78 lines
2.4 KiB
Python
78 lines
2.4 KiB
Python
"""Utility functions for visualization.
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For licensing see accompanying LICENSE file.
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Copyright (C) 2025 Apple Inc. All Rights Reserved.
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"""
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from __future__ import annotations
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import numpy as np
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import torch
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from matplotlib import pyplot as plt
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METRIC_DEPTH_MAX_CLAMP_METER = 50.0
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def colorize_depth(depth: torch.Tensor, val_max: float = 10.0) -> torch.Tensor:
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"""Colorize depth map."""
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depth_channels = depth.shape[-3]
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# When we have a general depth/disparity map, output the color map as is.
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if depth_channels == 1:
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return colorize_scalar_map(
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depth.squeeze(-3), val_min=0.0, val_max=val_max, color_map="turbo"
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)
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# When we have a multi-layered depth/disparity map,
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# we concatenate the color maps horizontally and output it.
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else:
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colored_depths = []
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for c in range(depth_channels):
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colored_depths.append(
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colorize_scalar_map(
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depth[..., c, :, :], val_min=0.0, val_max=val_max, color_map="turbo"
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)
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)
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return torch.cat(colored_depths, dim=-1)
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def colorize_alpha(alpha: torch.Tensor) -> torch.Tensor:
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"""Colorize alpha map."""
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return colorize_scalar_map(alpha.squeeze(-3), val_min=0.0, val_max=1.0, color_map="coolwarm")
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def colorize_scalar_map(
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scalar_map: torch.Tensor, val_min=0.0, val_max=1.0, color_map: str = "jet"
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) -> torch.Tensor:
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"""Colorize a scalar map of.
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Args:
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scalar_map: Map of with format BHW.
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val_min: Minimu value to display.
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val_max: Maximum value to display.
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color_map: Which color map to use. Will be passed to matplotlob.
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Returns:
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A colorized image with format BHWC.
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"""
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if scalar_map.ndim not in (2, 3, 4):
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raise ValueError("Only scalar maps of 2 or 3 or 4 dimensions supported.")
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cmap = plt.get_cmap(color_map)
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scalar_map_np = scalar_map.detach().cpu().float().numpy()
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scalar_map_np = (scalar_map_np - val_min) / (val_max - val_min)
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scalar_map_np = np.clip(scalar_map_np, a_min=0.0, a_max=1.0)
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color_map_np = cmap(scalar_map_np)[..., :3]
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tensor = torch.as_tensor(color_map_np * 255.0, dtype=torch.uint8)
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if tensor.ndim == 3:
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return tensor.permute(2, 0, 1)
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elif tensor.ndim == 4:
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return tensor.permute(0, 3, 1, 2)
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elif tensor.ndim == 5:
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return tensor.permute(0, 1, 4, 2, 3)
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else:
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assert False, "Invalid tensor shape encountered."
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