Install working tensorflow or pytorch via standard conda environment workflow.
The recommended conda-based install process works smoothly:
$ # Create a fresh environment| """ | |
| A micro event loop library implementation from scratch. | |
| This library provides a minimal but feature-complete asynchronous event loop | |
| implementation for educational purposes. It demonstrates the core concepts of | |
| asynchronous programming including: | |
| - Task scheduling and management | |
| - I/O multiplexing with non-blocking sockets | |
| - Timeouts and sleep functionality |
| import cv2 | |
| import numpy as np | |
| def in_front_of_both_cameras(first_points, second_points, rot, trans): | |
| # check if the point correspondences are in front of both images | |
| rot_inv = rot | |
| for first, second in zip(first_points, second_points): | |
| first_z = np.dot(rot[0, :] - second[0]*rot[2, :], trans) / np.dot(rot[0, :] - second[0]*rot[2, :], second) | |
| first_3d_point = np.array([first[0] * first_z, second[0] * first_z, first_z]) |