273 lines
7.8 KiB
Python
273 lines
7.8 KiB
Python
"""色准（ΔE2000 / 标准色）相关纯算法。
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Step 1 重构：从 pqAutomationApp.PQAutomationApp 中原样搬迁以下方法，
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去掉 self 参数，改为模块级纯函数：
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- calculate_delta_e_2000
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- calculate_color_accuracy
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- get_accuracy_color_standards
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"""
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import math
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import numpy as np
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D65_X = 0.3127
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D65_Y = 0.3290
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# Calman ColorChecker 参考 xy（与 Calman dE2000 对齐；比较时使用实测 Y 作为目标 Y）
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_ACCURACY_REFERENCE_XY = {
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"White": (0.3127, 0.3282),
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"Gray 80": (0.3128, 0.3283),
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"Gray 65": (0.3118, 0.3270),
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"Gray 50": (0.3122, 0.3282),
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"Gray 35": (0.3124, 0.3278),
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"Dark Skin": (0.4042, 0.3686),
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"Light Skin": (0.3774, 0.3562),
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"Blue Sky": (0.2535, 0.2671),
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"Foliage": (0.3379, 0.4287),
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"Blue Flower": (0.2691, 0.2484),
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"Bluish Green": (0.2578, 0.3544),
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"Orange": (0.5047, 0.4088),
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"Purplish Blue": (0.2166, 0.1857),
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"Moderate Red": (0.4554, 0.3098),
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"Purple": (0.2889, 0.2135),
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"Yellow Green": (0.3771, 0.4937),
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"Orange Yellow": (0.4578, 0.4416),
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"Blue (Legacy)": (0.1851, 0.1238),
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"Green (Legacy)": (0.3008, 0.4976),
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"Red (Legacy)": (0.5435, 0.3200),
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"Yellow (Legacy)": (0.4430, 0.4717),
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"Magenta (Legacy)": (0.3735, 0.2428),
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"Cyan (Legacy)": (0.2093, 0.2679),
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"100% Red": (0.6424, 0.3274),
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"100% Green": (0.2935, 0.6024),
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"100% Blue": (0.1615, 0.0610),
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"100% Cyan": (0.2302, 0.3340),
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"100% Magenta": (0.3300, 0.1513),
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"100% Yellow": (0.4152, 0.5047),
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}
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# 29 色 SDR 标准色板（Legacy 色块仍保留 RGB 定义供图案发送）
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_SDR_COLOR_PATTERNS = [
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("White", 255, 255, 255),
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("Gray 80", 230, 230, 230),
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("Gray 65", 209, 209, 209),
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("Gray 50", 186, 186, 186),
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("Gray 35", 158, 158, 158),
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("Dark Skin", 115, 82, 66),
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("Light Skin", 194, 150, 130),
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("Blue Sky", 94, 122, 156),
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("Foliage", 89, 107, 66),
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("Blue Flower", 130, 128, 176),
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("Bluish Green", 99, 189, 168),
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("Orange", 217, 120, 41),
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("Purplish Blue", 74, 92, 163),
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("Moderate Red", 194, 84, 97),
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("Purple", 92, 61, 107),
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("Yellow Green", 158, 186, 64),
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("Orange Yellow", 230, 161, 46),
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("Blue (Legacy)", 51, 61, 150),
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("Green (Legacy)", 71, 148, 71),
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("Red (Legacy)", 176, 48, 59),
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("Yellow (Legacy)", 237, 199, 33),
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("Magenta (Legacy)", 186, 84, 145),
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("Cyan (Legacy)", 0, 133, 163),
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("100% Red", 255, 0, 0),
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("100% Green", 0, 255, 0),
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("100% Blue", 0, 0, 255),
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("100% Cyan", 0, 255, 255),
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("100% Magenta", 255, 0, 255),
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("100% Yellow", 255, 255, 0),
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]
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def _resolve_reference_xy(name):
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return _ACCURACY_REFERENCE_XY.get(name, (D65_X, D65_Y))
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def get_accuracy_reference_y(name, white_lv):
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"""
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返回图表/表格用的参考亮度（Calman 目标 Y 比例，White=100 缩放）。
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注意：ΔE2000 计算使用实测 Y 作为目标 Y（与 Calman 一致），此函数仅供展示。
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"""
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del name
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if white_lv <= 0:
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return 100.0
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return white_lv
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def get_accuracy_color_standards(test_type):
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"""
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获取色准测试的标准 xy 色度坐标（Calman 兼容参考值）。
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Args:
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test_type: 测试类型 ("sdr_movie" 或 "hdr_movie")
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Returns:
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dict: {color_name: (x, y), ...}
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"""
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del test_type
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return {name: _resolve_reference_xy(name) for name, _, _, _ in _SDR_COLOR_PATTERNS}
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def _xyY_to_lab(x, y, Y):
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if y == 0:
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return 0.0, 0.0, 0.0
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X = x * Y / y
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Z = (1 - x - y) * Y / y
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Xn, Yn, Zn = 95.047, 100.000, 108.883
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def f(t):
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delta = 6.0 / 29.0
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if t > delta ** 3:
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return t ** (1.0 / 3.0)
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return t / (3 * delta ** 2) + 4.0 / 29.0
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xr, yr, zr = X / Xn, Y / Yn, Z / Zn
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fx, fy, fz = f(xr), f(yr), f(zr)
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return 116 * fy - 16, 500 * (fx - fy), 200 * (fy - fz)
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def _delta_e_2000_from_lab(L1, a1, b1, L2, a2, b2):
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L_bar = (L1 + L2) / 2.0
