def __call__(self, results):
if results.get(f'{self.item}_root_removed', False):
raise RuntimeError('The transform RelativeJointRandomFlip should '
f'not be applied to {self.item} whose root '
'joint has been removed and joint indices have '
'been changed')
if np.random.rand() <= self.flip_prob:
flip_pairs = results['ann_info']['flip_pairs']
# flip joint coordinates
assert self.item in results
joints = results[self.item]
joints_flipped = fliplr_regression(joints,
flip_pairs,
center_mode='root',
center_index=self.root_index)
results[self.item] = joints_flipped
# flip joint visibility
if self.vis_item is not None:
assert self.vis_item in results
visible = results[self.vis_item]
visible_flipped = visible.copy()
for left, right in flip_pairs:
visible_flipped[..., left, :] = visible[..., right, :]
visible_flipped[..., right, :] = visible[..., left, :]
results[self.vis_item] = visible_flipped
return results
def __call__(self, results):
if results.get(f'{self.item}_root_removed', False):
raise RuntimeError('The transform RelativeJointRandomFlip should '
f'not be applied to {self.item} whose root '
'joint has been removed and joint indices have '
'been changed')
if np.random.rand() <= self.flip_prob:
flip_pairs = results['ann_info']['flip_pairs']
# flip joint coordinates
for i, item in enumerate(self.item):
assert item in results
joints = results[item]
joints_flipped = fliplr_regression(joints, flip_pairs,
**self.flip_cfg[i])
results[item] = joints_flipped
# flip joint visibility
for vis_item in self.vis_item:
assert vis_item in results
visible = results[vis_item]
visible_flipped = visible.copy()
for left, right in flip_pairs:
visible_flipped[..., left, :] = visible[..., right, :]
visible_flipped[..., right, :] = visible[..., left, :]
results[vis_item] = visible_flipped
# flip horizontal distortion coefficients
if self.flip_camera:
if self.static_camera:
camera_param = copy.deepcopy(self.camera_param)
else:
assert 'camera_param' in results, \
'Camera parameters are missing.'
camera_param = results['camera_param']
assert 'c' in camera_param
camera_param['c'][0] *= -1
if 'p' in camera_param:
camera_param['p'][0] *= -1
if 'camera_param' not in results:
results['camera_param'] = dict()
results['camera_param'].update(camera_param)
return results
def inference_model(self, x, flip_pairs=None):
"""Inference function.
Returns:
output_regression (np.ndarray): Output regression.
Args:
x (torch.Tensor[N, K, 2]): Input features.
flip_pairs (None | list[tuple()):
Pairs of keypoints which are mirrored.
"""
output = self.forward(x)
if flip_pairs is not None:
output_regression = fliplr_regression(
output.detach().cpu().numpy(), flip_pairs)
else:
output_regression = output.detach().cpu().numpy()
return output_regression