重构-抽纯函数

app/resources.py

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+"""资源与全局样式工具。
+
+Step 0 重构：将原先散落在 pqAutomationApp.py 顶部的
+get_resource_path / load_icon / backgroud_style_set 三个辅助函数
+迁移到独立模块，保持行为完全一致。
+"""
+
+import os
+import sys
+
+import ttkbootstrap as ttk
+from PIL import Image, ImageTk
+
+
+# 项目根目录（app/ 的上一级）。开发环境下用它作为资源查找基准，
+# 从而兼容从项目根目录启动的 pqAutomationApp.py。
+_PROJECT_ROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+
+
+def get_resource_path(relative_path):
+    """
+    获取资源文件的绝对路径（兼容开发环境和打包后）
+
+    Args:
+        relative_path: 相对路径，如 "assets/cie.png"
+
+    Returns:
+        str: 资源文件的绝对路径
+    """
+    try:
+        # PyInstaller 打包后的临时文件夹路径
+        base_path = sys._MEIPASS
+    except AttributeError:
+        # 开发环境：使用项目根目录
+        base_path = _PROJECT_ROOT
+
+    return os.path.join(base_path, relative_path)
+
+
+def load_icon(png_path):
+    """加载并调整图标大小为 24x24（原注释写 64x64，实际 resize 为 24x24，保持原行为）"""
+    img = Image.open(get_resource_path(png_path))
+    img = img.resize((24, 24), Image.LANCZOS)
+    return ImageTk.PhotoImage(img)
+
+
+def backgroud_style_set():
+    style = ttk.Style()
+    # 移除背景色设置,使用默认背景色
+    style.configure(
+        "SidebarSelected.TButton",
+        #   anchor="w",
+        padding=10,
+        background="#005470",
+    )

