python三维重建代码

三维重建是一个比较复杂的问题，需要使用多种技术和算法。以下是一个简单的例子，使用Python和OpenCV库进行基本的三维重建。

首先，需要准备两张图片，一张是左视图，一张是右视图。这两张图片应该是同一场景的不同角度拍摄的。

import cv2
import numpy as np

# 读取左视图和右视图图片
imgL = cv2.imread('left.png')
imgR = cv2.imread('right.png')

接下来，需要对图片进行预处理，包括灰度化、去噪和特征点提取。

# 将图片转为灰度图
grayL = cv2.cvtColor(imgL, cv2.COLOR_BGR2GRAY)
grayR = cv2.cvtColor(imgR, cv2.COLOR_BGR2GRAY)

# 去噪
blurL = cv2.GaussianBlur(grayL, (5, 5), 0)
blurR = cv2.GaussianBlur(grayR, (5, 5), 0)

# 特征点提取
sift = cv2.xfeatures2d.SIFT_create()
kpL, desL = sift.detectAndCompute(blurL, None)
kpR, desR = sift.detectAndCompute(blurR, None)

接下来，需要进行特征点匹配，使用OpenCV库中的BFMatcher算法进行匹配。

# 特征点匹配
bf = cv2.BFMatcher()
matches = bf.knnMatch(desL, desR, k=2)

# 挑选出好的匹配点
good = []
for m, n in matches:
    if m.distance < 0.75 * n.distance:
        good.append(m)

# 绘制匹配结果
imgMatch = cv2.drawMatches(imgL, kpL, imgR, kpR, good, None, flags=cv2.DrawMatchesFlags_NOT_DRAW_SINGLE_POINTS)

接下来，需要进行立体匹配，使用OpenCV库中的StereoSGBM算法。

# 立体匹配
stereo = cv2.StereoSGBM_create(numDisparities=16, blockSize=15)
disparity = stereo.compute(grayL, grayR)

# 归一化
disp = cv2.normalize(disparity, None, alpha=0, beta=255, norm_type=cv2.NORM_MINMAX, dtype=cv2.CV_8U)

最后，使用OpenCV库中的reprojectImageTo3D函数将视差图转换为三维点云。

# 将视差图转换为三维点云
focal_length = 718.856
Q = np.float32([[1, 0, 0, -imgL.shape[1]/2],
                [0, 1, 0, -imgL.shape[0]/2],
                [0, 0, 0, focal_length],
                [0, 0, 1, 0]])
points = cv2.reprojectImageTo3D(disparity, Q)

完整代码如下：

import cv2
import numpy as np

# 读取左视图和右视图图片
imgL = cv2.imread('left.png')
imgR = cv2.imread('right.png')

# 将图片转为灰度图
grayL = cv2.cvtColor(imgL, cv2.COLOR_BGR2GRAY)
grayR = cv2.cvtColor(imgR, cv2.COLOR_BGR2GRAY)

# 去噪
blurL = cv2.GaussianBlur(grayL, (5, 5), 0)
blurR = cv2.GaussianBlur(grayR, (5, 5), 0)

# 特征点提取
sift = cv2.xfeatures2d.SIFT_create()
kpL, desL = sift.detectAndCompute(blurL, None)
kpR, desR = sift.detectAndCompute(blurR, None)

# 特征点匹配
bf = cv2.BFMatcher()
matches = bf.knnMatch(desL, desR, k=2)

# 挑选出好的匹配点
good = []
for m, n in matches:
    if m.distance < 0.75 * n.distance:
        good.append(m)

# 绘制匹配结果
imgMatch = cv2.drawMatches(imgL, kpL, imgR, kpR, good, None, flags=cv2.DrawMatchesFlags_NOT_DRAW_SINGLE_POINTS)

# 立体匹配
stereo = cv2.StereoSGBM_create(numDisparities=16, blockSize=15)
disparity = stereo.compute(grayL, grayR)

# 归一化
disp = cv2.normalize(disparity, None, alpha=0, beta=255, norm_type=cv2.NORM_MINMAX, dtype=cv2.CV_8U)

# 将视差图转换为三维点云
focal_length = 718.856
Q = np.float32([[1, 0, 0, -imgL.shape[1]/2],
                [0, 1, 0, -imgL.shape[0]/2],
                [0, 0, 0, focal_length],
                [0, 0, 1, 0]])
points = cv2.reprojectImageTo3D(disparity, Q)

# 显示结果
cv2.imshow('imgMatch', imgMatch)
cv2.imshow('disparity', disp)
cv2.waitKey(0)
cv2.destroyAllWindows()