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| # Copyright (c) OpenMMLab. All rights reserved. | |
| import tempfile | |
| import numpy as np | |
| from mmcv import Config | |
| from mmpose.datasets import DATASETS | |
| from mmpose.datasets.builder import build_dataset | |
| def test_body3d_h36m_dataset(): | |
| # Test Human3.6M dataset | |
| dataset = 'Body3DH36MDataset' | |
| dataset_class = DATASETS.get(dataset) | |
| dataset_info = Config.fromfile( | |
| 'configs/_base_/datasets/h36m.py').dataset_info | |
| # test single-frame input | |
| data_cfg = dict( | |
| num_joints=17, | |
| seq_len=1, | |
| seq_frame_interval=1, | |
| joint_2d_src='pipeline', | |
| joint_2d_det_file=None, | |
| causal=False, | |
| need_camera_param=True, | |
| camera_param_file='tests/data/h36m/cameras.pkl') | |
| _ = dataset_class( | |
| ann_file='tests/data/h36m/test_h36m_body3d.npz', | |
| img_prefix='tests/data/h36m', | |
| data_cfg=data_cfg, | |
| dataset_info=dataset_info, | |
| pipeline=[], | |
| test_mode=False) | |
| custom_dataset = dataset_class( | |
| ann_file='tests/data/h36m/test_h36m_body3d.npz', | |
| img_prefix='tests/data/h36m', | |
| data_cfg=data_cfg, | |
| dataset_info=dataset_info, | |
| pipeline=[], | |
| test_mode=True) | |
| assert custom_dataset.dataset_name == 'h36m' | |
| assert custom_dataset.test_mode is True | |
| _ = custom_dataset[0] | |
| results = [] | |
| for result in custom_dataset: | |
| results.append({ | |
| 'preds': result['target'][None, ...], | |
| 'target_image_paths': [result['target_image_path']], | |
| }) | |
| metrics = ['mpjpe', 'p-mpjpe', 'n-mpjpe'] | |
| infos = custom_dataset.evaluate(results, metric=metrics) | |
| np.testing.assert_almost_equal(infos['MPJPE'], 0.0) | |
| np.testing.assert_almost_equal(infos['P-MPJPE'], 0.0) | |
| np.testing.assert_almost_equal(infos['N-MPJPE'], 0.0) | |
| # test multi-frame input with joint_2d_src = 'detection' | |
| data_cfg = dict( | |
| num_joints=17, | |
| seq_len=27, | |
| seq_frame_interval=1, | |
| causal=True, | |
| temporal_padding=True, | |
| joint_2d_src='detection', | |
| joint_2d_det_file='tests/data/h36m/test_h36m_2d_detection.npy', | |
| need_camera_param=True, | |
| camera_param_file='tests/data/h36m/cameras.pkl') | |
| _ = dataset_class( | |
| ann_file='tests/data/h36m/test_h36m_body3d.npz', | |
| img_prefix='tests/data/h36m', | |
| data_cfg=data_cfg, | |
| dataset_info=dataset_info, | |
| pipeline=[], | |
| test_mode=False) | |
| custom_dataset = dataset_class( | |
| ann_file='tests/data/h36m/test_h36m_body3d.npz', | |
| img_prefix='tests/data/h36m', | |
| data_cfg=data_cfg, | |
| dataset_info=dataset_info, | |
| pipeline=[], | |
| test_mode=True) | |
| assert custom_dataset.test_mode is True | |
| _ = custom_dataset[0] | |
| results = [] | |
| for result in custom_dataset: | |
| results.append({ | |
| 'preds': result['target'][None, ...], | |
| 'target_image_paths': [result['target_image_path']], | |
| }) | |
| metrics = ['mpjpe', 'p-mpjpe', 'n-mpjpe'] | |
| infos = custom_dataset.evaluate(results, metric=metrics) | |
| np.testing.assert_almost_equal(infos['MPJPE'], 0.0) | |
| np.testing.assert_almost_equal(infos['P-MPJPE'], 0.0) | |
| np.testing.assert_almost_equal(infos['N-MPJPE'], 0.0) | |
| def test_body3d_semi_supervision_dataset(): | |
| # Test Body3d Semi-supervision Dataset | |
| dataset_info = Config.fromfile( | |
| 'configs/_base_/datasets/h36m.py').dataset_info | |
| # load labeled dataset | |
| labeled_data_cfg = dict( | |
| num_joints=17, | |
| seq_len=27, | |
| seq_frame_interval=1, | |
| causall=False, | |
| temporal_padding=True, | |
| joint_2d_src='gt', | |
