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import copy
import os.path as osp
import mmcv
import numpy as np
import pytest
from mmcv.utils import build_from_cfg
from PIL import Image
from mmseg.datasets.builder import PIPELINES
def test_resize():
# test assertion if img_scale is a list
with pytest.raises(AssertionError):
transform = dict(type='Resize', img_scale=[1333, 800], keep_ratio=True)
build_from_cfg(transform, PIPELINES)
# test assertion if len(img_scale) while ratio_range is not None
with pytest.raises(AssertionError):
transform = dict(
type='Resize',
img_scale=[(1333, 800), (1333, 600)],
ratio_range=(0.9, 1.1),
keep_ratio=True)
build_from_cfg(transform, PIPELINES)
# test assertion for invalid multiscale_mode
with pytest.raises(AssertionError):
transform = dict(
type='Resize',
img_scale=[(1333, 800), (1333, 600)],
keep_ratio=True,
multiscale_mode='2333')
build_from_cfg(transform, PIPELINES)
transform = dict(type='Resize', img_scale=(1333, 800), keep_ratio=True)
resize_module = build_from_cfg(transform, PIPELINES)
results = dict()
# (288, 512, 3)
img = mmcv.imread(
osp.join(osp.dirname(__file__), '../data/color.jpg'), 'color')
results['img'] = img
results['img_shape'] = img.shape
results['ori_shape'] = img.shape
# Set initial values for default meta_keys
results['pad_shape'] = img.shape
results['scale_factor'] = 1.0
resized_results = resize_module(results.copy())
assert resized_results['img_shape'] == (750, 1333, 3)
# test keep_ratio=False
transform = dict(
type='Resize',
img_scale=(1280, 800),
multiscale_mode='value',
keep_ratio=False)
resize_module = build_from_cfg(transform, PIPELINES)
resized_results = resize_module(results.copy())
assert resized_results['img_shape'] == (800, 1280, 3)
# test multiscale_mode='range'
transform = dict(
type='Resize',
img_scale=[(1333, 400), (1333, 1200)],
multiscale_mode='range',
keep_ratio=True)
resize_module = build_from_cfg(transform, PIPELINES)
resized_results = resize_module(results.copy())
assert max(resized_results['img_shape'][:2]) <= 1333
assert min(resized_results['img_shape'][:2]) >= 400
assert min(resized_results['img_shape'][:2]) <= 1200
# test multiscale_mode='value'
transform = dict(
type='Resize',
img_scale=[(1333, 800), (1333, 400)],
multiscale_mode='value',
keep_ratio=True)
resize_module = build_from_cfg(transform, PIPELINES)
resized_results = resize_module(results.copy())
assert resized_results['img_shape'] in [(750, 1333, 3), (400, 711, 3)]
# test multiscale_mode='range'
transform = dict(
type='Resize',
img_scale=(1333, 800),
ratio_range=(0.9, 1.1),
keep_ratio=True)
resize_module = build_from_cfg(transform, PIPELINES)
resized_results = resize_module(results.copy())
