├── README.md
└── python_depth_to_point_cloud.py


/README.md:
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 1 | # python_depth_to_point_cloud
 2 | Transform depth and RGB image pairs into a .ply file and show it.
 3 | 
 4 | ## Getting Started
 5 | Download or copy the .py file to utilize the full funtionality.
 6 | 
 7 | ### Prerequisites
 8 | Python3.7 and some several Python libraries.
 9 | 
10 | ### Installing
11 | Install all related libraries in terminal,
12 | ```
13 | pip install Pillow pandas numpy open3d-python
14 | ```
15 | Note: open3d-python might have some problems in version, but you can still get the .ply file via other libs and choose other point cloud lib to show the point cloud directly.
16 | 
17 | 
18 | 
19 | 


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/python_depth_to_point_cloud.py:
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 1 | from PIL import Image
 2 | import pandas as pd
 3 | import numpy as np
 4 | from open3d import read_point_cloud, draw_geometries
 5 | import time
 6 | 
 7 | 
 8 | class point_cloud_generator():
 9 | 
10 |     def __init__(self, rgb_file, depth_file, pc_file, focal_length, scalingfactor):
11 |         self.rgb_file = rgb_file
12 |         self.depth_file = depth_file
13 |         self.pc_file = pc_file
14 |         self.focal_length = focal_length
15 |         self.scalingfactor = scalingfactor
16 |         self.rgb = Image.open(rgb_file)
17 |         self.depth = Image.open(depth_file).convert('I')
18 |         self.width = self.rgb.size[0]
19 |         self.height = self.rgb.size[1]
20 | 
21 |     def calculate(self):
22 |         t1=time.time()
23 |         depth = np.asarray(self.depth).T
24 |         self.Z = depth / self.scalingfactor
25 |         X = np.zeros((self.width, self.height))
26 |         Y = np.zeros((self.width, self.height))
27 |         for i in range(self.width):
28 |             X[i, :] = np.full(X.shape[1], i)
29 | 
30 |         self.X = ((X - self.width / 2) * self.Z) / self.focal_length
31 |         for i in range(self.height):
32 |             Y[:, i] = np.full(Y.shape[0], i)
33 |         self.Y = ((Y - self.height / 2) * self.Z) / self.focal_length
34 | 
35 |         df=np.zeros((6,self.width*self.height))
36 |         df[0] = self.X.T.reshape(-1)
37 |         df[1] = -self.Y.T.reshape(-1)
38 |         df[2] = -self.Z.T.reshape(-1)
39 |         img = np.array(self.rgb)
40 |         df[3] = img[:, :, 0:1].reshape(-1)
41 |         df[4] = img[:, :, 1:2].reshape(-1)
42 |         df[5] = img[:, :, 2:3].reshape(-1)
43 |         self.df=df
44 |         t2=time.time()
45 |         print('calcualte 3d point cloud Done.',t2-t1)
46 | 
47 |     def write_ply(self):
48 |         t1=time.time()
49 |         float_formatter = lambda x: "%.4f" % x
50 |         points =[]
51 |         for i in self.df.T:
52 |             points.append("{} {} {} {} {} {} 0\n".format
53 |                           (float_formatter(i[0]), float_formatter(i[1]), float_formatter(i[2]),
54 |                            int(i[3]), int(i[4]), int(i[5])))
55 | 
56 |         file = open(self.pc_file, "w")
57 |         file.write('''ply
58 |         format ascii 1.0
59 |         element vertex %d
60 |         property float x
61 |         property float y
62 |         property float z
63 |         property uchar red
64 |         property uchar green
65 |         property uchar blue
66 |         property uchar alpha
67 |         end_header
68 |         %s
69 |         ''' % (len(points), "".join(points)))
70 |         file.close()
71 | 
72 |         t2=time.time()
73 |         print("Write into .ply file Done.",t2-t1)
74 | 
75 |     def show_point_cloud(self):
76 |         pcd = read_point_cloud(self.pc_file)
77 |         draw_geometries([pcd])
78 | 
79 | a = point_cloud_generator('p.png', 'd.png', 'pc1.ply',
80 |                           focal_length=50, scalingfactor=1000)
81 | a.calculate()
82 | a.write_ply()
83 | a.show_point_cloud()
84 | df = a.df
85 | np.save('pc.npy',df)
86 | 


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