如何从 npy 文件中获取 plot 3D 点云？

Question

我有一些由包含 3D 点云的 LIDAR 读数创建的 Numpy 二进制文件。 我希望能够通过从文件中读取每个点云的自上而下（正交）视图来 plot。 我查找了各种 3D 点云库，例如 Open3d、pyntcloud 等，但它们都不适用于 NPY 文件。 我怎样才能 plot 他们？

我不是在这里要求图书馆推荐。 我只是在寻找可以继续的可能方向，因为我还没有找到通过从 NPY 文件中读取 plot 点云的方法。

编辑：当我使用np.load()从其中一个文件中读取数据时，它看起来像这样：

array([[(-0.       ,  0.        ,  0.        , 0.        , 857827240, 1579782324),
    (-0.       ,  0.        ,  0.        , 0.        , 857882120, 1579782324),
    (-0.       ,  0.        ,  0.        , 0.        , 857937680, 1579782324),
    ...,
    (-0.       , -0.        ,  0.        , 0.        , 957653240, 1579782324),
    (-0.       , -0.        ,  0.        , 0.        , 957709120, 1579782324),
    (-0.       , -0.        ,  0.        , 0.        , 957764680, 1579782324)],
   [(15.622366 , -8.086195  ,  5.7023315 , 0.00392157, 857828544, 1579782324),
    (16.292194 , -8.503972  ,  5.8512874 , 0.07843138, 857883424, 1579782324),
    (15.855744 , -8.374023  ,  5.767106  , 0.02352941, 857938984, 1579782324),
    ...,
    (16.500275 , -9.402869  ,  6.0786157 , 0.01568628, 957654544, 1579782324),
    (16.197226 , -9.334285  ,  6.023082  , 0.00392157, 957710424, 1579782324),
    (16.260717 , -9.463429  ,  6.0455737 , 0.00392157, 957765984, 1579782324)],
   [(16.526688 , -8.541684  ,  4.6792016 , 0.00392157, 857829848, 1579782324),
    (15.844723 , -8.292216  ,  4.5818253 , 0.        , 857884728, 1579782324),
    (15.915991 , -8.414634  ,  4.5984206 , 0.00392157, 857940288, 1579782324),
    ...,
    (15.649654 , -8.954793  ,  4.6751213 , 0.01176471, 957655848, 1579782324),
    (17.318968 , -9.951033  ,  4.9357953 , 0.01176471, 957711728, 1579782324),
    (16.125185 , -9.398413  ,  4.7603803 , 0.00392157, 957767288, 1579782324)],
   ...,
   [( 2.5268526, -1.6420269 , -0.24141277, 0.02745098, 857780808, 1579782324),
    ( 2.529189 , -1.6714373 , -0.24518971, 0.03137255, 857836368, 1579782324),
    ( 2.5140662, -1.6922294 , -0.24403782, 0.03137255, 857891248, 1579782324),
    ...,
    ( 1.7650445, -1.4837685 , -0.2509078 , 0.02745098, 957606808, 1579782324),
    ( 1.742465 , -1.5004072 , -0.24779865, 0.02352941, 957662368, 1579782324),
    ( 1.7232444, -1.5187881 , -0.245681  , 0.02745098, 957718248, 1579782324)],
   [(-2.7442074,  0.9481321 ,  1.1273874 , 0.        , 857786024, 1579782324),
    (-2.7466307,  0.94417626,  1.1274364 , 0.        , 857841584, 1579782324),
    (-2.749064 ,  0.94022495,  1.1274853 , 0.        , 857896464, 1579782324),
    ...,
    (-3.4345033,  1.3002251 ,  1.1344001 , 0.        , 957612024, 1579782324),
    (-3.4270716,  1.2909878 ,  1.1304668 , 0.        , 957667584, 1579782324),
    (-3.4362614,  1.2907308 ,  1.1331499 , 0.        , 957723464, 1579782324)],
   [(-3.1056237,  1.1257029 ,  1.1556424 , 0.        , 857782112, 1579782324),
    (-3.1041813,  1.1214051 ,  1.1539782 , 0.        , 857837672, 1579782324),
    (-3.102756 ,  1.1170869 ,  1.1523142 , 0.        , 857892552, 1579782324),
    ...,
    (-3.779868 ,  1.4852207 ,  1.1581781 , 0.        , 957608112, 1579782324),
    (-3.8071766,  1.4963622 ,  1.1718962 , 0.        , 957663672, 1579782324),
    (-3.7931492,  1.4851598 ,  1.163371  , 0.        , 957719552, 1579782324)]],
  dtype=[('x', '<f4'), ('y', '<f4'), ('z', '<f4'), ('intensity', '<f4'), ('t_low', '<u4'), ('t_high', '<u4')])

当我尝试 plot 时，正如@Dorian 建议的那样：

    x = data[:, 0]
    y = data[:, 1]
    z = data[:, 2]
    fig = plt.figure(figsize=(8, 8))
    ax = fig.add_subplot(111, projection='3d')
    ax.scatter(x, y, z)
    plt.show()

