| import numpy as np |
| import torch |
|
|
|
|
| def angle_axis(angle, axis): |
| |
| r"""Returns a 4x4 rotation matrix that performs a rotation around axis by angle |
| |
| Parameters |
| ---------- |
| angle : float |
| Angle to rotate by |
| axis: np.ndarray |
| Axis to rotate about |
| |
| Returns |
| ------- |
| torch.Tensor |
| 3x3 rotation matrix |
| """ |
| u = axis / np.linalg.norm(axis) |
| cosval, sinval = np.cos(angle), np.sin(angle) |
|
|
| |
| cross_prod_mat = np.array([[0.0, -u[2], u[1]], |
| [u[2], 0.0, -u[0]], |
| [-u[1], u[0], 0.0]]) |
|
|
| R = torch.from_numpy( |
| cosval * np.eye(3) |
| + sinval * cross_prod_mat |
| + (1.0 - cosval) * np.outer(u, u) |
| ) |
| |
| return R.float() |
|
|
|
|
| class PointcloudScale(object): |
| def __init__(self, lo=0.8, hi=1.25): |
| self.lo, self.hi = lo, hi |
|
|
| def __call__(self, points): |
| scaler = np.random.uniform(self.lo, self.hi) |
| points[:, 0:3] *= scaler |
| return points |
|
|
|
|
| class PointcloudRotate(object): |
| def __init__(self, axis=np.array([0.0, 1.0, 0.0])): |
| self.axis = axis |
|
|
| def __call__(self, points): |
| rotation_angle = np.random.uniform() * 2 * np.pi |
| rotation_matrix = angle_axis(rotation_angle, self.axis) |
|
|
| normals = points.size(1) > 3 |
| if not normals: |
| return torch.matmul(points, rotation_matrix.t()) |
| else: |
| pc_xyz = points[:, 0:3] |
| pc_normals = points[:, 3:] |
| points[:, 0:3] = torch.matmul(pc_xyz, rotation_matrix.t()) |
| points[:, 3:] = torch.matmul(pc_normals, rotation_matrix.t()) |
|
|
| return points |
|
|
|
|
| class PointcloudRotatePerturbation(object): |
| def __init__(self, angle_sigma=0.06, angle_clip=0.18): |
| self.angle_sigma, self.angle_clip = angle_sigma, angle_clip |
|
|
| def _get_angles(self): |
| angles = np.clip( |
| self.angle_sigma * np.random.randn(3), -self.angle_clip, self.angle_clip |
| ) |
|
|
| return angles |
|
|
| def __call__(self, points): |
| angles = self._get_angles() |
| Rx = angle_axis(angles[0], np.array([1.0, 0.0, 0.0])) |
| Ry = angle_axis(angles[1], np.array([0.0, 1.0, 0.0])) |
| Rz = angle_axis(angles[2], np.array([0.0, 0.0, 1.0])) |
|
|
| rotation_matrix = torch.matmul(torch.matmul(Rz, Ry), Rx) |
|
|
| normals = points.size(1) > 3 |
| if not normals: |
| return torch.matmul(points, rotation_matrix.t()) |
| else: |
| pc_xyz = points[:, 0:3] |
| pc_normals = points[:, 3:] |
| points[:, 0:3] = torch.matmul(pc_xyz, rotation_matrix.t()) |
| points[:, 3:] = torch.matmul(pc_normals, rotation_matrix.t()) |
|
|
| return points |
|
|
|
|
| class PointcloudJitter(object): |
| def __init__(self, std=0.01, clip=0.05): |
| self.std, self.clip = std, clip |
|
|
| def __call__(self, points): |
| jittered_data = ( |
| points.new(points.size(0), 3) |
| .normal_(mean=0.0, std=self.std) |
| .clamp_(-self.clip, self.clip) |
| ) |
| points[:, 0:3] += jittered_data |
| return points |
|
|
|
|
| class PointcloudTranslate(object): |
| def __init__(self, translate_range=0.1): |
| self.translate_range = translate_range |
|
|
| def __call__(self, points): |
| translation = np.random.uniform(-self.translate_range, self.translate_range) |
| points[:, 0:3] += translation |
| return points |
|
|
|
|
| class PointcloudToTensor(object): |
| def __call__(self, points): |
| return torch.from_numpy(points).float() |
|
|
|
|
| class PointcloudRandomInputDropout(object): |
| def __init__(self, max_dropout_ratio=0.875): |
| assert max_dropout_ratio >= 0 and max_dropout_ratio < 1 |
| self.max_dropout_ratio = max_dropout_ratio |
|
|
| def __call__(self, points): |
| pc = points.numpy() |
|
|
| dropout_ratio = np.random.random() * self.max_dropout_ratio |
| drop_idx = np.where(np.random.random((pc.shape[0])) <= dropout_ratio)[0] |
| if len(drop_idx) > 0: |
| pc[drop_idx] = pc[0] |
|
|
| return torch.from_numpy(pc).float() |
|
|
