diff --git a/.vscode/launch.json b/.vscode/launch.json
index c40731e..c361c4e 100644
--- a/.vscode/launch.json
+++ b/.vscode/launch.json
@@ -10,7 +10,7 @@
             "request": "launch",
             "program": "${file}",
             "console": "integratedTerminal",
-            "args": "sample/DSC_9657s.jpg"
+            "args": "sample/TIAS26_MCC.jpg"
         }
     ]
 }
\ No newline at end of file
diff --git a/README.md b/README.md
index 379fe5d..6b71615 100644
--- a/README.md
+++ b/README.md
@@ -19,6 +19,9 @@
 （`numpy.ndarray`）です。配列は `uint8` 型、形状 `(高さ, 幅, 3)` の
 BGR画像（OpenCVの標準形式）を指定してください。入力配列は変更しません。
 
+横向き（6列×4行）と縦向き（4列×6行）の格子を検出し、色票の向きを補正します。
+明るい背景に囲まれた色票（`sample/TIAS26_MCC.jpg` など）にも対応しています。
+
 ```python
 import cv2
 
diff --git a/colorchecker.py b/colorchecker.py
index a0c007d..6e72372 100644
--- a/colorchecker.py
+++ b/colorchecker.py
@@ -180,9 +180,9 @@
         np.ones((5, 5), np.uint8)
     )
 
-    contours, _ = cv2.findContours(
+    contours, hierarchy = cv2.findContours(
         mask,
-        cv2.RETR_EXTERNAL,
+        cv2.RETR_TREE,
         cv2.CHAIN_APPROX_SIMPLE
     )
 
@@ -192,7 +192,17 @@
 
     candidates = []
 
-    for contour in contours:
+    for index, contour in enumerate(contours):
+
+        # 明るい背景の内側にある色票も探索する。
+        # 奇数階層は暗い領域（穴）の輪郭なので候補に含めない。
+        depth = 0
+        parent = hierarchy[0, index, 3]
+        while parent != -1:
+            depth += 1
+            parent = hierarchy[0, parent, 3]
+        if depth % 2:
+            continue
 
         x, y, w, h = cv2.boundingRect(contour)
 
@@ -335,7 +345,9 @@
 
 def warp_from_cells(image,
                     cells,
-                    cell_size=140):
+                    cell_size=140,
+                    rows=4,
+                    cols=6):
 
     src_points = []
     dst_points = []
@@ -349,10 +361,17 @@
         ])
 
         # 正規化画像上の色票中心
-        dst_points.append([
-            (col + 0.5) * cell_size,
-            (row + 0.5) * cell_size
-        ])
+        if (rows, cols) == (6, 4):
+            # 縦向きの格子は90度回転した座標へ直接射影する。
+            dst_points.append([
+                (rows - row - 0.5) * cell_size,
+                (col + 0.5) * cell_size
+            ])
+        else:
+            dst_points.append([
+                (col + 0.5) * cell_size,
+                (row + 0.5) * cell_size
+            ])
 
     src_points = np.asarray(
         src_points,
@@ -741,27 +760,22 @@
             )
         )
 
-        grid = fit_grid(
-            candidates
-        )
+        for rows, cols in ((4, 6), (6, 4)):
+            grid = fit_grid(candidates, rows=rows, cols=cols)
+            if grid is None:
+                continue
 
-        if grid is None:
-            continue
-
-        score, cells = grid
-
-        if (
-            best is None
-            or score > best["score"]
-        ):
-
-            best = {
-                "score": score,
-                "threshold": threshold,
-                "candidates": candidates,
-                "cells": cells,
-                "mask": mask
-            }
+            score, cells = grid
+            if best is None or score > best["score"]:
+                best = {
+                    "score": score,
+                    "threshold": threshold,
+                    "candidates": candidates,
+                    "cells": cells,
+                    "mask": mask,
+                    "rows": rows,
+                    "cols": cols,
+                }
 
     if best is None:
 
@@ -775,7 +789,9 @@
         warp_from_cells(
             image,
             best["cells"],
-            cell_size
+            cell_size,
+            rows=best["rows"],
+            cols=best["cols"],
         )
     )
 
diff --git a/test_colorchecker.py b/test_colorchecker.py
index 22fdf7e..acb2037 100644
--- a/test_colorchecker.py
+++ b/test_colorchecker.py
@@ -7,7 +7,7 @@
 import cv2
 import numpy as np
 
-from colorchecker import ResultCode, detect_colorchecker
+from colorchecker import ResultCode, detect_colorchecker, read_image
 
 
 class ColorCheckerTests(unittest.TestCase):
@@ -90,16 +90,38 @@
             np.testing.assert_allclose(measured, colors.T, atol=1)
             self.assertEqual(stdout.getvalue(), '')
             self.assertEqual(list(Path(directory).iterdir()), [path])
-            for source in (str(path), image, image[:, ::-1]):
+            # 明るい背景に囲まれた色票と、縦向き・反転を含む全方向。
+            surrounded = np.pad(image, ((30, 30), (30, 30), (0, 0)),
+                                constant_values=240)
+            sources = [str(path), image, image[:, ::-1]]
+            sources.extend(np.rot90(surrounded, k) for k in range(4))
+            sources.extend(np.rot90(surrounded[:, ::-1], k) for k in range(4))
+            for source in sources:
                 with self.subTest(input_type=type(source)):
                     original = source.copy() if isinstance(source, np.ndarray) else None
                     code, measured, images = detect_colorchecker(filename=source)
                     self.assertEqual(code, ResultCode.SUCCESS)
-                    self.assert_images(images, image.shape)
+                    self.assert_images(images, source.shape if original is not None
+                                       else image.shape)
                     np.testing.assert_allclose(measured, colors.T, atol=1)
                     if original is not None:
                         np.testing.assert_array_equal(source, original)
 
+    def test_tias26_checker(self):
+        path = Path(__file__).parent / 'sample' / 'TIAS26_MCC.jpg'
+        image = read_image(path)
+        code, measured, images = detect_colorchecker(image)
+        self.assertEqual(code, ResultCode.SUCCESS)
+        self.assert_images(images, image.shape)
+
+        # 原画像で確認した24色票の内部と比較し、位置・向き・順序も検証。
+        expected = np.empty((3, 24))
+        for row, y in enumerate((600, 900, 1190, 1475, 1750, 2020)):
+            for col, x in enumerate((480, 800, 1110, 1420)):
+                rgb = image[y - 20:y + 20, x - 20:x + 20, ::-1].mean(axis=(0, 1))
+                expected[:, (3 - col) * 6 + row] = rgb
+        np.testing.assert_allclose(measured, expected, atol=8)
+
 
 if __name__ == '__main__':
     unittest.main()