我收到以下错误：

TypeError                                 Traceback (most recent call last)
<ipython-input-20-d6d9ea7be681> in <module>
      1 fig = plt.figure(figsize=(8, 8))
      2 ax = fig.add_subplot(111, projection='3d')
----> 3 ax.scatter(x, y, z)
      4 plt.show()

~/anaconda3/envs/pointclouds/lib/python3.8/site-packages/mpl_toolkits/mplot3d/axes3d.py in scatter(self, xs, ys, zs, zdir, s, c, depthshade, *args, **kwargs)
   2325         xs, ys, zs, s, c = cbook.delete_masked_points(xs, ys, zs, s, c)
   2326 
-> 2327         patches = super().scatter(xs, ys, s=s, c=c, *args, **kwargs)
   2328         art3d.patch_collection_2d_to_3d(patches, zs=zs, zdir=zdir,
   2329                                         depthshade=depthshade)

~/anaconda3/envs/pointclouds/lib/python3.8/site-packages/matplotlib/__init__.py in inner(ax, data, *args, **kwargs)
   1597     def inner(ax, *args, data=None, **kwargs):
   1598         if data is None:
-> 1599             return func(ax, *map(sanitize_sequence, args), **kwargs)
   1600 
   1601         bound = new_sig.bind(ax, *args, **kwargs)

~/anaconda3/envs/pointclouds/lib/python3.8/site-packages/matplotlib/axes/_axes.py in scatter(self, x, y, s, c, marker, cmap, norm, vmin, vmax, alpha, linewidths, verts, edgecolors, plotnonfinite, **kwargs)
   4459         else:
   4460             x, y, s, c, colors, edgecolors, linewidths = \
-> 4461                 cbook._combine_masks(
   4462                     x, y, s, c, colors, edgecolors, linewidths)
   4463 

~/anaconda3/envs/pointclouds/lib/python3.8/site-packages/matplotlib/cbook/__init__.py in _combine_masks(*args)
   1122                 x = safe_masked_invalid(x)
   1123                 seqlist[i] = True
-> 1124                 if np.ma.is_masked(x):
   1125                     masks.append(np.ma.getmaskarray(x))
   1126             margs.append(x)  # Possibly modified.

~/anaconda3/envs/pointclouds/lib/python3.8/site-packages/numpy/ma/core.py in is_masked(x)
   6520     if m is nomask:
   6521         return False
-> 6522     elif m.any():
   6523         return True
   6524     return False

~/anaconda3/envs/pointclouds/lib/python3.8/site-packages/numpy/core/_methods.py in _any(a, axis, dtype, out, keepdims)
     43 
     44 def _any(a, axis=None, dtype=None, out=None, keepdims=False):
---> 45     return umr_any(a, axis, dtype, out, keepdims)
     46 
     47 def _all(a, axis=None, dtype=None, out=None, keepdims=False):

TypeError: cannot perform reduce with flexible type

这里有一小部分数据。

Answer 1

matplotlib.pyplot将是我个人的 go 选项。

您没有提供任何数据或数据的保存方式，因此我假设点云的点保存在Nx3维numpy数组中：

data = np.load('file.npy')
x = data[:, 0]
y = data[:, 1]
z = data[:, 2]

import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import proj3d

fig = plt.figure(figsize=(8, 8))
ax = fig.add_subplot(111, projection='3d')

ax.scatter(x, y, z)
plt.show()

如果您只想拥有 2D（自上而下的视图），请不要使用 3D 投影并忽略您的 z 值：

fig = plt.figure(figsize=(8, 8))
ax = fig.add_subplot(111)

ax.scatter(x, y)
plt.show()

Answer 2

您的数据包含结构化 arrays ，可以按如下方式访问：

import numpy as np

data = np.load("LIDAR_0.npy")
# data.dtype is [('x', '<f4'), ('y', '<f4'), ('z', '<f4'), ('intensity', '<f4'), ('t_low', '<u4'), ('t_high', '<u4')]

# coordinates
xs = data["x"]
ys = data["y"]
zs = data["z"]
# attribute
t_low = data["t_low"]

之后，您可以创建一个 3D plot 类似于这个问题：

import matplotlib.pyplot as plt

fig = plt.figure(figsize=(12,7))
ax = fig.add_subplot(projection='3d')
img = ax.scatter(xs, ys, zs, c=t_low, cmap=plt.hot())
fig.colorbar(img)

ax.set_xlabel('X')
ax.set_ylabel('Y')
ax.set_zlabel('Z')

plt.show()

结果：

或者您可以创建一个 2D plot（例如沿 z 轴查看）：

fig = plt.figure(figsize=(12,7))
ax = fig.add_subplot()
img = ax.scatter(xs, ys, c=t_low, cmap=plt.hot())
fig.colorbar(img)

ax.set_xlabel('X')
ax.set_ylabel('Y')

plt.show()

结果：

注意：主要将 go 归功于已经指出正确方向的 Dorian。