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import numpy as np
import torch
import torch.nn.functional as F
import utils.geom
class Vox_util(object):
def __init__(self, Z, Y, X, scene_centroid, bounds, pad=None, assert_cube=False):
self.XMIN, self.XMAX, self.YMIN, self.YMAX, self.ZMIN, self.ZMAX = bounds
B, D = list(scene_centroid.shape)
self.Z, self.Y, self.X = Z, Y, X
scene_centroid = scene_centroid.detach().cpu().numpy()
x_centroid, y_centroid, z_centroid = scene_centroid[0]
self.XMIN += x_centroid
self.XMAX += x_centroid
self.YMIN += y_centroid
self.YMAX += y_centroid
self.ZMIN += z_centroid
self.ZMAX += z_centroid
self.default_vox_size_X = (self.XMAX-self.XMIN)/float(X)
self.default_vox_size_Y = (self.YMAX-self.YMIN)/float(Y)
self.default_vox_size_Z = (self.ZMAX-self.ZMIN)/float(Z)
if pad:
Z_pad, Y_pad, X_pad = pad
self.ZMIN -= self.default_vox_size_Z * Z_pad
self.ZMAX += self.default_vox_size_Z * Z_pad
self.YMIN -= self.default_vox_size_Y * Y_pad
self.YMAX += self.default_vox_size_Y * Y_pad
self.XMIN -= self.default_vox_size_X * X_pad
self.XMAX += self.default_vox_size_X * X_pad
if assert_cube:
# we assume cube voxels
if (not np.isclose(self.default_vox_size_X, self.default_vox_size_Y)) or (not np.isclose(self.default_vox_size_X, self.default_vox_size_Z)):
print('Z, Y, X', Z, Y, X)
print('bounds for this iter:',
'X = %.2f to %.2f' % (self.XMIN, self.XMAX),
'Y = %.2f to %.2f' % (self.YMIN, self.YMAX),
'Z = %.2f to %.2f' % (self.ZMIN, self.ZMAX),
)
print('self.default_vox_size_X', self.default_vox_size_X)
print('self.default_vox_size_Y', self.default_vox_size_Y)
print('self.default_vox_size_Z', self.default_vox_size_Z)
assert(np.isclose(self.default_vox_size_X, self.default_vox_size_Y))
assert(np.isclose(self.default_vox_size_X, self.default_vox_size_Z))
def Ref2Mem(self, xyz, Z, Y, X, assert_cube=False):
# xyz is B x N x 3, in ref coordinates
# transforms ref coordinates into mem coordinates
B, N, C = list(xyz.shape)
device = xyz.device
assert(C==3)
mem_T_ref = self.get_mem_T_ref(B, Z, Y, X, assert_cube=assert_cube, device=device)
xyz = utils.geom.apply_4x4(mem_T_ref, xyz)
return xyz
def Mem2Ref(self, xyz_mem, Z, Y, X, assert_cube=False):
# xyz is B x N x 3, in mem coordinates
# transforms mem coordinates into ref coordinates
B, N, C = list(xyz_mem.shape)
ref_T_mem = self.get_ref_T_mem(B, Z, Y, X, assert_cube=assert_cube, device=xyz_mem.device)
xyz_ref = utils.geom.apply_4x4(ref_T_mem, xyz_mem)
return xyz_ref
def get_mem_T_ref(self, B, Z, Y, X, assert_cube=False, device='cuda'):
vox_size_X = (self.XMAX-self.XMIN)/float(X)
vox_size_Y = (self.YMAX-self.YMIN)/float(Y)
vox_size_Z = (self.ZMAX-self.ZMIN)/float(Z)
if assert_cube:
if (not np.isclose(vox_size_X, vox_size_Y)) or (not np.isclose(vox_size_X, vox_size_Z)):
print('Z, Y, X', Z, Y, X)
print('bounds for this iter:',
'X = %.2f to %.2f' % (self.XMIN, self.XMAX),
'Y = %.2f to %.2f' % (self.YMIN, self.YMAX),
'Z = %.2f to %.2f' % (self.ZMIN, self.ZMAX),
)
print('vox_size_X', vox_size_X)
print('vox_size_Y', vox_size_Y)
print('vox_size_Z', vox_size_Z)
assert(np.isclose(vox_size_X, vox_size_Y))
assert(np.isclose(vox_size_X, vox_size_Z))
# translation
# (this makes the left edge of the leftmost voxel correspond to XMIN)
center_T_ref = utils.geom.eye_4x4(B, device=device)
