diff --git a/Spectrum.xlsx b/Spectrum.xlsx
index 80fb006..6f01b24 100644
--- a/Spectrum.xlsx
+++ b/Spectrum.xlsx
Binary files differ
diff --git "a/TCC\350\250\210\346\270\254\345\206\231\347\234\237.pptx" "b/TCC\350\250\210\346\270\254\345\206\231\347\234\237.pptx"
deleted file mode 100644
index abdbb3c..0000000
--- "a/TCC\350\250\210\346\270\254\345\206\231\347\234\237.pptx"
+++ /dev/null
Binary files differ
diff --git a/eval_chart.py b/eval_chart.py
index a139371..ebd1627 100644
--- a/eval_chart.py
+++ b/eval_chart.py
@@ -1,45 +1,297 @@
 import openpyxl
 import numpy as np
 import matplotlib.pyplot as plt
+import cv2
+from sklearn.linear_model import LinearRegression
+import sys
 
 
-def excel_range(filename, r):
+def read_excel(filename: str, range_str: str, sheet_no: int = 0) -> np.array:
+    """Excelから数値データ取得
+
+    Args:
+        filename (str): ファイル名
+        range_str (str): セル範囲文字列 'A1:B2'
+
+    Returns:
+        np.array: セル値配列
+    """
     wb = openpyxl.load_workbook(filename)
-    sheet = wb[wb.sheetnames[0]]
-    return [[cell.value for cell in sheet[r][i]] for i in range(len(sheet[r]))]
+    sheet = wb[wb.sheetnames[sheet_no]]
+    return np.array(
+        [
+            [cell.value for cell in sheet[range_str][i]]
+            for i in range(len(sheet[range_str]))
+        ]
+    )
 
 
-led_sp = np.float32(excel_range("Spectrum.xlsx", "B2:AF2")[0])
-cmf_sp = np.float32(excel_range("Spectrum.xlsx", "B3:AF5"))
-filter_sp = np.float32(excel_range("Spectrum.xlsx", "B6:AF8"))
-tcc_sp = np.float32(excel_range("handy_tcc.xlsx", "F2:AJ25")) / 100
-# print("led=", led_sp)
+def extend_rgb(rgb_arr: np.array) -> np.array:
+    """RGB値の高次項を拡張する
 
-sum_y = led_sp.dot(cmf_sp[1])
-k = 100.0 / sum_y
+    Args:
+        rgb_arr (np.array): RGB値の配列 1x3, Nx3
 
-tcc_xyz = np.zeros((24, 3), np.float32)
-for patch_no in range(24):
-    tcc_xyz[patch_no, 0] = (led_sp * tcc_sp[patch_no]).dot(cmf_sp[0]) * k
-    tcc_xyz[patch_no, 1] = (led_sp * tcc_sp[patch_no]).dot(cmf_sp[1]) * k
-    tcc_xyz[patch_no, 2] = (led_sp * tcc_sp[patch_no]).dot(cmf_sp[2]) * k
-    # print(patch_no, tri_x, tri_y, tri_z, sep="\t")
-print("tcc_xyz=")
-print(tcc_xyz)
+    Returns:
+        np.array: 拡張した配列 1x11, Nx11
+    """
 
-white_patch = 0
-white_balance = np.float32([240, 240, 240])
-raw_rgb = np.float32(
-    [(led_sp * tcc_sp[white_patch]).dot(filter_sp[i]) for i in range(3)]
-)
-print("raw_rgb=", raw_rgb)
-rgb_gain = white_balance / raw_rgb
-print("rgb_gain=", rgb_gain)
-tcc_rgb = np.zeros((24, 3), np.float32)
-for patch_no in range(24):
-    tcc_rgb[patch_no, 0] = (led_sp * tcc_sp[patch_no]).dot(filter_sp[0]) * rgb_gain[0]
-    tcc_rgb[patch_no, 1] = (led_sp * tcc_sp[patch_no]).dot(filter_sp[1]) * rgb_gain[1]
-    tcc_rgb[patch_no, 2] = (led_sp * tcc_sp[patch_no]).dot(filter_sp[2]) * rgb_gain[2]
