Spaces:
Runtime error
Runtime error
| # Copyright 2022 the Regents of the University of California, Nerfstudio Team and contributors. All rights reserved. | |
| # | |
| # Licensed under the Apache License, Version 2.0 (the "License"); | |
| # you may not use this file except in compliance with the License. | |
| # You may obtain a copy of the License at | |
| # | |
| # http://www.apache.org/licenses/LICENSE-2.0 | |
| # | |
| # Unless required by applicable law or agreed to in writing, software | |
| # distributed under the License is distributed on an "AS IS" BASIS, | |
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
| # See the License for the specific language governing permissions and | |
| # limitations under the License. | |
| """ Helper functions for visualizing outputs """ | |
| from dataclasses import dataclass | |
| # from utils.typing import * | |
| from typing import * | |
| import matplotlib | |
| import torch | |
| from jaxtyping import Bool, Float | |
| from torch import Tensor | |
| from utils import colors | |
| Colormaps = Literal[ | |
| "default", "turbo", "viridis", "magma", "inferno", "cividis", "gray", "pca" | |
| ] | |
| class ColormapOptions: | |
| """Options for colormap""" | |
| colormap: Colormaps = "default" | |
| """ The colormap to use """ | |
| normalize: bool = False | |
| """ Whether to normalize the input tensor image """ | |
| colormap_min: float = 0 | |
| """ Minimum value for the output colormap """ | |
| colormap_max: float = 1 | |
| """ Maximum value for the output colormap """ | |
| invert: bool = False | |
| """ Whether to invert the output colormap """ | |
| def apply_colormap( | |
| image: Float[Tensor, "*bs channels"], | |
| colormap_options: ColormapOptions = ColormapOptions(), | |
| eps: float = 1e-9, | |
| ) -> Float[Tensor, "*bs rgb"]: | |
| """ | |
| Applies a colormap to a tensor image. | |
| If single channel, applies a colormap to the image. | |
| If 3 channel, treats the channels as RGB. | |
| If more than 3 channel, applies a PCA reduction on the dimensions to 3 channels | |
| Args: | |
| image: Input tensor image. | |
| eps: Epsilon value for numerical stability. | |
| Returns: | |
| Tensor with the colormap applied. | |
| """ | |
| # default for rgb images | |
| if image.shape[-1] == 3: | |
| return image | |
| # rendering depth outputs | |
| if image.shape[-1] == 1 and torch.is_floating_point(image): | |
| output = image | |
| if colormap_options.normalize: | |
| output = output - torch.min(output) | |
| output = output / (torch.max(output) + eps) | |
| output = ( | |
| output * (colormap_options.colormap_max - colormap_options.colormap_min) | |
| + colormap_options.colormap_min | |
| ) | |
| output = torch.clip(output, 0, 1) | |
| if colormap_options.invert: | |
| output = 1 - output | |
| return apply_float_colormap(output, colormap=colormap_options.colormap) | |
| # rendering boolean outputs | |
| if image.dtype == torch.bool: | |
| return apply_boolean_colormap(image) | |
| if image.shape[-1] > 3: | |
| return apply_pca_colormap(image) | |
| raise NotImplementedError | |
| def apply_float_colormap( | |
| image: Float[Tensor, "*bs 1"], colormap: Colormaps = "viridis" | |
| ) -> Float[Tensor, "*bs rgb"]: | |
| """Convert single channel to a color image. | |
| Args: | |
| image: Single channel image. | |
| colormap: Colormap for image. | |
| Returns: | |
| Tensor: Colored image with colors in [0, 1] | |
| """ | |
| if colormap == "default": | |
| colormap = "turbo" | |
| image = torch.nan_to_num(image, 0) | |
| if colormap == "gray": | |
| return image.repeat(1, 1, 3) | |
| image = image.clamp(0, 1) | |
| image_long = (image * 255).long() | |