center_T_ref[:,0,3] = -self.XMIN-vox_size_X/2.0
center_T_ref[:,1,3] = -self.YMIN-vox_size_Y/2.0
center_T_ref[:,2,3] = -self.ZMIN-vox_size_Z/2.0
# scaling
# (this makes the right edge of the rightmost voxel correspond to XMAX)
mem_T_center = utils.geom.eye_4x4(B, device=device)
mem_T_center[:,0,0] = 1./vox_size_X
mem_T_center[:,1,1] = 1./vox_size_Y
mem_T_center[:,2,2] = 1./vox_size_Z
mem_T_ref = utils.geom.matmul2(mem_T_center, center_T_ref)
return mem_T_ref
def get_ref_T_mem(self, B, Z, Y, X, assert_cube=False, device='cuda'):
mem_T_ref = self.get_mem_T_ref(B, Z, Y, X, assert_cube=assert_cube, device=device)
# note safe_inverse is inapplicable here,
# since the transform is nonrigid
ref_T_mem = mem_T_ref.inverse()
return ref_T_mem
def get_inbounds(self, xyz, Z, Y, X, already_mem=False, padding=0.0, assert_cube=False):
# xyz is B x N x 3
# padding should be 0 unless you are trying to account for some later cropping
if not already_mem:
xyz = self.Ref2Mem(xyz, Z, Y, X, assert_cube=assert_cube)
x = xyz[:,:,0]
y = xyz[:,:,1]
z = xyz[:,:,2]
x_valid = ((x-padding)>-0.5).byte() & ((x+padding)<float(X-0.5)).byte()
y_valid = ((y-padding)>-0.5).byte() & ((y+padding)<float(Y-0.5)).byte()
z_valid = ((z-padding)>-0.5).byte() & ((z+padding)<float(Z-0.5)).byte()
nonzero = (~(z==0.0)).byte()
inbounds = x_valid & y_valid & z_valid & nonzero
return inbounds.bool()
def voxelize_xyz(self, xyz_ref, Z, Y, X, already_mem=False, assert_cube=False, clean_eps=0):
B, N, D = list(xyz_ref.shape)
assert(D==3)
if already_mem:
xyz_mem = xyz_ref
else:
xyz_mem = self.Ref2Mem(xyz_ref, Z, Y, X, assert_cube=assert_cube)
xyz_zero = self.Ref2Mem(xyz_ref[:,0:1]*0, Z, Y, X, assert_cube=assert_cube)
vox = self.get_occupancy(xyz_mem, Z, Y, X, clean_eps=clean_eps, xyz_zero=xyz_zero)
return vox
def voxelize_xyz_and_feats(self, xyz_ref, feats, Z, Y, X, already_mem=False, assert_cube=False, clean_eps=0):
B, N, D = list(xyz_ref.shape)
B2, N2, D2 = list(feats.shape)
assert(D==3)
assert(B==B2)
assert(N==N2)
if already_mem:
xyz_mem = xyz_ref
else:
xyz_mem = self.Ref2Mem(xyz_ref, Z, Y, X, assert_cube=assert_cube)
xyz_zero = self.Ref2Mem(xyz_ref[:,0:1]*0, Z, Y, X, assert_cube=assert_cube)
feats = self.get_feat_occupancy(xyz_mem, feats, Z, Y, X, clean_eps=clean_eps, xyz_zero=xyz_zero)
return feats
def get_occupancy(self, xyz, Z, Y, X, clean_eps=0, xyz_zero=None):
# xyz is B x N x 3 and in mem coords
# we want to fill a voxel tensor with 1's at these inds
B, N, C = list(xyz.shape)
assert(C==3)
# these papers say simple 1/0 occupancy is ok:
# http://openaccess.thecvf.com/content_cvpr_2018/papers/Yang_PIXOR_Real-Time_3d_CVPR_2018_paper.pdf
# http://openaccess.thecvf.com/content_cvpr_2018/papers/Luo_Fast_and_Furious_CVPR_2018_paper.pdf
# cont fusion says they do 8-neighbor interp
# voxelnet does occupancy but with a bit of randomness in terms of the reflectance value i think
inbounds = self.get_inbounds(xyz, Z, Y, X, already_mem=True)
x, y, z = xyz[:,:,0], xyz[:,:,1], xyz[:,:,2]
mask = torch.zeros_like(x)
mask[inbounds] = 1.0
if xyz_zero is not None:
# only take points that are beyond a thresh of zero
dist = torch.norm(xyz_zero-xyz, dim=2)
mask[dist < 0.1] = 0
if clean_eps > 0:
# only take points that are already near centers
xyz_round = torch.round(xyz) # B, N, 3
