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788b37f78f
Thanks for your contribution and we appreciate it a lot. The following instructions would make your pull request more healthy and more easily get feedback. If you do not understand some items, don't worry, just make the pull request and seek help from maintainers. ## Motivation Please describe the motivation of this PR and the goal you want to achieve through this PR. ## Modification Please briefly describe what modification is made in this PR. 1. add `NYUDataset`class 2. add script to process NYU dataset 3. add transforms for loading depth map 4. add docs & unittest ## BC-breaking (Optional) Does the modification introduce changes that break the backward-compatibility of the downstream repos? If so, please describe how it breaks the compatibility and how the downstream projects should modify their code to keep compatibility with this PR. ## Use cases (Optional) If this PR introduces a new feature, it is better to list some use cases here, and update the documentation. ## Checklist 1. Pre-commit or other linting tools are used to fix the potential lint issues. 5. The modification is covered by complete unit tests. If not, please add more unit test to ensure the correctness. 6. If the modification has potential influence on downstream projects, this PR should be tested with downstream projects, like MMDet or MMDet3D. 7. The documentation has been modified accordingly, like docstring or example tutorials.
296 lines
11 KiB
Python
296 lines
11 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
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import copy
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import os.path as osp
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import tempfile
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import mmcv
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import numpy as np
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from mmcv.transforms import LoadImageFromFile
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from mmseg.datasets.transforms import LoadAnnotations # noqa
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from mmseg.datasets.transforms import (LoadBiomedicalAnnotation,
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LoadBiomedicalData,
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LoadBiomedicalImageFromFile,
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LoadDepthAnnotation,
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LoadImageFromNDArray)
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class TestLoading:
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@classmethod
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def setup_class(cls):
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cls.data_prefix = osp.join(osp.dirname(__file__), '../data')
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def test_load_img(self):
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results = dict(img_path=osp.join(self.data_prefix, 'color.jpg'))
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transform = LoadImageFromFile()
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results = transform(copy.deepcopy(results))
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assert results['img_path'] == osp.join(self.data_prefix, 'color.jpg')
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assert results['img'].shape == (288, 512, 3)
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assert results['img'].dtype == np.uint8
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assert results['ori_shape'] == results['img'].shape[:2]
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assert repr(transform) == transform.__class__.__name__ + \
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"(ignore_empty=False, to_float32=False, color_type='color'," + \
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" imdecode_backend='cv2', backend_args=None)"
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# to_float32
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transform = LoadImageFromFile(to_float32=True)
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results = transform(copy.deepcopy(results))
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assert results['img'].dtype == np.float32
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# gray image
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results = dict(img_path=osp.join(self.data_prefix, 'gray.jpg'))
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transform = LoadImageFromFile()
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results = transform(copy.deepcopy(results))
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assert results['img'].shape == (288, 512, 3)
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assert results['img'].dtype == np.uint8
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transform = LoadImageFromFile(color_type='unchanged')
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results = transform(copy.deepcopy(results))
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assert results['img'].shape == (288, 512)
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assert results['img'].dtype == np.uint8
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def test_load_seg(self):
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seg_path = osp.join(self.data_prefix, 'seg.png')
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results = dict(
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seg_map_path=seg_path, reduce_zero_label=True, seg_fields=[])
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transform = LoadAnnotations()
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results = transform(copy.deepcopy(results))
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assert results['gt_seg_map'].shape == (288, 512)
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assert results['gt_seg_map'].dtype == np.uint8
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assert repr(transform) == transform.__class__.__name__ + \
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"(reduce_zero_label=True, imdecode_backend='pillow', " + \
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'backend_args=None)'
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# reduce_zero_label
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transform = LoadAnnotations(reduce_zero_label=True)
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results = transform(copy.deepcopy(results))
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assert results['gt_seg_map'].shape == (288, 512)
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assert results['gt_seg_map'].dtype == np.uint8
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def test_load_seg_custom_classes(self):
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test_img = np.random.rand(10, 10)
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test_gt = np.zeros_like(test_img)
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test_gt[2:4, 2:4] = 1
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test_gt[2:4, 6:8] = 2
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test_gt[6:8, 2:4] = 3
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test_gt[6:8, 6:8] = 4
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tmp_dir = tempfile.TemporaryDirectory()
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img_path = osp.join(tmp_dir.name, 'img.jpg')
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gt_path = osp.join(tmp_dir.name, 'gt.png')
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mmcv.imwrite(test_img, img_path)
