diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..2c33560 --- /dev/null +++ b/.gitignore @@ -0,0 +1,2 @@ +sample/ + diff --git a/.vscode/launch.json b/.vscode/launch.json new file mode 100644 index 0000000..c40731e --- /dev/null +++ b/.vscode/launch.json @@ -0,0 +1,16 @@ +{ + // IntelliSense を使用して利用可能な属性を学べます。 + // 既存の属性の説明をホバーして表示します。 + // 詳細情報は次を確認してください: https://go.microsoft.com/fwlink/?linkid=830387 + "version": "0.2.0", + "configurations": [ + { + "name": "Python デバッガー: 引数を含む現在のファイル", + "type": "debugpy", + "request": "launch", + "program": "${file}", + "console": "integratedTerminal", + "args": "sample/DSC_9657s.jpg" + } + ] +} \ No newline at end of file diff --git a/README.md b/README.md new file mode 100644 index 0000000..379fe5d --- /dev/null +++ b/README.md @@ -0,0 +1,67 @@ +# ColorChecker 検出ライブラリ + +`colorchecker.py` をアプリから import できる場所に配置してください。 +依存パッケージは `pip install -r requirements.txt` で導入できます。 + +```python +from colorchecker import ResultCode, detect_colorchecker + +code, rgb, images = detect_colorchecker("sample/DSC_9657s.jpg") +if code == ResultCode.SUCCESS: + print(rgb.shape) # (3, 24) + red, green, blue = rgb + print(rgb[:, 0]) # 1番目の色票の R, G, B +else: + print(f"処理失敗: {int(code)}") +``` + +入力は画像ファイルのパス（`str` または `pathlib.Path`）、または画像データ +（`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。 + +```python +import cv2 + +image = cv2.imread("sample/DSC_9657s.jpg") +code, rgb, images = detect_colorchecker(image) +``` + +戻り値は `(ResultCode, numpy.ndarray, dict[str, numpy.ndarray])` で、RGB配列は常に `(3, 24)` の +`float64` です。行は R・G・B、列は `PATCH_NAMES` の順で、向き補正後の +6列×4行の色票を左上から横方向に並べます。成功時の値は0～255の平均値です。 + +第3の戻り値 `images` は次の処理画像を格納した辞書です（すべて `uint8`）。 + +| キー | 内容 | 形状 | +| --- | --- | --- | +| `warped` | 射影変換・向き補正後のBGR画像 | `(560, 840, 3)` | +| `mask` | 検出に採用した二値マスク（入力画像の座標系、値は0または255） | `(入力の高さ, 入力の幅)` | +| `debug` | `warped` に計測領域と色票番号を描画したBGR画像 | `(560, 840, 3)` | + +```python +if code == ResultCode.SUCCESS: + cv2.imwrite("warped.png", images["warped"]) + cv2.imwrite("mask.png", images["mask"]) + cv2.imwrite("debug.png", images["debug"]) +``` + +以前の2変数へのアンパックは、`code, rgb, images = ...` に変更してください。 +処理画像が不要な場合は `code, rgb, _ = ...` と書けます。 + +| コード | 定数 | 意味 | +| --- | --- | --- | +| 0 | `SUCCESS` | 成功 | +| 1 | `IMAGE_READ_ERROR` | ファイルの読み込み・デコード失敗、または入力配列の型・形状が不正 | +| 2 | `DETECTION_FAILED` | 色票の格子検出・射影変換の推定に失敗 | +| 3 | `PROCESSING_ERROR` | OpenCVの処理エラー、または計測値が不正 | + +失敗時はRGB配列の全要素が `NaN`、処理画像は空の辞書 `{}` です。ライブラリ関数は結果を表示せず、 +CSVや画像ファイルも保存しません。 + +従来のコマンドラインでの表示・CSVとデバッグ画像の保存は引き続き利用できます。 + +```powershell +python main2.py "sample/DSC_9657s.jpg" +``` + +テスト: `python -m unittest test_colorchecker.py` diff --git a/colorchecker.py b/colorchecker.py new file mode 100644 index 0000000..a0c007d --- /dev/null +++ b/colorchecker.py @@ -0,0 +1,792 @@ +"""ColorChecker Classic 24 detection and RGB measurement.""" + +from enum import IntEnum +from os import PathLike +from pathlib import Path + +import cv2 +import numpy as np + + +class ResultCode(IntEnum): + """画像ファイルを処理した結果。整数としても比較できます。""" + + SUCCESS = 0 + IMAGE_READ_ERROR = 1 + DETECTION_FAILED = 2 + PROCESSING_ERROR = 3 + + +def read_image(filename: str | PathLike[str]) -> np.ndarray | None: + """日本語を含むパスからBGR画像を読み込みます。""" + data = np.frombuffer(Path(filename).read_bytes(), dtype=np.uint8) + if data.size == 0: + return None + return cv2.imdecode(data, cv2.IMREAD_COLOR) + + +def detect_colorchecker( + filename: str | PathLike[str] | np.ndarray +) -> tuple[ResultCode, np.ndarray, dict[str, np.ndarray]]: + """画像ファイルまたは画像配列から24色票のRGB値を取得します。 + + Args: + filename: ファイルパス、またはuint8型・shape=(高さ, 幅, 3)のBGR画像。 + 入力配列は変更しません。引数名は既存のキーワード呼び出しと互換です。 + + Returns: + (結果コード, RGB配列, 処理画像の辞書)。RGB配列は常にshape=(3, 24)、dtype=float64。 + 行はR, G, B、列はPATCH_NAMES順（向き補正後の左上から行優先）。 + 成功時の値は0～255の平均値、失敗時は全要素NaNです。 + 読み込み失敗・不正な入力=1、検出失敗=2、計測などの処理失敗=3。 + + 処理画像はwarped（向き補正後のBGR画像）、mask（入力座標の二値画像）、 + debug（計測領域と色票番号を描画したBGR画像）。すべてuint8型です。 + 失敗時の処理画像の辞書は空です。 + + この関数は標準出力やCSV・デバッグ画像の保存を行いません。 + """ + empty = np.full((3, 24), np.nan, dtype=np.float64) + if isinstance(filename, np.ndarray): + if (filename.dtype != np.uint8 or filename.ndim != 3 + or filename.shape[2] != 3 or filename.size == 0): + return ResultCode.IMAGE_READ_ERROR, empty, {} + image = filename + elif isinstance(filename, (str, PathLike)): + try: + image = read_image(filename) + except (OSError, ValueError, TypeError, cv2.error): + return ResultCode.IMAGE_READ_ERROR, empty, {} + if image is None: + return ResultCode.IMAGE_READ_ERROR, empty, {} + else: + return ResultCode.IMAGE_READ_ERROR, empty, {} + + try: + warped, _, detection_info = detect_colorchecker_image(image) + except RuntimeError: + return ResultCode.DETECTION_FAILED, empty, {} + except (cv2.error, ValueError, FloatingPointError): + return ResultCode.PROCESSING_ERROR, empty, {} + + try: + results, debug = measure_patches(warped) + rgb = np.array( + [[patch[channel] for patch in results] for channel in ("R", "G", "B")], + dtype=np.float64, + ) + if rgb.shape != (3, 24) or not np.isfinite(rgb).all(): + return ResultCode.PROCESSING_ERROR, empty, {} + except (cv2.error, ValueError, FloatingPointError): + return ResultCode.PROCESSING_ERROR, empty, {} + + return ResultCode.SUCCESS, rgb, { + "warped": warped, + "mask": detection_info["mask"], + "debug": debug, + } + + +PATCH_NAMES = [ + "Dark Skin", "Light Skin", "Blue Sky", + "Foliage", "Blue Flower", "Bluish Green", + + "Orange", "Purplish Blue", "Moderate Red", + "Purple", "Yellow Green", "Orange Yellow", + + "Blue", "Green", "Red", + "Yellow", "Magenta", "Cyan", + + "White", "Neutral 8", "Neutral 6.5", + "Neutral 5", "Neutral 3.5", "Black" +] + + +# ============================================================ +# 1次元クラスタリング +# ============================================================ + +def cluster_1d(values, k): + + data = np.asarray( + values, + dtype=np.float32 + ).reshape(-1, 1) + + if len(data) < k: + raise RuntimeError( + "候補数が不足しています" + ) + + criteria = ( + cv2.TERM_CRITERIA_EPS + + cv2.TERM_CRITERIA_MAX_ITER, + 100, + 0.1 + ) + + _, labels, centers = cv2.kmeans( + data, + k, + None, + criteria, + 20, + cv2.KMEANS_PP_CENTERS + ) + + # 座標の小さい順に + # 0,1,2,... となるようラベルを並べ直す + order = np.argsort(centers[:, 0]) + + remap = np.empty(k, dtype=np.int32) + + remap[order] = np.arange(k) + + labels = remap[labels.ravel()] + + centers = centers[order, 0] + + return labels, centers + + +# ============================================================ +# 色票候補の検出 +# ============================================================ + +def find_patch_candidates(image, threshold): + + hsv = cv2.cvtColor( + image, + cv2.COLOR_BGR2HSV + ) + + value = hsv[:, :, 2] + + mask = np.uint8( + value > threshold + ) * 255 + + # 小さなノイズ除去 + mask = cv2.morphologyEx( + mask, + cv2.MORPH_OPEN, + np.ones((3, 3), np.uint8) + ) + + # 色票内部の小さな穴を埋める + mask = cv2.morphologyEx( + mask, + cv2.MORPH_CLOSE, + np.ones((5, 5), np.uint8) + ) + + contours, _ = cv2.findContours( + mask, + cv2.RETR_EXTERNAL, + cv2.CHAIN_APPROX_SIMPLE + ) + + height, width = image.shape[:2] + + image_area = height * width + + candidates = [] + + for contour in contours: + + x, y, w, h = cv2.boundingRect(contour) + + area = cv2.contourArea(contour) + + # 画像全体に対する面積で絞る + if not ( + 0.0015 * image_area + < area + < 0.035 * image_area + ): + continue + + aspect = w / max(h, 1) + + # 色票はおおむね正方形 + if not ( + 0.50 + < aspect + < 2.0 + ): + continue + + rectangularity = ( + area / max(w * h, 1) + ) + + # 長方形として十分埋まっているか + if rectangularity < 0.68: + continue + + candidates.append({ + "contour": contour, + "x": x, + "y": y, + "w": w, + "h": h, + "cx": x + w / 2.0, + "cy": y + h / 2.0, + "area": area + }) + + return candidates, mask + + +# ============================================================ +# 6 x 4 格子への割り当て +# ============================================================ + +def fit_grid(candidates, + rows=4, + cols=6): + + # 多少のパッチが検出できなくてもよい + if len(candidates) < 12: + return None + + row_labels, row_centers = cluster_1d( + [p["cy"] for p in candidates], + rows + ) + + col_labels, col_centers = cluster_1d( + [p["cx"] for p in candidates], + cols + ) + + cells = {} + + for i, patch in enumerate(candidates): + + row = int(row_labels[i]) + col = int(col_labels[i]) + + key = (row, col) + + # 同一セルに複数候補が入った場合は + # 大きいものを採用 + if ( + key not in cells + or patch["area"] + > cells[key]["area"] + ): + cells[key] = patch + + if len(cells) < 12: + return None + + # -------------------------------------------------------- + # 格子間隔の均一性 + # -------------------------------------------------------- + + dx = np.diff(col_centers) + dy = np.diff(row_centers) + + if ( + np.min(dx) <= 0 + or np.min(dy) <= 0 + ): + return None + + regularity = ( + np.std(dx) / np.mean(dx) + + + np.std(dy) / np.mean(dy) + ) + + # -------------------------------------------------------- + # 色票サイズの均一性 + # -------------------------------------------------------- + + areas = np.array( + [ + p["area"] + for p in cells.values() + ], + dtype=np.float64 + ) + + area_cv = ( + np.std(areas) + / + np.mean(areas) + ) + + # パッチ数が多いほど高得点 + # 格子が乱れているほど減点 + score = ( + len(cells) + - 2.0 * regularity + - area_cv + ) + + return score, cells + + +# ============================================================ +# ColorCheckerを正面向きへ変換 +# ============================================================ + +def warp_from_cells(image, + cells, + cell_size=140): + + src_points = [] + dst_points = [] + + for (row, col), patch in cells.items(): + + # 実画像上の色票中心 + src_points.append([ + patch["cx"], + patch["cy"] + ]) + + # 正規化画像上の色票中心 + dst_points.append([ + (col + 0.5) * cell_size, + (row + 0.5) * cell_size + ]) + + src_points = np.asarray( + src_points, + dtype=np.float32 + ) + + dst_points = np.asarray( + dst_points, + dtype=np.float32 + ) + + # 多数の色票中心を使って + # RANSACでHomographyを推定 + H, inliers = cv2.findHomography( + src_points, + dst_points, + cv2.RANSAC, + 5.0 + ) + + if H is None: + raise RuntimeError( + "Homographyを推定できませんでした" + ) + + warped = cv2.warpPerspective( + image, + H, + ( + 6 * cell_size, + 4 * cell_size + ) + ) + + return warped, H, inliers + + +# ============================================================ +# ColorCheckerの向きを判定 +# ============================================================ + +def patch_hsv_statistics( + warped, + fraction=0.50): + + height, width = warped.shape[:2] + + cell_w = width / 6.0 + cell_h = height / 4.0 + + hsv = cv2.cvtColor( + warped, + cv2.COLOR_BGR2HSV + ) + + saturation = np.zeros( + (4, 6), + dtype=np.float64 + ) + + value = np.zeros( + (4, 6), + dtype=np.float64 + ) + + for row in range(4): + + for col in range(6): + + cx = ( + col + 0.5 + ) * cell_w + + cy = ( + row + 0.5 + ) * cell_h + + half_w = ( + cell_w + * fraction + / 2.0 + ) + + half_h = ( + cell_h + * fraction + / 2.0 + ) + + x0 = int(cx - half_w) + x1 = int(cx + half_w) + + y0 = int(cy - half_h) + y1 = int(cy + half_h) + + roi = hsv[ + y0:y1, + x0:x1 + ] + + saturation[ + row, + col + ] = roi[:, :, 1].mean() + + value[ + row, + col + ] = roi[:, :, 2].mean() + + return saturation, value + + +def normalize_orientation(warped): + + # 通常画像だけでなく + # 180度回転や左右反転にも対応 + variants = [ + warped, + cv2.rotate( + warped, + cv2.ROTATE_180 + ), + cv2.flip( + warped, + 1 + ), + cv2.flip( + warped, + 0 + ) + ] + + best_score = -1e100 + + best_image = None + + for img in variants: + + sat, val = ( + patch_hsv_statistics(img) + ) + + # ---------------------------------------------------- + # 最下段は無彩色列のはず + # ---------------------------------------------------- + + row_saturation = ( + sat.mean(axis=1) + ) + + gray_advantage = ( + np.mean( + row_saturation[:3] + ) + - + row_saturation[3] + ) + + # ---------------------------------------------------- + # 白 → 黒 + # 左から右へ暗くなるはず + # ---------------------------------------------------- + + x = np.arange( + 6, + dtype=np.float64 + ) + + slope = np.polyfit( + x, + val[3], + 1 + )[0] + + score = gray_advantage + + # 負の傾きなら加点 + if slope < 0: + score += ( + -slope * 0.6 + ) + else: + score -= ( + slope * 2.0 + ) + + if score > best_score: + + best_score = score + + best_image = img + + return best_image + + +# ============================================================ +# RGB測定 +# ============================================================ + +def measure_patches( + warped, + cell_size=140, + roi_fraction=0.55): + + results = [] + + debug = warped.copy() + + for row in range(4): + + for col in range(6): + + cx = ( + col + 0.5 + ) * cell_size + + cy = ( + row + 0.5 + ) * cell_size + + half_w = ( + cell_size + * roi_fraction + / 2 + ) + + half_h = ( + cell_size + * roi_fraction + / 2 + ) + + x0 = int(cx - half_w) + x1 = int(cx + half_w) + + y0 = int(cy - half_h) + y1 = int(cy + half_h) + + roi = warped[ + y0:y1, + x0:x1 + ] + + pixels = ( + roi + .reshape(-1, 3) + .astype(np.float32) + ) + + # ------------------------------------------------ + # ハイライト・ゴミ等への対策 + # + # RGB各チャンネルを独立に切ると + # 色自体が変化する可能性があるため、 + # 輝度で上下5%を除外する + # ------------------------------------------------ + + luminance = ( + 0.114 * pixels[:, 0] + + + 0.587 * pixels[:, 1] + + + 0.299 * pixels[:, 2] + ) + + low, high = np.percentile( + luminance, + [5, 95] + ) + + valid = ( + (luminance >= low) + & + (luminance <= high) + ) + + filtered = pixels[valid] + + if len(filtered) < 10: + filtered = pixels + + mean_bgr = ( + filtered.mean(axis=0) + ) + + B, G, R = mean_bgr + + index = ( + row * 6 + + col + ) + + results.append({ + "index": index + 1, + "name": PATCH_NAMES[index], + "R": float(R), + "G": float(G), + "B": float(B) + }) + + # デバッグ描画 + cv2.rectangle( + debug, + (x0, y0), + (x1, y1), + (0, 255, 0), + 2 + ) + + cv2.putText( + debug, + str(index + 1), + (x0 + 5, y0 + 22), + cv2.FONT_HERSHEY_SIMPLEX, + 0.6, + (0, 255, 0), + 2, + cv2.LINE_AA + ) + + return results, debug + + +# ============================================================ +# ColorChecker検出 +# ============================================================ + +def detect_colorchecker_image( + image, + cell_size=140): + + hsv = cv2.cvtColor( + image, + cv2.COLOR_BGR2HSV + ) + + value = hsv[:, :, 2] + + # -------------------------------------------------------- + # Otsu法から基準閾値を求める + # -------------------------------------------------------- + + otsu_threshold, _ = cv2.threshold( + value, + 0, + 255, + cv2.THRESH_BINARY + + cv2.THRESH_OTSU + ) + + # -------------------------------------------------------- + # 