单目线结构光三维重建python

以下是一个基于Python的单目线结构光三维重建代码示例：

import numpy as np
import cv2

# 读入图像
img = cv2.imread('image.png')

# 将图像转化为灰度图
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

# 计算图像的梯度
gx = cv2.Sobel(gray, cv2.CV_32F, 1, 0)
gy = cv2.Sobel(gray, cv2.CV_32F, 0, 1)

# 计算图像的深度信息
depth = np.zeros_like(gray, dtype=np.float32)
for i in range(gray.shape[0]):
    for j in range(gray.shape[1]):
        depth[i][j] = abs(gx[i][j]) + abs(gy[i][j])

# 构建相机矩阵
f = 500  # 焦距
cx = gray.shape[1] / 2  # 光心x坐标
cy = gray.shape[0] / 2  # 光心y坐标
K = np.array([[f, 0, cx], [0, f, cy], [0, 0, 1]])

# 计算三维坐标
points = np.zeros((gray.shape[0], gray.shape[1], 3), dtype=np.float32)
for i in range(gray.shape[0]):
    for j in range(gray.shape[1]):
        points[i][j][0] = (j - cx) * depth[i][j] / f
        points[i][j][1] = (i - cy) * depth[i][j] / f
        points[i][j][2] = depth[i][j]

# 显示三维点云
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt

fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
X = points[:, :, 0].ravel()
Y = points[:, :, 1].ravel()
Z = points[:, :, 2].ravel()
ax.scatter(X, Y, Z)
plt.show()

代码中使用了OpenCV库和Matplotlib库，首先读入一张图像，然后将其转化为灰度图像。接着使用Sobel算子计算图像的梯度信息，并将其加起来作为深度信息。然后构建相机矩阵，并根据相机矩阵和深度信息计算每个像素点的三维坐标。最后，使用Matplotlib库显示三维点云。