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1c0ffb3 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 | """Utilities for mapping normalized target modalities to physical ranges."""
from __future__ import annotations
import numpy as np
TARGET_CHANNELS = ["D", "Delta", "eta", "theta", "psi", "R"]
def normalized_modalities_to_physical(target, channel_axis=0, clip=False):
"""Map normalized grayscale modalities to nominal physical ranges.
Use this only for targets encoded as
``png_uint8_normalized_to_float32_0_1``. If a split already stores physical
Lu-Chipman values, do not apply this conversion again.
"""
target = np.asarray(target, dtype=np.float32)
values = np.moveaxis(target, channel_axis, 0)
if values.shape[0] != 6:
raise ValueError(f"Expected 6 target channels, got shape {target.shape}")
g = np.clip(values, 0.0, 1.0) if clip else values
physical = np.empty_like(g, dtype=np.float32)
physical[0] = g[0]
physical[1] = g[1]
physical[2] = np.pi * g[2]
physical[3] = np.pi * (g[3] - 0.5)
physical[4] = np.pi * (g[4] - 0.5)
physical[5] = np.pi * g[5]
return np.moveaxis(physical, 0, channel_axis)
def physical_modalities_to_normalized(target, channel_axis=0, clip=False):
"""Map physical target modalities to normalized grayscale ranges."""
target = np.asarray(target, dtype=np.float32)
values = np.moveaxis(target, channel_axis, 0)
if values.shape[0] != 6:
raise ValueError(f"Expected 6 target channels, got shape {target.shape}")
normalized = np.empty_like(values, dtype=np.float32)
normalized[0] = values[0]
normalized[1] = values[1]
normalized[2] = values[2] / np.pi
normalized[3] = values[3] / np.pi + 0.5
normalized[4] = values[4] / np.pi + 0.5
normalized[5] = values[5] / np.pi
if clip:
normalized = np.clip(normalized, 0.0, 1.0)
return np.moveaxis(normalized, 0, channel_axis)
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