File size: 1,102 Bytes
5013bb8 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 | import numpy as np
import torch
def get_proj_mat(intrins, rots, trans):
K = np.eye(4)
K[:3, :3] = intrins
R = np.eye(4)
R[:3, :3] = rots.transpose(-1, -2)
T = np.eye(4)
T[:3, 3] = -trans
RT = R @ T
return K @ RT
def perspective(cam_coords, proj_mat):
pix_coords = proj_mat @ cam_coords
valid_idx = pix_coords[2, :] > 0
pix_coords = pix_coords[:, valid_idx]
pix_coords = pix_coords[:2, :] / (pix_coords[2, :] + 1e-7)
pix_coords = pix_coords.transpose(1, 0)
return pix_coords
def label_onehot_decoding(onehot):
return torch.argmax(onehot, axis=0)
def label_onehot_encoding(label, num_classes=4):
H, W = label.shape
onehot = torch.zeros((num_classes, H, W))
onehot.scatter_(0, label[None].long(), 1)
return onehot
def gen_dx_bx(xbound, ybound, zbound):
dx = torch.Tensor([row[2] for row in [xbound, ybound, zbound]])
bx = torch.Tensor([row[0] + row[2] / 2.0 for row in [xbound, ybound, zbound]])
nx = torch.LongTensor([(row[1] - row[0]) / row[2] for row in [xbound, ybound, zbound]])
return dx, bx, nx
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