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import os, torch
import numpy as np
import torch.nn.functional as F
def build_patch_offset(h_patch_size):
offsets = torch.arange(-h_patch_size, h_patch_size + 1)
return torch.stack(torch.meshgrid(offsets, offsets)[::-1], dim=-1).view(1, -1, 2) # nb_pixels_patch * 2
def gen_rays_from_single_image(H, W, image, intrinsic, c2w, depth=None, mask=None):
"""
generate rays in world space, for image image
:param H:
:param W:
:param intrinsics: [3,3]
:param c2ws: [4,4]
:return:
"""
device = image.device
ys, xs = torch.meshgrid(torch.linspace(0, H - 1, H),
torch.linspace(0, W - 1, W), indexing="ij") # pytorch's meshgrid has indexing='ij'
p = torch.stack([xs, ys, torch.ones_like(ys)], dim=-1) # H, W, 3
# normalized ndc uv coordinates, (-1, 1)
ndc_u = 2 * xs / (W - 1) - 1
ndc_v = 2 * ys / (H - 1) - 1
rays_ndc_uv = torch.stack([ndc_u, ndc_v], dim=-1).view(-1, 2).float().to(device)
intrinsic_inv = torch.inverse(intrinsic)
p = p.view(-1, 3).float().to(device) # N_rays, 3
p = torch.matmul(intrinsic_inv[None, :3, :3], p[:, :, None]).squeeze() # N_rays, 3
rays_v = p / torch.linalg.norm(p, ord=2, dim=-1, keepdim=True) # N_rays, 3
rays_v = torch.matmul(c2w[None, :3, :3], rays_v[:, :, None]).squeeze() # N_rays, 3
rays_o = c2w[None, :3, 3].expand(rays_v.shape) # N_rays, 3
image = image.permute(1, 2, 0)
color = image.view(-1, 3)
depth = depth.view(-1, 1) if depth is not None else None
mask = mask.view(-1, 1) if mask is not None else torch.ones([H * W, 1]).to(device)
sample = {
'rays_o': rays_o,
'rays_v': rays_v,
'rays_ndc_uv': rays_ndc_uv,
'rays_color': color,
# 'rays_depth': depth,
'rays_mask': mask,
'rays_norm_XYZ_cam': p # - XYZ_cam, before multiply depth
}
if depth is not None:
sample['rays_depth'] = depth
return sample
def gen_random_rays_from_single_image(H, W, N_rays, image, intrinsic, c2w, depth=None, mask=None, dilated_mask=None,
importance_sample=False, h_patch_size=3):
"""
generate random rays in world space, for a single image
:param H:
:param W:
:param N_rays:
:param image: [3, H, W]
:param intrinsic: [3,3]
:param c2w: [4,4]
:param depth: [H, W]
:param mask: [H, W]
:return:
"""
device = image.device
if dilated_mask is None:
dilated_mask = mask
if not importance_sample:
pixels_x = torch.randint(low=0, high=W, size=[N_rays])
pixels_y = torch.randint(low=0, high=H, size=[N_rays])
elif importance_sample and dilated_mask is not None: # sample more pts in the valid mask regions
pixels_x_1 = torch.randint(low=0, high=W, size=[N_rays // 4])
pixels_y_1 = torch.randint(low=0, high=H, size=[N_rays // 4])
ys, xs = torch.meshgrid(torch.linspace(0, H - 1, H),
torch.linspace(0, W - 1, W), indexing="ij") # pytorch's meshgrid has indexing='ij'
p = torch.stack([xs, ys], dim=-1) # H, W, 2
try:
p_valid = p[dilated_mask > 0] # [num, 2]
random_idx = torch.randint(low=0, high=p_valid.shape[0], size=[N_rays // 4 * 3])
except:
print("dilated_mask.shape: ", dilated_mask.shape)
print("dilated_mask valid number", dilated_mask.sum())
raise ValueError("hhhh")
p_select = p_valid[random_idx] # [N_rays//2, 2]
pixels_x_2 = p_select[:, 0]
pixels_y_2 = p_select[:, 1]
pixels_x = torch.cat([pixels_x_1, pixels_x_2], dim=0).to(torch.int64)
pixels_y = torch.cat([pixels_y_1, pixels_y_2], dim=0).to(torch.int64)
# - crop patch from images
offsets = build_patch_offset(h_patch_size).to(device)
grid_patch = torch.stack([pixels_x, pixels_y], dim=-1).view(-1, 1, 2) + offsets.float() # [N_pts, Npx, 2]
patch_mask = (pixels_x > h_patch_size) * (pixels_x < (W - h_patch_size)) * (pixels_y > h_patch_size) * (
pixels_y < H - h_patch_size) # [N_pts]
grid_patch_u = 2 * grid_patch[:, :, 0] / (W - 1) - 1
grid_patch_v = 2 * grid_patch[:, :, 1] / (H - 1) - 1
grid_patch_uv = torch.stack([grid_patch_u, grid_patch_v], dim=-1) # [N_pts, Npx, 2]
