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1. support FaceDetectionPipeline inference
Link: https://code.alibaba-inc.com/Ali-MaaS/MaaS-lib/codereview/9470723
85 lines
3.1 KiB
Python
85 lines
3.1 KiB
Python
# Copyright (c) Alibaba, Inc. and its affiliates.
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import os.path as osp
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import tempfile
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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.fileio import File
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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 ModelFile, Tasks
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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, bboxes, kpss, scores):
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bboxes = np.array(bboxes)
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kpss = np.array(kpss)
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scores = np.array(scores)
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img = cv2.imread(img_path)
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assert img is not None, f"Can't read img: {img_path}"
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for i in range(len(scores)):
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bbox = bboxes[i].astype(np.int32)
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kps = kpss[i].reshape(-1, 2).astype(np.int32)
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score = scores[i]
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x1, y1, x2, y2 = bbox
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cv2.rectangle(img, (x1, y1), (x2, y2), (255, 0, 0), 2)
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for kp in kps:
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cv2.circle(img, tuple(kp), 1, (0, 0, 255), 1)
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cv2.putText(
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img,
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f'{score:.2f}', (x1, y2),
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1,
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1.0, (0, 255, 0),
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thickness=1,
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lineType=8)
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cv2.imwrite('result.png', img)
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print(
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f'Found {len(scores)} faces, output written to {osp.abspath("result.png")}'
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)
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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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# alternatively:
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# input_location = '/dir/to/images'
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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[OutputKeys.BOXES],
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result[OutputKeys.KEYPOINTS],
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result[OutputKeys.SCORES])
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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[OutputKeys.BOXES],
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result[OutputKeys.KEYPOINTS],
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result[OutputKeys.SCORES])
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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[OutputKeys.BOXES],
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result[OutputKeys.KEYPOINTS],
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result[OutputKeys.SCORES])
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if __name__ == '__main__':
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unittest.main()
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