複数の閾値を試す + # + # 黒票・濃い灰色票は検出されなくてもよい + # -------------------------------------------------------- + + thresholds = sorted( + set( + int( + np.clip( + otsu_threshold * scale, + 25, + 180 + ) + ) + for scale in [ + 0.45, + 0.55, + 0.65, + 0.80, + 1.00, + 1.15 + ] + ) + ) + + best = None + + for threshold in thresholds: + + candidates, mask = ( + find_patch_candidates( + image, + threshold + ) + ) + + grid = fit_grid( + candidates + ) + + 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 + } + + if best is None: + + raise RuntimeError( + "ColorCheckerの" + "6x4色票格子を" + "検出できませんでした" + ) + + warped, H, inliers = ( + warp_from_cells( + image, + best["cells"], + cell_size + ) + ) + + warped = normalize_orientation( + warped + ) + + return ( + warped, + H, + best + ) + + diff --git a/main.py b/main.py new file mode 100644 index 0000000..3148825 --- /dev/null +++ b/main.py @@ -0,0 +1,101 @@ +import argparse +import csv +import sys +from pathlib import Path + +import cv2 + +from colorchecker import ( + detect_colorchecker, + PATCH_NAMES, + ResultCode, +) + + +# ============================================================ +# Main +# ============================================================ + +def main(): + + parser = argparse.ArgumentParser( + description="画像からColorCheckerを検出し、RGB値を計測します" + ) + parser.add_argument( + "filename", + help="入力画像ファイルのパス" + ) + args = parser.parse_args() + code, rgb, images = detect_colorchecker(args.filename) + if code != ResultCode.SUCCESS: + print( + f"ColorChecker処理失敗: {code.name} ({int(code)}): {args.filename}", + file=sys.stderr, + ) + return int(code) + + results = [ + { + "index": index + 1, + "name": name, + "R": float(rgb[0, index]), + "G": float(rgb[1, index]), + "B": float(rgb[2, index]), + } + for index, name in enumerate(PATCH_NAMES) + ] + + print() + print( f"{'No.':>3} " f"{'Name':<18} " f"{'R':>8} " f"{'G':>8} " f"{'B':>8}" ) + for r in results: + print( + f"{r['index']:3d} " + f"{r['name']:<18} " + f"{r['R']:8.2f} " + f"{r['G']:8.2f} " + f"{r['B']:8.2f}" + ) + + # -------------------------------------------------------- + # CSV保存 + # -------------------------------------------------------- + input_path = Path(args.filename) + output_path = input_path.with_name( + f"{input_path.stem}_rgb.csv" + ) + with open( + output_path, + "w", + newline="", + encoding="utf-8" + ) as f: + + writer = csv.writer(f) + + writer.writerow( [ "No", "Name", "R", "G", "B" ] ) + for r in results: + writer.writerow([ + r["index"], + r["name"], + r["R"], + r["G"], + r["B"] + ]) + print() + print( f"{output_path} saved" ) + + # -------------------------------------------------------- + # デバッグ画像保存 + # -------------------------------------------------------- + for suffix, key in (("warped", "warped"), ("patches", "debug"), ("mask", "mask")): + output_path = input_path.with_name( + f"{input_path.stem}_{suffix}{input_path.suffix}" + ) + cv2.imwrite(str(output_path), images[key]) + print(f"{output_path} saved") + + return int(ResultCode.SUCCESS) + + +if __name__ == "__main__": + sys.exit(main()) diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 0000000..6f1e523 --- /dev/null +++ b/requirements.txt @@ -0,0 +1,2 @@ +opencv-python +numpy \ No newline at end of file diff --git a/test_colorchecker.py b/test_colorchecker.py new file mode 100644 index 0000000..22fdf7e --- /dev/null +++ b/test_colorchecker.py @@ -0,0 +1,105 @@ +import contextlib +import io +from pathlib import Path +import tempfile +import unittest + +import cv2 +import numpy as np + +from colorchecker import ResultCode, detect_colorchecker + + +class