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https://github.com/modelscope/modelscope.git
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802f90d2e1
* first version * fix minor bugs * add test images * TorchModel & docstr * modify pipeline implementation * minor * add datasets * add extractor * add update * add augmentor * add flow_viz * add frame_utils * add utils * add raft_model * add dense_optical_flow_estimation_pipeline * add image_utils * add test_dense_optical_flow_estimation * test * update cv/__init__ * [3]update cv/__init__ * update submodule data/test * correct yapf * fix bugs * move test data * update submodule * minor * update submodule --------- Co-authored-by: kejie <kejie.qkj@alibaba-inc.com>
40 lines
1.3 KiB
Python
40 lines
1.3 KiB
Python
# Copyright (c) Alibaba, Inc. and its affiliates.
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import unittest
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import cv2
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import numpy as np
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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 DenseOpticalFlowEstimationTest(unittest.TestCase):
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def setUp(self) -> None:
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self.task = 'dense-optical-flow-estimation'
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self.model_id = 'Damo_XR_Lab/cv_raft_dense-optical-flow_things'
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@unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
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def test_dense_optical_flow_estimation(self):
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input_location = [[
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'data/test/images/dense_flow1.png',
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'data/test/images/dense_flow2.png',
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# 'modelscope/models/cv/dense_optical_flow_estimation/data/test/images/dense_flow1.png',
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# 'modelscope/models/cv/dense_optical_flow_estimation/data/test/images/dense_flow2.png'
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]]
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estimator = pipeline(
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Tasks.dense_optical_flow_estimation, model=self.model_id)
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result = estimator(input_location)
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# flow = result[0][OutputKeys.FLOWS]
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flow_vis = result[0][OutputKeys.FLOWS_COLOR]
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cv2.imwrite('result.jpg', flow_vis)
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print('test_dense_optical_flow_estimation DONE')
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if __name__ == '__main__':
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unittest.main()
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