Python利用PyVista进行mesh的色彩映射的实现

最近项目中需要对mesh做一个色彩映射，无意间发现vtk的封装库pyvista相当好用，就试了试，在此做一个总结。

PyVista简介

PyVista是什么

PyVista 是一个：

VTK for humans”，可视化工具包(VTK)的高级API
空间数据的网格数据结构与滤波方法
使3D绘图更加简单，可用于大型/复杂数据的图像化

PyVista(以前的vtki)是可视化工具包(VTK)的一个助手模块，它采用了一种不同的方法，通过NumPy和直接数组访问与VTK进行接口。这个包提供了一个python化的、文档化良好的接口，展示了VTK强大的可视化后端，以方便对空间引用的数据集进行快速原型化、分析和可视化集成。

该模块可用于演示文稿和研究论文的科学绘图，以及其他依赖网格的Python模块的支持模块。

参考：https://docs.pyvista.org/index.html

github

官方教程

pyvista和其他3D可视化工具比较

参考：https://github.com/pyvista/pyvista/issues/146

pyvista使用

安装

pip install pyvista -i https://pypi.tuna.tsinghua.edu.cn/simple

I/O读取及可视化

mesh类型

pyvista支持读取大多数常见的mesh文件类型，比如PLY，VTK，STL ,OBJ ,BYU 等，一些不常见的mesh文件类型，比如FEniCS/Dolfin_ XML format

（很遗憾，pyvista不支持点云PCD格式，不过可以通过pcdpy、pclpy、python-pcl等库来读取pcd文件）

import pyvista as pv
# 读取
mesh = pv.read(\'pointCloudData/data.vtk\')
# 显示
mesh.plot()
# 其他类似
mesh = pv.read(\'pointCloudData/data.ply\')
……

图片类型

支持读取图片类型数据JPEG, TIFF, PNG等

# 读取
image = pv.read(\'my_image.jpg\')
# 显示
image.plot(rgb=True, cpos=\"xy\")

# 其余图片类型类似
……

mesh彩色映射

项目中需要用到根据高度来对mesh进行彩色映射，在pyvista中大概有四种方法

自定义

代码

import pyvista as pv
import matplotlib.pyplot as plt
from matplotlib.colors import ListedColormap
import numpy as np

def mesh_cmp_custom(mesh, name):
 \"\"\"
 自定义色彩映射
 :param mesh: 输入mesh
 :param name: 比较数据的名字
 :return:
 \"\"\"
 pts = mesh.points
 mesh[name] = pts[:, 1]
 # Define the colors we want to use
 blue = np.array([12 / 256, 238 / 256, 246 / 256, 1])
 black = np.array([11 / 256, 11 / 256, 11 / 256, 1])
 grey = np.array([189 / 256, 189 / 256, 189 / 256, 1])
 yellow = np.array([255 / 256, 247 / 256, 0 / 256, 1])
 red = np.array([1, 0, 0, 1])

 c_min = mesh[name].min()
 c_max = mesh[name].max()
 c_scale = c_max - c_min

 mapping = np.linspace(c_min, c_max, 256)
 newcolors = np.empty((256, 4))
 newcolors[mapping >= (c_scale * 0.8 + c_min)] = red
 newcolors[mapping < (c_scale * 0.8 + c_min)] = grey
 newcolors[mapping < (c_scale * 0.55 + c_min)] = yellow
 newcolors[mapping < (c_scale * 0.3 + c_min)] = blue
 newcolors[mapping < (c_scale * 0.1 + c_min)] = black

 # Make the colormap from the listed colors
 my_colormap = ListedColormap(newcolors)
 mesh.plot(scalars=name, cmap=my_colormap)

if __name__ == \'__main__\':
 mesh = pv.read(\'pointCloudData/1.ply\')
 mesh_cmp_custom(mesh, \'y_height\')

效果：

使用pyvista自带的cmp

函数mesh.plot(scalars=name, cmap=\'viridis_r\')

