diff --git a/ColorConversion.py b/ColorConversion.py
new file mode 100644
index 0000000..f3f464f
--- /dev/null
+++ b/ColorConversion.py
@@ -0,0 +1,282 @@
+import matplotlib.pyplot as plt
+import openpyxl
+import numpy as np
+from scipy import interpolate
+from sklearn.linear_model import LinearRegression
+
+
+# エクセルから指定範囲読み込み
+def read_cell_range(sheet, start_cell, end_cell):
+    cell_range = sheet[start_cell:end_cell]
+    cell_values = []
+
+    for row in cell_range:
+        for cell in row:
+            cell_values.append(cell.value)
+
+    return np.array(cell_values)
+
+
+# Cubic補間
+def interporate(x_org, y_org, x_target):
+    intp = interpolate.interp1d(x_org, y_org, kind="cubic")
+    return intp(x_target)
+
+
+# XYZ値算出
+def calc_XYZ(reflectance):
+    # global light, x_bar, y_bar, z_bar, K
+    X = np.sum(reflectance * light * x_bar) / K
+    Y = np.sum(reflectance * light * y_bar) / K
+    Z = np.sum(reflectance * light * z_bar) / K
+    return [X, Y, Z]
+
+
+# MCCで校正したゲインでRGB値算出
+def calc_mcc_rgb(reflectance):
+    # global light, cam_r, cam_g, cam_b, gain_mcc_r, gain_mcc_g, gain_mcc_b
+    r = np.sum(reflectance * light * cam_r) * gain_mcc_r
+    g = np.sum(reflectance * light * cam_g) * gain_mcc_g
+    b = np.sum(reflectance * light * cam_b) * gain_mcc_b
+    return [r, g, b]
+
+
+# TCCで校正したゲインでRGB値算出
+def calc_tcc_rgb(reflectance):
+    # global light, cam_r, cam_g, cam_b, gain_tcc_r, gain_tcc_g, gain_tcc_b
+    r = np.sum(reflectance * light * cam_r) * gain_tcc_r
+    g = np.sum(reflectance * light * cam_g) * gain_tcc_g
+    b = np.sum(reflectance * light * cam_b) * gain_tcc_b
+    return [r, g, b]
+
+
+# XYZ->L*a*b*変換の非線形変換
+def f(t):
+    if t > (6.0 / 29.0) ** 3:
+        return t ** (1.0 / 3.0)
+    else:
+        return ((29.0 / 3.0) ** 3 * t + 16.0) / 116.0
+
+
+# XYZ -> L*a*b* 変換
+def xyz_to_lab(xyz):
+    global X_ref, Y_ref, Z_ref
+    L = 116.0 * f(xyz[1] / Y_ref) - 16.0
+    a = 500.0 * (f(xyz[0] / X_ref) - f(xyz[1] / Y_ref))
+    b = 200.0 * (f(xyz[1] / Y_ref) - f(xyz[2] / Z_ref))
+    return [L, a, b]
+
+
+# CIE分光データから等色関数取得
+wbCIE = openpyxl.load_workbook("SpectrumDataCIE.xlsx")
+wsCIE = wbCIE["Sheet1"]
+wavelen_org = read_cell_range(wsCIE, "A5", "A99")
+x_bar_org = read_cell_range(wsCIE, "B5", "B99")
+y_bar_org = read_cell_range(wsCIE, "C5", "C99")
+z_bar_org = read_cell_range(wsCIE, "D5", "D99")
+# 400～700nm, 10nm刻み に補間
+wavelen = np.linspace(400, 700, 31)
+x_bar = interporate(wavelen_org, x_bar_org, wavelen)
+y_bar = interporate(wavelen_org, y_bar_org, wavelen)
+z_bar = interporate(wavelen_org, z_bar_org, wavelen)
+# グラフ出力
