、3种三维重建方法实现视差估计 python代码
时间: 2023-07-24 15:56:43 浏览: 113
以下是三种三维重建方法的视差估计Python代码示例:
1. 基于SIFT特征匹配的视差估计:
```python
import cv2
import numpy as np
# 读取左右相机的图像
imgL = cv2.imread('left.jpg', 0)
imgR = cv2.imread('right.jpg', 0)
# 创建SIFT对象
sift = cv2.xfeatures2d.SIFT_create()
# 对左右图像提取SIFT特征和描述符
kp1, des1 = sift.detectAndCompute(imgL, None)
kp2, des2 = sift.detectAndCompute(imgR, None)
# 创建暴力匹配器对象
bf = cv2.BFMatcher()
# 对左右图像的SIFT描述符进行匹配
matches = bf.knnMatch(des1, des2, k=2)
# 根据Lowe's ratio test筛选匹配点
good = []
for m, n in matches:
if m.distance < 0.75 * n.distance:
good.append(m)
# 计算视差
disparity = np.zeros_like(imgL)
for m in good:
x, y = kp1[m.queryIdx].pt
x1, y1 = kp2[m.trainIdx].pt
disparity[int(y), int(x)] = x - x1
# 显示视差图像
cv2.imshow('disparity', disparity)
cv2.waitKey(0)
cv2.destroyAllWindows()
```
2. 基于BM算法的视差估计:
```python
import cv2
import numpy as np
# 读取左右相机的图像
imgL = cv2.imread('left.jpg', 0)
imgR = cv2.imread('right.jpg', 0)
# 创建立体匹配对象
stereo = cv2.StereoBM_create(numDisparities=16, blockSize=15)
# 计算视差
disparity = stereo.compute(imgL, imgR)
# 显示视差图像
cv2.imshow('disparity', disparity)
cv2.waitKey(0)
cv2.destroyAllWindows()
```
3. 基于SGBM算法的视差估计:
```python
import cv2
import numpy as np
# 读取左右相机的图像
imgL = cv2.imread('left.jpg', 0)
imgR = cv2.imread('right.jpg', 0)
# 创建立体匹配对象
stereo = cv2.StereoSGBM_create(numDisparities=16, blockSize=15)
# 计算视差
disparity = stereo.compute(imgL, imgR)
# 显示视差图像
cv2.imshow('disparity', disparity)
cv2.waitKey(0)
cv2.destroyAllWindows()
```
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