mirror of
https://github.com/wassname/Pointnet2_PyTorch.git
synced 2026-06-27 16:00:07 +08:00
erikwijmans
78 lines
2.0 KiB
Python
78 lines
2.0 KiB
Python
import torch
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import numpy as np
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class PointcloudScale(object):
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def __init__(self, mean=2.0, std=1.0, clip=1.8):
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self.mean, self.std, self.clip = mean, std, clip
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def __call__(self, points):
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scaler = points.new(1).normal_(
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mean=self.mean, std=self.std
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).clamp_(max(self.mean - self.clip, 0.01), self.mean + self.clip)
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return scaler * points
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class PointcloudRotate(object):
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def __init__(self, x_axis=False, z_axis=True):
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assert x_axis or z_axis
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self.x, self.z = x_axis, z_axis
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def _get_angles(self):
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rotation_angle = np.random.uniform() * 2 * np.pi
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cosval = np.cos(rotation_angle)
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sinval = np.sin(rotation_angle)
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return cosval, sinval
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def __call__(self, points):
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if self.z:
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sinval, cosval = self._get_angles()
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Rz = points.new([[cosval, sinval, 0], [-sinval, cosval, 0],
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[0, 0, 1]])
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else:
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Rz = torch.eye(3)
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if self.x:
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sinval, cosval = self._get_angles()
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Rx = points.new([[1, 0, 0], [0, cosval, sinval],
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[0, -sinval, cosval]])
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else:
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Rx = torch.eye(3)
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rot_mat = Rx @ Rz
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return points @ rot_mat
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class PointcloudJitter(object):
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def __init__(self, std=0.01, clip=0.03):
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self.std, self.clip = std, clip
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def __call__(self, points):
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jittered_data = points.new(*points.size()).normal_(
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mean=0.0, std=self.std
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).clamp_(-self.clip, self.clip)
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return points + jittered_data
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class PointcloudTranslate(object):
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def __init__(self, std=1.0, clip=3.0):
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self.std, self.clip = std, clip
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def __call__(self, points):
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translation = points.new(3).normal_(
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mean=0.0, std=self.std
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).clamp_(-self.clip, self.clip)
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return points + translation
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class PointcloudToTensor(object):
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def __call__(self, points):
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return torch.from_numpy(points).float()
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