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viz_util.py
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viz_util.py
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''' Visualization code for point clouds and 3D bounding boxes with mayavi.
Modified by Charles R. Qi
Date: September 2017
Ref: https://github.com/hengck23/didi-udacity-2017/blob/master/baseline-04/kitti_data/draw.py
'''
import numpy as np
import mayavi.mlab as mlab
try:
raw_input # Python 2
except NameError:
raw_input = input # Python 3
def normalize(vec):
"""normalizes an Nd list of vectors or a single vector
to unit length.
The vector is **not** changed in place.
For zero-length vectors, the result will be np.nan.
:param numpy.array vec: an Nd array with the final dimension
being vectors
::
numpy.array([ x, y, z ])
Or an NxM array::
numpy.array([
[x1, y1, z1],
[x2, y2, z2]
]).
:rtype: A numpy.array the normalized value
"""
# calculate the length
# this is a duplicate of length(vec) because we
# always want an array, even a 0-d array.
return (vec.T / np.sqrt(np.sum(vec**2,axis=-1))).T
def rotation_matrix_numpy0(axis, theta, dtype=None):
#dtype = dtype or axis.dtype
# make sure the vector is normalized
if not np.isclose(np.linalg.norm(axis), 1.):
axis = normalize(axis)
thetaOver2 = theta * 0.5
sinThetaOver2 = np.sin(thetaOver2)
return np.array(
[
sinThetaOver2 * axis[0],
sinThetaOver2 * axis[1],
sinThetaOver2 * axis[2],
np.cos(thetaOver2)
]
)
def rotation_matrix_numpy(axis, theta):
mat = np.eye(3,3)
axis = axis/np.sqrt(np.dot(axis, axis))
a = np.cos(theta/2.)
b, c, d = -axis*np.sin(theta/2.)
return np.array([[a*a+b*b-c*c-d*d, 2*(b*c-a*d), 2*(b*d+a*c)],
[2*(b*c+a*d), a*a+c*c-b*b-d*d, 2*(c*d-a*b)],
[2*(b*d-a*c), 2*(c*d+a*b), a*a+d*d-b*b-c*c]])
def rotx(t):
''' 3D Rotation about the x-axis. '''
c = np.cos(t)
s = np.sin(t)
return np.array([[1, 0, 0],
[0, c, -s],
[0, s, c]])
def roty(t):
''' Rotation about the y-axis. '''
c = np.cos(t)
s = np.sin(t)
return np.array([[c, 0, s],
[0, 1, 0],
[-s, 0, c]])
def rotz(t):
''' Rotation about the z-axis. '''
c = np.cos(t)
s = np.sin(t)
return np.array([[c, -s, 0],
[s, c, 0],
[0, 0, 1]])
def draw_lidar_simple(pc, color=None):
''' Draw lidar points. simplest set up. '''
fig = mlab.figure(figure=None, bgcolor=(0,0,0), fgcolor=None, engine=None, size=(1600, 1000))
if color is None: color = pc[:,2]
#draw points
mlab.points3d(pc[:,0], pc[:,1], pc[:,2], color, color=None, mode='point', colormap = 'gnuplot', scale_factor=1, figure=fig)
#draw origin
mlab.points3d(0, 0, 0, color=(1,1,1), mode='sphere', scale_factor=0.2)
#draw axis
axes=np.array([
[2.,0.,0.,0.],
[0.,2.,0.,0.],
[0.,0.,2.,0.],
],dtype=np.float64)
mlab.plot3d([0, axes[0,0]], [0, axes[0,1]], [0, axes[0,2]], color=(1,0,0), tube_radius=None, figure=fig)
mlab.plot3d([0, axes[1,0]], [0, axes[1,1]], [0, axes[1,2]], color=(0,1,0), tube_radius=None, figure=fig)
mlab.plot3d([0, axes[2,0]], [0, axes[2,1]], [0, axes[2,2]], color=(0,0,1), tube_radius=None, figure=fig)