patch_color = F.grid_sample(image[None, :, :, :], grid_patch_uv[None, :, :, :], mode='bilinear',
padding_mode='zeros',align_corners=True)[0] # [3, N_pts, Npx]
patch_color = patch_color.permute(1, 2, 0).contiguous()
# normalized ndc uv coordinates, (-1, 1)
ndc_u = 2 * pixels_x / (W - 1) - 1
ndc_v = 2 * pixels_y / (H - 1) - 1
rays_ndc_uv = torch.stack([ndc_u, ndc_v], dim=-1).view(-1, 2).float().to(device)
image = image.permute(1, 2, 0) # H ,W, C
color = image[(pixels_y, pixels_x)] # N_rays, 3
if mask is not None:
mask = mask[(pixels_y, pixels_x)] # N_rays
patch_mask = patch_mask * mask # N_rays
mask = mask.view(-1, 1)
else:
mask = torch.ones([N_rays, 1])
if depth is not None:
depth = depth[(pixels_y, pixels_x)] # N_rays
depth = depth.view(-1, 1)
intrinsic_inv = torch.inverse(intrinsic)
p = torch.stack([pixels_x, pixels_y, torch.ones_like(pixels_y)], dim=-1).float().to(device) # N_rays, 3
p = torch.matmul(intrinsic_inv[None, :3, :3], p[:, :, None]).squeeze() # N_rays, 3
rays_v = p / torch.linalg.norm(p, ord=2, dim=-1, keepdim=True) # N_rays, 3
rays_v = torch.matmul(c2w[None, :3, :3], rays_v[:, :, None]).squeeze() # N_rays, 3
rays_o = c2w[None, :3, 3].expand(rays_v.shape) # N_rays, 3
sample = {
'rays_o': rays_o,
'rays_v': rays_v,
'rays_ndc_uv': rays_ndc_uv,
'rays_color': color,
# 'rays_depth': depth,
'rays_mask': mask,
'rays_norm_XYZ_cam': p, # - XYZ_cam, before multiply depth,
'rays_patch_color': patch_color,
'rays_patch_mask': patch_mask.view(-1, 1)
}
if depth is not None:
sample['rays_depth'] = depth
return sample
def gen_random_rays_of_patch_from_single_image(H, W, N_rays, num_neighboring_pts, patch_size,
image, intrinsic, c2w, depth=None, mask=None):
"""
generate random rays in world space, for a single image
sample rays from local patches
:param H:
:param W:
:param N_rays: the number of center rays of patches
:param image: [3, H, W]
:param intrinsic: [3,3]
:param c2w: [4,4]
:param depth: [H, W]
:param mask: [H, W]
:return:
"""
device = image.device
patch_radius_max = patch_size // 2
unit_u = 2 / (W - 1)
unit_v = 2 / (H - 1)
pixels_x_center = torch.randint(low=patch_size, high=W - patch_size, size=[N_rays])
pixels_y_center = torch.randint(low=patch_size, high=H - patch_size, size=[N_rays])
# normalized ndc uv coordinates, (-1, 1)
ndc_u_center = 2 * pixels_x_center / (W - 1) - 1
ndc_v_center = 2 * pixels_y_center / (H - 1) - 1
ndc_uv_center = torch.stack([ndc_u_center, ndc_v_center], dim=-1).view(-1, 2).float().to(device)[:, None,
:] # [N_rays, 1, 2]
shift_u, shift_v = torch.rand([N_rays, num_neighboring_pts, 1]), torch.rand(
[N_rays, num_neighboring_pts, 1]) # uniform distribution of [0,1)
shift_u = 2 * (shift_u - 0.5) # mapping to [-1, 1)
shift_v = 2 * (shift_v - 0.5)
# - avoid sample points which are too close to center point
shift_uv = torch.cat([(shift_u * patch_radius_max) * unit_u, (shift_v * patch_radius_max) * unit_v],
dim=-1) # [N_rays, num_npts, 2]
neighboring_pts_uv = ndc_uv_center + shift_uv # [N_rays, num_npts, 2]
sampled_pts_uv = torch.cat([ndc_uv_center, neighboring_pts_uv], dim=1) # concat the center point
# sample the gts
color = F.grid_sample(image[None, :, :, :], sampled_pts_uv[None, :, :, :], mode='bilinear',
align_corners=True)[0] # [3, N_rays, num_npts]
depth = F.grid_sample(depth[None, None, :, :], sampled_pts_uv[None, :, :, :], mode='bilinear',
align_corners=True)[0] # [1, N_rays, num_npts]
mask = F.grid_sample(mask[None, None, :, :].to(torch.float32), sampled_pts_uv[None, :, :, :], mode='nearest',
align_corners=True).to(torch.int64)[0] # [1, N_rays, num_npts]
intrinsic_inv = torch.inverse(intrinsic)
sampled_pts_uv = sampled_pts_uv.view(N_rays * (1 + num_neighboring_pts), 2)
color = color.permute(1, 2, 0).contiguous().view(N_rays * (1 + num_neighboring_pts), 3)
