| |
| from unittest import TestCase |
|
|
| import numpy as np |
|
|
| from mmpose.codecs import ImagePoseLifting |
| from mmpose.registry import KEYPOINT_CODECS |
|
|
|
|
| class TestImagePoseLifting(TestCase): |
|
|
| def setUp(self) -> None: |
| keypoints = (0.1 + 0.8 * np.random.rand(1, 17, 2)) * [192, 256] |
| keypoints = np.round(keypoints).astype(np.float32) |
| keypoints_visible = np.random.randint(2, size=(1, 17)) |
| lifting_target = (0.1 + 0.8 * np.random.rand(17, 3)) |
| lifting_target_visible = np.random.randint(2, size=(17, )) |
| encoded_wo_sigma = np.random.rand(1, 17, 3) |
|
|
| self.keypoints_mean = np.random.rand(17, 2).astype(np.float32) |
| self.keypoints_std = np.random.rand(17, 2).astype(np.float32) + 1e-6 |
| self.target_mean = np.random.rand(17, 3).astype(np.float32) |
| self.target_std = np.random.rand(17, 3).astype(np.float32) + 1e-6 |
|
|
| self.data = dict( |
| keypoints=keypoints, |
| keypoints_visible=keypoints_visible, |
| lifting_target=lifting_target, |
| lifting_target_visible=lifting_target_visible, |
| encoded_wo_sigma=encoded_wo_sigma) |
|
|
| def build_pose_lifting_label(self, **kwargs): |
| cfg = dict(type='ImagePoseLifting', num_keypoints=17, root_index=0) |
| cfg.update(kwargs) |
| return KEYPOINT_CODECS.build(cfg) |
|
|
| def test_build(self): |
| codec = self.build_pose_lifting_label() |
| self.assertIsInstance(codec, ImagePoseLifting) |
|
|
| def test_encode(self): |
| keypoints = self.data['keypoints'] |
| keypoints_visible = self.data['keypoints_visible'] |
| lifting_target = self.data['lifting_target'] |
| lifting_target_visible = self.data['lifting_target_visible'] |
|
|
| |
| codec = self.build_pose_lifting_label() |
| encoded = codec.encode(keypoints, keypoints_visible, lifting_target, |
| lifting_target_visible) |
|
|
| self.assertEqual(encoded['keypoint_labels'].shape, (1, 17, 2)) |
| self.assertEqual(encoded['lifting_target_label'].shape, (17, 3)) |
| self.assertEqual(encoded['lifting_target_weights'].shape, (17, )) |
| self.assertEqual(encoded['trajectory_weights'].shape, (17, )) |
| self.assertEqual(encoded['target_root'].shape, (3, )) |
|
|
| |
| codec = self.build_pose_lifting_label( |
| remove_root=True, save_index=True) |
| encoded = codec.encode(keypoints, keypoints_visible, lifting_target, |
| lifting_target_visible) |
|
|
| self.assertTrue('target_root_removed' in encoded |
| and 'target_root_index' in encoded) |
| self.assertEqual(encoded['lifting_target_weights'].shape, (16, )) |
| self.assertEqual(encoded['keypoint_labels'].shape, (1, 17, 2)) |
| self.assertEqual(encoded['lifting_target_label'].shape, (16, 3)) |
| self.assertEqual(encoded['target_root'].shape, (3, )) |
|
|
| |
| codec = self.build_pose_lifting_label( |
| keypoints_mean=self.keypoints_mean, |
| keypoints_std=self.keypoints_std, |
| target_mean=self.target_mean, |
| target_std=self.target_std) |
| encoded = codec.encode(keypoints, keypoints_visible, lifting_target, |
| lifting_target_visible) |
|
|
| self.assertEqual(encoded['keypoint_labels'].shape, (1, 17, 2)) |
| self.assertEqual(encoded['lifting_target_label'].shape, (17, 3)) |
|
|
| def test_decode(self): |
| lifting_target = self.data['lifting_target'] |
| encoded_wo_sigma = self.data['encoded_wo_sigma'] |
|
|
| codec = self.build_pose_lifting_label() |
|
|
| decoded, scores = codec.decode( |
| encoded_wo_sigma, target_root=lifting_target[..., 0, :]) |
|
|
| self.assertEqual(decoded.shape, (1, 17, 3)) |
| self.assertEqual(scores.shape, (1, 17)) |
|
|
| codec = self.build_pose_lifting_label(remove_root=True) |
|
|
| decoded, scores = codec.decode( |
| encoded_wo_sigma, target_root=lifting_target[..., 0, :]) |
|
|
| self.assertEqual(decoded.shape, (1, 18, 3)) |
| self.assertEqual(scores.shape, (1, 18)) |
|
|
| def test_cicular_verification(self): |
| keypoints = self.data['keypoints'] |
| keypoints_visible = self.data['keypoints_visible'] |
| lifting_target = self.data['lifting_target'] |
| lifting_target_visible = self.data['lifting_target_visible'] |
|
|
| |
| codec = self.build_pose_lifting_label() |
| encoded = codec.encode(keypoints, keypoints_visible, lifting_target, |
| lifting_target_visible) |
|
|
| _keypoints, _ = codec.decode( |
| np.expand_dims(encoded['lifting_target_label'], axis=0), |
| target_root=lifting_target[..., 0, :]) |
|
|
| self.assertTrue( |
| np.allclose( |
| np.expand_dims(lifting_target, axis=0), _keypoints, atol=5.)) |
|
|
| |
| codec = self.build_pose_lifting_label(remove_root=True) |
| encoded = codec.encode(keypoints, keypoints_visible, lifting_target, |
| lifting_target_visible) |
|
|
| _keypoints, _ = codec.decode( |
| np.expand_dims(encoded['lifting_target_label'], axis=0), |
| target_root=lifting_target[..., 0, :]) |
|
|
| self.assertTrue( |
| np.allclose( |
| np.expand_dims(lifting_target, axis=0), _keypoints, atol=5.)) |
|
|
| |
| codec = self.build_pose_lifting_label( |
| keypoints_mean=self.keypoints_mean, |
| keypoints_std=self.keypoints_std, |
| target_mean=self.target_mean, |
| target_std=self.target_std) |
| encoded = codec.encode(keypoints, keypoints_visible, lifting_target, |
| lifting_target_visible) |
|
|
| _keypoints, _ = codec.decode( |
| np.expand_dims(encoded['lifting_target_label'], axis=0), |
| target_root=lifting_target[..., 0, :]) |
|
|
| self.assertTrue( |
| np.allclose( |
| np.expand_dims(lifting_target, axis=0), _keypoints, atol=5.)) |
|
|