-    # print(patch_no, tri_x, tri_y, tri_z, sep="\t")
-print("tcc_rgb=")
-print(tcc_rgb)
+    conv_2d = 0
+    if rgb_arr.ndim == 1:
+        rgb_arr = rgb_arr.reshape([1, len(rgb_arr)])
+        conv_2d = 1
+    extended = np.array(
+        [
+            [
+                1,
+                rgb[0],
+                rgb[1],
+                rgb[2],
+                rgb[0] ** 2,
+                rgb[1] ** 2,
+                rgb[2] ** 2,
+                rgb[0] * rgb[1],
+                rgb[1] * rgb[2],
+                rgb[2] * rgb[0],
+                rgb[0] * rgb[1] * rgb[2],
+            ]
+            for rgb in rgb_arr
+        ]
+    )
+    if conv_2d == 1:
+        return extended[0]
+    else:
+        return extended
+
+
+def lab_func(t: float) -> float:
+    """XYZ->LAB変換に使用する暗部補正付き関数
+
+    Args:
+        t (float): 値
+
+    Returns:
+        float: 変換結果
+    """
+    if t > 0.008856:
+        return t ** (1.0 / 3.0)
+    else:
+        return 7.787 * t + 16.0 / 116.0
+
+
+def xyz_to_lab(xyz_arr: np.array, white_xyz: np.array) -> np.array:
+    """XYZからCIELABへ変換
+
+    Args:
+        xyz_arr (np.array): XYZ三刺激値の配列 1x3, Nx3
+        white_xyz (np.array): 白色点の三刺激値
+
+    Returns:
+        np.array: L*a*b*の配列 1x3, Nx3
+    """
+
+    conv_2d = 0
+    if xyz_arr.ndim == 1:
+        xyz_arr = xyz_arr.reshape([1, len(xyz_arr)])
+        conv_2d = 1
+    lab_arr = np.array(
+        [
+            [
+                116.0 * lab_func(xyz[1] / white_xyz[1]) - 16.0,
+                500.0
+                * (lab_func(xyz[0] / white_xyz[0]) - lab_func(xyz[1] / white_xyz[1])),
+                200.0
+                * (lab_func(xyz[1] / white_xyz[1]) - lab_func(xyz[2] / white_xyz[2])),
+            ]
+            for xyz in xyz_arr
+        ]
+    )
+    if conv_2d == 1:
+        return lab_arr[0]
+    else:
+        return lab_arr
+
+
+if __name__ == "__main__":
+    # --------------------------------------------------------------
+    # Excelからデータ取得
+    light_sp = read_excel("Spectrum.xlsx", "B2:AF2")[0]
+    cmf_sp_arr = read_excel("Spectrum.xlsx", "B3:AF5")
+    filter_sp_arr = read_excel("Spectrum.xlsx", "B6:AF8")
+    tongue_sp_arr = read_excel("Spectrum.xlsx", "B9:AF16")
+
+    # tcc_name = "handy_tcc"
+    # tcc_name = "takano_tcc"
+    tcc_name = "sugawara_tcc_mat8485"
+    tcc_sp_arr = read_excel(tcc_name + ".xlsx", "F2:AJ25") / 100.0
+    white_patch = (
+        0 if tcc_name == "handy_tcc" else 12
+    )  # ホワイトバランスに用いる色票番号
+
+    # --------------------------------------------------------------
+    # 白色点の計算
+    white_xyz = np.array([light_sp.dot(cmf_sp) for cmf_sp in cmf_sp_arr])
+    k = 100.0 / white_xyz[1]
+    white_xyz *= k
+    print("白色点 XYZ=", white_xyz)
+
+    # --------------------------------------------------------------
+    # TCCのXYZ, L*a*b* 算出（正解値）
+    tcc_xyz_arr = np.array(
