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55 lines
2.0 KiB
Python
55 lines
2.0 KiB
Python
# Copyright (c) Alibaba, Inc. and its affiliates.
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import os.path as osp
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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.msdatasets import MsDataset
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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.cv.image_utils import draw_face_detection_result
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from modelscope.utils.test_utils import test_level
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class FaceDetectionTest(unittest.TestCase):
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def setUp(self) -> None:
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self.model_id = 'damo/cv_resnet_facedetection_scrfd10gkps'
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def show_result(self, img_path, detection_result):
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img = draw_face_detection_result(img_path, detection_result)
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cv2.imwrite('result.png', img)
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print(f'output written to {osp.abspath("result.png")}')
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@unittest.skipUnless(test_level() >= 1, 'skip test in current test level')
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def test_run_with_dataset(self):
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input_location = ['data/test/images/face_detection.png']
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dataset = MsDataset.load(input_location, target='image')
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face_detection = pipeline(Tasks.face_detection, model=self.model_id)
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# note that for dataset output, the inference-output is a Generator that can be iterated.
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result = face_detection(dataset)
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result = next(result)
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self.show_result(input_location[0], result)
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@unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
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def test_run_modelhub(self):
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face_detection = pipeline(Tasks.face_detection, model=self.model_id)
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img_path = 'data/test/images/face_detection.png'
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result = face_detection(img_path)
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self.show_result(img_path, result)
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@unittest.skipUnless(test_level() >= 2, 'skip test in current test level')
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def test_run_modelhub_default_model(self):
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face_detection = pipeline(Tasks.face_detection)
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img_path = 'data/test/images/face_detection.png'
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result = face_detection(img_path)
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self.show_result(img_path, result)
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if __name__ == '__main__':
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unittest.main()
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