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* upload human normal estimation model * upload human normal estimation unittest * update human normal estimation * update test data --------- Co-authored-by: 葭润 <ranqing.rq@alibaba-inc.com> Co-authored-by: ranqing <ranqing@Sundays-Mac.local> Co-authored-by: suluyana <suluyan_sly@163.com>
38 lines
1.2 KiB
Python
38 lines
1.2 KiB
Python
# Copyright (c) Alibaba, Inc. and its affiliates.
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import os
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import os.path
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import unittest
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import cv2
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from modelscope.outputs import OutputKeys
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from modelscope.pipelines import pipeline
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from modelscope.utils.constant import Tasks
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from modelscope.utils.test_utils import test_level
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class HumanNormalEstimationTest(unittest.TestCase):
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def setUp(self) -> None:
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self.task = 'human-normal-estimation'
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self.model_id = 'Damo_XR_Lab/cv_human_monocular-normal-estimation'
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@unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
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def test_image_normal_estimation(self):
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cur_dir = os.path.dirname(
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os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
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input_location = f'{cur_dir}/data/test/images/human_normal_estimation.png'
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estimator = pipeline(
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Tasks.human_normal_estimation, model=self.model_id)
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result = estimator(input_location)
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normals_vis = result[OutputKeys.NORMALS_COLOR]
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input_img = cv2.imread(input_location)
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normals_vis = cv2.resize(
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normals_vis, dsize=(input_img.shape[1], input_img.shape[0]))
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cv2.imwrite('result.jpg', normals_vis)
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if __name__ == '__main__':
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unittest.main()
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