+        [
+            [
+                (light_sp * tcc_sp_arr[p]).dot(cmf_sp_arr[i]) * k
+                for i in range(len(cmf_sp_arr))
+            ]
+            for p in range(len(tcc_sp_arr))
+        ]
+    )
+    # print("tcc_xyz=")
+    # print(tcc_xyz)
+    tcc_lab_arr = xyz_to_lab(tcc_xyz_arr, white_xyz)
+    print("TCC L*a*b* 正解値=")
+    print(tcc_lab_arr)
+
+    # --------------------------------------------------------------
+    # カメラシミュレータのホワイトバランス
+    white_balance = np.array([0.9, 0.9, 0.9])  # ホワイトバランスのRGB値
+    raw_rgb = np.array(
+        [
+            (light_sp * tcc_sp_arr[white_patch]).dot(filter_sp_arr[i])
+            for i in range(len(filter_sp_arr))
+        ]
+    )
+    # print("raw_rgb=", raw_rgb)
+    rgb_gain = white_balance / raw_rgb
+    # print("rgb_gain=", rgb_gain)
+
+    # --------------------------------------------------------------
+    # カメラシミュレータによる色票のRGB値算出
+    tcc_rgb_arr = np.array(
+        [
+            [
+                (light_sp * tcc_sp_arr[p]).dot(filter_sp_arr[i]) * rgb_gain[i]
+                for i in range(len(filter_sp_arr))
+            ]
+            for p in range(len(tcc_sp_arr))
+        ]
+    )
+    # print("TCC RGB=")
+    # print(tcc_rgb_arr)
+    # print(extend_rgb(tcc_rgb))
+
+    # --------------------------------------------------------------
+    # カメラシミュレータRGBからXYZ推定行列の算出
+    # tcc_rgb_input_arr = tcc_rgb_arr
+    tcc_rgb_input_arr = extend_rgb(tcc_rgb_arr)
+    conv_rgb_to_xyz = LinearRegression()
+    conv_rgb_to_xyz.fit(tcc_rgb_input_arr, tcc_xyz_arr)
+
+    # --------------------------------------------------------------
+    # カメラシミュレータRGB値からXYZ, L*a*b*の推定
+    tcc_xyz_pred_arr = conv_rgb_to_xyz.predict(tcc_rgb_input_arr)
+    # print("TCC XYZ 推定値=")
+    # print(tcc_xyz_pred_arr)
+    tcc_lab_pred_arr = xyz_to_lab(tcc_xyz_pred_arr, white_xyz)
+    print("TCC L*a*b* 推定値=")
+    print(tcc_lab_pred_arr)
+
+    # --------------------------------------------------------------
+    # TCC 正解値と推定値の色差計算
+    delta_e_arr = np.array(
+        [
+            np.linalg.norm(tcc_lab_pred_arr[i] - tcc_lab_arr[i], ord=2)
+            for i in range(len(tcc_lab_arr))
+        ]
+    )
+    delta_e = np.mean(delta_e_arr)
+    print("TCC 推定色差 DeltaE=", delta_e)
+
+    # --------------------------------------------------------------
+    # 舌のXYZ, L*a*b* 算出（正解値）
+    tongue_xyz_arr = np.array(
+        [
+            [
+                (light_sp * tongue_sp_arr[p]).dot(cmf_sp_arr[i]) * k
+                for i in range(len(cmf_sp_arr))
+            ]
+            for p in range(len(tongue_sp_arr))
+        ]
+    )
+    # print("tongue_xyz=")
+    # print(tongue_xyz_arr)
+    tongue_lab_arr = xyz_to_lab(tongue_xyz_arr, white_xyz)
+    print("舌 L*a*b* 正解値=")
+    print(tongue_lab_arr)
+