mlab.view(azimuth=180, elevation=70, focalpoint=[ 12.0909996 , -1.04700089, -2.03249991], distance=62.0, figure=fig)
return fig
#pts_mode='sphere'
def draw_lidar(pc,
color=None,
fig=None,
bgcolor=(0,0,0),
pts_scale=0.3,
pts_mode='sphere',
pts_color=None,
color_by_intensity=False,
pc_label=False):
''' Draw lidar points
Args:
pc: numpy array (n,3) of XYZ
color: numpy array (n) of intensity or whatever
fig: mayavi figure handler, if None create new one otherwise will use it
Returns:
fig: created or used fig
'''
#ind = (pc[:,2]< -1.65)
#pc = pc[ind]
pts_mode='point'
print("====================",pc.shape)
if fig is None: fig = mlab.figure(figure=None, bgcolor=bgcolor, fgcolor=None, engine=None, size=(1600, 1000))
if color is None: color = pc[:,2]
if pc_label: color = pc[:,4]
if color_by_intensity: color = pc[:,2]
mlab.points3d(pc[:,0], pc[:,1], pc[:,2], color, color=pts_color, mode=pts_mode, colormap = 'gnuplot', scale_factor=pts_scale, figure=fig)
#draw origin
mlab.points3d(0, 0, 0, color=(1,1,1), mode='sphere', scale_factor=0.2)
#draw axis
axes=np.array([
[2.,0.,0.,0.],
[0.,2.,0.,0.],
[0.,0.,2.,0.],
],dtype=np.float64)
mlab.plot3d([0, axes[0,0]], [0, axes[0,1]], [0, axes[0,2]], color=(1,0,0), tube_radius=None, figure=fig)
mlab.plot3d([0, axes[1,0]], [0, axes[1,1]], [0, axes[1,2]], color=(0,1,0), tube_radius=None, figure=fig)
mlab.plot3d([0, axes[2,0]], [0, axes[2,1]], [0, axes[2,2]], color=(0,0,1), tube_radius=None, figure=fig)
# draw fov (todo: update to real sensor spec.)
fov=np.array([ # 45 degree
[20., 20., 0.,0.],
[20.,-20., 0.,0.],
],dtype=np.float64)
mlab.plot3d([0, fov[0,0]], [0, fov[0,1]], [0, fov[0,2]], color=(1,1,1), tube_radius=None, line_width=1, figure=fig)
mlab.plot3d([0, fov[1,0]], [0, fov[1,1]], [0, fov[1,2]], color=(1,1,1), tube_radius=None, line_width=1, figure=fig)
# draw square region
TOP_Y_MIN=-20
TOP_Y_MAX=20
TOP_X_MIN=0
TOP_X_MAX=40
TOP_Z_MIN=-2.0
TOP_Z_MAX=0.4
x1 = TOP_X_MIN
x2 = TOP_X_MAX
y1 = TOP_Y_MIN
y2 = TOP_Y_MAX
mlab.plot3d([x1, x1], [y1, y2], [0,0], color=(0.5,0.5,0.5), tube_radius=0.1, line_width=1, figure=fig)
mlab.plot3d([x2, x2], [y1, y2], [0,0], color=(0.5,0.5,0.5), tube_radius=0.1, line_width=1, figure=fig)
mlab.plot3d([x1, x2], [y1, y1], [0,0], color=(0.5,0.5,0.5), tube_radius=0.1, line_width=1, figure=fig)
mlab.plot3d([x1, x2], [y2, y2], [0,0], color=(0.5,0.5,0.5), tube_radius=0.1, line_width=1, figure=fig)
#mlab.orientation_axes()
mlab.view(azimuth=180, elevation=70, focalpoint=[ 12.0909996 , -1.04700089, -2.03249991], distance=62.0, figure=fig)
return fig
def draw_gt_boxes3d(gt_boxes3d, fig, color=(1,1,1), line_width=1, draw_text=True, text_scale=(1,1,1), color_list=None):
''' Draw 3D bounding boxes
Args:
gt_boxes3d: numpy array (n,8,3) for XYZs of the box corners
fig: mayavi figure handler
color: RGB value tuple in range (0,1), box line color
line_width: box line width
draw_text: boolean, if true, write box indices beside boxes
text_scale: three number tuple
color_list: a list of RGB tuple, if not None, overwrite color.