assert max(resized_results['img_shape'][:2]) <= 1333 * 1.1
# test img_scale=None and ratio_range is tuple.
# img shape: (288, 512, 3)
transform = dict(
type='Resize', img_scale=None, ratio_range=(0.5, 2.0), keep_ratio=True)
resize_module = build_from_cfg(transform, PIPELINES)
resized_results = resize_module(results.copy())
assert int(288 * 0.5) <= resized_results['img_shape'][0] <= 288 * 2.0
assert int(512 * 0.5) <= resized_results['img_shape'][1] <= 512 * 2.0
def test_flip():
# test assertion for invalid prob
with pytest.raises(AssertionError):
transform = dict(type='RandomFlip', prob=1.5)
build_from_cfg(transform, PIPELINES)
# test assertion for invalid direction
with pytest.raises(AssertionError):
transform = dict(type='RandomFlip', prob=1, direction='horizonta')
build_from_cfg(transform, PIPELINES)
transform = dict(type='RandomFlip', prob=1)
flip_module = build_from_cfg(transform, PIPELINES)
results = dict()
img = mmcv.imread(
osp.join(osp.dirname(__file__), '../data/color.jpg'), 'color')
original_img = copy.deepcopy(img)
seg = np.array(
Image.open(osp.join(osp.dirname(__file__), '../data/seg.png')))
original_seg = copy.deepcopy(seg)
results['img'] = img
results['gt_semantic_seg'] = seg
results['seg_fields'] = ['gt_semantic_seg']
results['img_shape'] = img.shape
results['ori_shape'] = img.shape
# Set initial values for default meta_keys
results['pad_shape'] = img.shape
results['scale_factor'] = 1.0
results = flip_module(results)
flip_module = build_from_cfg(transform, PIPELINES)
results = flip_module(results)
assert np.equal(original_img, results['img']).all()
assert np.equal(original_seg, results['gt_semantic_seg']).all()
def test_random_crop():
# test assertion for invalid random crop
with pytest.raises(AssertionError):
transform = dict(type='RandomCrop', crop_size=(-1, 0))
build_from_cfg(transform, PIPELINES)
results = dict()
img = mmcv.imread(
osp.join(osp.dirname(__file__), '../data/color.jpg'), 'color')
seg = np.array(
Image.open(osp.join(osp.dirname(__file__), '../data/seg.png')))
results['img'] = img
results['gt_semantic_seg'] = seg
results['seg_fields'] = ['gt_semantic_seg']
results['img_shape'] = img.shape
results['ori_shape'] = img.shape
# Set initial values for default meta_keys
results['pad_shape'] = img.shape
results['scale_factor'] = 1.0
h, w, _ = img.shape
transform = dict(type='RandomCrop', crop_size=(h - 20, w - 20))
crop_module = build_from_cfg(transform, PIPELINES)
results = crop_module(results)
assert results['img'].shape[:2] == (h - 20, w - 20)
assert results['img_shape'][:2] == (h - 20, w - 20)
assert results['gt_semantic_seg'].shape[:2] == (h - 20, w - 20)
def test_pad():
# test assertion if both size_divisor and size is None
with pytest.raises(AssertionError):
transform = dict(type='Pad')
build_from_cfg(transform, PIPELINES)
transform = dict(type='Pad', size_divisor=32)
transform = build_from_cfg(transform, PIPELINES)
results = dict()
img = mmcv.imread(
osp.join(osp.dirname(__file__), '../data/color.jpg'), 'color')
original_img = copy.deepcopy(img)
results['img'] = img
results['img_shape'] = img.shape
results['ori_shape'] = img.shape
# Set initial values for default meta_keys
results['pad_shape'] = img.shape
results['scale_factor'] = 1.0
results = transform(results)
# original img already divisible by 32
assert np.equal(results['img'], original_img).all()
img_shape = results['img'].shape
assert img_shape[0] % 32 == 0
assert img_shape[1] % 32 == 0
resize_transform = dict(
type='Resize', img_scale=(1333, 800), keep_ratio=True)
resize_module = build_from_cfg(resize_transform, PIPELINES)
results = resize_module(results)
results = transform(results)
img_shape = results['img'].shape
assert img_shape[0] % 32 == 0
assert img_shape[1] % 32 == 0
def test_rotate():
# test assertion degree should be tuple[float] or float
with pytest.raises(AssertionError):
transform = dict(type='RandomRotate', prob=0.5, degree=-10)
build_from_cfg(transform, PIPELINES)
# test assertion degree should be tuple[float] or float
with pytest.raises(AssertionError):
transform = dict(type='RandomRotate', prob=0.5, degree=(10., 20., 30.))
build_from_cfg(transform, PIPELINES)
transform = dict(type='RandomRotate', degree=10., prob=1.)
