File size: 9,263 Bytes
19b8852 |
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 |
"""
Copyright [2022] [Paul-Edouard Sarlin and Philipp Lindenberger]
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
3D visualization based on plotly.
Works for a small number of points and cameras, might be slow otherwise.
1) Initialize a figure with `init_figure`
2) Add 3D points, camera frustums, or both as a pycolmap.Reconstruction
Written by Paul-Edouard Sarlin and Philipp Lindenberger.
"""
# Slightly modified by Dmytro Mishkin
from typing import Optional
import numpy as np
import pycolmap
import plotly.graph_objects as go
### Some helper functions for geometry
def qvec2rotmat(qvec):
return np.array([
[1 - 2 * qvec[2]**2 - 2 * qvec[3]**2,
2 * qvec[1] * qvec[2] - 2 * qvec[0] * qvec[3],
2 * qvec[3] * qvec[1] + 2 * qvec[0] * qvec[2]],
[2 * qvec[1] * qvec[2] + 2 * qvec[0] * qvec[3],
1 - 2 * qvec[1]**2 - 2 * qvec[3]**2,
2 * qvec[2] * qvec[3] - 2 * qvec[0] * qvec[1]],
[2 * qvec[3] * qvec[1] - 2 * qvec[0] * qvec[2],
2 * qvec[2] * qvec[3] + 2 * qvec[0] * qvec[1],
1 - 2 * qvec[1]**2 - 2 * qvec[2]**2]])
def to_homogeneous(points):
pad = np.ones((points.shape[:-1]+(1,)), dtype=points.dtype)
return np.concatenate([points, pad], axis=-1)
def t_to_proj_center(qvec, tvec):
Rr = qvec2rotmat(qvec)
tt = (-Rr.T) @ tvec
return tt
def calib(params):
out = np.eye(3)
if len(params) == 3:
out[0,0] = params[0]
out[1,1] = params[0]
out[0,2] = params[1]
out[1,2] = params[2]
else:
out[0,0] = params[0]
out[1,1] = params[1]
out[0,2] = params[2]
out[1,2] = params[3]
return out
### Plotting functions
def init_figure(height: int = 800) -> go.Figure:
"""Initialize a 3D figure."""
fig = go.Figure()
axes = dict(
visible=False,
showbackground=False,
showgrid=False,
showline=False,
showticklabels=True,
autorange=True,
)
fig.update_layout(
template="plotly_dark",
height=height,
scene_camera=dict(
eye=dict(x=0., y=-.1, z=-2),
up=dict(x=0, y=-1., z=0),
projection=dict(type="orthographic")),
scene=dict(
xaxis=axes,
yaxis=axes,
zaxis=axes,
aspectmode='data',
dragmode='orbit',
),
margin=dict(l=0, r=0, b=0, t=0, pad=0),
legend=dict(
orientation="h",
yanchor="top",
y=0.99,
xanchor="left",
x=0.1
),
)
return fig
def plot_lines_3d(
fig: go.Figure,
pts: np.ndarray,
color: str = 'rgba(255, 255, 255, 1)',
ps: int = 2,
colorscale: Optional[str] = None,
name: Optional[str] = None):
"""Plot a set of 3D points."""
x = pts[..., 0]
y = pts[..., 1]
z = pts[..., 2]
traces = [go.Scatter3d(x=x1, y=y1, z=z1,
mode='lines',
line=dict(color=color, width=2)) for x1, y1, z1 in zip(x,y,z)]
for t in traces:
fig.add_trace(t)
fig.update_traces(showlegend=False)
def plot_points(
fig: go.Figure,
pts: np.ndarray,
color: str = 'rgba(255, 0, 0, 1)',
ps: int = 2,
colorscale: Optional[str] = None,
name: Optional[str] = None):
"""Plot a set of 3D points."""
x, y, z = pts.T
tr = go.Scatter3d(
x=x, y=y, z=z, mode='markers', name=name, legendgroup=name,
marker=dict(
size=ps, color=color, line_width=0.0, colorscale=colorscale))
fig.add_trace(tr)
def plot_camera(
fig: go.Figure,
R: np.ndarray,
t: np.ndarray,
K: np.ndarray,
color: str = 'rgb(0, 0, 255)',
name: Optional[str] = None,
legendgroup: Optional[str] = None,
size: float = 1.0):
"""Plot a camera frustum from pose and intrinsic matrix."""
