如何用Python快速通过深度图片生成点云图像并预览

• 原理
• • 代码

原理

在机器人及其他应用中，除了传统的RGB三通道相机以外，常常还会配置有深度传感器。比较常用的有Kinect，Lidar等等。博主前段时间被研究所导师布置了一个用深度图（depth image）和RGB图片来生成点云的任务。一开始谷歌百度了很久发现居然没有python的解法。实际上这是一个非常简单的问题，只要计算一下二维到三维的映射就可以了。
我自己写了一个转换函数可以直接生成点云了，原始repo在：Github代码库

代码

from PIL import Image
import pandas as pd
import numpy as np
from open3d import read_point_cloud, draw_geometries
import time


class point_cloud_generator():

    def __init__(self, rgb_file, depth_file, pc_file, focal_length, scalingfactor):
        self.rgb_file = rgb_file
        self.depth_file = depth_file
        self.pc_file = pc_file
        self.focal_length = focal_length
        self.scalingfactor = scalingfactor
        self.rgb = Image.open(rgb_file)
        self.depth = Image.open(depth_file).convert('I')
        self.width = self.rgb.size[0]
        self.height = self.rgb.size[1]

    def calculate(self):
        t1=time.time()
        depth = np.asarray(self.depth).T
        self.Z = depth / self.scalingfactor
        X = np.zeros((self.width, self.height))
        Y = np.zeros((self.width, self.height))
        for i in range(self.width):
            X[i, :] = np.full(X.shape[1], i)

        self.X = ((X - self.width / 2) * self.Z) / self.focal_length
        for i in range(self.height):
            Y[:, i] = np.full(Y.shape[0], i)
        self.Y = ((Y - self.height / 2) * self.Z) / self.focal_length

        df=np.zeros((6,self.width*self.height))
        df[0] = self.X.T.reshape(-1)
        df[1] = -self.Y.T.reshape(-1)
        df[2] = -self.Z.T.reshape(-1)
        img = np.array(self.rgb)
        df[3] = img[:, :, 0:1].reshape(-1)
        df[4] = img[:, :, 1:2].reshape(-1)
        df[5] = img[:, :, 2:3].reshape(-1)
        self.df=df
        t2=time.time()
        print('calcualte 3d point cloud Done.',t2-t1)

    def write_ply(self):
        t1=time.time()
        float_formatter = lambda x: "%.4f" % x
        points =[]
        for i in self.df.T:
            points.append("{} {} {} {} {} {} 0\n".format
                          (float_formatter(i[0]), float_formatter(i[1]), float_formatter(i[2]),
                           int(i[3]), int(i[4]), int(i[5])))

        file = open(self.pc_file, "w")
        file.write('''ply
        format ascii 1.0
        element vertex %d
        property float x
        property float y
        property float z
        property uchar red
        property uchar green
        property uchar blue
        property uchar alpha
        end_header
        %s
        ''' % (len(points), "".join(points)))
        file.close()

        t2=time.time()
        print("Write into .ply file Done.",t2-t1)

    def show_point_cloud(self):
        pcd = read_point_cloud(self.pc_file)
        draw_geometries([pcd])

a = point_cloud_generator('p.png', 'd.png', 'pc1.ply',
                          focal_length=50, scalingfactor=1000)
a.calculate()
a.write_ply()
a.show_point_cloud()
df = a.df
np.save('pc.npy',df)

在原代码文件夹里放入RGB图片 ‘p.png’,深度图’d.png’直接运行就可以预览点云啦。
具体的坐标转换原理网上已经有很多解释了，比如坐标转换原理，大家觉得有帮助帮忙在github上点个星吧谢谢！