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