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# Copyright (c) OpenMMLab. All rights reserved.
import numpy as np
import pytest
from mmengine.testing import assert_dict_has_keys
from numpy.testing import assert_array_almost_equal
from mmaction.datasets.transforms import CenterCrop, ImgAug
def check_flip(origin_imgs, result_imgs, flip_type):
"""Check if the origin_imgs are flipped correctly into result_imgs in
different flip_types."""
n, _, _, _ = np.shape(origin_imgs)
if flip_type == 'horizontal':
for i in range(n):
if np.any(result_imgs[i] != np.fliplr(origin_imgs[i])):
return False
else:
# yapf: disable
for i in range(n):
if np.any(result_imgs[i] != np.transpose(np.fliplr(np.transpose(origin_imgs[i], (1, 0, 2))), (1, 0, 2))): # noqa:E501
return False
# yapf: enable
return True
class TestAugumentations:
@staticmethod
def test_ImgAug():
with pytest.raises(ValueError):
# transforms only support one string, 'default'
ImgAug(transforms='test')
with pytest.raises(ValueError):
# transforms only support string or list of dicts
# or iaa.Augmenter object
ImgAug(transforms=dict(type='Rotate'))
with pytest.raises(AssertionError):
# each dict must have a `type` key
ImgAug(transforms=[dict(rotate=(-30, 30))])
with pytest.raises(AttributeError):
# `type` must be available in ImgAug
ImgAug(transforms=[dict(type='BlaBla')])
with pytest.raises(TypeError):
# `type` must be str or iaa available type
ImgAug(transforms=[dict(type=CenterCrop)])
from imgaug import augmenters as iaa
# check default configs
target_keys = ['imgs', 'img_shape', 'modality']
imgs = list(np.random.randint(0, 255, (1, 64, 64, 3)).astype(np.uint8))
results = dict(imgs=imgs, modality='RGB')
default_ImgAug = ImgAug(transforms='default')
default_results = default_ImgAug(results)
assert_dict_has_keys(default_results, target_keys)
assert default_results['img_shape'] == (64, 64)
# check flip (both images and bboxes)
target_keys = ['imgs', 'gt_bboxes', 'proposals', 'img_shape']
imgs = list(np.random.rand(1, 64, 64, 3).astype(np.float32))
results = dict(
imgs=imgs,
modality='RGB',
proposals=np.array([[0, 0, 25, 35]]),
img_shape=(64, 64),
gt_bboxes=np.array([[0, 0, 25, 35]]))
ImgAug_flip = ImgAug(transforms=[dict(type='Fliplr')])
flip_results = ImgAug_flip(results)
assert assert_dict_has_keys(flip_results, target_keys)
assert check_flip(imgs, flip_results['imgs'], 'horizontal')
assert_array_almost_equal(flip_results['gt_bboxes'],
np.array([[39, 0, 64, 35]]))
assert_array_almost_equal(flip_results['proposals'],
np.array([[39, 0, 64, 35]]))
transforms = iaa.Sequential([iaa.Fliplr()])
assert repr(ImgAug_flip) == f'ImgAug(transforms={transforms})'
# check crop (both images and bboxes)
target_keys = ['crop_bbox', 'gt_bboxes', 'imgs', 'img_shape']
imgs = list(np.random.rand(1, 122, 122, 3))
results = dict(
imgs=imgs,
modality='RGB',
img_shape=(122, 122),
gt_bboxes=np.array([[1.5, 2.5, 110, 64]]))
ImgAug_center_crop = ImgAug(transforms=[
dict(
type=iaa.CropToFixedSize,
width=100,
height=100,
position='center')
])
crop_results = ImgAug_center_crop(results)
assert_dict_has_keys(crop_results, target_keys)
assert_array_almost_equal(crop_results['gt_bboxes'],
np.array([[0., 0., 99., 53.]]))
assert 'proposals' not in results
transforms = iaa.Sequential(
[iaa.CropToFixedSize(width=100, height=100, position='center')])
assert repr(ImgAug_center_crop) == f'ImgAug(transforms={transforms})'
# check resize (images only)
target_keys = ['imgs', 'img_shape']
imgs = list(np.random.rand(1, 64, 64, 3))
results = dict(imgs=imgs, modality='RGB')
transforms = iaa.Resize(32)
ImgAug_resize = ImgAug(transforms=transforms)
resize_results = ImgAug_resize(results)
assert_dict_has_keys(resize_results, target_keys)
assert resize_results['img_shape'] == (32, 32)
assert repr(ImgAug_resize) == f'ImgAug(transforms={transforms})'
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