OpenCV 3 Computer Vision with Python Cookbook

How to do it

You need to complete the following steps:

1. Import the necessary modules.

import cv2import numpy as np

2. Generate the test camera's projection matrices:

P1 = np.eye(3, 4, dtype=np.float32)P2 = np.eye(3, 4, dtype=np.float32)P2[0, 3] = -1

3. Generate the test points:

N = 5points3d = np.empty((4, N), np.float32)points3d[:3, :] = np.random.randn(3, N)points3d[3, :] = 1

4. Project the 3D points into two views and add noise:

points1 = P1 @ points3dpoints1 = points1[:2, :] / points1[2, :]points1[:2, :] += np.random.randn(2, N) * 1e-2points2 = P2 @ points3dpoints2 = points2[:2, :] / points2[2, :]points2[:2, :] += np.random.randn(2, N) * 1e-2

5. Reconstruct the points from noisy observations:

points3d_reconstr = cv2.triangulatePoints(P1, ...