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hsi_to_rgb.py
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from pathlib import Path
import numpy as np
from PIL import Image
def load_light_distribution(name="lamp_spectrum.csv"):
sd_light_source = np.loadtxt(name, skiprows=1, dtype="float")
sd_light_source = sd_light_source[np.where(sd_light_source[:, 0] >= 400)]
# rindx = np.where(sd_light_source[:, 0] >= 400) and np.where(sd_light_source[:, 0] <= 600)
sd_light_source = sd_light_source[:, 1:2]
# print("sum", np.sum(sd_light_source))
sd_light_source = sd_light_source[::20]
sd_light_source = sd_light_source[:44]
# print(sd_light_source.shape)
return sd_light_source
def load_illuminantA(name="A.csv"):
sd_light_source = np.loadtxt(name, skiprows=1, dtype="float")
sd_light_source = sd_light_source[np.where(sd_light_source[:, 0] >= 400)]
sd_light_source = sd_light_source[:, 1:2]
# print("sum",np.sum(sd_light_source))
# sd_light_source = sd_light_source / np.max(sd_light_source)
sd_light_source = sd_light_source[::2]
sd_light_source = sd_light_source[:44]
# print(sd_light_source)
return sd_light_source
def hsi_to_ci31931_rgb(himg, dist_name):
pass
def hsi_to_rgb(himg, dist_name):
"""
input: ハイパースペクトル画像 HSI（numpy型）
return: RGB画像（Image objedct）
"""
# 計測時にノイズとして負の値になった値を0にする
np.where(himg < 0, 0, himg)
cmf = np.loadtxt("./csvs/CIE1931-2deg-XYZ.csv", delimiter=",")
# HSIが400nm以上のため等色関数も400nm以上のみを利用
cmf = cmf[np.where(cmf[:, 0] >= 400)]
# 光源の分光分布の5nm刻みをHSIと同じ10nm刻みに変更
cmf = cmf[::2]
cmf = cmf[:44, :]
stem = Path(dist_name).stem
if stem in ["A"]:
# 標準光源Aは780nmまでを可視光としているため，HSIと等色関数も780nmm以下に制限
nhimg = himg[:, :, :39]
cmf = cmf[:39, :]
sd_light_source = load_illuminantA(name=dist_name)
elif stem in ["D65"]:
nhimg = himg[:, :, :44]
cmf = cmf[:44, :]
sd_light_source = load_illuminantA(name=dist_name)
else:
nhimg = himg[:, :, :44]
sd_light_source = load_light_distribution(name=dist_name)
flag_const_100 = False
ncmf = cmf[:, 1:]
nmf_multi_ld = ncmf * sd_light_source
x = nmf_multi_ld[:, 0]
y = nmf_multi_ld[:, 1]
z = nmf_multi_ld[:, 2]
if flag_const_100:
k = 100 / np.sum(y)
# print(np.sum(y))
else:
k = 1 / np.sum(y)
# print(np.sum(y))
X = np.sum(x * nhimg, axis=2)
Y = np.sum(y * nhimg, axis=2)
Z = np.sum(z * nhimg, axis=2)
XYZ = np.stack([X, Y, Z], 2)
# print(np.max(XYZ), np.min(XYZ))
# print(np.max(Y*k), np.min(Y*k))
XYZ = XYZ * k
XYZ.shape
xyz_to_r = np.array([3.2406255, -1.537208, -0.4986286])
r = np.dot(XYZ, xyz_to_r)
xyz_to_g = np.array([-0.9689307, 1.8757561, 0.0415175])
g = np.dot(XYZ, xyz_to_g)
xyz_to_b = np.array([0.0557101, -0.2040211, 1.0569959])
b = np.dot(XYZ, xyz_to_b)
rgb_img2 = np.stack([r, g, b], axis=2)
rgb_img2 = np.where(rgb_img2 < 0, 0, rgb_img2)
if flag_const_100:
# HSI画像配布元と同じガンマ補正（ガンマ=0.6）をする
# print(np.max(rgb_img2))
rgb_img2 = np.power(rgb_img2/255, 0.6)
else:
# XYZからsRGBへのレンダリングするためのガンマ補正
# print(np.max(255*rgb_img2))
rgb_img2 = np.where(rgb_img2 <= 0.0031308, 12.92 * rgb_img2, 1.055 * np.power(rgb_img2, 1/2.4) - 0.055)
rgb_img2 = np.clip(rgb_img2, 0, 1)
if flag_const_100:
img = Image.fromarray(np.uint8(255*rgb_img2))
else:
img = Image.fromarray(np.uint8(255*rgb_img2))
return img