| subset=1, | |
| subjects=['S1'], | |
| need_camera_param=True, | |
| camera_param_file='tests/data/h36m/cameras.pkl') | |
| labeled_dataset_cfg = dict( | |
| type='Body3DH36MDataset', | |
| ann_file='tests/data/h36m/test_h36m_body3d.npz', | |
| img_prefix='tests/data/h36m', | |
| data_cfg=labeled_data_cfg, | |
| dataset_info=dataset_info, | |
| pipeline=[]) | |
| # load unlabled data | |
| unlabeled_data_cfg = dict( | |
| num_joints=17, | |
| seq_len=27, | |
| seq_frame_interval=1, | |
| causal=False, | |
| temporal_padding=True, | |
| joint_2d_src='gt', | |
| subjects=['S5', 'S7', 'S8'], | |
| need_camera_param=True, | |
| camera_param_file='tests/data/h36m/cameras.pkl', | |
| need_2d_label=True) | |
| unlabeled_dataset_cfg = dict( | |
| type='Body3DH36MDataset', | |
| ann_file='tests/data/h36m/test_h36m_body3d.npz', | |
| img_prefix='tests/data/h36m', | |
| data_cfg=unlabeled_data_cfg, | |
| dataset_info=dataset_info, | |
| pipeline=[ | |
| dict( | |
| type='Collect', | |
| keys=[('input_2d', 'unlabeled_input')], | |
| meta_name='metas', | |
| meta_keys=[]) | |
| ]) | |
| # combine labeled and unlabeled dataset to form a new dataset | |
| dataset = 'Body3DSemiSupervisionDataset' | |
| dataset_class = DATASETS.get(dataset) | |
| custom_dataset = dataset_class(labeled_dataset_cfg, unlabeled_dataset_cfg) | |
| item = custom_dataset[0] | |
| assert custom_dataset.labeled_dataset.dataset_name == 'h36m' | |
| assert 'unlabeled_input' in item.keys() | |
| unlabeled_dataset = build_dataset(unlabeled_dataset_cfg) | |
| assert len(unlabeled_dataset) == len(custom_dataset) | |
| def test_body3d_mpi_inf_3dhp_dataset(): | |
| # Test MPI-INF-3DHP dataset | |
| dataset = 'Body3DMpiInf3dhpDataset' | |
| dataset_class = DATASETS.get(dataset) | |
| dataset_info = Config.fromfile( | |
| 'configs/_base_/datasets/mpi_inf_3dhp.py').dataset_info | |
| # Test single-frame input on trainset | |
| single_frame_train_data_cfg = dict( | |
| num_joints=17, | |
| seq_len=1, | |
| seq_frame_interval=1, | |
| joint_2d_src='pipeline', | |
| joint_2d_det_file=None, | |
| causal=False, | |
| need_camera_param=True, | |
| camera_param_file='tests/data/mpi_inf_3dhp/cameras_train.pkl') | |
| # Test single-frame input on testset | |
| single_frame_test_data_cfg = dict( | |
| num_joints=17, | |
| seq_len=1, | |
| seq_frame_interval=1, | |
| joint_2d_src='gt', | |
| joint_2d_det_file=None, | |
| causal=False, | |
| need_camera_param=True, | |
| camera_param_file='tests/data/mpi_inf_3dhp/cameras_test.pkl') | |
| # Test multi-frame input on trainset | |
| multi_frame_train_data_cfg = dict( | |
| num_joints=17, | |
| seq_len=27, | |
| seq_frame_interval=1, | |
| joint_2d_src='gt', | |
| joint_2d_det_file=None, | |
| causal=True, | |
| temporal_padding=True, | |
| need_camera_param=True, | |
| camera_param_file='tests/data/mpi_inf_3dhp/cameras_train.pkl') | |
| # Test multi-frame input on testset | |
| multi_frame_test_data_cfg = dict( | |
| num_joints=17, | |
| seq_len=27, | |
| seq_frame_interval=1, | |
| joint_2d_src='pipeline', | |
| joint_2d_det_file=None, | |
| causal=False, | |
| temporal_padding=True, | |
| need_camera_param=True, | |
| camera_param_file='tests/data/mpi_inf_3dhp/cameras_test.pkl') | |
| ann_files = [ | |
| 'tests/data/mpi_inf_3dhp/test_3dhp_train.npz', | |
| 'tests/data/mpi_inf_3dhp/test_3dhp_test.npz' | |
| ] * 2 | |
| data_cfgs = [ | |
| single_frame_train_data_cfg, single_frame_test_data_cfg, | |
| multi_frame_train_data_cfg, multi_frame_test_data_cfg | |
| ] | |
| for ann_file, data_cfg in zip(ann_files, data_cfgs): | |
| _ = dataset_class( | |
| ann_file=ann_file, | |
| img_prefix='tests/data/mpi_inf_3dhp', | |