dist = torch.norm(xyz_round - xyz, dim=2)
mask[dist > clean_eps] = 0
# set the invalid guys to zero
# we then need to zero out 0,0,0
# (this method seems a bit clumsy)
x = x*mask
y = y*mask
z = z*mask
x = torch.round(x)
y = torch.round(y)
z = torch.round(z)
x = torch.clamp(x, 0, X-1).int()
y = torch.clamp(y, 0, Y-1).int()
z = torch.clamp(z, 0, Z-1).int()
x = x.view(B*N)
y = y.view(B*N)
z = z.view(B*N)
dim3 = X
dim2 = X * Y
dim1 = X * Y * Z
base = torch.arange(0, B, dtype=torch.int32, device=xyz.device)*dim1
base = torch.reshape(base, [B, 1]).repeat([1, N]).view(B*N)
vox_inds = base + z * dim2 + y * dim3 + x
voxels = torch.zeros(B*Z*Y*X, device=xyz.device).float()
voxels[vox_inds.long()] = 1.0
# zero out the singularity
voxels[base.long()] = 0.0
voxels = voxels.reshape(B, 1, Z, Y, X)
# B x 1 x Z x Y x X
return voxels
def get_feat_occupancy(self, xyz, feat, Z, Y, X, clean_eps=0, xyz_zero=None):
# xyz is B x N x 3 and in mem coords
# feat is B x N x D
# we want to fill a voxel tensor with 1's at these inds
B, N, C = list(xyz.shape)
B2, N2, D2 = list(feat.shape)
assert(C==3)
assert(B==B2)
assert(N==N2)
# these papers say simple 1/0 occupancy is ok:
# http://openaccess.thecvf.com/content_cvpr_2018/papers/Yang_PIXOR_Real-Time_3d_CVPR_2018_paper.pdf
# http://openaccess.thecvf.com/content_cvpr_2018/papers/Luo_Fast_and_Furious_CVPR_2018_paper.pdf
# cont fusion says they do 8-neighbor interp
# voxelnet does occupancy but with a bit of randomness in terms of the reflectance value i think
inbounds = self.get_inbounds(xyz, Z, Y, X, already_mem=True)
x, y, z = xyz[:,:,0], xyz[:,:,1], xyz[:,:,2]
mask = torch.zeros_like(x)
mask[inbounds] = 1.0
if xyz_zero is not None:
# only take points that are beyond a thresh of zero
dist = torch.norm(xyz_zero-xyz, dim=2)
mask[dist < 0.1] = 0
if clean_eps > 0:
# only take points that are already near centers
xyz_round = torch.round(xyz) # B, N, 3
dist = torch.norm(xyz_round - xyz, dim=2)
mask[dist > clean_eps] = 0
# set the invalid guys to zero
# we then need to zero out 0,0,0
# (this method seems a bit clumsy)
x = x*mask # B, N
y = y*mask
z = z*mask
feat = feat*mask.unsqueeze(-1) # B, N, D
x = torch.round(x)
y = torch.round(y)
z = torch.round(z)
x = torch.clamp(x, 0, X-1).int()
y = torch.clamp(y, 0, Y-1).int()
z = torch.clamp(z, 0, Z-1).int()
# permute point orders
perm = torch.randperm(N)
x = x[:, perm]
y = y[:, perm]
z = z[:, perm]
feat = feat[:, perm]
x = x.view(B*N)
y = y.view(B*N)
z = z.view(B*N)
feat = feat.view(B*N, -1)
dim3 = X
dim2 = X * Y
dim1 = X * Y * Z
base = torch.arange(0, B, dtype=torch.int32, device=xyz.device)*dim1
base = torch.reshape(base, [B, 1]).repeat([1, N]).view(B*N)
vox_inds = base + z * dim2 + y * dim3 + x
feat_voxels = torch.zeros((B*Z*Y*X, D2), device=xyz.device).float()
feat_voxels[vox_inds.long()] = feat
# zero out the singularity
feat_voxels[base.long()] = 0.0
feat_voxels = feat_voxels.reshape(B, Z, Y, X, D2).permute(0, 4, 1, 2, 3)
# B x C x Z x Y x X
return feat_voxels
def unproject_image_to_mem(self, rgb_camB, pixB_T_camA, camB_T_camA, Z, Y, X, assert_cube=False, xyz_camA=None):
# rgb_camB is B x C x H x W
# pixB_T_camA is B x 4 x 4
# rgb lives in B pixel coords
# we want everything in A memory coords
# this puts each C-dim pixel in the rgb_camB
# along a ray in the voxelgrid
B, C, H, W = list(rgb_camB.shape)