| image_long_min = torch.min(image_long) | |
| image_long_max = torch.max(image_long) | |
| assert image_long_min >= 0, f"the min value is {image_long_min}" | |
| assert image_long_max <= 255, f"the max value is {image_long_max}" | |
| return torch.tensor(matplotlib.colormaps[colormap].colors, device=image.device)[ | |
| image_long[..., 0] | |
| ] | |
| def apply_depth_colormap( | |
| depth: Float[Tensor, "*bs 1"], | |
| accumulation: Optional[Float[Tensor, "*bs 1"]] = None, | |
| near_plane: Optional[float] = None, | |
| far_plane: Optional[float] = None, | |
| colormap_options: ColormapOptions = ColormapOptions(), | |
| ) -> Float[Tensor, "*bs rgb"]: | |
| """Converts a depth image to color for easier analysis. | |
| Args: | |
| depth: Depth image. | |
| accumulation: Ray accumulation used for masking vis. | |
| near_plane: Closest depth to consider. If None, use min image value. | |
| far_plane: Furthest depth to consider. If None, use max image value. | |
| colormap: Colormap to apply. | |
| Returns: | |
| Colored depth image with colors in [0, 1] | |
| """ | |
| near_plane = near_plane or float(torch.min(depth)) | |
| far_plane = far_plane or float(torch.max(depth)) | |
| depth = (depth - near_plane) / (far_plane - near_plane + 1e-10) | |
| depth = torch.clip(depth, 0, 1) | |
| # depth = torch.nan_to_num(depth, nan=0.0) # TODO(ethan): remove this | |
| colored_image = apply_colormap(depth, colormap_options=colormap_options) | |
| if accumulation is not None: | |
| colored_image = colored_image * accumulation + (1 - accumulation) | |
| return colored_image | |
| def apply_boolean_colormap( | |
| image: Bool[Tensor, "*bs 1"], | |
| true_color: Float[Tensor, "*bs rgb"] = colors.WHITE, | |
| false_color: Float[Tensor, "*bs rgb"] = colors.BLACK, | |
| ) -> Float[Tensor, "*bs rgb"]: | |
| """Converts a depth image to color for easier analysis. | |
| Args: | |
| image: Boolean image. | |
| true_color: Color to use for True. | |
| false_color: Color to use for False. | |
| Returns: | |
| Colored boolean image | |
| """ | |
| colored_image = torch.ones(image.shape[:-1] + (3,)) | |
| colored_image[image[..., 0], :] = true_color | |
| colored_image[~image[..., 0], :] = false_color | |
| return colored_image | |
| def apply_pca_colormap(image: Float[Tensor, "*bs dim"]) -> Float[Tensor, "*bs rgb"]: | |
| """Convert feature image to 3-channel RGB via PCA. The first three principle | |
| components are used for the color channels, with outlier rejection per-channel | |
| Args: | |
| image: image of arbitrary vectors | |
| Returns: | |
| Tensor: Colored image | |
| """ | |
| original_shape = image.shape | |
| image = image.view(-1, image.shape[-1]) | |
| _, _, v = torch.pca_lowrank(image) | |
| image = torch.matmul(image, v[..., :3]) | |
| d = torch.abs(image - torch.median(image, dim=0).values) | |
| mdev = torch.median(d, dim=0).values | |
| s = d / mdev | |
| m = 3.0 # this is a hyperparam controlling how many std dev outside for outliers | |
| rins = image[s[:, 0] < m, 0] | |
| gins = image[s[:, 1] < m, 1] | |
| bins = image[s[:, 2] < m, 2] | |
| image[:, 0] -= rins.min() | |
| image[:, 1] -= gins.min() | |
| image[:, 2] -= bins.min() | |
| image[:, 0] /= rins.max() - rins.min() | |
| image[:, 1] /= gins.max() - gins.min() | |
| image[:, 2] /= bins.max() - bins.min() | |
| image = torch.clamp(image, 0, 1) | |
| image_long = (image * 255).long() | |
| image_long_min = torch.min(image_long) | |
| image_long_max = torch.max(image_long) | |
| assert image_long_min >= 0, f"the min value is {image_long_min}" | |
| assert image_long_max <= 255, f"the max value is {image_long_max}" | |
| return image.view(*original_shape[:-1], 3) | |