mirror of
https://github.com/modelscope/modelscope.git
synced 2025-12-16 16:27:45 +01:00
7e0af3dddc
1. support FaceDetectionPipeline inference
Link: https://code.alibaba-inc.com/Ali-MaaS/MaaS-lib/codereview/9470723
43 lines
1.4 KiB
Python
43 lines
1.4 KiB
Python
# Copyright (c) Alibaba, Inc. and its affiliates.
|
|
import os.path as osp
|
|
import tempfile
|
|
import unittest
|
|
|
|
import cv2
|
|
import numpy as np
|
|
|
|
from modelscope.fileio import File
|
|
from modelscope.msdatasets import MsDataset
|
|
from modelscope.outputs import OutputKeys
|
|
from modelscope.pipelines import pipeline
|
|
from modelscope.utils.constant import ModelFile, Tasks
|
|
from modelscope.utils.test_utils import test_level
|
|
|
|
|
|
class FaceRecognitionTest(unittest.TestCase):
|
|
|
|
def setUp(self) -> None:
|
|
self.recog_model_id = 'damo/cv_ir101_facerecognition_cfglint'
|
|
self.det_model_id = 'damo/cv_resnet_facedetection_scrfd10gkps'
|
|
|
|
@unittest.skipUnless(test_level() >= 1, 'skip test in current test level')
|
|
def test_face_compare(self):
|
|
img1 = 'data/test/images/face_recognition_1.png'
|
|
img2 = 'data/test/images/face_recognition_2.png'
|
|
|
|
face_detection = pipeline(
|
|
Tasks.face_detection, model=self.det_model_id)
|
|
face_recognition = pipeline(
|
|
Tasks.face_recognition,
|
|
face_detection=face_detection,
|
|
model=self.recog_model_id)
|
|
# note that for dataset output, the inference-output is a Generator that can be iterated.
|
|
emb1 = face_recognition(img1)[OutputKeys.IMG_EMBEDDING]
|
|
emb2 = face_recognition(img2)[OutputKeys.IMG_EMBEDDING]
|
|
sim = np.dot(emb1[0], emb2[0])
|
|
print(f'Cos similarity={sim:.3f}, img1:{img1} img2:{img2}')
|
|
|
|
|
|
if __name__ == '__main__':
|
|
unittest.main()
|