# Copyright (c) OpenMMLab. All rights reserved.
import copy
import os.path as osp
import tempfile
import mmcv
import numpy as np
from mmcv.transforms import LoadImageFromFile
from mmseg.datasets.transforms import (LoadAnnotations,
LoadBiomedicalAnnotation,
LoadBiomedicalData,
LoadBiomedicalImageFromFile,
LoadImageFromNDArray)
class TestLoading:
@classmethod
def setup_class(cls):
cls.data_prefix = osp.join(osp.dirname(__file__), '../data')
def test_load_img(self):
results = dict(img_path=osp.join(self.data_prefix, 'color.jpg'))
transform = LoadImageFromFile()
results = transform(copy.deepcopy(results))
assert results['img_path'] == osp.join(self.data_prefix, 'color.jpg')
assert results['img'].shape == (288, 512, 3)
assert results['img'].dtype == np.uint8
assert results['ori_shape'] == results['img'].shape[:2]
assert repr(transform) == transform.__class__.__name__ + \
"(ignore_empty=False, to_float32=False, color_type='color'," + \
" imdecode_backend='cv2', file_client_args={'backend': 'disk'})"
# to_float32
transform = LoadImageFromFile(to_float32=True)
results = transform(copy.deepcopy(results))
assert results['img'].dtype == np.float32
# gray image
results = dict(img_path=osp.join(self.data_prefix, 'gray.jpg'))
transform = LoadImageFromFile()
results = transform(copy.deepcopy(results))
assert results['img'].shape == (288, 512, 3)
assert results['img'].dtype == np.uint8
transform = LoadImageFromFile(color_type='unchanged')
results = transform(copy.deepcopy(results))
assert results['img'].shape == (288, 512)
assert results['img'].dtype == np.uint8
def test_load_seg(self):
seg_path = osp.join(self.data_prefix, 'seg.png')
results = dict(
seg_map_path=seg_path, reduce_zero_label=True, seg_fields=[])
transform = LoadAnnotations()
results = transform(copy.deepcopy(results))
assert results['gt_seg_map'].shape == (288, 512)
assert results['gt_seg_map'].dtype == np.uint8
assert repr(transform) == transform.__class__.__name__ + \
"(reduce_zero_label=True,imdecode_backend='pillow')" + \
"file_client_args={'backend': 'disk'})"
# reduce_zero_label
transform = LoadAnnotations(reduce_zero_label=True)
results = transform(copy.deepcopy(results))
assert results['gt_seg_map'].shape == (288, 512)
assert results['gt_seg_map'].dtype == np.uint8
def test_load_seg_custom_classes(self):
test_img = np.random.rand(10, 10)
test_gt = np.zeros_like(test_img)
test_gt[2:4, 2:4] = 1
test_gt[2:4, 6:8] = 2
test_gt[6:8, 2:4] = 3
test_gt[6:8, 6:8] = 4
tmp_dir = tempfile.TemporaryDirectory()
img_path = osp.join(tmp_dir.name, 'img.jpg')
gt_path = osp.join(tmp_dir.name, 'gt.png')
mmcv.imwrite(test_img, img_path)
mmcv.imwrite(test_gt, gt_path)
# test only train with label with id 3
results = dict(
img_path=img_path,
seg_map_path=gt_path,
label_map={
0: 0,
1: 0,
2: 0,
3: 1,
4: 0
},
reduce_zero_label=False,
seg_fields=[])
load_imgs = LoadImageFromFile()
results = load_imgs(copy.deepcopy(results))
load_anns = LoadAnnotations()
results = load_anns(copy.deepcopy(results))
gt_array = results['gt_seg_map']
true_mask = np.zeros_like(gt_array)
true_mask[6:8, 2:4] = 1
assert results['seg_fields'] == ['gt_seg_map']
assert gt_array.shape == (10, 10)
assert gt_array.dtype == np.uint8
np.testing.assert_array_equal(gt_array, true_mask)
# test only train with label with id 4 and 3
results = dict(
img_path=osp.join(self.data_prefix, 'color.jpg'),
seg_map_path=gt_path,
label_map={
0: 0,
1: 0,
2: 0,
3: 2,
4: 1
},
reduce_zero_label=False,
seg_fields=[])
load_imgs = LoadImageFromFile()
results = load_imgs(copy.deepcopy(results))
load_anns = LoadAnnotations()
results = load_anns(copy.deepcopy(results))
gt_array = results['gt_seg_map']
true_mask = np.zeros_like(gt_array)
true_mask[6:8, 2:4] = 2
true_mask[6:8, 6:8] = 1
assert results['seg_fields'] == ['gt_seg_map']
assert gt_array.shape == (10, 10)
assert gt_array.dtype == np.uint8
np.testing.assert_array_equal(gt_array, true_mask)
