Spaces:
Running
Running
File size: 11,890 Bytes
b7eedf7 |
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 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 |
import torch
import torch.nn.functional as F
pixel_coords = None
def set_id_grid(depth):
global pixel_coords
b, h, w = depth.size()
i_range = torch.arange(0, h).view(1, h, 1).expand(
1, h, w).type_as(depth) # [1, H, W]
j_range = torch.arange(0, w).view(1, 1, w).expand(
1, h, w).type_as(depth) # [1, H, W]
ones = torch.ones(1, h, w).type_as(depth)
pixel_coords = torch.stack((j_range, i_range, ones), dim=1) # [1, 3, H, W]
def check_sizes(input, input_name, expected):
condition = [input.ndimension() == len(expected)]
for i, size in enumerate(expected):
if size.isdigit():
condition.append(input.size(i) == int(size))
assert(all(condition)), "wrong size for {}, expected {}, got {}".format(
input_name, 'x'.join(expected), list(input.size()))
def pixel2cam(depth, intrinsics_inv):
global pixel_coords
"""Transform coordinates in the pixel frame to the camera frame.
Args:
depth: depth maps -- [B, H, W]
intrinsics_inv: intrinsics_inv matrix for each element of batch -- [B, 3, 3]
Returns:
array of (u,v,1) cam coordinates -- [B, 3, H, W]
"""
b, h, w = depth.size()
if (pixel_coords is None) or pixel_coords.size(2) < h:
set_id_grid(depth)
current_pixel_coords = pixel_coords[:, :, :h, :w].expand(
b, 3, h, w).reshape(b, 3, -1) # [B, 3, H*W]
cam_coords = (intrinsics_inv @ current_pixel_coords).reshape(b, 3, h, w)
out = depth.unsqueeze(1) * cam_coords
return out
def cam2pixel(cam_coords, proj_c2p_rot, proj_c2p_tr, padding_mode):
"""Transform coordinates in the camera frame to the pixel frame.
Args:
cam_coords: pixel coordinates defined in the first camera coordinates system -- [B, 4, H, W]
proj_c2p_rot: rotation matrix of cameras -- [B, 3, 4]
proj_c2p_tr: translation vectors of cameras -- [B, 3, 1]
Returns:
array of [-1,1] coordinates -- [B, 2, H, W]
"""
b, _, h, w = cam_coords.size()
cam_coords_flat = cam_coords.reshape(b, 3, -1) # [B, 3, H*W]
if proj_c2p_rot is not None:
pcoords = proj_c2p_rot @ cam_coords_flat
else:
pcoords = cam_coords_flat
if proj_c2p_tr is not None:
pcoords = pcoords + proj_c2p_tr # [B, 3, H*W]
X = pcoords[:, 0]
Y = pcoords[:, 1]
Z = pcoords[:, 2].clamp(min=1e-3)
# Normalized, -1 if on extreme left, 1 if on extreme right (x = w-1) [B, H*W]
X_norm = 2*(X / Z)/(w-1) - 1
Y_norm = 2*(Y / Z)/(h-1) - 1 # Idem [B, H*W]
pixel_coords = torch.stack([X_norm, Y_norm], dim=2) # [B, H*W, 2]
return pixel_coords.reshape(b, h, w, 2)
def euler2mat(angle):
"""Convert euler angles to rotation matrix.
Reference: https://github.com/pulkitag/pycaffe-utils/blob/master/rot_utils.py#L174
Args:
angle: rotation angle along 3 axis (in radians) -- size = [B, 3]
Returns:
Rotation matrix corresponding to the euler angles -- size = [B, 3, 3]
"""
B = angle.size(0)
x, y, z = angle[:, 0], angle[:, 1], angle[:, 2]
cosz = torch.cos(z)
sinz = torch.sin(z)
zeros = z.detach()*0
ones = zeros.detach()+1
zmat = torch.stack([cosz, -sinz, zeros,
sinz, cosz, zeros,
zeros, zeros, ones], dim=1).reshape(B, 3, 3)
cosy = torch.cos(y)
siny = torch.sin(y)
ymat = torch.stack([cosy, zeros, siny,
zeros, ones, zeros,
-siny, zeros, cosy], dim=1).reshape(B, 3, 3)
cosx = torch.cos(x)
sinx = torch.sin(x)
xmat = torch.stack([ones, zeros, zeros,
zeros, cosx, -sinx,
zeros, sinx, cosx], dim=1).reshape(B, 3, 3)
rotMat = xmat @ ymat @ zmat
return rotMat
def quat2mat(quat):
"""Convert quaternion coefficients to rotation matrix.
