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# Copyright (c) OpenMMLab. All rights reserved.
from copy import deepcopy
from typing import Optional, Tuple
import numpy as np
from mmpose.registry import KEYPOINT_CODECS
from .base import BaseKeypointCodec
from .utils import camera_to_image_coord
@KEYPOINT_CODECS.register_module()
class MotionBERTLabel(BaseKeypointCodec):
r"""Generate keypoint and label coordinates for `MotionBERT`_ by Zhu et al
(2022).
Note:
- instance number: N
- keypoint number: K
- keypoint dimension: D
- pose-lifitng target dimension: C
Args:
num_keypoints (int): The number of keypoints in the dataset.
root_index (int): Root keypoint index in the pose. Default: 0.
remove_root (bool): If true, remove the root keypoint from the pose.
Default: ``False``.
save_index (bool): If true, store the root position separated from the
original pose, only takes effect if ``remove_root`` is ``True``.
Default: ``False``.
concat_vis (bool): If true, concat the visibility item of keypoints.
Default: ``False``.
rootrel (bool): If true, the root keypoint will be set to the
coordinate origin. Default: ``False``.
mode (str): Indicating whether the current mode is 'train' or 'test'.
Default: ``'test'``.
"""
auxiliary_encode_keys = {
'lifting_target', 'lifting_target_visible', 'camera_param', 'factor'
}
instance_mapping_table = dict(
lifting_target='lifting_target',
lifting_target_visible='lifting_target_visible',
)
label_mapping_table = dict(
trajectory_weights='trajectory_weights',
lifting_target_label='lifting_target_label',
lifting_target_weight='lifting_target_weight')
def __init__(self,
num_keypoints: int,
root_index: int = 0,
remove_root: bool = False,
save_index: bool = False,
concat_vis: bool = False,
rootrel: bool = False,
mode: str = 'test'):
super().__init__()
self.num_keypoints = num_keypoints
self.root_index = root_index
self.remove_root = remove_root
self.save_index = save_index
self.concat_vis = concat_vis
self.rootrel = rootrel
assert mode.lower() in {'train', 'test'
}, (f'Unsupported mode {mode}, '
'mode should be one of ("train", "test").')
self.mode = mode.lower()
def encode(self,
keypoints: np.ndarray,
keypoints_visible: Optional[np.ndarray] = None,
lifting_target: Optional[np.ndarray] = None,
lifting_target_visible: Optional[np.ndarray] = None,
camera_param: Optional[dict] = None,
factor: Optional[np.ndarray] = None) -> dict:
"""Encoding keypoints from input image space to normalized space.
Args:
keypoints (np.ndarray): Keypoint coordinates in shape (B, T, K, D).
keypoints_visible (np.ndarray, optional): Keypoint visibilities in
shape (B, T, K).
lifting_target (np.ndarray, optional): 3d target coordinate in
shape (T, K, C).
lifting_target_visible (np.ndarray, optional): Target coordinate in
shape (T, K, ).
camera_param (dict, optional): The camera parameter dictionary.
factor (np.ndarray, optional): The factor mapping camera and image
coordinate in shape (T, ).
Returns:
encoded (dict): Contains the following items:
- keypoint_labels (np.ndarray): The processed keypoints in
shape like (N, K, D).
- keypoint_labels_visible (np.ndarray): The processed
keypoints' weights in shape (N, K, ) or (N, K-1, ).
- lifting_target_label: The processed target coordinate in
shape (K, C) or (K-1, C).
- lifting_target_weight (np.ndarray): The target weights in
shape (K, ) or (K-1, ).
- factor (np.ndarray): The factor mapping camera and image
coordinate in shape (T, 1).
"""
if keypoints_visible is None:
keypoints_visible = np.ones(keypoints.shape[:2], dtype=np.float32)
# set initial value for `lifting_target_weight`
if lifting_target_visible is None:
lifting_target_visible = np.ones(
lifting_target.shape[:-1], dtype=np.float32)
lifting_target_weight = lifting_target_visible
else:
valid = lifting_target_visible > 0.5
lifting_target_weight = np.where(valid, 1., 0.).astype(np.float32)
if camera_param is None:
camera_param = dict()
encoded = dict()
assert lifting_target is not None
lifting_target_label = lifting_target.copy()
keypoint_labels = keypoints.copy()
assert keypoint_labels.ndim in {
2, 3
}, (f'Keypoint labels should have 2 or 3 dimensions, '
f'but got {keypoint_labels.ndim}.')
if keypoint_labels.ndim == 2:
keypoint_labels = keypoint_labels[None, ...]