# test with removing a class and reducing zero label simultaneously
results = dict(
img_path=img_path,
seg_map_path=gt_path,
# since reduce_zero_label is True, there are only 4 real classes.
# if the full set of classes is ["A", "B", "C", "D"], the
# following label map simulates the dataset option
# classes=["A", "C", "D"] which removes class "B".
label_map={
0: 0,
1: 255, # simulate removing class 1
2: 1,
3: 2
},
reduce_zero_label=True, # reduce zero label
seg_fields=[])
load_imgs = LoadImageFromFile()
results = load_imgs(copy.deepcopy(results))
# reduce zero label
load_anns = LoadAnnotations()
results = load_anns(copy.deepcopy(results))
gt_array = results['gt_seg_map']
true_mask = np.ones_like(gt_array) * 255 # all zeros get mapped to 255
true_mask[2:4, 2:4] = 0 # 1s are reduced to class 0 mapped to class 0
true_mask[2:4, 6:8] = 255 # 2s are reduced to class 1 which is removed
true_mask[6:8, 2:4] = 1 # 3s are reduced to class 2 mapped to class 1
true_mask[6:8, 6:8] = 2 # 4s are reduced to class 3 mapped to class 2
assert results['seg_fields'] == ['gt_seg_map']
assert gt_array.shape == (10, 10)
assert gt_array.dtype == np.uint8
np.testing.assert_array_equal(gt_array, true_mask)
# test no custom classes
results = dict(
img_path=img_path,
seg_map_path=gt_path,
reduce_zero_label=False,
seg_fields=[])
load_imgs = LoadImageFromFile()
results = load_imgs(copy.deepcopy(results))
load_anns = LoadAnnotations()
results = load_anns(copy.deepcopy(results))
gt_array = results['gt_seg_map']
assert results['seg_fields'] == ['gt_seg_map']
assert gt_array.shape == (10, 10)
assert gt_array.dtype == np.uint8
np.testing.assert_array_equal(gt_array, test_gt)
tmp_dir.cleanup()
def test_load_image_from_ndarray(self):
results = {'img': np.zeros((256, 256, 3), dtype=np.uint8)}
transform = LoadImageFromNDArray()
results = transform(results)
assert results['img'].shape == (256, 256, 3)
assert results['img'].dtype == np.uint8
assert results['img_shape'] == (256, 256)
assert results['ori_shape'] == (256, 256)
# to_float32
transform = LoadImageFromNDArray(to_float32=True)
results = transform(copy.deepcopy(results))
assert results['img'].dtype == np.float32
# test repr
transform = LoadImageFromNDArray()
assert repr(transform) == ('LoadImageFromNDArray('
'ignore_empty=False, '
'to_float32=False, '
"color_type='color', "
"imdecode_backend='cv2', "
"file_client_args={'backend': 'disk'})")
def test_load_biomedical_img(self):
results = dict(
img_path=osp.join(self.data_prefix, 'biomedical.nii.gz'))
transform = LoadBiomedicalImageFromFile()
results = transform(copy.deepcopy(results))
assert results['img_path'] == osp.join(self.data_prefix,
'biomedical.nii.gz')
assert len(results['img'].shape) == 4
assert results['img'].dtype == np.float32
assert results['ori_shape'] == results['img'].shape[1:]
assert repr(transform) == ('LoadBiomedicalImageFromFile('
"decode_backend='nifti', "
'to_xyz=False, '
'to_float32=True, '
"file_client_args={'backend': 'disk'})")
def test_load_biomedical_annotation(self):
results = dict(
seg_map_path=osp.join(self.data_prefix, 'biomedical_ann.nii.gz'))
transform = LoadBiomedicalAnnotation()
results = transform(copy.deepcopy(results))
assert len(results['gt_seg_map'].shape) == 3
assert results['gt_seg_map'].dtype == np.float32
def test_load_biomedical_data(self):
input_results = dict(
img_path=osp.join(self.data_prefix, 'biomedical.npy'))
transform = LoadBiomedicalData(with_seg=True)
results = transform(copy.deepcopy(input_results))
assert results['img_path'] == osp.join(self.data_prefix,
'biomedical.npy')
assert results['img'][0].shape == results['gt_seg_map'].shape
assert results['img'].dtype == np.float32
assert results['ori_shape'] == results['img'].shape[1:]
assert repr(transform) == ('LoadBiomedicalData('
'with_seg=True, '
"decode_backend='numpy', "
'to_xyz=False, '
"file_client_args={'backend': 'disk'})")
transform = LoadBiomedicalData(with_seg=False)
results = transform(copy.deepcopy(input_results))
assert len(results['img'].shape) == 4
assert results.get('gt_seg_map') is None
assert repr(transform) == ('LoadBiomedicalData('
'with_seg=False, '
"decode_backend='numpy', "
'to_xyz=False, '
"file_client_args={'backend': 'disk'})")