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45 lines
1.9 KiB
Python
45 lines
1.9 KiB
Python
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# Copyright (c) Alibaba, Inc. and its affiliates.
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import unittest
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from easycv.predictors.hand_keypoints_predictor import HandKeypointsPredictor
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from easycv.utils.config_tools import mmcv_config_fromfile
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MM_DEFAULT_HAND_DETECTION_SSDLITE_MODEL_PATH = 'https://download.openmmlab.com/mmpose/mmdet_pretrained/' \
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'ssdlite_mobilenetv2_scratch_600e_onehand-4f9f8686_20220523.pth'
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MM_DEFAULT_HAND_DETECTION_SSDLITE_CONFIG_FILE = 'data/test/pose/hand/configs/hand_keypoints_predictor.py'
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class HandKeypointsPredictorTest(unittest.TestCase):
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def setUp(self):
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print(('Testing %s.%s' % (type(self).__name__, self._testMethodName)))
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self.image_path = 'data/test/pose/hand/data/hand.jpg'
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self.save_image_path = 'data/test/pose/hand/data/hand_result.jpg'
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self.model_path = 'data/test/pose/hand/hrnet_w18_256x256.pth'
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self.model_config_path = 'configs/pose/hand/hrnet_w18_coco_wholebody_hand_256x256_dark.py'
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def test_single(self):
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config = mmcv_config_fromfile(self.model_config_path)
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predict_pipeline = HandKeypointsPredictor(
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model_path=self.model_path,
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config_file=config,
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detection_predictor_config=dict(
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type='DetectionPredictor',
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model_path=MM_DEFAULT_HAND_DETECTION_SSDLITE_MODEL_PATH,
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config_file=MM_DEFAULT_HAND_DETECTION_SSDLITE_CONFIG_FILE,
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score_threshold=0.5))
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output = predict_pipeline(self.image_path)[0]
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keypoints = output['keypoints']
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boxes = output['boxes']
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image_show = predict_pipeline.show_result(
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self.image_path, keypoints, boxes, save_path=self.save_image_path)
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self.assertEqual(keypoints.shape[0], 1)
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self.assertEqual(keypoints.shape[1], 21)
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self.assertEqual(keypoints.shape[2], 3)
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if __name__ == '__main__':
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unittest.main()
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