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mmcv.imwrite(test_gt, gt_path)
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# test only train with label with id 3
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results = dict(
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img_path=img_path,
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seg_map_path=gt_path,
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label_map={
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0: 0,
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1: 0,
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2: 0,
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3: 1,
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4: 0
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},
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reduce_zero_label=False,
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seg_fields=[])
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load_imgs = LoadImageFromFile()
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results = load_imgs(copy.deepcopy(results))
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load_anns = LoadAnnotations()
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results = load_anns(copy.deepcopy(results))
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gt_array = results['gt_seg_map']
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true_mask = np.zeros_like(gt_array)
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true_mask[6:8, 2:4] = 1
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assert results['seg_fields'] == ['gt_seg_map']
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assert gt_array.shape == (10, 10)
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assert gt_array.dtype == np.uint8
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np.testing.assert_array_equal(gt_array, true_mask)
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# test only train with label with id 4 and 3
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results = dict(
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img_path=osp.join(self.data_prefix, 'color.jpg'),
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seg_map_path=gt_path,
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label_map={
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0: 0,
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1: 0,
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2: 0,
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3: 2,
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4: 1
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},
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reduce_zero_label=False,
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seg_fields=[])
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load_imgs = LoadImageFromFile()
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results = load_imgs(copy.deepcopy(results))
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load_anns = LoadAnnotations()
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results = load_anns(copy.deepcopy(results))
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gt_array = results['gt_seg_map']
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true_mask = np.zeros_like(gt_array)
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true_mask[6:8, 2:4] = 2
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true_mask[6:8, 6:8] = 1
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assert results['seg_fields'] == ['gt_seg_map']
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assert gt_array.shape == (10, 10)
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assert gt_array.dtype == np.uint8
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np.testing.assert_array_equal(gt_array, true_mask)
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# test with removing a class and reducing zero label simultaneously
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results = dict(
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img_path=img_path,
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seg_map_path=gt_path,
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# since reduce_zero_label is True, there are only 4 real classes.
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# if the full set of classes is ["A", "B", "C", "D"], the
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# following label map simulates the dataset option
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# classes=["A", "C", "D"] which removes class "B".
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label_map={
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0: 0,
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1: 255, # simulate removing class 1
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2: 1,
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3: 2
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},
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reduce_zero_label=True, # reduce zero label
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seg_fields=[])
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load_imgs = LoadImageFromFile()
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results = load_imgs(copy.deepcopy(results))
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# reduce zero label
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load_anns = LoadAnnotations()
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results = load_anns(copy.deepcopy(results))
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gt_array = results['gt_seg_map']
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true_mask = np.ones_like(gt_array) * 255 # all zeros get mapped to 255
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true_mask[2:4, 2:4] = 0 # 1s are reduced to class 0 mapped to class 0
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true_mask[2:4, 6:8] = 255 # 2s are reduced to class 1 which is removed
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true_mask[6:8, 2:4] = 1 # 3s are reduced to class 2 mapped to class 1
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true_mask[6:8, 6:8] = 2 # 4s are reduced to class 3 mapped to class 2
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assert results['seg_fields'] == ['gt_seg_map']
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assert gt_array.shape == (10, 10)
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assert gt_array.dtype == np.uint8
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np.testing.assert_array_equal(gt_array, true_mask)
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# test no custom classes
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results = dict(
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img_path=img_path,
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seg_map_path=gt_path,
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reduce_zero_label=False,
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seg_fields=[])
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load_imgs = LoadImageFromFile()
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results = load_imgs(copy.deepcopy(results))
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load_anns = LoadAnnotations()
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results = load_anns(copy.deepcopy(results))
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gt_array = results['gt_seg_map']
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assert results['seg_fields'] == ['gt_seg_map']
