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import torch
import numpy as np
class Quad:
def __init__(self, points, format='NP2'):
self._rect = None
self.tensorized = False
self._points = None
self.set_points(points, format)
@property
def points(self):
return self._points
def set_points(self, new_points, format='NP2'):
order = (format.index('N'), format.index('P'), format.index('2'))
if isinstance(new_points, torch.Tensor):
self._points = new_points.permute(*order)
self.tensorized = True
else:
points = np.array(new_points, dtype=np.float32)
self._points = points.transpose(*order)
if self.tensorized:
self.tensorized = False
self.tensor
@points.setter
def points(self, new_points):
self.set_points(new_points)
@property
def tensor(self):
if not self.tensorized:
self._points = torch.from_numpy(self._points)
return self._points
def to(self, device):
self._points.to(device)
return self._points
def __iter__(self):
for i in range(self._points.shape[0]):
if self.tensorized:
yield self.tensor[i]
else:
yield self.points[i]
def rect(self):
if self._rect is None:
self._rect = self.rectify()
return self._rect
def __getitem__(self, *args, **kwargs):
return self._points.__getitem__(*args, **kwargs)
def numpy(self):
if not self.tensorized:
return self._points
return self._points.cpu().data.numpy()
def rectify(self):
if self.tensorized:
return self.rectify_tensor()
xmin = self._points[:, :, 0].min(axis=1)
ymin = self._points[:, :, 1].min(axis=1)
xmax = self._points[:, :, 0].max(axis=1)
ymax = self._points[:, :, 1].max(axis=1)
return np.stack([xmin, ymin, xmax, ymax], axis=1)
def rectify_tensor(self):
xmin, _ = self.tensor[:, :, 0].min(dim=1, keepdim=True)
ymin, _ = self.tensor[:, :, 1].min(dim=1, keepdim=True)
xmax, _ = self.tensor[:, :, 0].max(dim=1, keepdim=True)
ymax, _ = self.tensor[:, :, 1].max(dim=1, keepdim=True)
return torch.cat([xmin, ymin, xmax, ymax], dim=1)
def __getattribute__(self, name):
try:
return super().__getattribute__(name)
except AttributeError:
return self._points.__getattribute__(name)
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