# Normalize the 2D keypoint coordinate with image width and height
_camera_param = deepcopy(camera_param)
assert 'w' in _camera_param and 'h' in _camera_param, (
'Camera parameters should contain "w" and "h".')
w, h = _camera_param['w'], _camera_param['h']
keypoint_labels[
..., :2] = keypoint_labels[..., :2] / w * 2 - [1, h / w]
# convert target to image coordinate
T = keypoint_labels.shape[0]
factor_ = np.array([4] * T, dtype=np.float32).reshape(T, )
if 'f' in _camera_param and 'c' in _camera_param:
lifting_target_label, factor_ = camera_to_image_coord(
self.root_index, lifting_target_label, _camera_param)
if self.mode == 'train':
w, h = w / 1000, h / 1000
lifting_target_label[
..., :2] = lifting_target_label[..., :2] / w * 2 - [1, h / w]
lifting_target_label[..., 2] = lifting_target_label[..., 2] / w * 2
lifting_target_label[..., :, :] = lifting_target_label[
..., :, :] - lifting_target_label[...,
self.root_index:self.root_index +
1, :]
if factor is None or factor[0] == 0:
factor = factor_
if factor.ndim == 1:
factor = factor[:, None]
if self.mode == 'test':
lifting_target_label *= factor[..., None]
if self.concat_vis:
keypoints_visible_ = keypoints_visible
if keypoints_visible.ndim == 2:
keypoints_visible_ = keypoints_visible[..., None]
keypoint_labels = np.concatenate(
(keypoint_labels, keypoints_visible_), axis=2)
encoded['keypoint_labels'] = keypoint_labels
encoded['keypoint_labels_visible'] = keypoints_visible
encoded['lifting_target_label'] = lifting_target_label
encoded['lifting_target_weight'] = lifting_target_weight
encoded['lifting_target'] = lifting_target_label
encoded['lifting_target_visible'] = lifting_target_visible
encoded['factor'] = factor
return encoded
def decode(
self,
encoded: np.ndarray,
w: Optional[np.ndarray] = None,
h: Optional[np.ndarray] = None,
factor: Optional[np.ndarray] = None,
) -> Tuple[np.ndarray, np.ndarray]:
"""Decode keypoint coordinates from normalized space to input image
space.
Args:
encoded (np.ndarray): Coordinates in shape (N, K, C).
w (np.ndarray, optional): The image widths in shape (N, ).
Default: ``None``.
h (np.ndarray, optional): The image heights in shape (N, ).
Default: ``None``.
factor (np.ndarray, optional): The factor for projection in shape
(N, ). Default: ``None``.
Returns:
keypoints (np.ndarray): Decoded coordinates in shape (N, K, C).
scores (np.ndarray): The keypoint scores in shape (N, K).
"""
keypoints = encoded.copy()
scores = np.ones(keypoints.shape[:-1], dtype=np.float32)
if self.rootrel:
keypoints[..., 0, :] = 0
if w is not None and w.size > 0:
assert w.shape == h.shape, (f'w and h should have the same shape, '
f'but got {w.shape} and {h.shape}.')
assert w.shape[0] == keypoints.shape[0], (
f'w and h should have the same batch size, '
f'but got {w.shape[0]} and {keypoints.shape[0]}.')
assert w.ndim in {1,
2}, (f'w and h should have 1 or 2 dimensions, '
f'but got {w.ndim}.')
if w.ndim == 1:
w = w[:, None]
h = h[:, None]
trans = np.append(
np.ones((w.shape[0], 1)), h / w, axis=1)[:, None, :]
keypoints[..., :2] = (keypoints[..., :2] + trans) * w[:, None] / 2
keypoints[..., 2:] = keypoints[..., 2:] * w[:, None] / 2
if factor is not None and factor.size > 0:
assert factor.shape[0] == keypoints.shape[0], (
f'factor should have the same batch size, '
f'but got {factor.shape[0]} and {keypoints.shape[0]}.')
keypoints *= factor[..., None]
keypoints[..., :, :] = keypoints[..., :, :] - keypoints[
..., self.root_index:self.root_index + 1, :]
keypoints /= 1000.
return keypoints, scores
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