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assert gt_array.shape == (10, 10)
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assert gt_array.dtype == np.uint8
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np.testing.assert_array_equal(gt_array, test_gt)
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tmp_dir.cleanup()
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def test_load_image_from_ndarray(self):
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results = {'img': np.zeros((256, 256, 3), dtype=np.uint8)}
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transform = LoadImageFromNDArray()
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results = transform(results)
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assert results['img'].shape == (256, 256, 3)
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assert results['img'].dtype == np.uint8
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assert results['img_shape'] == (256, 256)
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assert results['ori_shape'] == (256, 256)
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# to_float32
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transform = LoadImageFromNDArray(to_float32=True)
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results = transform(copy.deepcopy(results))
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assert results['img'].dtype == np.float32
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# test repr
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transform = LoadImageFromNDArray()
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assert repr(transform) == ('LoadImageFromNDArray('
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'ignore_empty=False, '
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'to_float32=False, '
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"color_type='color', "
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"imdecode_backend='cv2', "
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'backend_args=None)')
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def test_load_biomedical_img(self):
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results = dict(
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img_path=osp.join(self.data_prefix, 'biomedical.nii.gz'))
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transform = LoadBiomedicalImageFromFile()
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results = transform(copy.deepcopy(results))
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assert results['img_path'] == osp.join(self.data_prefix,
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'biomedical.nii.gz')
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assert len(results['img'].shape) == 4
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assert results['img'].dtype == np.float32
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assert results['ori_shape'] == results['img'].shape[1:]
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assert repr(transform) == ('LoadBiomedicalImageFromFile('
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"decode_backend='nifti', "
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'to_xyz=False, '
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'to_float32=True, '
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'backend_args=None)')
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def test_load_biomedical_annotation(self):
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results = dict(
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seg_map_path=osp.join(self.data_prefix, 'biomedical_ann.nii.gz'))
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transform = LoadBiomedicalAnnotation()
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results = transform(copy.deepcopy(results))
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assert len(results['gt_seg_map'].shape) == 3
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assert results['gt_seg_map'].dtype == np.float32
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def test_load_biomedical_data(self):
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input_results = dict(
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img_path=osp.join(self.data_prefix, 'biomedical.npy'))
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transform = LoadBiomedicalData(with_seg=True)
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results = transform(copy.deepcopy(input_results))
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assert results['img_path'] == osp.join(self.data_prefix,
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'biomedical.npy')
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assert results['img'][0].shape == results['gt_seg_map'].shape
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assert results['img'].dtype == np.float32
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assert results['ori_shape'] == results['img'].shape[1:]
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assert repr(transform) == ('LoadBiomedicalData('
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'with_seg=True, '
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"decode_backend='numpy', "
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'to_xyz=False, '
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'backend_args=None)')
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transform = LoadBiomedicalData(with_seg=False)
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results = transform(copy.deepcopy(input_results))
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assert len(results['img'].shape) == 4
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assert results.get('gt_seg_map') is None
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assert repr(transform) == ('LoadBiomedicalData('
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'with_seg=False, '
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"decode_backend='numpy', "
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'to_xyz=False, '
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'backend_args=None)')
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def test_load_depth_annotation(self):
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input_results = dict(
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img_path='tests/data/pseudo_nyu_dataset/images/'
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'bookstore_0001d_00001.jpg',
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depth_map_path='tests/data/pseudo_nyu_dataset/'
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'annotations/bookstore_0001d_00001.png',
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category_id=-1,
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seg_fields=[])
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transform = LoadDepthAnnotation(depth_rescale_factor=0.001)
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results = transform(input_results)
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assert 'gt_depth_map' in results
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assert results['gt_depth_map'].shape[:2] == mmcv.imread(
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input_results['depth_map_path']).shape[:2]
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assert results['gt_depth_map'].dtype == np.float32
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assert 'gt_depth_map' in results['seg_fields']
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