# coding=utf-8 from __future__ import absolute_import from __future__ import division from __future__ import print_function import os import unittest import cv2 import numpy as np import paddle.fluid as fluid import paddlehub as hub pic_dir = '../image_dataset/keypoint_detection' class TestFaceLocate(unittest.TestCase): @classmethod def setUpClass(self): """Prepare the environment once before execution of all tests.\n""" self.face_locate = hub.Module(name="face_landmark_localization") @classmethod def tearDownClass(self): """clean up the environment after the execution of all tests.\n""" self.face_locate = None def setUp(self): "Call setUp() to prepare environment\n" self.test_prog = fluid.Program() def tearDown(self): "Call tearDown to restore environment.\n" self.test_prog = None def test_context(self): self.face_locate.context() def test_single_pic(self): with fluid.program_guard(self.test_prog): pics_path_list = [ os.path.join(pic_dir, f) for f in os.listdir(pic_dir) ] for pic_path in pics_path_list: result = self.face_locate.keypoint_detection( paths=[pic_path], use_gpu=True, visualization=True) print(result) def test_ndarray(self): with fluid.program_guard(self.test_prog): pics_path_list = [ os.path.join(pic_dir, f) for f in os.listdir(pic_dir) ] pics_ndarray = list() for pic_path in pics_path_list: im = cv2.imread(pic_path) result = self.face_locate.keypoint_detection( images=np.expand_dims(im, axis=0), output_dir='ndarray_output', use_gpu=True, visualization=True) print(result) if __name__ == "__main__": suite = unittest.TestSuite() suite.addTest(TestFaceLocate('test_single_pic')) suite.addTest(TestFaceLocate('test_ndarray')) runner = unittest.TextTestRunner(verbosity=2) runner.run(suite)