135 lines
5.0 KiB
Python
135 lines
5.0 KiB
Python
# -*- coding: utf-8 -*-
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# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
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import logging
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import numpy as np
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import unittest
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from unittest import mock
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from detectron2.config import get_cfg
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from detectron2.data import detection_utils
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from detectron2.data import transforms as T
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from detectron2.utils.logger import setup_logger
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logger = logging.getLogger(__name__)
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class TestTransforms(unittest.TestCase):
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def setUp(self):
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setup_logger()
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def test_apply_rotated_boxes(self):
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np.random.seed(125)
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cfg = get_cfg()
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is_train = True
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transform_gen = detection_utils.build_transform_gen(cfg, is_train)
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image = np.random.rand(200, 300)
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image, transforms = T.apply_transform_gens(transform_gen, image)
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image_shape = image.shape[:2] # h, w
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assert image_shape == (800, 1200)
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annotation = {"bbox": [179, 97, 62, 40, -56]}
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boxes = np.array([annotation["bbox"]], dtype=np.float64) # boxes.shape = (1, 5)
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transformed_bbox = transforms.apply_rotated_box(boxes)[0]
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expected_bbox = np.array([484, 388, 248, 160, 56], dtype=np.float64)
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err_msg = "transformed_bbox = {}, expected {}".format(transformed_bbox, expected_bbox)
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assert np.allclose(transformed_bbox, expected_bbox), err_msg
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def test_apply_rotated_boxes_unequal_scaling_factor(self):
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np.random.seed(125)
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h, w = 400, 200
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newh, neww = 800, 800
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image = np.random.rand(h, w)
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transform_gen = []
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transform_gen.append(T.Resize(shape=(newh, neww)))
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image, transforms = T.apply_transform_gens(transform_gen, image)
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image_shape = image.shape[:2] # h, w
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assert image_shape == (newh, neww)
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boxes = np.array(
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[
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[150, 100, 40, 20, 0],
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[150, 100, 40, 20, 30],
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[150, 100, 40, 20, 90],
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[150, 100, 40, 20, -90],
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],
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dtype=np.float64,
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)
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transformed_boxes = transforms.apply_rotated_box(boxes)
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expected_bboxes = np.array(
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[
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[600, 200, 160, 40, 0],
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[600, 200, 144.22205102, 52.91502622, 49.10660535],
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[600, 200, 80, 80, 90],
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[600, 200, 80, 80, -90],
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],
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dtype=np.float64,
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)
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err_msg = "transformed_boxes = {}, expected {}".format(transformed_boxes, expected_bboxes)
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assert np.allclose(transformed_boxes, expected_bboxes), err_msg
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def test_print_transform_gen(self):
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t = T.RandomCrop("relative", (100, 100))
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self.assertTrue(str(t) == "RandomCrop(crop_type='relative', crop_size=(100, 100))")
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t = T.RandomFlip(prob=0.5)
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self.assertTrue(str(t) == "RandomFlip(prob=0.5)")
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t = T.RandomFlip()
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self.assertTrue(str(t) == "RandomFlip()")
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def test_random_apply_prob_out_of_range_check(self):
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# GIVEN
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test_probabilities = {0.0: True, 0.5: True, 1.0: True, -0.01: False, 1.01: False}
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# WHEN
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for given_probability, is_valid in test_probabilities.items():
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# THEN
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if not is_valid:
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self.assertRaises(AssertionError, T.RandomApply, None, prob=given_probability)
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else:
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T.RandomApply(T.NoOpTransform(), prob=given_probability)
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def test_random_apply_wrapping_transform_gen_probability_occured_evaluation(self):
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# GIVEN
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transform_mock = mock.MagicMock(name="MockTransform", spec=T.TransformGen)
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image_mock = mock.MagicMock(name="MockImage")
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random_apply = T.RandomApply(transform_mock, prob=0.001)
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# WHEN
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with mock.patch.object(random_apply, "_rand_range", return_value=0.0001):
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transform = random_apply.get_transform(image_mock)
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# THEN
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transform_mock.get_transform.assert_called_once_with(image_mock)
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self.assertIsNot(transform, transform_mock)
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def test_random_apply_wrapping_std_transform_probability_occured_evaluation(self):
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# GIVEN
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transform_mock = mock.MagicMock(name="MockTransform", spec=T.Transform)
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image_mock = mock.MagicMock(name="MockImage")
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random_apply = T.RandomApply(transform_mock, prob=0.001)
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# WHEN
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with mock.patch.object(random_apply, "_rand_range", return_value=0.0001):
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transform = random_apply.get_transform(image_mock)
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# THEN
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self.assertIs(transform, transform_mock)
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def test_random_apply_probability_not_occured_evaluation(self):
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# GIVEN
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transform_mock = mock.MagicMock(name="MockTransform", spec=T.TransformGen)
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image_mock = mock.MagicMock(name="MockImage")
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random_apply = T.RandomApply(transform_mock, prob=0.001)
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# WHEN
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with mock.patch.object(random_apply, "_rand_range", return_value=0.9):
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transform = random_apply.get_transform(image_mock)
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# THEN
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transform_mock.get_transform.assert_not_called()
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self.assertIsInstance(transform, T.NoOpTransform)
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