# 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'])