transform = build_from_cfg(transform, PIPELINES)
assert str(transform) == f'RandomRotate(' \
f'prob={1.}, ' \
f'degree=({-10.}, {10.}), ' \
f'pad_val={0}, ' \
f'seg_pad_val={255}, ' \
f'center={None}, ' \
f'auto_bound={False})'
results = dict()
img = mmcv.imread(
osp.join(osp.dirname(__file__), '../data/color.jpg'), 'color')
h, w, _ = img.shape
seg = np.array(
Image.open(osp.join(osp.dirname(__file__), '../data/seg.png')))
results['img'] = img
results['gt_semantic_seg'] = seg
results['seg_fields'] = ['gt_semantic_seg']
results['img_shape'] = img.shape
results['ori_shape'] = img.shape
# Set initial values for default meta_keys
results['pad_shape'] = img.shape
results['scale_factor'] = 1.0
results = transform(results)
assert results['img'].shape[:2] == (h, w)
assert results['gt_semantic_seg'].shape[:2] == (h, w)
def test_normalize():
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
to_rgb=True)
transform = dict(type='Normalize', **img_norm_cfg)
transform = build_from_cfg(transform, PIPELINES)
results = dict()
img = mmcv.imread(
osp.join(osp.dirname(__file__), '../data/color.jpg'), 'color')
original_img = copy.deepcopy(img)
results['img'] = img
results['img_shape'] = img.shape
results['ori_shape'] = img.shape
# Set initial values for default meta_keys
results['pad_shape'] = img.shape
results['scale_factor'] = 1.0
results = transform(results)
mean = np.array(img_norm_cfg['mean'])
std = np.array(img_norm_cfg['std'])
converted_img = (original_img[..., ::-1] - mean) / std
assert np.allclose(results['img'], converted_img)
def test_rgb2gray():
# test assertion out_channels should be greater than 0
with pytest.raises(AssertionError):
transform = dict(type='RGB2Gray', out_channels=-1)
build_from_cfg(transform, PIPELINES)
# test assertion weights should be tuple[float]
with pytest.raises(AssertionError):
transform = dict(type='RGB2Gray', out_channels=1, weights=1.1)
build_from_cfg(transform, PIPELINES)
# test out_channels is None
transform = dict(type='RGB2Gray')
transform = build_from_cfg(transform, PIPELINES)
assert str(transform) == f'RGB2Gray(' \
f'out_channels={None}, ' \
f'weights={(0.299, 0.587, 0.114)})'
results = dict()
img = mmcv.imread(
osp.join(osp.dirname(__file__), '../data/color.jpg'), 'color')
h, w, c = img.shape
seg = np.array(
Image.open(osp.join(osp.dirname(__file__), '../data/seg.png')))
results['img'] = img
results['gt_semantic_seg'] = seg
results['seg_fields'] = ['gt_semantic_seg']
results['img_shape'] = img.shape
results['ori_shape'] = img.shape
# Set initial values for default meta_keys
results['pad_shape'] = img.shape
results['scale_factor'] = 1.0
results = transform(results)
assert results['img'].shape == (h, w, c)
assert results['img_shape'] == (h, w, c)
assert results['ori_shape'] == (h, w, c)
# test out_channels = 2
transform = dict(type='RGB2Gray', out_channels=2)
transform = build_from_cfg(transform, PIPELINES)
assert str(transform) == f'RGB2Gray(' \
f'out_channels={2}, ' \
f'weights={(0.299, 0.587, 0.114)})'
results = dict()
img = mmcv.imread(
osp.join(osp.dirname(__file__), '../data/color.jpg'), 'color')
h, w, c = img.shape
seg = np.array(
Image.open(osp.join(osp.dirname(__file__), '../data/seg.png')))
results['img'] = img
results['gt_semantic_seg'] = seg
results['seg_fields'] = ['gt_semantic_seg']
results['img_shape'] = img.shape
results['ori_shape'] = img.shape
# Set initial values for default meta_keys
results['pad_shape'] = img.shape
results['scale_factor'] = 1.0
results = transform(results)
assert results['img'].shape == (h, w, 2)
assert results['img_shape'] == (h, w, 2)
assert results['ori_shape'] == (h, w, c)
def test_adjust_gamma():
# test assertion if gamma <= 0
with pytest.raises(AssertionError):
transform = dict(type='AdjustGamma', gamma=0)
build_from_cfg(transform, PIPELINES)
# test assertion if gamma is list
with pytest.raises(AssertionError):
transform = dict(type='AdjustGamma', gamma=[1.2])
build_from_cfg(transform, PIPELINES)
# test with gamma = 1.2
transform = dict(type='AdjustGamma', gamma=1.2)
transform = build_from_cfg(transform, PIPELINES)
