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