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import numpy as np
import trimesh
from pytorch360convert import e2c, c2e
def erp_to_cubemap(erp_tensor, face_w = 768, cube_format = "stack", mode = "bilinear", **kwargs):
return e2c(erp_tensor, face_w=face_w, cube_format=cube_format, mode=mode, **kwargs)
def cubemap_to_erp(cube_tensor, erp_h = 1024, erp_w = 2048, cube_format = "stack", mode = "bilinear", **kwargs):
return c2e(cube_tensor, h=erp_h, w=erp_w, cube_format=cube_format, mode=mode, **kwargs)
def roll_augment(data, shift_x):
if data.ndim == 2:
data = data[:, :, np.newaxis]
originally_2d = True
else:
originally_2d = False
if data.ndim == 3 and data.shape[0] != 3:
data = np.moveaxis(data, -1, 0)
moved_axis = True
else:
moved_axis = False
data_rolled = np.roll(data, int(shift_x), axis=2)
if moved_axis:
data_rolled = np.moveaxis(data_rolled, 0, -1)
if originally_2d:
data_rolled = data_rolled[:, :, 0]
return data_rolled
def roll_normal(normal, shift_x):
if normal.ndim == 2:
normal = normal[:, :, np.newaxis]
originally_2d = True
else:
originally_2d = False
if normal.ndim == 3 and normal.shape[0] != 3:
normal = np.moveaxis(normal, -1, 0)
moved_axis = True
else:
moved_axis = False
_, H, W = normal.shape
angle = - 2.0 * np.pi * (shift_x / float(W))
cos_a, sin_a = np.cos(angle), np.sin(angle)
R = np.array([
[ cos_a, 0.0, -sin_a],
[ 0.0, 1.0, 0.0 ],
[ sin_a, 0.0, cos_a]
], dtype=normal.dtype)
n_flat = normal.reshape(3, -1)
normal = (R @ n_flat).reshape(3, H, W)
if moved_axis:
normal = np.moveaxis(normal, 0, -1)
if originally_2d:
normal = normal[:, :, 0]
return normal
def compute_scale_and_shift(pred_g, targ_g, mask_g = None, eps = 0.0, fit_shift = True):
if mask_g is None:
mask_g = torch.ones_like(pred_g, dtype=torch.bool)
if pred_g.shape[0] == 6:
pred_g = pred_g.view(1, 6, pred_g.shape[2], pred_g.shape[3])
targ_g = targ_g.view(1, 6, targ_g.shape[2], targ_g.shape[3])
mask_g = mask_g.view(1, 6, mask_g.shape[2], mask_g.shape[3])
elif pred_g.shape[0] == 1 and pred_g.dim() == 3:
pred_g = pred_g.unsqueeze(0)
targ_g = targ_g.unsqueeze(0)
mask_g = mask_g.unsqueeze(0)
mask_g = mask_g.to(dtype=pred_g.dtype)
a_00 = torch.sum(mask_g * pred_g * pred_g, dim=(1, 2, 3))
a_01 = torch.sum(mask_g * pred_g, dim=(1, 2, 3))
a_11 = torch.sum(mask_g, dim=(1, 2, 3))
b_0 = torch.sum(mask_g * pred_g * targ_g, dim=(1, 2, 3))
b_1 = torch.sum(mask_g * targ_g, dim=(1, 2, 3))
if fit_shift:
det = a_00 * a_11 - a_01 * a_01
det = det + eps
scale = torch.zeros_like(b_0)
shift = torch.zeros_like(b_1)
valid = det > 0
scale[valid] = (a_11[valid] * b_0[valid] - a_01[valid] * b_1[valid]) / det[valid]
shift[valid] = (-a_01[valid] * b_0[valid] + a_00[valid] * b_1[valid]) / det[valid]
return scale, shift
else:
denom = a_00 + eps
scale = b_0 / denom
shift = torch.zeros_like(scale)
return scale, shift
def compute_shift(pred, targ, mask, eps = 1e-6):
if pred.shape[0] == 6:
pred = pred.view(1, 6, *pred.shape[2:])
targ = targ.view(1, 6, *targ.shape[2:])
mask = mask.view(1, 6, *mask.shape[2:])
w = mask.float()
num = torch.sum(w * (targ - pred), dim=(1,2,3))
den = torch.sum(w, dim=(1,2,3)).clamp_min(eps)
beta = num / den
return beta
def get_positional_encoding(H, W, pixel_center = True, hw = 96):
jj = np.arange(W, dtype=np.float64)
ii = np.arange(H, dtype=np.float64)
if pixel_center:
jj = jj + 0.5
ii = ii + 0.5
U = (jj / W) * 2.0 - 1.0
V = (ii / H) * 2.0 - 1.0
U, V = np.meshgrid(U, V, indexing='xy')
erp = np.stack([U, V], axis=-1)