W, H = K[0, 2]*2, K[1, 2]*2
corners = np.array([[0, 0], [W, 0], [W, H], [0, H], [0, 0]])
if size is not None:
image_extent = max(size * W / 1024.0, size * H / 1024.0)
world_extent = max(W, H) / (K[0, 0] + K[1, 1]) / 0.5
scale = 0.5 * image_extent / world_extent
else:
scale = 1.0
corners = to_homogeneous(corners) @ np.linalg.inv(K).T
corners = (corners / 2 * scale) @ R.T + t
x, y, z = corners.T
rect = go.Scatter3d(
x=x, y=y, z=z, line=dict(color=color), legendgroup=legendgroup,
name=name, marker=dict(size=0.0001), showlegend=False)
fig.add_trace(rect)
x, y, z = np.concatenate(([t], corners)).T
i = [0, 0, 0, 0]
j = [1, 2, 3, 4]
k = [2, 3, 4, 1]
pyramid = go.Mesh3d(
x=x, y=y, z=z, color=color, i=i, j=j, k=k,
legendgroup=legendgroup, name=name, showlegend=False)
fig.add_trace(pyramid)
triangles = np.vstack((i, j, k)).T
vertices = np.concatenate(([t], corners))
tri_points = np.array([
vertices[i] for i in triangles.reshape(-1)
])
x, y, z = tri_points.T
pyramid = go.Scatter3d(
x=x, y=y, z=z, mode='lines', legendgroup=legendgroup,
name=name, line=dict(color=color, width=1), showlegend=False)
fig.add_trace(pyramid)
def plot_camera_colmap(
fig: go.Figure,
image: pycolmap.Image,
camera: pycolmap.Camera,
name: Optional[str] = None,
**kwargs):
"""Plot a camera frustum from PyCOLMAP objects"""
intr = calib(camera.params)
if intr[0][0] > 10000:
print("Bad camera")
return
plot_camera(
fig,
qvec2rotmat(image.qvec).T,
t_to_proj_center(image.qvec, image.tvec),
intr,#calibration_matrix(),
name=name or str(image.id),
**kwargs)
def plot_cameras(
fig: go.Figure,
reconstruction,#: pycolmap.Reconstruction,
**kwargs):
"""Plot a camera as a cone with camera frustum."""
for image_id, image in reconstruction["images"].items():
plot_camera_colmap(
fig, image, reconstruction["cameras"][image.camera_id], **kwargs)
def plot_reconstruction(
fig: go.Figure,
rec,
color: str = 'rgb(0, 0, 255)',
name: Optional[str] = None,
points: bool = True,
cameras: bool = True,
cs: float = 1.0,
single_color_points=False,
camera_color='rgba(0, 255, 0, 0.5)'):
# rec is result of loading reconstruction from "read_write_colmap.py"
# Filter outliers
xyzs = []
rgbs = []
for k, p3D in rec['points'].items():
xyzs.append(p3D.xyz)
rgbs.append(p3D.rgb)
if points:
plot_points(fig, np.array(xyzs), color=color if single_color_points else np.array(rgbs), ps=1, name=name)
if cameras:
plot_cameras(fig, rec, color=camera_color, legendgroup=name, size=cs)
def plot_pointcloud(
fig: go.Figure,
pts: np.ndarray,
colors: np.ndarray,
ps: int = 2,
name: Optional[str] = None):
"""Plot a set of 3D points."""
plot_points(fig, np.array(pts), color=colors, ps=ps, name=name)
def plot_triangle_mesh(
fig: go.Figure,
vert: np.ndarray,
colors: np.ndarray,
triangles: np.ndarray,
name: Optional[str] = None):
"""Plot a triangle mesh."""
tr = go.Mesh3d(
x=vert[:,0],
y=vert[:,1],
z=vert[:,2],
vertexcolor = np.clip(255*colors, 0, 255),
# i, j and k give the vertices of triangles
# here we represent the 4 triangles of the tetrahedron surface
i=triangles[:,0],
j=triangles[:,1],
k=triangles[:,2],
name=name,
showscale=False
)
fig.add_trace(tr)
def plot_estimate_and_gt(pred_vertices, pred_connections, gt_vertices=None, gt_connections=None):
fig3d = init_figure()
c1 = (30, 20, 255)
img_color = [c1 for _ in range(len(pred_vertices))]
plot_points(fig3d, pred_vertices, color = img_color, ps = 10)
lines = []
for c in pred_connections:
v1 = pred_vertices[c[0]]
v2 = pred_vertices[c[1]]
lines.append(np.stack([v1, v2], axis=0))
plot_lines_3d(fig3d, np.array(lines), img_color, ps=4)
if gt_vertices is not None:
c2 = (30, 255, 20)
img_color2 = [c2 for _ in range(len(gt_vertices))]
plot_points(fig3d, gt_vertices, color = img_color2, ps = 10)
if gt_connections is not None:
gt_lines = []
for c in gt_connections:
v1 = gt_vertices[c[0]]
v2 = gt_vertices[c[1]]
gt_lines.append(np.stack([v1, v2], axis=0))
plot_lines_3d(fig3d, np.array(gt_lines), img_color2, ps=4)
fig3d.show()
return fig3d
|