results = dict()
img = mmcv.imread(
osp.join(osp.dirname(__file__), '../data/color.jpg'), 'color')
original_img = copy.deepcopy(img)
results['img'] = img
results['img_shape'] = img.shape
results['ori_shape'] = img.shape
# Set initial values for default meta_keys
results['pad_shape'] = img.shape
results['scale_factor'] = 1.0
results = transform(results)
inv_gamma = 1.0 / 1.2
table = np.array([((i / 255.0)**inv_gamma) * 255
for i in np.arange(0, 256)]).astype('uint8')
converted_img = mmcv.lut_transform(
np.array(original_img, dtype=np.uint8), table)
assert np.allclose(results['img'], converted_img)
assert str(transform) == f'AdjustGamma(gamma={1.2})'
def test_rerange():
# test assertion if min_value or max_value is illegal
with pytest.raises(AssertionError):
transform = dict(type='Rerange', min_value=[0], max_value=[255])
build_from_cfg(transform, PIPELINES)
# test assertion if min_value >= max_value
with pytest.raises(AssertionError):
transform = dict(type='Rerange', min_value=1, max_value=1)
build_from_cfg(transform, PIPELINES)
# test assertion if img_min_value == img_max_value
with pytest.raises(AssertionError):
transform = dict(type='Rerange', min_value=0, max_value=1)
transform = build_from_cfg(transform, PIPELINES)
results = dict()
results['img'] = np.array([[1, 1], [1, 1]])
transform(results)
img_rerange_cfg = dict()
transform = dict(type='Rerange', **img_rerange_cfg)
transform = build_from_cfg(transform, PIPELINES)
results = dict()
img = mmcv.imread(
osp.join(osp.dirname(__file__), '../data/color.jpg'), 'color')
original_img = copy.deepcopy(img)
results['img'] = img
results['img_shape'] = img.shape
results['ori_shape'] = img.shape
# Set initial values for default meta_keys
results['pad_shape'] = img.shape
results['scale_factor'] = 1.0
results = transform(results)
min_value = np.min(original_img)
max_value = np.max(original_img)
converted_img = (original_img - min_value) / (max_value - min_value) * 255
assert np.allclose(results['img'], converted_img)
assert str(transform) == f'Rerange(min_value={0}, max_value={255})'
def test_CLAHE():
# test assertion if clip_limit is None
with pytest.raises(AssertionError):
transform = dict(type='CLAHE', clip_limit=None)
build_from_cfg(transform, PIPELINES)
# test assertion if tile_grid_size is illegal
with pytest.raises(AssertionError):
transform = dict(type='CLAHE', tile_grid_size=(8.0, 8.0))
build_from_cfg(transform, PIPELINES)
# test assertion if tile_grid_size is illegal
with pytest.raises(AssertionError):
transform = dict(type='CLAHE', tile_grid_size=(9, 9, 9))
build_from_cfg(transform, PIPELINES)
transform = dict(type='CLAHE', clip_limit=2)
transform = build_from_cfg(transform, PIPELINES)
results = dict()
img = mmcv.imread(
osp.join(osp.dirname(__file__), '../data/color.jpg'), 'color')
original_img = copy.deepcopy(img)
results['img'] = img
results['img_shape'] = img.shape
results['ori_shape'] = img.shape
# Set initial values for default meta_keys
results['pad_shape'] = img.shape
results['scale_factor'] = 1.0
results = transform(results)
converted_img = np.empty(original_img.shape)
for i in range(original_img.shape[2]):
converted_img[:, :, i] = mmcv.clahe(
np.array(original_img[:, :, i], dtype=np.uint8), 2, (8, 8))
assert np.allclose(results['img'], converted_img)
assert str(transform) == f'CLAHE(clip_limit={2}, tile_grid_size={(8, 8)})'
def test_seg_rescale():
results = dict()
seg = np.array(
Image.open(osp.join(osp.dirname(__file__), '../data/seg.png')))
results['gt_semantic_seg'] = seg
results['seg_fields'] = ['gt_semantic_seg']
h, w = seg.shape
transform = dict(type='SegRescale', scale_factor=1. / 2)
rescale_module = build_from_cfg(transform, PIPELINES)
rescale_results = rescale_module(results.copy())
assert rescale_results['gt_semantic_seg'].shape == (h // 2, w // 2)
transform = dict(type='SegRescale', scale_factor=1)
rescale_module = build_from_cfg(transform, PIPELINES)
rescale_results = rescale_module(results.copy())
assert rescale_results['gt_semantic_seg'].shape == (h, w)
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