58 lines
2.2 KiB
Python
58 lines
2.2 KiB
Python
import copy
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import os.path as osp
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import numpy as np
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from mmcls.datasets.pipelines import LoadImageFromFile
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class TestLoading(object):
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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(
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img_prefix=self.data_prefix, img_info=dict(filename='color.jpg'))
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transform = LoadImageFromFile()
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results = transform(copy.deepcopy(results))
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assert results['filename'] == 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['img_shape'] == (288, 512, 3)
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assert results['ori_shape'] == (288, 512, 3)
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np.testing.assert_equal(results['img_norm_cfg']['mean'],
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np.zeros(3, dtype=np.float32))
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assert repr(transform) == transform.__class__.__name__ + \
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"(to_float32=False, color_type='color', " + \
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"file_client_args={'backend': 'disk'})"
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# no img_prefix
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results = dict(
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img_prefix=None, img_info=dict(filename='tests/data/color.jpg'))
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transform = LoadImageFromFile()
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results = transform(copy.deepcopy(results))
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assert results['filename'] == 'tests/data/color.jpg'
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assert results['img'].shape == (288, 512, 3)
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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(
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img_prefix=self.data_prefix, img_info=dict(filename='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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np.testing.assert_equal(results['img_norm_cfg']['mean'],
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np.zeros(1, dtype=np.float32))
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