40 lines
1.5 KiB
Python
40 lines
1.5 KiB
Python
# import numpy as np
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import pandas as pd
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from numpy.testing import assert_allclose
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from pytest import mark
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# @mark.smoke
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# @mark.body
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@mark.facial
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class FacialTest:
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def test_get_landmark(self, processing_facial_activity):
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df_act = pd.read_csv("tests/test_data/landmark.csv")
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res = processing_facial_activity.get_landmark()
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assert_allclose(df_act.mean(), res.mean(), rtol=0.1, atol=1e-8)
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assert_allclose(df_act.std(), res.std(), rtol=0.1, atol=1e-8)
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def test_get_action_unit(self, processing_facial_activity):
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df_act = pd.read_csv("tests/test_data/action_unit.csv")
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res = processing_facial_activity.get_action_unit()
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assert_allclose(df_act.mean(), res.mean(), rtol=0.1, atol=1e-8)
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assert_allclose(df_act.std(), res.std(), rtol=0.1, atol=1e-8)
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def test_get_asymmetry(self, processing_facial_activity):
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actual_mean = [2.58260995, 3.34416172, 3.0563894, 2.94777878]
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actual_std = [1.74161635, 2.17995634, 2.19173686, 1.82435901]
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res = processing_facial_activity.get_asymmetry()
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assert_allclose(actual_mean, res.mean(), rtol=0.1, atol=1e-8)
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assert_allclose(actual_std, res.std(), rtol=0.1, atol=1e-8)
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def test_get_expressivity(self, processing_facial_activity):
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df_act = pd.read_csv("tests/test_data/expressivity.csv")
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res = processing_facial_activity.get_expressivity()
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assert_allclose(df_act.mean(), res.mean(), rtol=0.35, atol=1e-8)
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assert_allclose(df_act.std(), res.std(), rtol=0.35, atol=1e-8)
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