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| import numpy as np | |
| def save_samples_truncted_prob(fname, points, prob): | |
| ''' | |
| Save the visualization of sampling to a ply file. | |
| Red points represent positive predictions. | |
| Green points represent negative predictions. | |
| :param fname: File name to save | |
| :param points: [N, 3] array of points | |
| :param prob: [N, 1] array of predictions in the range [0~1] | |
| :return: | |
| ''' | |
| r = (prob > 0.5).reshape([-1, 1]) * 255 | |
| g = (prob < 0.5).reshape([-1, 1]) * 255 | |
| b = np.zeros(r.shape) | |
| to_save = np.concatenate([points, r, g, b], axis=-1) | |
| return np.savetxt(fname, | |
| to_save, | |
| fmt='%.6f %.6f %.6f %d %d %d', | |
| comments='', | |
| header=( | |
| 'ply\nformat ascii 1.0\nelement vertex {:d}\nproperty float x\nproperty float y\nproperty float z\nproperty uchar red\nproperty uchar green\nproperty uchar blue\nend_header').format( | |
| points.shape[0]) | |
| ) | |
| def save_samples_rgb(fname, points, rgb): | |
| ''' | |
| Save the visualization of sampling to a ply file. | |
| Red points represent positive predictions. | |
| Green points represent negative predictions. | |
| :param fname: File name to save | |
| :param points: [N, 3] array of points | |
| :param rgb: [N, 3] array of rgb values in the range [0~1] | |
| :return: | |
| ''' | |
| to_save = np.concatenate([points, rgb * 255], axis=-1) | |
| return np.savetxt(fname, | |
| to_save, | |
| fmt='%.6f %.6f %.6f %d %d %d', | |
| comments='', | |
| header=( | |
| 'ply\nformat ascii 1.0\nelement vertex {:d}\nproperty float x\nproperty float y\nproperty float z\nproperty uchar red\nproperty uchar green\nproperty uchar blue\nend_header').format( | |
| points.shape[0]) | |
| ) | |