+fig = plt.figure(figsize=(12, 8))
+ax1 = fig.add_subplot(2, 3, 1)
+ax1.plot(wavelen, x_bar, linestyle="solid", color="red")
+ax1.plot(wavelen, y_bar, linestyle="solid", color="green")
+ax1.plot(wavelen, z_bar, linestyle="solid", color="blue")
+ax1.set_title("color matching function")
+
+# カメラの分光感度特性を取得
+wbSzk = openpyxl.load_workbook("SpectrumDataSuzuki.xlsx")
+wsSzk = wbSzk["Sheet1"]
+cam_r = read_cell_range(wsSzk, "B4", "B34")
+cam_g = read_cell_range(wsSzk, "C4", "C34")
+cam_b = read_cell_range(wsSzk, "D4", "D34")
+ax2 = fig.add_subplot(2, 3, 2)
+ax2.plot(wavelen, cam_r, linestyle="solid", color="red")
+ax2.plot(wavelen, cam_g, linestyle="solid", color="green")
+ax2.plot(wavelen, cam_b, linestyle="solid", color="blue")
+ax2.set_title("Lw115C camera spectral sensitivity")
+
+# D65, TIAS光源の分光特性を取得
+wavelen2_org = np.linspace(300, 830, 107)
+d65_org = read_cell_range(wsCIE, "X6", "X112")
+d65 = interporate(wavelen2_org, d65_org, wavelen)
+d65_max = d65.max(axis=None)
+d65 = d65 / d65_max
+tias = read_cell_range(wsSzk, "F4", "F34")
+tias_max = tias.max(axis=None)
+tias = tias / tias_max
+ax3 = fig.add_subplot(2, 3, 3)
+ax3.plot(wavelen, tias, linestyle="solid", color="red")
+ax3.plot(wavelen, d65, linestyle="solid", color="orange")
+ax3.set_title("Illuminations spectrum")
+print("Illuminaton selection = TIAS")
+light = tias
+
+# サンプル（肌パッチ，舌モデル）の分光反射率を取得
+sample = np.zeros((5, 31))
+sample[0, :] = read_cell_range(wsSzk, "H4", "H34") / 10000.0  # 肌パッチ1
+sample[1, :] = read_cell_range(wsSzk, "I4", "I34") / 10000.0  # 肌パッチ2
+sample[2, :] = read_cell_range(wsSzk, "J4", "J34") / 10000.0  # 舌モデルA
+sample[3, :] = read_cell_range(wsSzk, "K4", "K34") / 10000.0  # 舌モデルB
+sample[4, :] = read_cell_range(wsSzk, "L4", "L34") / 10000.0  # 舌モデルC
+ax4 = fig.add_subplot(2, 3, 4)
+ax4.plot(wavelen, sample[0, :], linestyle="solid")
+ax4.plot(wavelen, sample[1, :], linestyle="solid")
+ax4.plot(wavelen, sample[2, :], linestyle="solid")
+ax4.plot(wavelen, sample[3, :], linestyle="solid")
+ax4.plot(wavelen, sample[4, :], linestyle="solid")
+ax4.set_title("Samples' reflentance")
+
+# MCC 24色の分光反射率を取得
+wsMcc = wbSzk["Sheet2"]
+mcc = np.zeros((24, wavelen.shape[0]))
+ax5 = fig.add_subplot(2, 3, 5)
+for i in range(mcc.shape[0]):
+    mcc[i, :] = read_cell_range(wsMcc, chr(66 + i) + "3", chr(66 + i) + "33") / 10000
+    ax5.plot(wavelen, mcc[i, :], linestyle="solid")
+ax5.set_title("Macbeth CC reflectance")
+
+# 舌診色票TCC 24色の分光反射率を取得
+wsTcc = wbSzk["Sheet3"]
+tcc = np.zeros((24, wavelen.shape[0]))
+ax6 = fig.add_subplot(2, 3, 6)
+for i in range(24):
+    tcc[i, :] = read_cell_range(wsTcc, chr(66 + i) + "3", chr(66 + i) + "33") / 10000