+    # --------------------------------------------------------------
+    # カメラシミュレータによる舌のRGB値算出
+    tongue_rgb_arr = np.array(
+        [
+            [
+                (light_sp * tongue_sp_arr[p]).dot(filter_sp_arr[i]) * rgb_gain[i]
+                for i in range(len(filter_sp_arr))
+            ]
+            for p in range(len(tongue_sp_arr))
+        ]
+    )
+    print("舌 RGB=")
+    print(tongue_rgb_arr)
+
+    # --------------------------------------------------------------
+    # 舌のカメラシミュレータRGB値からXYZ, L*a*b*の推定
+    tongue_rgb_input_arr = extend_rgb(tongue_rgb_arr)
+    tongue_xyz_pred_arr = conv_rgb_to_xyz.predict(tongue_rgb_input_arr)
+    print("舌 XYZ 推定値=")
+    print(tongue_xyz_pred_arr)
+    tongue_lab_pred_arr = xyz_to_lab(tongue_xyz_pred_arr, white_xyz)
+    print("舌 L*a*b* 推定値=")
+    print(tongue_lab_pred_arr)
+
+    # --------------------------------------------------------------
+    # 舌 正解値と推定値の色差計算
+    delta_e_arr = np.array(
+        [
+            np.linalg.norm(tongue_lab_pred_arr[i] - tongue_lab_arr[i], ord=2)
+            for i in range(len(tongue_lab_arr))
+        ]
+    )
+    delta_e = np.mean(delta_e_arr)
+    print("舌 推定色差 DeltaE=", delta_e)
+
+    fig = plt.figure(figsize=(15, 8))
+    ax_ab = fig.add_subplot(1, 2, 1, xlabel="a*", ylabel="b*")
+    plt.grid(True)
+    ax_ab.set_title(tcc_name + "  a* - b*")
+    ax_ab.set_xlim(-40, 60)
+    ax_ab.set_ylim(-40, 60)
+    ax_ab.text(-35, 55, "red:GT, black:Estim, green:tcc")
+    for i in range(len(tongue_lab_arr)):
+        ax_ab.plot(
+            [tongue_lab_arr[i, 1], tongue_lab_pred_arr[i, 1]],
+            [tongue_lab_arr[i, 2], tongue_lab_pred_arr[i, 2]],
+            "-",
+            c="gray",
+        )
+    ax_ab.plot(tongue_lab_arr[:, 1], tongue_lab_arr[:, 2], "o", c="r")
+    ax_ab.plot(tongue_lab_pred_arr[:, 1], tongue_lab_pred_arr[:, 2], "o", c="k")
+    ax_ab.plot(tcc_lab_arr[:, 1], tcc_lab_arr[:, 2], "o", c="g")
+
+    ax_aL = fig.add_subplot(1, 2, 2, xlabel="a*", ylabel="L*")
+    plt.grid(True)
+    ax_aL.set_title(tcc_name + "  a* - L*")
+    ax_aL.set_xlim(-40, 60)
+    ax_aL.set_ylim(0, 100)
+    ax_aL.text(-35, 95, "red:GT, black:Estim, green:tcc")
+    for i in range(len(tongue_lab_arr)):
+        ax_aL.plot(
+            [tongue_lab_arr[i, 1], tongue_lab_pred_arr[i, 1]],
+            [tongue_lab_arr[i, 0], tongue_lab_pred_arr[i, 0]],
+            "-",
+            c="gray",
+        )
+    ax_aL.plot(tongue_lab_arr[:, 1], tongue_lab_arr[:, 0], "o", c="r")
+    ax_aL.plot(tongue_lab_pred_arr[:, 1], tongue_lab_pred_arr[:, 0], "o", c="k")
+    ax_aL.plot(tcc_lab_arr[:, 1], tcc_lab_arr[:, 0], "o", c="g")
+
+    plt.show()
diff --git a/requirements.txt b/requirements.txt
index 7bc79d3..861396c 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -2,3 +2,4 @@
 numpy
 openpyxl
 pillow
+opencv-python