Returns:
fig: updated fig
'''
num = len(gt_boxes3d)
for n in range(num):
b = gt_boxes3d[n]
if color_list is not None:
color = color_list[n]
if draw_text: mlab.text3d(b[4,0], b[4,1], b[4,2], '%d'%n, scale=text_scale, color=color, figure=fig)
for k in range(0,4):
#http://docs.enthought.com/mayavi/mayavi/auto/mlab_helper_functions.html
i,j=k,(k+1)%4
mlab.plot3d([b[i,0], b[j,0]], [b[i,1], b[j,1]], [b[i,2], b[j,2]], color=color, tube_radius=None, line_width=line_width, figure=fig)
i,j=k+4,(k+1)%4 + 4
mlab.plot3d([b[i,0], b[j,0]], [b[i,1], b[j,1]], [b[i,2], b[j,2]], color=color, tube_radius=None, line_width=line_width, figure=fig)
i,j=k,k+4
mlab.plot3d([b[i,0], b[j,0]], [b[i,1], b[j,1]], [b[i,2], b[j,2]], color=color, tube_radius=None, line_width=line_width, figure=fig)
#mlab.show(1)
#mlab.view(azimuth=180, elevation=70, focalpoint=[ 12.0909996 , -1.04700089, -2.03249991], distance=62.0, figure=fig)
return fig
def xyzwhl2eight(xyzwhl):
''' Draw 3d bounding box in image
qs: (8,3) array of vertices for the 3d box in following order:
7 -------- 6
/| /|
4 -------- 5 .
| | | |
. 3 -------- 2
|/ |/
0 -------- 1
'''
x,y,z,w,h,l = xyzwhl[:6]
box8 = np.array([
[x+w/2, x+w/2, x-w/2, x-w/2, x+w/2, x+w/2, x-w/2, x-w/2 ],
[y-h/2, y+h/2, y+h/2, y-h/2, y-h/2, y+h/2, y+h/2, y-h/2 ],
[z-l/2, z-l/2, z-l/2, z-l/2, z+l/2, z+l/2, z+l/2, z+l/2 ]
])
return box8.T
def draw_xyzwhl(gt_boxes3d, fig, color=(1,1,1), line_width=1, draw_text=True, text_scale=(1,1,1), color_list=None, rot=False):
''' Draw 3D bounding boxes
Args:
gt_boxes3d: numpy array (n,8,3) for XYZs of the box corners
fig: mayavi figure handler
color: RGB value tuple in range (0,1), box line color
line_width: box line width
draw_text: boolean, if true, write box indices beside boxes
text_scale: three number tuple
color_list: a list of RGB tuple, if not None, overwrite color.
Returns:
fig: updated fig
'''
num = len(gt_boxes3d)
for n in range(num):
print(gt_boxes3d[n])
box6 = gt_boxes3d[n]
b = xyzwhl2eight(box6)
if rot:
b = b.dot(rotz(box6[7]))
#b = b.dot(rotx(box6[6]))
#print(rotz(box6[6]))
#b = b.dot( rotz(box6[6]).dot(rotz(box6[7])) )
vec = np.array([-1,1,0])
b = b.dot(rotation_matrix_numpy(vec, box6[6]))
#b = b.dot(roty(box6[7]))
print(b.shape,b)
if color_list is not None:
color = color_list[n]
#if draw_text: mlab.text3d(b[4,0], b[4,1], b[4,2], '%d'%n, scale=text_scale, color=color, figure=fig)
for k in range(0,4):
#http://docs.enthought.com/mayavi/mayavi/auto/mlab_helper_functions.html
i,j=k,(k+1)%4
mlab.plot3d([b[i,0], b[j,0]], [b[i,1], b[j,1]], [b[i,2], b[j,2]], color=color, tube_radius=None, line_width=line_width, figure=fig)
i,j=k+4,(k+1)%4 + 4
mlab.plot3d([b[i,0], b[j,0]], [b[i,1], b[j,1]], [b[i,2], b[j,2]], color=color, tube_radius=None, line_width=line_width, figure=fig)
i,j=k,k+4
mlab.plot3d([b[i,0], b[j,0]], [b[i,1], b[j,1]], [b[i,2], b[j,2]], color=color, tube_radius=None, line_width=line_width, figure=fig)
#mlab.show(1)
#mlab.view(azimuth=180, elevation=70, focalpoint=[ 12.0909996 , -1.04700089, -2.03249991], distance=62.0, figure=fig)
return fig
if __name__=='__main__':
pc = np.loadtxt('mayavi/kitti_sample_scan.txt')
fig = draw_lidar(pc)
mlab.savefig('pc_view.jpg', figure=fig)
raw_input()