+    ax6.plot(wavelen, tcc[i, :], linestyle="solid")
+ax6.set_title("Tongue CC reflectance")
+
+# 参照白色のXYZ値とK値を算出
+K = np.sum(light * y_bar)
+X_ref = np.sum(light * x_bar) / K
+Z_ref = np.sum(light * z_bar) / K
+Y_ref = 1.0
+print("Xw, Yw, Zw = {:.3}, {:.3}, {:.3}".format(X_ref, Y_ref, Z_ref))
+
+# MCCのXYZ値算出
+mcc_xyz = np.zeros((mcc.shape[0], 3))
+for i in range(mcc.shape[0]):
+    mcc_xyz[i, :] = calc_XYZ(mcc[i])
+print("MCC XYZ=")
+print(mcc_xyz)
+
+# カメラで撮影したMCC 24色のRGB値を算出
+# MCC 19番（白パッチ）=200,200,200 でゲイン校正
+calib_target = 18
+calib_value = 200
+gain_mcc_r = calib_value / np.sum(mcc[calib_target] * light * cam_r)
+gain_mcc_g = calib_value / np.sum(mcc[calib_target] * light * cam_g)
+gain_mcc_b = calib_value / np.sum(mcc[calib_target] * light * cam_b)
+mcc_rgb = np.zeros((mcc.shape[0], 3))
+for i in range(mcc.shape[0]):
+    mcc_rgb[i, :] = calc_mcc_rgb(mcc[i])
+print("MCC RGB=")
+print(mcc_rgb)
+
+# MCCを使って RGB->XYZ 変換行列算出
+rgb2xyz_mcc = LinearRegression()
+rgb2xyz_mcc.fit(mcc_rgb, mcc_xyz)
+
+# MCCの変換行列を使ってMCCの24色をRGB->XYZ変換（検証）
+pred_mcc_xyz = rgb2xyz_mcc.predict(mcc_rgb)
+print("MCC XYZ Converted by MCC=")
+print(pred_mcc_xyz)
+
+
+# サンプルのXYZ値算出
+sample_xyz = np.zeros((sample.shape[0], 3))
+sample_lab = np.zeros((sample.shape[0], 3))
+for i in range(sample.shape[0]):
+    sample_xyz[i, :] = calc_XYZ(sample[i])
+    sample_lab[i, :] = xyz_to_lab(sample_xyz[i, :])
+print("Sample XYZ=")
+print(sample_xyz)
+print("Sample L*a*b*=")
+print(sample_lab)
+
+# サンプルのRGB値算出
+sample_rgb = np.zeros((sample.shape[0], 3))
+for i in range(sample.shape[0]):
+    sample_rgb[i, :] = calc_mcc_rgb(sample[i])
+print("Sample RGB=")
+print(sample_rgb)
+
+# MCCの変換行列を使ってサンプルをRGB->XYZ変換（テスト）
+pred_sample_xyz_by_mcc = rgb2xyz_mcc.predict(sample_rgb)
+print("Sample XYZ Converted by MCC=")
+print(pred_sample_xyz_by_mcc)
+pred_sample_lab_by_mcc = np.zeros((pred_sample_xyz_by_mcc.shape[0], 3))
+for i in range(pred_sample_xyz_by_mcc.shape[0]):
+    pred_sample_lab_by_mcc[i, :] = xyz_to_lab(pred_sample_xyz_by_mcc[i, :])
+print("Sample L*a*b* Converted by MCC=")
+print(pred_sample_lab_by_mcc)
+
+# MCCの変換行列を使ったサンプルL*a*b*値の色差を計算
+sample_delta_e_by_mcc = []
+for i in range(pred_sample_lab_by_mcc.shape[0]):
+    sample_delta_e_by_mcc.append(
+        np.linalg.norm(sample_lab[i, :] - pred_sample_lab_by_mcc[i, :])
+    )
+print("MCCで変換行列を算出した場合のサンプルの色差")
+print("肌パッチ1 = ", sample_delta_e_by_mcc[0])
+print("肌パッチ2 = ", sample_delta_e_by_mcc[1])
+print("舌モデルA = ", sample_delta_e_by_mcc[2])
+print("舌モデルB = ", sample_delta_e_by_mcc[3])
+print("舌モデルC = ", sample_delta_e_by_mcc[4])
