| from typing import * | |
| import numpy as np | |
| import matplotlib | |
| def colorize_depth(depth: np.ndarray, mask: np.ndarray = None, normalize: bool = True, cmap: str = 'Spectral') -> np.ndarray: | |
| if mask is None: | |
| depth = np.where(depth > 0, depth, np.nan) | |
| else: | |
| depth = np.where((depth > 0) & mask, depth, np.nan) | |
| disp = 1 / depth | |
| if normalize: | |
| min_disp, max_disp = np.nanquantile(disp, 0.001), np.nanquantile(disp, 0.99) | |
| disp = (disp - min_disp) / (max_disp - min_disp) | |
| colored = np.nan_to_num(matplotlib.colormaps[cmap](1.0 - disp)[..., :3], 0) | |
| colored = np.ascontiguousarray((colored.clip(0, 1) * 255).astype(np.uint8)) | |
| return colored | |
| def colorize_depth_affine(depth: np.ndarray, mask: np.ndarray = None, cmap: str = 'Spectral') -> np.ndarray: | |
| if mask is not None: | |
| depth = np.where(mask, depth, np.nan) | |
| min_depth, max_depth = np.nanquantile(depth, 0.001), np.nanquantile(depth, 0.999) | |
| depth = (depth - min_depth) / (max_depth - min_depth) | |
| colored = np.nan_to_num(matplotlib.colormaps[cmap](depth)[..., :3], 0) | |
| colored = np.ascontiguousarray((colored.clip(0, 1) * 255).astype(np.uint8)) | |
| return colored | |
| def colorize_disparity(disparity: np.ndarray, mask: np.ndarray = None, normalize: bool = True, cmap: str = 'Spectral') -> np.ndarray: | |
| if mask is not None: | |
| disparity = np.where(mask, disparity, np.nan) | |
| if normalize: | |
| min_disp, max_disp = np.nanquantile(disparity, 0.001), np.nanquantile(disparity, 0.999) | |
| disparity = (disparity - min_disp) / (max_disp - min_disp) | |
| colored = np.nan_to_num(matplotlib.colormaps[cmap](1.0 - disparity)[..., :3], 0) | |
| colored = np.ascontiguousarray((colored.clip(0, 1) * 255).astype(np.uint8)) | |
| return colored | |
| def colorize_segmentation(segmentation: np.ndarray, cmap: str = 'Set1') -> np.ndarray: | |
| colored = matplotlib.colormaps[cmap]((segmentation % 20) / 20)[..., :3] | |
| colored = np.ascontiguousarray((colored.clip(0, 1) * 255).astype(np.uint8)) | |
| return colored | |
| def colorize_normal(normal: np.ndarray, mask: np.ndarray = None) -> np.ndarray: | |
| if mask is not None: | |
| normal = np.where(mask[..., None], normal, 0) | |
| normal = normal * [0.5, -0.5, -0.5] + 0.5 | |
| normal = (normal.clip(0, 1) * 255).astype(np.uint8) | |
| return normal | |
| def colorize_error_map(error_map: np.ndarray, mask: np.ndarray = None, cmap: str = 'plasma', value_range: Tuple[float, float] = None): | |
| vmin, vmax = value_range if value_range is not None else (np.nanmin(error_map), np.nanmax(error_map)) | |
| cmap = matplotlib.colormaps[cmap] | |
| colorized_error_map = cmap(((error_map - vmin) / (vmax - vmin)).clip(0, 1))[..., :3] | |
| if mask is not None: | |
| colorized_error_map = np.where(mask[..., None], colorized_error_map, 0) | |
| colorized_error_map = np.ascontiguousarray((colorized_error_map.clip(0, 1) * 255).astype(np.uint8)) | |
| return colorized_error_map | |