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import numpy as np
import plotly.express as px
import plotly.graph_objects as go
def vis_camera(RT_list, rescale_T=1):
fig = go.Figure()
showticklabels = True
visible = True
scene_bounds = 2
base_radius = 2.5
zoom_scale = 1.5
fov_deg = 50.0
edges = [(0, 1), (0, 2), (0, 3), (1, 2), (2, 3), (3, 1), (3, 4)]
colors = px.colors.qualitative.Plotly
cone_list = []
n = len(RT_list)
for i, RT in enumerate(RT_list):
R = RT[:,:3]
T = RT[:,-1]/rescale_T
cone = calc_cam_cone_pts_3d(R, T, fov_deg)
cone_list.append((cone, (i*1/n, "green"), f"view_{i}"))
for (cone, clr, legend) in cone_list:
for (i, edge) in enumerate(edges):
(x1, x2) = (cone[edge[0], 0], cone[edge[1], 0])
(y1, y2) = (cone[edge[0], 1], cone[edge[1], 1])
(z1, z2) = (cone[edge[0], 2], cone[edge[1], 2])
fig.add_trace(go.Scatter3d(
x=[x1, x2], y=[y1, y2], z=[z1, z2], mode='lines',
line=dict(color=clr, width=3),
name=legend, showlegend=(i == 0)))
fig.update_layout(
height=500,
autosize=True,
# hovermode=False,
margin=go.layout.Margin(l=0, r=0, b=0, t=0),
showlegend=True,
legend=dict(
yanchor='bottom',
y=0.01,
xanchor='right',
x=0.99,
),
scene=dict(
aspectmode='manual',
aspectratio=dict(x=1, y=1, z=1.0),
camera=dict(
center=dict(x=0.0, y=0.0, z=0.0),
up=dict(x=0.0, y=-1.0, z=0.0),
eye=dict(x=scene_bounds/2, y=-scene_bounds/2, z=-scene_bounds/2),
),
xaxis=dict(
range=[-scene_bounds, scene_bounds],
showticklabels=showticklabels,
visible=visible,
),
yaxis=dict(
range=[-scene_bounds, scene_bounds],
showticklabels=showticklabels,
visible=visible,
),
zaxis=dict(
range=[-scene_bounds, scene_bounds],
showticklabels=showticklabels,
visible=visible,
)
))
return fig
def calc_cam_cone_pts_3d(R_W2C, T_W2C, fov_deg, scale=0.1, set_canonical=False, first_frame_RT=None):
fov_rad = np.deg2rad(fov_deg)
R_W2C_inv = np.linalg.inv(R_W2C)
# Camera pose center:
T = np.zeros_like(T_W2C) - T_W2C
T = np.dot(R_W2C_inv, T)
cam_x = T[0]
cam_y = T[1]
cam_z = T[2]
if set_canonical:
T = np.zeros_like(T_W2C)
T = np.dot(first_frame_RT[:,:3], T) + first_frame_RT[:,-1]
T = T - T_W2C
T = np.dot(R_W2C_inv, T)
cam_x = T[0]
cam_y = T[1]
cam_z = T[2]
# vertex
corn1 = np.array([np.tan(fov_rad / 2.0), 0.5*np.tan(fov_rad / 2.0), 1.0]) *scale
corn2 = np.array([-np.tan(fov_rad / 2.0), 0.5*np.tan(fov_rad / 2.0), 1.0]) *scale
corn3 = np.array([0, -0.25*np.tan(fov_rad / 2.0), 1.0]) *scale
corn4 = np.array([0, -0.5*np.tan(fov_rad / 2.0), 1.0]) *scale
corn1 = corn1 - T_W2C
corn2 = corn2 - T_W2C
corn3 = corn3 - T_W2C
corn4 = corn4 - T_W2C
corn1 = np.dot(R_W2C_inv, corn1)
corn2 = np.dot(R_W2C_inv, corn2)
corn3 = np.dot(R_W2C_inv, corn3)
corn4 = np.dot(R_W2C_inv, corn4)
# Now attach as offset to actual 3D camera position:
corn_x1 = corn1[0]
corn_y1 = corn1[1]
corn_z1 = corn1[2]
corn_x2 = corn2[0]
corn_y2 = corn2[1]
corn_z2 = corn2[2]
corn_x3 = corn3[0]
corn_y3 = corn3[1]
corn_z3 = corn3[2]
corn_x4 = corn4[0]
corn_y4 = corn4[1]
corn_z4 = corn4[2]
xs = [cam_x, corn_x1, corn_x2, corn_x3, corn_x4, ]
ys = [cam_y, corn_y1, corn_y2, corn_y3, corn_y4, ]
zs = [cam_z, corn_z1, corn_z2, corn_z3, corn_z4, ]
return np.array([xs, ys, zs]).T
# T_base = [
# [1.,0.,0.], ## W2C x 的正方向： 相机朝左 left
# [-1.,0.,0.], ## W2C x 的负方向： 相机朝右 right
# [0., 1., 0.], ## W2C y 的正方向： 相机朝上 up
# [0.,-1.,0.], ## W2C y 的负方向： 相机朝下 down
# [0.,0.,1.], ## W2C z 的正方向： 相机往前 zoom out
# [0.,0.,-1.], ## W2C z 的负方向： 相机往前 zoom in
# ]
# radius = 1
# n = 16
# # step =
# look_at = np.array([0, 0, 0.8]).reshape(3,1)
# # look_at = np.array([0, 0, 0.2]).reshape(3,1)
# T_list = []
# base_R = np.array([[1., 0., 0.],
# [0., 1., 0.],
# [0., 0., 1.]])
# res = []
# res_forsave = []
# T_range = 1.8
# for i in range(0, 16):
# # theta = (1)*np.pi*i/n
# R = base_R[:,:3]
# T = np.array([0.,0.,1.]).reshape(3,1) * (i/n)*2
# RT = np.concatenate([R,T], axis=1)
# res.append(RT)
# fig = vis_camera(res)
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