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C1 = math.sqrt(a1 ** 2 + b1 ** 2)
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C2 = math.sqrt(a2 ** 2 + b2 ** 2)
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C_bar = (C1 + C2) / 2.0
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G = 0.5 * (1 - math.sqrt(C_bar ** 7 / (C_bar ** 7 + 25 ** 7)))
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a1_prime = a1 * (1 + G)
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a2_prime = a2 * (1 + G)
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C1_prime = math.sqrt(a1_prime ** 2 + b1 ** 2)
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C2_prime = math.sqrt(a2_prime ** 2 + b2 ** 2)
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C_bar_prime = (C1_prime + C2_prime) / 2.0
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def calc_hue(a_prime, b):
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if a_prime == 0 and b == 0:
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return 0
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h = math.atan2(b, a_prime) * 180 / math.pi
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if h < 0:
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h += 360
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return h
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h1_prime = calc_hue(a1_prime, b1)
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h2_prime = calc_hue(a2_prime, b2)
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if C1_prime == 0 or C2_prime == 0:
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delta_h_prime = 0
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else:
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delta_h = h2_prime - h1_prime
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if abs(delta_h) <= 180:
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delta_h_prime = delta_h
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elif delta_h > 180:
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delta_h_prime = delta_h - 360
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else:
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delta_h_prime = delta_h + 360
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if C1_prime == 0 or C2_prime == 0:
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H_bar_prime = h1_prime + h2_prime
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elif abs(h1_prime - h2_prime) <= 180:
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H_bar_prime = (h1_prime + h2_prime) / 2.0
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elif h1_prime + h2_prime < 360:
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H_bar_prime = (h1_prime + h2_prime + 360) / 2.0
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else:
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H_bar_prime = (h1_prime + h2_prime - 360) / 2.0
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delta_L_prime = L2 - L1
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delta_C_prime = C2_prime - C1_prime
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delta_H_prime = (
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2
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* math.sqrt(C1_prime * C2_prime)
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* math.sin(math.radians(delta_h_prime / 2.0))
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)
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S_L = 1 + (0.015 * (L_bar - 50) ** 2) / math.sqrt(20 + (L_bar - 50) ** 2)
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S_C = 1 + 0.045 * C_bar_prime
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T = (
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1
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- 0.17 * math.cos(math.radians(H_bar_prime - 30))
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+ 0.24 * math.cos(math.radians(2 * H_bar_prime))
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+ 0.32 * math.cos(math.radians(3 * H_bar_prime + 6))
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- 0.20 * math.cos(math.radians(4 * H_bar_prime - 63))
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)
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S_H = 1 + 0.015 * C_bar_prime * T
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delta_theta = 30 * math.exp(-(((H_bar_prime - 275) / 25) ** 2))
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R_C = 2 * math.sqrt(C_bar_prime ** 7 / (C_bar_prime ** 7 + 25 ** 7))
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R_T = -R_C * math.sin(math.radians(2 * delta_theta))
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kL = kC = kH = 1.0
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return math.sqrt(
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(delta_L_prime / (kL * S_L)) ** 2
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+ (delta_C_prime / (kC * S_C)) ** 2
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+ (delta_H_prime / (kH * S_H)) ** 2
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+ R_T * (delta_C_prime / (kC * S_C)) * (delta_H_prime / (kH * S_H))
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)
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def calculate_delta_e_2000(
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measured_x,
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measured_y,
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measured_lv,
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standard_x,
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standard_y,
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standard_lv=None,
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):
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"""
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计算 ΔE 2000 色差。
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Args:
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measured_x, measured_y: 测量的 xy 坐标
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measured_lv: 测量的亮度（cd/m²）
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standard_x, standard_y: 标准的 xy 坐标
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standard_lv: 标准亮度（cd/m²）；默认与 measured_lv 相同
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Returns:
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float: ΔE 2000 色差值
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"""
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if standard_lv is None:
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standard_lv = measured_lv
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L1, a1, b1 = _xyY_to_lab(measured_x, measured_y, measured_lv)
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L2, a2, b2 = _xyY_to_lab(standard_x, standard_y, standard_lv)
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return _delta_e_2000_from_lab(L1, a1, b1, L2, a2, b2)
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def calculate_accuracy_delta_e_2000(
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patch_name, measured_x, measured_y, measured_lv, white_lv
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):
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"""
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色准测试专用 ΔE2000（Calman 对齐）。
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Calman 在 ColorChecker 测试中对每块使用固定参考 xy，
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且目标 Y 取实测 Y（同亮度下比较色度差异）。
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"""
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del white_lv
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standard_x, standard_y = _resolve_reference_xy(patch_name)
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return calculate_delta_e_2000(
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measured_x,
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measured_y,
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measured_lv,
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standard_x,
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standard_y,
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measured_lv,
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)
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def calculate_color_accuracy(measured, standard):
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"""计算色差（简化版，欧氏距离 × 1000）"""
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delta_E = {}
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for color in measured.keys():
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dx = measured[color][0] - standard[color][0]
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dy = measured[color][1] - standard[color][1]
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delta_E[color] = np.sqrt(dx * dx + dy * dy) * 1000
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return delta_E
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