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| # TRI-VIDAR - Copyright 2022 Toyota Research Institute. All rights reserved. | |
| from copy import deepcopy | |
| import numpy as np | |
| from vidar.utils.data import keys_in | |
| from vidar.utils.decorators import iterate1 | |
| def duplicate_sample(sample, keys): | |
| """ | |
| Duplicates sample images and contexts to preserve their un-augmented versions. | |
| Parameters | |
| ---------- | |
| sample : Dict | |
| Input sample | |
| Returns | |
| ------- | |
| sample : Dict | |
| Sample including [+"_original"] keys with copies of images and contexts. | |
| """ | |
| for key in keys_in(sample, keys): | |
| sample[f'raw_{key}'] = deepcopy(sample[key]) | |
| # Return duplicated sample | |
| return sample | |
| def mask_depth_number(depth, num_points): | |
| """ | |
| Mask depth map to remove valid pixels given the target number of points to keep. | |
| Parameters | |
| ---------- | |
| depth : np.Array | |
| Depth map to be masked | |
| num_points : Int | |
| Number of input depth points that should be kept at each iteration | |
| Returns | |
| ------- | |
| depth : np.Array | |
| Masked depth map (modification done in-place!) | |
| """ | |
| # Find probability of maintaining | |
| total_points = depth.shape[0] * depth.shape[1] | |
| rnd = np.random.rand(depth.shape[0], depth.shape[1]) | |
| percentile = 100 * num_points / total_points | |
| # Mask depth map | |
| mask = rnd < np.percentile(rnd, q=100 - percentile) | |
| depth[mask] = 0.0 | |
| # Return depth map | |
| return depth | |
| def mask_depth_percentage(depth, percentage): | |
| """ | |
| Mask depth map to remove valid pixels given a range of percentages. | |
| Parameters | |
| ---------- | |
| depth : np.Array | |
| Depth map to be masked | |
| percentage : Tuple | |
| Min/Max percentages to be maintained (min, max) | |
| Returns | |
| ------- | |
| depth : np.Array | |
| Masked depth map (modification done in-place!) | |
| """ | |
| # Find probability of maintaining | |
| rnd = np.random.uniform(low=percentage[0], high=percentage[1], size=1)[0] | |
| # Mask depth map | |
| depth[np.random.rand(*depth.shape) > rnd] = 0.0 | |
| # Return depth map | |
| return depth | |
| def clip_depth(sample, max_value): | |
| """Clip depth map to a maximum range""" | |
| for i in range(len(sample)): | |
| if 'depth' in sample[i]: | |
| for ctx in sample[i]['depth'].keys(): | |
| sample[i]['depth'][ctx][sample[i]['depth'][ctx] > max_value] = max_value | |
| return sample | |
| def mask_depth_range(sample, depth_range): | |
| """Mask out depth map within a range""" | |
| for i in range(len(sample)): | |
| if 'depth' in sample[i]: | |
| for ctx in sample[i]['depth'].keys(): | |
| sample[i]['depth'][ctx][sample[i]['depth'][ctx] < depth_range[0]] = 0.0 | |
| sample[i]['depth'][ctx][sample[i]['depth'][ctx] > depth_range[1]] = 0.0 | |
| return sample | |