Args:
quat: first three coeff of quaternion of rotation. fourht is then computed to have a norm of 1 -- size = [B, 3]
Returns:
Rotation matrix corresponding to the quaternion -- size = [B, 3, 3]
"""
norm_quat = torch.cat([quat[:, :1].detach()*0 + 1, quat], dim=1)
norm_quat = norm_quat/norm_quat.norm(p=2, dim=1, keepdim=True)
w, x, y, z = norm_quat[:, 0], norm_quat[:,
1], norm_quat[:, 2], norm_quat[:, 3]
B = quat.size(0)
w2, x2, y2, z2 = w.pow(2), x.pow(2), y.pow(2), z.pow(2)
wx, wy, wz = w*x, w*y, w*z
xy, xz, yz = x*y, x*z, y*z
rotMat = torch.stack([w2 + x2 - y2 - z2, 2*xy - 2*wz, 2*wy + 2*xz,
2*wz + 2*xy, w2 - x2 + y2 - z2, 2*yz - 2*wx,
2*xz - 2*wy, 2*wx + 2*yz, w2 - x2 - y2 + z2], dim=1).reshape(B, 3, 3)
return rotMat
def pose_vec2mat(vec, rotation_mode='euler'):
"""
Convert 6DoF parameters to transformation matrix.
Args:s
vec: 6DoF parameters in the order of tx, ty, tz, rx, ry, rz -- [B, 6]
Returns:
A transformation matrix -- [B, 3, 4]
"""
translation = vec[:, :3].unsqueeze(-1) # [B, 3, 1]
rot = vec[:, 3:]
if rotation_mode == 'euler':
rot_mat = euler2mat(rot) # [B, 3, 3]
elif rotation_mode == 'quat':
rot_mat = quat2mat(rot) # [B, 3, 3]
transform_mat = torch.cat([rot_mat, translation], dim=2) # [B, 3, 4]
return transform_mat
def inverse_warp(img, depth, pose, intrinsics, rotation_mode='euler', padding_mode='zeros'):
"""
Inverse warp a source image to the target image plane.
Args:
img: the source image (where to sample pixels) -- [B, 3, H, W]
depth: depth map of the target image -- [B, H, W]
pose: 6DoF pose parameters from target to source -- [B, 6]
intrinsics: camera intrinsic matrix -- [B, 3, 3]
Returns:
projected_img: Source image warped to the target image plane
valid_points: Boolean array indicating point validity
"""
check_sizes(img, 'img', 'B3HW')
check_sizes(depth, 'depth', 'BHW')
check_sizes(pose, 'pose', 'B6')
check_sizes(intrinsics, 'intrinsics', 'B33')
batch_size, _, img_height, img_width = img.size()
cam_coords = pixel2cam(depth, intrinsics.inverse()) # [B,3,H,W]
pose_mat = pose_vec2mat(pose, rotation_mode) # [B,3,4]
# Get projection matrix for tgt camera frame to source pixel frame
proj_cam_to_src_pixel = intrinsics @ pose_mat # [B, 3, 4]
rot, tr = proj_cam_to_src_pixel[:, :, :3], proj_cam_to_src_pixel[:, :, -1:]
src_pixel_coords = cam2pixel(
cam_coords, rot, tr, padding_mode) # [B,H,W,2]
projected_img = F.grid_sample(
img, src_pixel_coords, padding_mode=padding_mode)
valid_points = src_pixel_coords.abs().max(dim=-1)[0] <= 1
return projected_img, valid_points
def cam2pixel2(cam_coords, proj_c2p_rot, proj_c2p_tr, padding_mode):
"""Transform coordinates in the camera frame to the pixel frame.
Args:
cam_coords: pixel coordinates defined in the first camera coordinates system -- [B, 4, H, W]
proj_c2p_rot: rotation matrix of cameras -- [B, 3, 4]
proj_c2p_tr: translation vectors of cameras -- [B, 3, 1]
Returns:
array of [-1,1] coordinates -- [B, 2, H, W]
"""
b, _, h, w = cam_coords.size()
cam_coords_flat = cam_coords.reshape(b, 3, -1) # [B, 3, H*W]
if proj_c2p_rot is not None:
pcoords = proj_c2p_rot @ cam_coords_flat
else:
pcoords = cam_coords_flat
if proj_c2p_tr is not None:
pcoords = pcoords + proj_c2p_tr # [B, 3, H*W]
X = pcoords[:, 0]
Y = pcoords[:, 1]
Z = pcoords[:, 2].clamp(min=1e-3)
# Normalized, -1 if on extreme left, 1 if on extreme right (x = w-1) [B, H*W]
X_norm = 2*(X / Z)/(w-1) - 1
Y_norm = 2*(Y / Z)/(h-1) - 1 # Idem [B, H*W]
if padding_mode == 'zeros':
X_mask = ((X_norm > 1)+(X_norm < -1)).detach()
# make sure that no point in warped image is a combinaison of im and gray
X_norm[X_mask] = 2
Y_mask = ((Y_norm > 1)+(Y_norm < -1)).detach()
Y_norm[Y_mask] = 2
pixel_coords = torch.stack([X_norm, Y_norm], dim=2) # [B, H*W, 2]
return pixel_coords.reshape(b, h, w, 2), Z.reshape(b, 1, h, w)
def inverse_warp2(img, depth, ref_depth, pose, intrinsics, padding_mode='zeros'):
"""
Inverse warp a source image to the target image plane.
