python结构光三维重建

Python可以使用OpenCV和NumPy等开源库进行结构光三维重建。以下是一个简单的示例代码：

import cv2
import numpy as np

# 加载结构光图像
pattern = cv2.imread('pattern.png', cv2.IMREAD_GRAYSCALE)
image1 = cv2.imread('image1.png', cv2.IMREAD_GRAYSCALE)
image2 = cv2.imread('image2.png', cv2.IMREAD_GRAYSCALE)

# 计算相位差
phase = np.arctan2(np.sin(image1 - image2), np.cos(image1 - image2))

# 计算深度信息
h, w = pattern.shape
scale = 8
depth = phase * scale / (2 * np.pi)
depth = cv2.resize(depth, (w, h))

# 显示深度图像
cv2.imshow('depth', depth)
cv2.waitKey(0)

# 三维重建
fx = fy = 1000
cx = w / 2
cy = h / 2
points = []
for y in range(h):
    for x in range(w):
        z = depth[y, x]
        if z != 0:
            points.append((x, y, z))
points = np.array(points)
points[:, 0] = (points[:, 0] - cx) * points[:, 2] / fx
points[:, 1] = (points[:, 1] - cy) * points[:, 2] / fy

# 将点云保存为PLY文件
with open('pointcloud.ply', 'w') as f:
    f.write('ply\n')
    f.write('format ascii 1.0\n')
    f.write('element vertex %d\n' % len(points))
    f.write('property float x\n')
    f.write('property float y\n')
    f.write('property float z\n')
    f.write('end_header\n')
    for point in points:
        f.write('%f %f %f\n' % tuple(point))

注：以上代码仅为示例，实际应用中需要根据具体情况进行修改和优化。