if xyz_camA is None:
xyz_memA = utils.basic.gridcloud3d(B, Z, Y, X, norm=False, device=pixB_T_camA.device)
xyz_camA = self.Mem2Ref(xyz_memA, Z, Y, X, assert_cube=assert_cube)
xyz_camB = utils.geom.apply_4x4(camB_T_camA, xyz_camA)
z = xyz_camB[:,:,2]
xyz_pixB = utils.geom.apply_4x4(pixB_T_camA, xyz_camA)
normalizer = torch.unsqueeze(xyz_pixB[:,:,2], 2)
EPS=1e-6
# z = xyz_pixB[:,:,2]
xy_pixB = xyz_pixB[:,:,:2]/torch.clamp(normalizer, min=EPS)
# this is B x N x 2
# this is the (floating point) pixel coordinate of each voxel
x, y = xy_pixB[:,:,0], xy_pixB[:,:,1]
# these are B x N
x_valid = (x>-0.5).bool() & (x<float(W-0.5)).bool()
y_valid = (y>-0.5).bool() & (y<float(H-0.5)).bool()
z_valid = (z>0.0).bool()
valid_mem = (x_valid & y_valid & z_valid).reshape(B, 1, Z, Y, X).float()
if (0):
# handwritten version
values = torch.zeros([B, C, Z*Y*X], dtype=torch.float32)
for b in list(range(B)):
values[b] = utils.samp.bilinear_sample_single(rgb_camB[b], x_pixB[b], y_pixB[b])
else:
# native pytorch version
y_pixB, x_pixB = utils.basic.normalize_grid2d(y, x, H, W)
# since we want a 3d output, we need 5d tensors
z_pixB = torch.zeros_like(x)
xyz_pixB = torch.stack([x_pixB, y_pixB, z_pixB], axis=2)
rgb_camB = rgb_camB.unsqueeze(2)
xyz_pixB = torch.reshape(xyz_pixB, [B, Z, Y, X, 3])
values = F.grid_sample(rgb_camB, xyz_pixB, align_corners=False)
values = torch.reshape(values, (B, C, Z, Y, X))
values = values * valid_mem
return values
def warp_tiled_to_mem(self, rgb_tileB, pixB_T_camA, camB_T_camA, Z, Y, X, DMIN, DMAX, assert_cube=False):
# rgb_tileB is B,C,D,H,W
# pixB_T_camA is B,4,4
# camB_T_camA is B,4,4
# rgb_tileB lives in B pixel coords but it has been tiled across the Z dimension
# we want everything in A memory coords
# this resamples the so that each C-dim pixel in rgb_tilB
# is put into its correct place in the voxelgrid
# (using the pinhole camera model)
B, C, D, H, W = list(rgb_tileB.shape)
xyz_memA = utils.basic.gridcloud3d(B, Z, Y, X, norm=False, device=pixB_T_camA.device)
xyz_camA = self.Mem2Ref(xyz_memA, Z, Y, X, assert_cube=assert_cube)
xyz_camB = utils.geom.apply_4x4(camB_T_camA, xyz_camA)
z_camB = xyz_camB[:,:,2]
# rgb_tileB has depth=DMIN in tile 0, and depth=DMAX in tile D-1
z_tileB = (D-1.0) * (z_camB-float(DMIN)) / float(DMAX-DMIN)
xyz_pixB = utils.geom.apply_4x4(pixB_T_camA, xyz_camA)
normalizer = torch.unsqueeze(xyz_pixB[:,:,2], 2)
EPS=1e-6
# z = xyz_pixB[:,:,2]
xy_pixB = xyz_pixB[:,:,:2]/torch.clamp(normalizer, min=EPS)
# this is B x N x 2
# this is the (floating point) pixel coordinate of each voxel
x, y = xy_pixB[:,:,0], xy_pixB[:,:,1]
# these are B x N
x_valid = (x>-0.5).bool() & (x<float(W-0.5)).bool()
y_valid = (y>-0.5).bool() & (y<float(H-0.5)).bool()
z_valid = (z_camB>0.0).bool()
valid_mem = (x_valid & y_valid & z_valid).reshape(B, 1, Z, Y, X).float()
z_tileB, y_pixB, x_pixB = utils.basic.normalize_grid3d(z_tileB, y, x, D, H, W)
xyz_pixB = torch.stack([x_pixB, y_pixB, z_tileB], axis=2)
xyz_pixB = torch.reshape(xyz_pixB, [B, Z, Y, X, 3])
values = F.grid_sample(rgb_tileB, xyz_pixB, align_corners=False)
values = torch.reshape(values, (B, C, Z, Y, X))
values = values * valid_mem
return values
def apply_mem_T_ref_to_lrtlist(self, lrtlist_cam, Z, Y, X, assert_cube=False):