depth = depth.permute(1, 2, 0).contiguous().view(N_rays * (1 + num_neighboring_pts), 1)
mask = mask.permute(1, 2, 0).contiguous().view(N_rays * (1 + num_neighboring_pts), 1)
pixels_x = (sampled_pts_uv[:, 0] + 1) * (W - 1) / 2
pixels_y = (sampled_pts_uv[:, 1] + 1) * (H - 1) / 2
p = torch.stack([pixels_x, pixels_y, torch.ones_like(pixels_y)], dim=-1).float().to(device) # N_rays*num_pts, 3
p = torch.matmul(intrinsic_inv[None, :3, :3], p[:, :, None]).squeeze() # N_rays*num_pts, 3
rays_v = p / torch.linalg.norm(p, ord=2, dim=-1, keepdim=True) # N_rays*num_pts, 3
rays_v = torch.matmul(c2w[None, :3, :3], rays_v[:, :, None]).squeeze() # N_rays*num_pts, 3
rays_o = c2w[None, :3, 3].expand(rays_v.shape) # N_rays*num_pts, 3
sample = {
'rays_o': rays_o,
'rays_v': rays_v,
'rays_ndc_uv': sampled_pts_uv,
'rays_color': color,
'rays_depth': depth,
'rays_mask': mask,
# 'rays_norm_XYZ_cam': p # - XYZ_cam, before multiply depth
}
return sample
def gen_random_rays_from_batch_images(H, W, N_rays, images, intrinsics, c2ws, depths=None, masks=None):
"""
:param H:
:param W:
:param N_rays:
:param images: [B,3,H,W]
:param intrinsics: [B, 3, 3]
:param c2ws: [B, 4, 4]
:param depths: [B,H,W]
:param masks: [B,H,W]
:return:
"""
assert len(images.shape) == 4
rays_o = []
rays_v = []
rays_color = []
rays_depth = []
rays_mask = []
for i in range(images.shape[0]):
sample = gen_random_rays_from_single_image(H, W, N_rays, images[i], intrinsics[i], c2ws[i],
depth=depths[i] if depths is not None else None,
mask=masks[i] if masks is not None else None)
rays_o.append(sample['rays_o'])
rays_v.append(sample['rays_v'])
rays_color.append(sample['rays_color'])
if depths is not None:
rays_depth.append(sample['rays_depth'])
if masks is not None:
rays_mask.append(sample['rays_mask'])
sample = {
'rays_o': torch.stack(rays_o, dim=0), # [batch, N_rays, 3]
'rays_v': torch.stack(rays_v, dim=0),
'rays_color': torch.stack(rays_color, dim=0),
'rays_depth': torch.stack(rays_depth, dim=0) if depths is not None else None,
'rays_mask': torch.stack(rays_mask, dim=0) if masks is not None else None
}
return sample
from scipy.spatial.transform import Rotation as Rot
from scipy.spatial.transform import Slerp
def gen_rays_between(c2w_0, c2w_1, intrinsic, ratio, H, W, resolution_level=1):
device = c2w_0.device
l = resolution_level
tx = torch.linspace(0, W - 1, W // l)
ty = torch.linspace(0, H - 1, H // l)
pixels_x, pixels_y = torch.meshgrid(tx, ty, indexing="ij")
p = torch.stack([pixels_x, pixels_y, torch.ones_like(pixels_y)], dim=-1).to(device) # W, H, 3
intrinsic_inv = torch.inverse(intrinsic[:3, :3])
p = torch.matmul(intrinsic_inv[None, None, :3, :3], p[:, :, :, None]).squeeze() # W, H, 3
rays_v = p / torch.linalg.norm(p, ord=2, dim=-1, keepdim=True) # W, H, 3
trans = c2w_0[:3, 3] * (1.0 - ratio) + c2w_1[:3, 3] * ratio
pose_0 = c2w_0.detach().cpu().numpy()
pose_1 = c2w_1.detach().cpu().numpy()
pose_0 = np.linalg.inv(pose_0)
pose_1 = np.linalg.inv(pose_1)
rot_0 = pose_0[:3, :3]
rot_1 = pose_1[:3, :3]
rots = Rot.from_matrix(np.stack([rot_0, rot_1]))
key_times = [0, 1]
key_rots = [rot_0, rot_1]
slerp = Slerp(key_times, rots)
rot = slerp(ratio)
pose = np.diag([1.0, 1.0, 1.0, 1.0])
pose = pose.astype(np.float32)
pose[:3, :3] = rot.as_matrix()
pose[:3, 3] = ((1.0 - ratio) * pose_0 + ratio * pose_1)[:3, 3]
pose = np.linalg.inv(pose)
c2w = torch.from_numpy(pose).to(device)
rot = torch.from_numpy(pose[:3, :3]).cuda()
trans = torch.from_numpy(pose[:3, 3]).cuda()
rays_v = torch.matmul(rot[None, None, :3, :3], rays_v[:, :, :, None]).squeeze() # W, H, 3
rays_o = trans[None, None, :3].expand(rays_v.shape) # W, H, 3
return c2w, rays_o.transpose(0, 1).contiguous().view(-1, 3), rays_v.transpose(0, 1).contiguous().view(-1, 3)
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