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on
L4
Running
on
L4
| import matplotlib | |
| import numpy as np | |
| import torch | |
| def colorize_depth_maps( | |
| depth_map, min_depth, max_depth, cmap="Spectral", valid_mask=None | |
| ): | |
| """ | |
| Colorize depth maps. | |
| """ | |
| assert len(depth_map.shape) >= 2, "Invalid dimension" | |
| if isinstance(depth_map, torch.Tensor): | |
| depth = depth_map.detach().squeeze().numpy() | |
| elif isinstance(depth_map, np.ndarray): | |
| depth = depth_map.copy().squeeze() | |
| # reshape to [ (B,) H, W ] | |
| if depth.ndim < 3: | |
| depth = depth[np.newaxis, :, :] | |
| # colorize | |
| cm = matplotlib.colormaps[cmap] | |
| depth = ((depth - min_depth) / (max_depth - min_depth)).clip(0, 1) | |
| img_colored_np = cm(depth, bytes=False)[:, :, :, 0:3] # value from 0 to 1 | |
| img_colored_np = np.rollaxis(img_colored_np, 3, 1) | |
| if valid_mask is not None: | |
| if isinstance(depth_map, torch.Tensor): | |
| valid_mask = valid_mask.detach().numpy() | |
| valid_mask = valid_mask.squeeze() # [H, W] or [B, H, W] | |
| if valid_mask.ndim < 3: | |
| valid_mask = valid_mask[np.newaxis, np.newaxis, :, :] | |
| else: | |
| valid_mask = valid_mask[:, np.newaxis, :, :] | |
| valid_mask = np.repeat(valid_mask, 3, axis=1) | |
| img_colored_np[~valid_mask] = 0 | |
| if isinstance(depth_map, torch.Tensor): | |
| img_colored = torch.from_numpy(img_colored_np).float() | |
| elif isinstance(depth_map, np.ndarray): | |
| img_colored = img_colored_np | |
| return img_colored | |
| def scale_depth_to_model(depth, camera_type='ortho'): | |
| """ | |
| Scale depth from the original range. | |
| """ | |
| assert camera_type == 'ortho' or camera_type == 'persp' | |
| w, h = depth.shape | |
| if camera_type == 'ortho': | |
| original_min = 9000 | |
| original_max = 17000 | |
| target_min = 2000 | |
| target_max = 62000 | |
| mask = depth != 0 | |
| # Scale depth to [0, 1] | |
| depth_normalized = np.zeros([w, h]) | |
| depth_normalized[mask] = (depth[mask] - original_min) / (original_max - original_min) | |
| # Scale depth to [2000, 60000] | |
| scaled_depth = np.zeros([w, h]) | |
| scaled_depth[mask] = depth_normalized[mask] * (target_max - target_min) + target_min | |
| else: | |
| original_min = 4000 | |
| original_max = 13000 | |
| target_min = 2000 | |
| target_max = 62000 | |
| mask = depth != 0 | |
| # Scale depth to [0, 1] | |
| depth_normalized = np.zeros([w, h]) | |
| depth_normalized[mask] = (depth[mask] - original_min) / (original_max - original_min) | |
| # Scale depth to [2000, 60000] | |
| scaled_depth = np.zeros([w, h]) | |
| scaled_depth[mask] = depth_normalized[mask] * (target_max - target_min) + target_min | |
| scaled_depth[scaled_depth > 62000] = 0 | |
| scaled_depth = scaled_depth / 65535. # [0, 1] | |
| return scaled_depth | |
| def rescale_depth_to_world(scaled_depth, camera_type='ortho'): | |
| """ | |
| Rescale depth from the scaled range back to the original range. | |
| """ | |
| assert camera_type == 'ortho' or camera_type == 'persp' | |
| scaled_depth = scaled_depth * 65535. | |
| w, h = scaled_depth.shape | |
| if camera_type == 'ortho': | |
| original_min = 9000 | |
| original_max = 17000 | |
| target_min = 2000 | |
| target_max = 62000 | |
| mask = scaled_depth != 0 | |
| rescaled_depth_norm = np.zeros([w, h]) | |
| # Rescale depth to [0, 1] | |
| rescaled_depth_norm[mask] = (scaled_depth[mask] - target_min) / (target_max - target_min) | |
| # Rescale depth to [9000, 17000] | |
| rescaled_depth = np.zeros([w, h]) | |
| rescaled_depth[mask] = rescaled_depth_norm[mask] * (original_max - original_min) + original_min | |
| else: | |
| original_min = 4000 | |
| original_max = 13000 | |
| target_min = 2000 | |
| target_max = 62000 | |
| mask = scaled_depth != 0 | |
| rescaled_depth_norm = np.zeros([w, h]) | |
| # Rescale depth to [0, 1] | |
| rescaled_depth_norm[mask] = (scaled_depth[mask] - target_min) / (target_max - target_min) | |
| # Rescale depth to [9000, 17000] | |
| rescaled_depth = np.zeros([w, h]) | |
| rescaled_depth[mask] = rescaled_depth_norm[mask] * (original_max - original_min) + original_min | |
| return rescaled_depth |