其中cmap支持的样式：

‘Accent\’, ‘Accent_r\’, ‘Blues\’, ‘Blues_r\’, ‘BrBG\’, ‘BrBG_r\’, ‘BuGn\’, ‘BuGn_r\’, ‘BuPu\’, ‘BuPu_r\’, ‘CMRmap\’, ‘CMRmap_r\’, ‘Dark2\’, ‘Dark2_r\’, ‘GnBu\’, ‘GnBu_r\’, ‘Greens\’, ‘Greens_r\’, ‘Greys\’, ‘Greys_r\’, ‘OrRd\’, ‘OrRd_r\’, ‘Oranges\’, ‘Oranges_r\’, ‘PRGn\’, ‘PRGn_r\’, ‘Paired\’, ‘Paired_r\’, ‘Pastel1\’, ‘Pastel1_r\’, ‘Pastel2\’, ‘Pastel2_r\’, ‘PiYG\’, ‘PiYG_r\’, ‘PuBu\’, ‘PuBuGn\’, ‘PuBuGn_r\’, ‘PuBu_r\’, ‘PuOr\’, ‘PuOr_r\’, ‘PuRd\’, ‘PuRd_r\’, ‘Purples\’, ‘Purples_r\’, ‘RdBu\’, ‘RdBu_r\’, ‘RdGy\’, ‘RdGy_r\’, ‘RdPu\’, ‘RdPu_r\’, ‘RdYlBu\’, ‘RdYlBu_r\’, ‘RdYlGn\’, ‘RdYlGn_r\’, ‘Reds\’, ‘Reds_r\’, ‘Set1\’, ‘Set1_r\’, ‘Set2\’, ‘Set2_r\’, ‘Set3\’, ‘Set3_r\’, ‘Spectral\’, ‘Spectral_r\’, ‘Wistia\’, ‘Wistia_r\’, ‘YlGn\’, ‘YlGnBu\’, ‘YlGnBu_r\’, ‘YlGn_r\’, ‘YlOrBr\’, ‘YlOrBr_r\’, ‘YlOrRd\’, ‘YlOrRd_r\’, ‘afmhot\’, ‘afmhot_r\’, ‘autumn\’, ‘autumn_r\’, ‘binary\’, ‘binary_r\’, ‘bone\’, ‘bone_r\’, ‘brg\’, ‘brg_r\’, ‘bwr\’, ‘bwr_r\’, ‘cividis\’, ‘cividis_r\’, ‘cool\’, ‘cool_r\’, ‘coolwarm\’, ‘coolwarm_r\’, ‘copper\’, ‘copper_r\’, ‘cubehelix\’, ‘cubehelix_r\’, ‘flag\’, ‘flag_r\’, ‘gist_earth\’, ‘gist_earth_r\’, ‘gist_gray\’, ‘gist_gray_r\’, ‘gist_heat\’, ‘gist_heat_r\’, ‘gist_ncar\’, ‘gist_ncar_r\’, ‘gist_rainbow\’, ‘gist_rainbow_r\’, ‘gist_stern\’, ‘gist_stern_r\’, ‘gist_yarg\’, ‘gist_yarg_r\’, ‘gnuplot\’, ‘gnuplot2\’, ‘gnuplot2_r\’, ‘gnuplot_r\’, ‘gray\’, ‘gray_r\’, ‘hot\’, ‘hot_r\’, ‘hsv\’, ‘hsv_r\’, ‘inferno\’, ‘inferno_r\’, ‘jet\’, ‘jet_r\’, ‘magma\’, ‘magma_r\’, ‘nipy_spectral\’, ‘nipy_spectral_r\’, ‘ocean\’, ‘ocean_r\’, ‘pink\’, ‘pink_r\’, ‘plasma\’, ‘plasma_r\’, ‘prism\’, ‘prism_r\’, ‘rainbow\’, ‘rainbow_r\’, ‘seismic\’, ‘seismic_r\’, ‘spring\’, ‘spring_r\’, ‘summer\’, ‘summer_r\’, ‘tab10\’, ‘tab10_r\’, ‘tab20\’, ‘tab20_r\’, ‘tab20b\’, ‘tab20b_r\’, ‘tab20c\’, ‘tab20c_r\’, ‘terrain\’, ‘terrain_r\’, ‘turbo\’, ‘turbo_r\’, ‘twilight\’, ‘twilight_r\’, ‘twilight_shifted\’, ‘twilight_shifted_r\’, ‘viridis\’, ‘viridis_r\’, ‘winter\’, ‘winter_r\’

代码

import pyvista as pv
def mesh_cmp(mesh, name):
 \"\"\"
  使用进行plot自带的色彩映射
  :param mesh: 输入mesh
  :param name: 比较数据的名字
  :return:
 \"\"\"
 pts = mesh.points
 mesh[name] = pts[:, 1]
 mesh.plot(scalars=name, cmap=\'viridis_r\')
 
if __name__ == \'__main__\':
 mesh = pv.read(\'vtkData/airplane.ply\')
 mesh_cmp(mesh, \'y_height\')

效果

使用Matplotlib的cmp

代码

import pyvista as pv
import matplotlib.pyplot as plt

def mesh_cmp_mpl(mesh, name):
 \"\"\"
  使用Matplotlib进行色彩映射
  :param mesh: 输入mesh
  :param name: 比较数据的名字
  :return:
  \"\"\"
 pts = mesh.points
 mesh[name] = pts[:, 1]
 mlp_cmap = plt.cm.get_cmap(\"viridis\", 25)
 mesh.plot(scalars=name, cmap=mlp_cmap)
 
if __name__ == \'__main__\':
 mesh = pv.read(\'vtkData/airplane.ply\')
 mesh_cmp_mpl(mesh, \'y_height\')

效果

使用colorcet的cmp

需要先安装colorcet：

pip install colorcet

使用方法和上面几种方法类似，若想使用colorcet的colormaps中的hot:

mesh.plot(scalars=name, cmap=“hot”)

代码

def mesh_cmp_colorcet(mesh, name):
 \"\"\"
  使用进行colorcet进行色彩映射
  :param mesh: 输入mesh
  :param name: 比较数据的名字
  :return:
 \"\"\"
 pts = mesh.points
 mesh[name] = pts[:, 1]
 mesh.plot(scalars=name, cmap=colorcet.fire)
 
if __name__ == \'__main__\':
 mesh = pv.read(\'vtkData/airplane.ply\')
 mesh_cmp_colorcet(mesh, \'y_height\')

效果：