ColorCheckerTests(unittest.TestCase): + def assert_failure(self, path, expected): + code, rgb, images = detect_colorchecker(path) + self.assertEqual(code, expected) + self.assertEqual(rgb.shape, (3, 24)) + self.assertEqual(rgb.dtype, np.float64) + self.assertTrue(np.isnan(rgb).all()) + self.assertEqual(images, {}) + + def assert_images(self, images, input_shape): + self.assertEqual(set(images), {'warped', 'mask', 'debug'}) + self.assertEqual(images['warped'].shape, (560, 840, 3)) + self.assertEqual(images['debug'].shape, (560, 840, 3)) + self.assertEqual(images['mask'].shape, input_shape[:2]) + for image in images.values(): + self.assertEqual(image.dtype, np.uint8) + self.assertTrue(np.isin(images['mask'], [0, 255]).all()) + self.assertFalse(np.array_equal(images['warped'], images['debug'])) + self.assertFalse(np.shares_memory(images['warped'], images['debug'])) + + def test_missing_file(self): + with tempfile.TemporaryDirectory() as directory: + self.assert_failure(Path(directory) / 'missing.png', ResultCode.IMAGE_READ_ERROR) + + def test_invalid_and_empty_file(self): + with tempfile.TemporaryDirectory() as directory: + path = Path(directory) / 'invalid.png' + for data in (b'', b'not an image'): + path.write_bytes(data) + self.assert_failure(path, ResultCode.IMAGE_READ_ERROR) + + def test_no_checker(self): + self.assert_failure(np.zeros((560, 840, 3), dtype=np.uint8), + ResultCode.DETECTION_FAILED) + with tempfile.TemporaryDirectory() as directory: + path = Path(directory) / 'blank.png' + cv2.imwrite(str(path), np.zeros((560, 840, 3), dtype=np.uint8)) + self.assert_failure(path, ResultCode.DETECTION_FAILED) + + def test_invalid_image_arrays(self): + for image in ( + np.zeros((0, 10, 3), dtype=np.uint8), + np.zeros((10, 10), dtype=np.uint8), + np.zeros((10, 10, 4), dtype=np.uint8), + np.zeros((10, 10, 3), dtype=np.float32), + np.array(1, dtype=np.uint8), + None, + 123, + ): + with self.subTest(shape=getattr(image, 'shape', None)): + self.assert_failure(image, ResultCode.IMAGE_READ_ERROR) + + def test_rgb_layout_and_unicode_path_without_output(self): + colors = np.array([ + [115, 82, 68], [194, 150, 130], [98, 122, 157], + [87, 108, 67], [133, 128, 177], [103, 189, 170], + [214, 126, 44], [80, 91, 166], [193, 90, 99], + [94, 60, 108], [157, 188, 64], [224, 163, 46], + [56, 61, 150], [70, 148, 73], [175, 54, 60], + [231, 199, 31], [187, 86, 149], [8, 133, 161], + [243, 243, 243], [200, 200, 200], [160, 160, 160], + [122, 122, 122], [85, 85, 85], [52, 52, 52], + ], dtype=np.uint8) + image = np.zeros((560, 840, 3), dtype=np.uint8) + for index, rgb in enumerate(colors): + row, col = divmod(index, 6) + image[row * 140 + 25:row * 140 + 115, + col * 140 + 25:col * 140 + 115] = rgb[::-1] + with tempfile.TemporaryDirectory() as directory: + path = Path(directory) / '色票 画像.png' + path.write_bytes(cv2.imencode('.png', image)[1].tobytes()) + stdout = io.StringIO() + with contextlib.redirect_stdout(stdout): + code, measured, images = detect_colorchecker(path) + self.assertEqual(code, ResultCode.SUCCESS) + self.assertEqual(measured.dtype, np.float64) + self.assert_images(images, image.shape) + 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]): + 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) + np.testing.assert_allclose(measured, colors.T, atol=1) + if original is not None: + np.testing.assert_array_equal(source, original) + + +if __name__ == '__main__': + unittest.main() diff --git a/xrite color checker.jpg b/xrite color checker.jpg new file mode 100644 index 0000000..41453d7 --- /dev/null +++ b/xrite color checker.jpg Binary files differ