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43 lines
1.3 KiB
Python
43 lines
1.3 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
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import unittest.mock as mock
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import numpy as np
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import mmocr.datasets.pipelines.transforms as transforms
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@mock.patch('%s.transforms.np.random.random_sample' % __name__)
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def test_scale_aspect_jitter(mock_random):
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img_scale = [(3000, 1000)] # unused
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ratio_range = (0.5, 1.5)
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aspect_ratio_range = (1, 1)
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multiscale_mode = 'value'
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long_size_bound = 2000
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short_size_bound = 640
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resize_type = 'long_short_bound'
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keep_ratio = False
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jitter = transforms.ScaleAspectJitter(
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img_scale=img_scale,
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ratio_range=ratio_range,
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aspect_ratio_range=aspect_ratio_range,
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multiscale_mode=multiscale_mode,
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long_size_bound=long_size_bound,
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short_size_bound=short_size_bound,
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resize_type=resize_type,
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keep_ratio=keep_ratio)
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mock_random.side_effect = [0.5]
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# test sample_from_range
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result = jitter.sample_from_range([100, 200])
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assert result == 150
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# test _random_scale
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results = {}
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results['img'] = np.zeros((4000, 1000))
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mock_random.side_effect = [0.5, 1]
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jitter._random_scale(results)
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# scale1 0.5， scale2=1 scale =0.5 650/1000, w, h
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# print(results['scale'])
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assert results['scale'] == (650, 2600)
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