erp_tensor = torch.from_numpy(erp).permute(2, 0, 1).float()
faces = erp_to_cubemap(erp_tensor, face_w=hw)
return faces
def unit_normals(n, eps = 1e-6):
assert n.dim() >= 3 and n.size(-3) == 3, "normals must have channel=3 at dim -3"
denom = torch.clamp(torch.linalg.norm(n, dim=-3, keepdim=True), min=eps)
return n / denom
def _erp_dirs(H, W, device=None, dtype=None):
u = (torch.arange(W, device=device, dtype=dtype) + 0.5) / W
v = (torch.arange(H, device=device, dtype=dtype) + 0.5) / H
theta = u * (2.0 * torch.pi) - torch.pi
phi = (0.5 - v) * torch.pi
theta = theta.view(1, W).expand(H, W)
phi = phi.view(H, 1).expand(H, W)
cosphi = torch.cos(phi)
sinphi = torch.sin(phi)
costhe = torch.cos(theta)
sinthe = torch.sin(theta)
x = cosphi * costhe
y = sinphi
z = -cosphi * sinthe
dirs = torch.stack([x, y, z], dim=0)
return dirs
def depth_to_normals_erp(depth, eps = 1e-6):
assert depth.dim() == 3 and depth.size(0) == 1, "depth must be (B,1,H,W)"
_, H, W = depth.shape
device, dtype = depth.device, depth.dtype
dirs = _erp_dirs(H, W, device=device, dtype=dtype)
P = depth * dirs
dtheta = 2.0 * torch.pi / W
dphi = torch.pi / H
P_l = torch.roll(P, shifts=+1, dims=-1)
P_r = torch.roll(P, shifts=-1, dims=-1)
dP_dtheta = (P_r - P_l) / (2.0 * dtheta)
P_u = torch.cat([P[:, :1, :], P[:, :-1, :]], dim=-2)
P_d = torch.cat([P[:, 1:, :], P[:, -1:, :]], dim=-2)
dP_dphi = (P_d - P_u) / (2.0 * dphi)
n = torch.cross(dP_dtheta, dP_dphi, dim=0)
n = unit_normals(n, eps=eps)
return n
def compute_edge_mask(depth, abs_thresh = 0.1, rel_thresh = 0.1):
assert depth.ndim == 2
depth = depth.astype(np.float32, copy=False)
valid = depth > 0
eps = 1e-6
edge = np.zeros_like(valid, dtype=bool)
d1 = depth[:, :-1]
d2 = depth[:, 1:]
v_pair = valid[:, :-1] & valid[:, 1:]
diff = np.abs(d1 - d2)
rel = diff / (np.minimum(d1, d2) + eps)
edge_pair = v_pair & (diff > abs_thresh) & (rel > rel_thresh)
edge[:, :-1] |= edge_pair
edge[:, 1:] |= edge_pair
d1 = depth[:-1, :]
d2 = depth[1:, :]
v_pair = valid[:-1, :] & valid[1:, :]
diff = np.abs(d1 - d2)
rel = diff / (np.minimum(d1, d2) + eps)
edge_pair = v_pair & (diff > abs_thresh) & (rel > rel_thresh)
edge[:-1, :] |= edge_pair
edge[1:, :] |= edge_pair
keep = valid & (~edge)
return keep
def erp_to_pointcloud(rgb, depth, mask = None):
assert rgb.ndim == 3 and rgb.shape[-1] == 3, "rgb must be (H, W, 3)"
assert depth.ndim == 2 and depth.shape[:2] == rgb.shape[:2], "depth must be (H, W) and match rgb H,W"
H, W, _ = rgb.shape
depth = depth.astype(np.float32, copy=False)
u = (np.arange(W, dtype=np.float32) + 0.5) / W
v = (np.arange(H, dtype=np.float32) + 0.5) / H
theta = u * (2.0 * np.pi) - np.pi
phi = (1 - v) * np.pi - (np.pi / 2.0)
theta, phi = np.meshgrid(theta, phi, indexing="xy")
cos_phi = np.cos(phi)
dir_x = cos_phi * np.cos(theta)
dir_y = np.sin(phi)
dir_z = cos_phi * np.sin(theta)
X = depth * dir_x
Y = depth * dir_y
Z = depth * dir_z
if mask is None:
keep = depth > 0
else:
keep = (mask.astype(bool)) & (depth > 0)
points = np.stack([X, Y, Z], axis=-1)[keep]
rgb_clamped = np.clip(rgb, -1.0, 1.0)
colors = ((rgb_clamped * 0.5 + 0.5) * 255.0).astype(np.uint8)
colors = colors.reshape(H, W, 3)[keep]
return points.astype(np.float32, copy=False), colors
def erp_to_point_cloud_glb(rgb, depth, mask=None, export_path=None):
points, colors = erp_to_pointcloud(rgb, depth, mask)
scene = trimesh.Scene()
scene.add_geometry(trimesh.PointCloud(vertices=points, colors=colors))
scene.export(export_path)
return scene
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