# lrtlist is B x N x 19, in cam coordinates
# transforms them into mem coordinates, including a scale change for the lengths
B, N, C = list(lrtlist_cam.shape)
assert(C==19)
mem_T_cam = self.get_mem_T_ref(B, Z, Y, X, assert_cube=assert_cube, device=lrtlist_cam.device)
def xyz2circles(self, xyz, radius, Z, Y, X, soft=True, already_mem=True, also_offset=False, grid=None):
# xyz is B x N x 3
# radius is B x N or broadcastably so
# output is B x N x Z x Y x X
B, N, D = list(xyz.shape)
assert(D==3)
if not already_mem:
xyz = self.Ref2Mem(xyz, Z, Y, X)
if grid is None:
grid_z, grid_y, grid_x = utils.basic.meshgrid3d(B, Z, Y, X, stack=False, norm=False, device=xyz.device)
# note the default stack is on -1
grid = torch.stack([grid_x, grid_y, grid_z], dim=1)
# this is B x 3 x Z x Y x X
xyz = xyz.reshape(B, N, 3, 1, 1, 1)
grid = grid.reshape(B, 1, 3, Z, Y, X)
# this is B x N x Z x Y x X
# round the xyzs, so that at least one value matches the grid perfectly,
# and we get a value of 1 there (since exp(0)==1)
xyz = xyz.round()
if torch.is_tensor(radius):
radius = radius.clamp(min=0.01)
if soft:
off = grid - xyz # B,N,3,Z,Y,X
# interpret radius as sigma
dist_grid = torch.sum(off**2, dim=2, keepdim=False)
# this is B x N x Z x Y x X
if torch.is_tensor(radius):
radius = radius.reshape(B, N, 1, 1, 1)
mask = torch.exp(-dist_grid/(2*radius*radius))
# zero out near zero
mask[mask < 0.001] = 0.0
# h = np.exp(-(x * x + y * y) / (2 * sigma * sigma))
# h[h < np.finfo(h.dtype).eps * h.max()] = 0
# return h
if also_offset:
return mask, off
else:
return mask
else:
assert(False) # something is wrong with this. come back later to debug
dist_grid = torch.norm(grid - xyz, dim=2, keepdim=False)
# this is 0 at/near the xyz, and increases by 1 for each voxel away
radius = radius.reshape(B, N, 1, 1, 1)
within_radius_mask = (dist_grid < radius).float()
within_radius_mask = torch.sum(within_radius_mask, dim=1, keepdim=True).clamp(0, 1)
return within_radius_mask
def xyz2circles_bev(self, xyz, radius, Z, Y, X, already_mem=True, also_offset=False):
# xyz is B x N x 3
# radius is B x N or broadcastably so
# output is B x N x Z x Y x X
B, N, D = list(xyz.shape)
assert(D==3)
if not already_mem:
xyz = self.Ref2Mem(xyz, Z, Y, X)
xz = torch.stack([xyz[:,:,0], xyz[:,:,2]], dim=2)
grid_z, grid_x = utils.basic.meshgrid2d(B, Z, X, stack=False, norm=False, device=xyz.device)
# note the default stack is on -1
grid = torch.stack([grid_x, grid_z], dim=1)
# this is B x 2 x Z x X
xz = xz.reshape(B, N, 2, 1, 1)
grid = grid.reshape(B, 1, 2, Z, X)
# these are ready to broadcast to B x N x Z x X
# round the points, so that at least one value matches the grid perfectly,
# and we get a value of 1 there (since exp(0)==1)
xz = xz.round()
if torch.is_tensor(radius):
radius = radius.clamp(min=0.01)
off = grid - xz # B,N,2,Z,X
# interpret radius as sigma
dist_grid = torch.sum(off**2, dim=2, keepdim=False)
# this is B x N x Z x X
if torch.is_tensor(radius):
radius = radius.reshape(B, N, 1, 1, 1)
mask = torch.exp(-dist_grid/(2*radius*radius))
# zero out near zero
mask[mask < 0.001] = 0.0
# add a Y dim
mask = mask.unsqueeze(-2)
off = off.unsqueeze(-2)
# # B,N,2,Z,1,X
if also_offset:
return mask, off
else:
return mask
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