| data_cfg=data_cfg, | |
| pipeline=[], | |
| dataset_info=dataset_info, | |
| test_mode=False) | |
| custom_dataset = dataset_class( | |
| ann_file=ann_file, | |
| img_prefix='tests/data/mpi_inf_3dhp', | |
| data_cfg=data_cfg, | |
| pipeline=[], | |
| dataset_info=dataset_info, | |
| test_mode=True) | |
| assert custom_dataset.test_mode is True | |
| _ = custom_dataset[0] | |
| results = [] | |
| for result in custom_dataset: | |
| results.append({ | |
| 'preds': result['target'][None, ...], | |
| 'target_image_paths': [result['target_image_path']], | |
| }) | |
| metrics = ['mpjpe', 'p-mpjpe', '3dpck', 'p-3dpck', '3dauc', 'p-3dauc'] | |
| infos = custom_dataset.evaluate(results, metric=metrics) | |
| np.testing.assert_almost_equal(infos['MPJPE'], 0.0) | |
| np.testing.assert_almost_equal(infos['P-MPJPE'], 0.0) | |
| np.testing.assert_almost_equal(infos['3DPCK'], 100.) | |
| np.testing.assert_almost_equal(infos['P-3DPCK'], 100.) | |
| np.testing.assert_almost_equal(infos['3DAUC'], 30 / 31 * 100) | |
| np.testing.assert_almost_equal(infos['P-3DAUC'], 30 / 31 * 100) | |
| def test_body3dmview_direct_panoptic_dataset(): | |
| # Test Mview-Panoptic dataset | |
| dataset = 'Body3DMviewDirectPanopticDataset' | |
| dataset_class = DATASETS.get(dataset) | |
| dataset_info = Config.fromfile( | |
| 'configs/_base_/datasets/panoptic_body3d.py').dataset_info | |
| space_size = [8000, 8000, 2000] | |
| space_center = [0, -500, 800] | |
| cube_size = [80, 80, 20] | |
| train_data_cfg = dict( | |
| image_size=[960, 512], | |
| heatmap_size=[[240, 128]], | |
| space_size=space_size, | |
| space_center=space_center, | |
| cube_size=cube_size, | |
| num_joints=15, | |
| seq_list=['160906_band1', '160906_band2'], | |
| cam_list=[(0, 12), (0, 6)], | |
| num_cameras=2, | |
| seq_frame_interval=1, | |
| subset='train', | |
| need_2d_label=True, | |
| need_camera_param=True, | |
| root_id=2) | |
| test_data_cfg = dict( | |
| image_size=[960, 512], | |
| heatmap_size=[[240, 128]], | |
| num_joints=15, | |
| space_size=space_size, | |
| space_center=space_center, | |
| cube_size=cube_size, | |
| seq_list=['160906_band1', '160906_band2'], | |
| cam_list=[(0, 12), (0, 6)], | |
| num_cameras=2, | |
| seq_frame_interval=1, | |
| subset='validation', | |
| need_2d_label=True, | |
| need_camera_param=True, | |
| root_id=2) | |
| with tempfile.TemporaryDirectory() as tmpdir: | |
| _ = dataset_class( | |
| ann_file=tmpdir + '/tmp_train.pkl', | |
| img_prefix='tests/data/panoptic_body3d/', | |
| data_cfg=train_data_cfg, | |
| pipeline=[], | |
| dataset_info=dataset_info, | |
| test_mode=False) | |
| with tempfile.TemporaryDirectory() as tmpdir: | |
| test_dataset = dataset_class( | |
| ann_file=tmpdir + '/tmp_validation.pkl', | |
| img_prefix='tests/data/panoptic_body3d', | |
| data_cfg=test_data_cfg, | |
| pipeline=[], | |
| dataset_info=dataset_info, | |
| test_mode=False) | |
| import copy | |
| gt_num = test_dataset.db_size // test_dataset.num_cameras | |
| results = [] | |
| for i in range(gt_num): | |
| index = test_dataset.num_cameras * i | |
| db_rec = copy.deepcopy(test_dataset.db[index]) | |
| joints_3d = db_rec['joints_3d'] | |
| joints_3d_vis = db_rec['joints_3d_visible'] | |
| num_gts = len(joints_3d) | |
| gt_pose = -np.ones((1, 10, test_dataset.num_joints, 5)) | |
| if num_gts > 0: | |
| gt_pose[0, :num_gts, :, :3] = np.array(joints_3d) | |
| gt_pose[0, :num_gts, :, 3] = np.array(joints_3d_vis)[:, :, 0] - 1.0 | |
| gt_pose[0, :num_gts, :, 4] = 1.0 | |
| results.append(dict(pose_3d=gt_pose, sample_id=[i])) | |
| _ = test_dataset.evaluate(results, metric=['mAP', 'mpjpe']) | |