Args:
img: the source image (where to sample pixels) -- [B, 3, H, W]
depth: depth map of the target image -- [B, 1, H, W]
ref_depth: the source depth map (where to sample depth) -- [B, 1, H, W]
pose: 6DoF pose parameters from target to source -- [B, 6]
intrinsics: camera intrinsic matrix -- [B, 3, 3]
Returns:
projected_img: Source image warped to the target image plane
valid_mask: Float array indicating point validity
projected_depth: sampled depth from source image
computed_depth: computed depth of source image using the target depth
"""
check_sizes(img, 'img', 'B3HW')
check_sizes(depth, 'depth', 'B1HW')
check_sizes(ref_depth, 'ref_depth', 'B1HW')
check_sizes(pose, 'pose', 'B6')
check_sizes(intrinsics, 'intrinsics', 'B33')
batch_size, _, img_height, img_width = img.size()
cam_coords = pixel2cam(depth.squeeze(1), intrinsics.inverse()) # [B,3,H,W]
pose_mat = pose_vec2mat(pose) # [B,3,4]
# Get projection matrix for tgt camera frame to source pixel frame
proj_cam_to_src_pixel = intrinsics @ pose_mat # [B, 3, 4]
rot, tr = proj_cam_to_src_pixel[:, :, :3], proj_cam_to_src_pixel[:, :, -1:]
src_pixel_coords, computed_depth = cam2pixel2(cam_coords, rot, tr, padding_mode) # [B,H,W,2]
projected_img = F.grid_sample(img, src_pixel_coords, padding_mode=padding_mode, align_corners=False)
projected_depth = F.grid_sample(ref_depth, src_pixel_coords, padding_mode=padding_mode, align_corners=False)
return projected_img, projected_depth, computed_depth
def inverse_rotation_warp(img, rot, intrinsics, padding_mode='zeros'):
b, _, h, w = img.size()
cam_coords = pixel2cam(torch.ones(b, h, w).type_as(img), intrinsics.inverse()) # [B,3,H,W]
rot_mat = euler2mat(rot) # [B, 3, 3]
# Get projection matrix for tgt camera frame to source pixel frame
proj_cam_to_src_pixel = intrinsics @ rot_mat # [B, 3, 3]
src_pixel_coords, computed_depth = cam2pixel2(cam_coords, proj_cam_to_src_pixel, None, padding_mode) # [B,H,W,2]
projected_img = F.grid_sample(img, src_pixel_coords, padding_mode=padding_mode, align_corners=True)
return projected_img
def grid_to_flow(grid):
b, h, w, _ = grid.size()
i_range = torch.arange(0, h).view(1, h, 1).expand(1, h, w).type_as(grid) # [1, H, W]
j_range = torch.arange(0, w).view(1, 1, w).expand(1, h, w).type_as(grid) # [1, H, W]
image_coords = torch.stack((j_range, i_range), dim=1) # [1, 2, H, W]
flow = torch.zeros_like(grid).type_as(grid)
flow[:, :, :, 0] = (grid[:, :, :, 0]+1) / 2 * (w-1)
flow[:, :, :, 1] = (grid[:, :, :, 1]+1) / 2 * (h-1)
flow = flow.permute([0, 3, 1, 2])
flow -= image_coords
return flow
def compute_translation_flow(depth, pose, intrinsics):
cam_coords = pixel2cam(depth.squeeze(1), intrinsics.inverse()) # [B,3,H,W]
pose_mat = pose_vec2mat(pose) # [B,3,4]
# Get projection matrix for tgt camera frame to source pixel frame
proj_cam_to_src_pixel = intrinsics @ pose_mat # [B, 3, 4]
rot, tr = proj_cam_to_src_pixel[:, :, :3], proj_cam_to_src_pixel[:, :, -1:]
grid_all, _ = cam2pixel2(cam_coords, rot, tr, padding_mode='zeros') # [B,H,W,2]
grid_rot, _ = cam2pixel2(cam_coords, rot, None, padding_mode='zeros') # [B,H,W,2]
flow_all = grid_to_flow(grid_all)
flow_rot = grid_to_flow(grid_rot)
flow_tr = (flow_all - flow_rot)
return flow_tr
|