask_list.BoxMaskList(
box_data=boxes, mask_data=masks)
def test_add_already_existed_field_bbox(self):
with self.assertRaises(ValueError):
self.box_mask_list.add_field('boxes',
np.array([[0, 0, 0, 1, 0]], dtype=float))
def test_add_already_existed_field_mask(self):
with self.assertRaises(ValueError):
self.box_mask_list.add_field('masks',
np.zeros([3, 3, 3], dtype=np.uint8))
def test_add_invalid_field_data(self):
with self.assertRaises(ValueError):
self.box_mask_list.add_field('scores', np.array([0.5, 0.7], dtype=float))
with self.assertRaises(ValueError):
self.box_mask_list.add_field('scores',
np.array([0.5, 0.7, 0.9, 0.1], dtype=float))
def test_add_single_dimensional_field_data(self):
box_mask_list = self.box_mask_list
scores = np.array([0.5, 0.7, 0.9], dtype=float)
box_mask_list.add_field('scores', scores)
self.assertTrue(np.allclose(scores, self.box_mask_list.get_field('scores')))
def test_add_multi_dimensional_field_data(self):
box_mask_list = self.box_mask_list
labels = np.array([[0, 0, 0, 1, 0], [0, 1, 0, 0, 0], [0, 0, 0, 0, 1]],
dtype=int)
box_mask_list.add_field('labels', labels)
self.assertTrue(np.allclose(labels, self.box_mask_list.get_field('labels')))
def test_get_extra_fields(self):
box_mask_list = self.box_mask_list
self.assertItemsEqual(box_mask_list.get_extra_fields(), ['masks'])
scores = np.array([0.5, 0.7, 0.9], dtype=float)
box_mask_list.add_field('scores', scores)
self.assertItemsEqual(box_mask_list.get_extra_fields(), ['masks', 'scores'])
labels = np.array([[0, 0, 0, 1, 0], [0, 1, 0, 0, 0], [0, 0, 0, 0, 1]],
dtype=int)
box_mask_list.add_field('labels', labels)
self.assertItemsEqual(box_mask_list.get_extra_fields(),
['masks', 'scores', 'labels'])
def test_get_coordinates(self):
y_min, x_min, y_max, x_max = self.box_mask_list.get_coordinates()
expected_y_min = np.array([3.0, 14.0, 0.0], dtype=float)
expected_x_min = np.array([4.0, 14.0, 0.0], dtype=float)
expected_y_max = np.array([6.0, 15.0, 20.0], dtype=float)
expected_x_max = np.array([8.0, 15.0, 20.0], dtype=float)
self.assertTrue(np.allclose(y_min, expected_y_min))
self.assertTrue(np.allclose(x_min, expected_x_min))
self.assertTrue(np.allclose(y_max, expected_y_max))
self.assertTrue(np.allclose(x_max, expected_x_max))
def test_num_boxes(self):
boxes = np.array([[0., 0., 100., 100.], [10., 30., 50., 70.]], dtype=float)
masks = np.zeros([2, 5, 5], dtype=np.uint8)
box_mask_list = np_box_mask_list.BoxMaskList(
box_data=boxes, mask_data=masks)
expected_num_boxes = 2
self.assertEquals(box_mask_list.num_boxes(), expected_num_boxes)
if __name__ == '__main__':
tf.test.main()