+
+
+# TCCのXYZ値算出
+tcc_xyz = np.zeros((tcc.shape[0], 3))
+for i in range(tcc.shape[0]):
+    tcc_xyz[i, :] = calc_XYZ(tcc[i])
+print("TCC XYZ=")
+print(tcc_xyz)
+
+# カメラで撮影したTCC 24色のRGB値を算出
+# TCC 13番（白パッチ）=200,200,200 でゲイン校正
+calib_target = 12
+calib_value = 200
+gain_tcc_r = calib_value / np.sum(tcc[calib_target] * light * cam_r)
+gain_tcc_g = calib_value / np.sum(tcc[calib_target] * light * cam_g)
+gain_tcc_b = calib_value / np.sum(tcc[calib_target] * light * cam_b)
+tcc_rgb = np.zeros((tcc.shape[0], 3))
+for i in range(tcc.shape[0]):
+    tcc_rgb[i, :] = calc_tcc_rgb(tcc[i])
+print("TCC RGB=")
+print(tcc_rgb)
+
+# TCCを使って RGB->XYZ 変換行列算出
+rgb2xyz_tcc = LinearRegression()
+rgb2xyz_tcc.fit(tcc_rgb, tcc_xyz)
+
+# TCCの変換行列を使ってTCCの24色をRGB->XYZ変換（検証）
+pred_tcc_xyz = rgb2xyz_tcc.predict(tcc_rgb)
+print("TCC XYZ Converted by TCC=")
+print(pred_tcc_xyz)
+
+# TCCの変換行列を使ってサンプルをRGB->XYZ変換（テスト）
+pred_sample_xyz_by_tcc = rgb2xyz_tcc.predict(sample_rgb)
+print("Sample XYZ Converted by TCC=")
+print(pred_sample_xyz_by_tcc)
+pred_sample_lab_by_tcc = np.zeros((pred_sample_xyz_by_tcc.shape[0], 3))
+for i in range(pred_sample_xyz_by_tcc.shape[0]):
+    pred_sample_lab_by_tcc[i, :] = xyz_to_lab(pred_sample_xyz_by_tcc[i, :])
+print("Sample L*a*b* Converted by TCC=")
+print(pred_sample_lab_by_tcc)
+
+# TCCの変換行列を使ったサンプルL*a*b*値の色差を計算
+sample_delta_e_by_tcc = []
+for i in range(pred_sample_lab_by_tcc.shape[0]):
+    sample_delta_e_by_tcc.append(
+        np.linalg.norm(sample_lab[i, :] - pred_sample_lab_by_tcc[i, :])
+    )
+print("TCCで変換行列を算出した場合のサンプルの色差")
+print("肌パッチ1 = ", sample_delta_e_by_tcc[0])
+print("肌パッチ2 = ", sample_delta_e_by_tcc[1])
+print("舌モデルA = ", sample_delta_e_by_tcc[2])
+print("舌モデルB = ", sample_delta_e_by_tcc[3])
+print("舌モデルC = ", sample_delta_e_by_tcc[4])
+
+plt.show()
diff --git a/README.md b/README.md
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--- /dev/null
+++ b/README.md
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+# Evaluating Color Checker
+
+カラーチェッカーの RGB->XYZ(Lab)変換精度評価スクリプト
+
+## 開発環境
+
+- Python
+- matplotlib
+- openpyxl
+- numpy
+- scipy
+- scikitlearn
diff --git a/SpectrumDataCIE.xlsx b/SpectrumDataCIE.xlsx
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index 0000000..7f26e90
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+++ b/SpectrumDataCIE.xlsx
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diff --git a/SpectrumDataSuzuki.xlsx b/SpectrumDataSuzuki.xlsx
new file mode 100644
index 0000000..31c6372
--- /dev/null
+++ b/SpectrumDataSuzuki.xlsx
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