File size: 4,299 Bytes
e4c5b8d | 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 | import numpy as np
import open3d as o3d
# from ROS camera convention to USD camera convention
U_R_TRANSFORM = np.array([[1, 0, 0, 0], [0, -1, 0, 0], [0, 0, -1, 0], [0, 0, 0, 1]])
# from USD camera convention to ROS camera convention
R_U_TRANSFORM = np.array([[1, 0, 0, 0], [0, -1, 0, 0], [0, 0, -1, 0], [0, 0, 0, 1]])
# from USD camera convention to World camera convention
W_U_TRANSFORM = np.array([[0, 0, -1, 0], [-1, 0, 0, 0], [0, 1, 0, 0], [0, 0, 0, 1]])
# from World camera convention to USD camera convention
U_W_TRANSFORM = np.array([[0, -1, 0, 0], [0, 0, 1, 0], [-1, 0, 0, 0], [0, 0, 0, 1]])
def depth2fgpcd(depth, mask, cam_params):
# depth: (h, w)
# fgpcd: (n, 3)
# mask: (h, w)
h, w = depth.shape
mask = np.logical_and(mask, depth > 0)
fgpcd = np.zeros((mask.sum(), 3))
fx, fy, cx, cy = cam_params
pos_x, pos_y = np.meshgrid(np.arange(w), np.arange(h))
pos_x = pos_x[mask]
pos_y = pos_y[mask]
fgpcd[:, 0] = (pos_x - cx) * depth[mask] / fx
fgpcd[:, 1] = (pos_y - cy) * depth[mask] / fy
fgpcd[:, 2] = depth[mask]
return fgpcd
def depth2pcd(depth,camera_proj_mat,camera_view_mat):
height, width=depth.shape
vinv = np.linalg.inv(camera_view_mat)
proj=camera_proj_mat
fu =2 /proj[0,0]
fv =2 /proj[1,1]
centerU=width/2
centerV = height/2
u = np.linspace(0, width - 1, width)
v = np.linspace(0, height - 1, height)
u,v = np.meshgrid(u,v,indexing="xy")
Z=depth
x_para=-Z*fu/width
y_para=Z*fv/height
X=(u - centerU)*x_para
Y=(v - centerV)*y_para
position = np.stack([X,Y,Z,np.ones_like(X)],axis=-1)
position = position.view(-1,4)
position = position @ vinv
points=position[:,:3]
return points
def depth2fgpcd_w(depth,mask,K):
# depth: (h, w)
# fgpcd: (n, 3)
# mask: (h, w)
# get_pointcloud
im_height, im_width = depth.shape[0], depth.shape[1]
valid_mask=np.logical_and(mask,depth>0)
if not valid_mask.any():
return np.zeros((0, 3))
ww = np.linspace(0.5, im_width - 0.5, im_width, dtype=np.float32)
hh = np.linspace(0.5, im_height - 0.5, im_height, dtype=np.float32)
xmap, ymap = np.meshgrid(ww, hh, indexing="xy")
# points_2d = np.column_stack((xmap.ravel(), ymap.ravel()))
points_2d = np.column_stack((xmap[mask], ymap[mask])) # (n, 2)
# get_world_points_from_image_coords
# depth =depth.flatten()
depth =depth[mask] # (n,)
homogenous=np.pad(points_2d,((0,0),(0,1)),mode="constant",constant_values=1.0)
points_in_camera_axes = np.matmul(
np.linalg.inv(K),
np.transpose(homogenous)*np.expand_dims(depth,0),
)
points_in_camera_frame=np.transpose(points_in_camera_axes)
return points_in_camera_frame
def np2o3d(pcd, color=None, seg=None):
# pcd: (n, 3)
# color: (n, 3)
pcd_dicts = {}
pcd_o3d = o3d.geometry.PointCloud()
pcd_o3d.points = o3d.utility.Vector3dVector(pcd)
if color is not None:
assert pcd.shape[0] == color.shape[0]
assert color.max() <= 1
assert color.min() >= 0
pcd_o3d.colors = o3d.utility.Vector3dVector(color)
for i, pos in enumerate(pcd_o3d.points):
pcd_dicts[tuple(pos)] = {
'color': pcd_o3d.colors[i],
'seg': seg[i]
}
return pcd_o3d, pcd_dicts
def depth2normal(d_im, K):
# :param d_im: (H, W) depth image in meters
# :param K: (3, 3) camera intrinsics
# :return (H, W, 3) normal image
H, W = d_im.shape
cx, cy, fx, fy = K[0, 2], K[1, 2], K[0, 0], K[1, 1]
pcd = np.zeros((H * W, 3))
xy_grid = np.mgrid[0:W, 0:H].T.reshape(-1, 2)
pcd[:, 0] = (xy_grid[:, 0] - cx) * d_im.reshape(-1) / fx
pcd[:, 1] = (xy_grid[:, 1] - cy) * d_im.reshape(-1) / fy
pcd[:, 2] = d_im.reshape(-1)
pcd = pcd.reshape(H, W, 3)
window = 10
pcd = np.pad(pcd, ((0, window), (0, window), (0, 0)), mode='edge') # shape (H+1, W+1, 3)
pcd_h_diff = pcd[window:, :W, :] - pcd[:-window, :W, :]
pcd_v_diff = pcd[:H, window:, :] - pcd[:H, :-window, :]
pcd_normals = np.cross(pcd_h_diff, pcd_v_diff) # shape (H, W, 3)
pcd_normals = pcd_normals / (np.linalg.norm(pcd_normals, axis=2, keepdims=True) + 1e-6) # shape (H, W, 3)
return pcd_normals |