479 lines
17 KiB
Python
479 lines
17 KiB
Python
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)
|