249 lines
6.9 KiB
Python
249 lines
6.9 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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def calculate_delta_e_2000(
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measured_x, measured_y, measured_lv, standard_x, standard_y
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):
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"""
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计算 ΔE 2000 色差（修正版）
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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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Returns:
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float: ΔE 2000 色差值
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"""
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# ========== 1. xy → XYZ（使用实际亮度）==========
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def xy_to_XYZ(x, y, Y):
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if y == 0:
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return 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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return X, Y, Z
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# 修复：使用实际测量的亮度
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X1, Y1, Z1 = xy_to_XYZ(measured_x, measured_y, measured_lv)
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# 修复：标准值使用相同的参考亮度（只比较色度差异）
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X2, Y2, Z2 = xy_to_XYZ(standard_x, standard_y, measured_lv)
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# ========== 2. XYZ → Lab（D65 白点）==========
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def XYZ_to_Lab(X, Y, Z):
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# D65 白点
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Xn, Yn, Zn = 95.047, 100.000, 108.883
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# 归一化
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xr = X / Xn
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yr = Y / Yn
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zr = Z / Zn
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# f(t) 函数
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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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else:
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return t / (3 * delta ** 2) + 4.0 / 29.0
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fx = f(xr)
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fy = f(yr)
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fz = f(zr)
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L = 116 * fy - 16
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a = 500 * (fx - fy)
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b = 200 * (fy - fz)
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return L, a, b
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L1, a1, b1 = XYZ_to_Lab(X1, Y1, Z1)
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L2, a2, b2 = XYZ_to_Lab(X2, Y2, Z2)
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# ========== 3. ΔE 2000 公式 ==========
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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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else:
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if 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 = 1.0
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kC = 1.0
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kH = 1.0
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delta_E = 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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return delta_E
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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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# 29 色 SDR 标准色板（保持与原实现一致）
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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 _rgb_to_xy_srgb(r, g, b):
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"""sRGB (8bit) → CIE 1931 xy"""
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r, g, b = r / 255.0, g / 255.0, b / 255.0
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def gamma_decode(c):
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if c <= 0.04045:
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return c / 12.92
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else:
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return ((c + 0.055) / 1.055) ** 2.4
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r_linear = gamma_decode(r)
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g_linear = gamma_decode(g)
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b_linear = gamma_decode(b)
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# sRGB → XYZ（D65 白点，IEC 61966-2-1）
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X = r_linear * 0.4124564 + g_linear * 0.3575761 + b_linear * 0.1804375
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Y = r_linear * 0.2126729 + g_linear * 0.7151522 + b_linear * 0.0721750
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Z = r_linear * 0.0193339 + g_linear * 0.1191920 + b_linear * 0.9503041
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total = X + Y + Z
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if total == 0:
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return 0.3127, 0.3290 # D65 白点
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return X / total, Y / total
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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（动态计算）
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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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# 注意：原实现对 sdr/hdr 使用同一张色板，这里保持原行为。
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del test_type # 参数保留以兼容调用方签名
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standards = {}
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for name, r, g, b in _SDR_COLOR_PATTERNS:
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standards[name] = _rgb_to_xy_srgb(r, g, b)
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return standards
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