app/tests/color_accuracy.py

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+"""色准（ΔE2000 / 标准色）相关纯算法。
+
+Step 1 重构：从 pqAutomationApp.PQAutomationApp 中原样搬迁以下方法，
+去掉 self 参数，改为模块级纯函数：
+    - calculate_delta_e_2000
+    - calculate_color_accuracy
+    - get_accuracy_color_standards
+"""
+
+import math
+
+import numpy as np
+
+
+def calculate_delta_e_2000(
+    measured_x, measured_y, measured_lv, standard_x, standard_y
+):
+    """
+    计算 ΔE 2000 色差（修正版）
+
+    Args:
+        measured_x, measured_y: 测量的 xy 坐标
+        measured_lv: 测量的亮度（cd/m²）
+        standard_x, standard_y: 标准的 xy 坐标
+
+    Returns:
+        float: ΔE 2000 色差值
+    """
+
+    # ========== 1. xy → XYZ（使用实际亮度）==========
+    def xy_to_XYZ(x, y, Y):
+        if y == 0:
+            return 0, 0, 0
+        X = x * Y / y
+        Z = (1 - x - y) * Y / y
+        return X, Y, Z
+
+    # 修复：使用实际测量的亮度
+    X1, Y1, Z1 = xy_to_XYZ(measured_x, measured_y, measured_lv)
+
+    # 修复：标准值使用相同的参考亮度（只比较色度差异）
+    X2, Y2, Z2 = xy_to_XYZ(standard_x, standard_y, measured_lv)
+
+    # ========== 2. XYZ → Lab（D65 白点）==========
+    def XYZ_to_Lab(X, Y, Z):
+        # D65 白点
+        Xn, Yn, Zn = 95.047, 100.000, 108.883
+
+        # 归一化
+        xr = X / Xn
+        yr = Y / Yn
+        zr = Z / Zn
+
+        # f(t) 函数
+        def f(t):
+            delta = 6.0 / 29.0
+            if t > delta ** 3:
+                return t ** (1.0 / 3.0)
+            else:
+                return t / (3 * delta ** 2) + 4.0 / 29.0
+
+        fx = f(xr)
+        fy = f(yr)
+        fz = f(zr)
+
+        L = 116 * fy - 16
+        a = 500 * (fx - fy)
+        b = 200 * (fy - fz)
+
+        return L, a, b
+
+    L1, a1, b1 = XYZ_to_Lab(X1, Y1, Z1)
+    L2, a2, b2 = XYZ_to_Lab(X2, Y2, Z2)
+
+    # ========== 3. ΔE 2000 公式 ==========
+    L_bar = (L1 + L2) / 2.0
+    C1 = math.sqrt(a1 ** 2 + b1 ** 2)
+    C2 = math.sqrt(a2 ** 2 + b2 ** 2)
+    C_bar = (C1 + C2) / 2.0
+
+    G = 0.5 * (1 - math.sqrt(C_bar ** 7 / (C_bar ** 7 + 25 ** 7)))
+
+    a1_prime = a1 * (1 + G)
+    a2_prime = a2 * (1 + G)
+
+    C1_prime = math.sqrt(a1_prime ** 2 + b1 ** 2)
+    C2_prime = math.sqrt(a2_prime ** 2 + b2 ** 2)
+    C_bar_prime = (C1_prime + C2_prime) / 2.0
+
+    def calc_hue(a_prime, b):
+        if a_prime == 0 and b == 0:
+            return 0
+        h = math.atan2(b, a_prime) * 180 / math.pi
+        if h < 0:
+            h += 360
+        return h
+
+    h1_prime = calc_hue(a1_prime, b1)
+    h2_prime = calc_hue(a2_prime, b2)
+
+    if C1_prime == 0 or C2_prime == 0:
+        delta_h_prime = 0
+    else:
+        delta_h = h2_prime - h1_prime
+        if abs(delta_h) <= 180:
+            delta_h_prime = delta_h
+        elif delta_h > 180:
+            delta_h_prime = delta_h - 360
+        else:
+            delta_h_prime = delta_h + 360
+
+    if C1_prime == 0 or C2_prime == 0:
+        H_bar_prime = h1_prime + h2_prime
+    else:
+        if abs(h1_prime - h2_prime) <= 180:
+            H_bar_prime = (h1_prime + h2_prime) / 2.0
+        elif h1_prime + h2_prime < 360:
+            H_bar_prime = (h1_prime + h2_prime + 360) / 2.0
+        else:
+            H_bar_prime = (h1_prime + h2_prime - 360) / 2.0
+
+    delta_L_prime = L2 - L1
+    delta_C_prime = C2_prime - C1_prime
+    delta_H_prime = (
+        2
+        * math.sqrt(C1_prime * C2_prime)
+        * math.sin(math.radians(delta_h_prime / 2.0))
+    )
+
+    S_L = 1 + (0.015 * (L_bar - 50) ** 2) / math.sqrt(20 + (L_bar - 50) ** 2)
+    S_C = 1 + 0.045 * C_bar_prime
+
+    T = (
+        1
+        - 0.17 * math.cos(math.radians(H_bar_prime - 30))
+        + 0.24 * math.cos(math.radians(2 * H_bar_prime))
+        + 0.32 * math.cos(math.radians(3 * H_bar_prime + 6))
+        - 0.20 * math.cos(math.radians(4 * H_bar_prime - 63))
+    )
+
+    S_H = 1 + 0.015 * C_bar_prime * T
+
+    delta_theta = 30 * math.exp(-(((H_bar_prime - 275) / 25) ** 2))
+    R_C = 2 * math.sqrt(C_bar_prime ** 7 / (C_bar_prime ** 7 + 25 ** 7))
+    R_T = -R_C * math.sin(math.radians(2 * delta_theta))
+
+    kL = 1.0
+    kC = 1.0
+    kH = 1.0
+
+    delta_E = math.sqrt(
+        (delta_L_prime / (kL * S_L)) ** 2
+        + (delta_C_prime / (kC * S_C)) ** 2
+        + (delta_H_prime / (kH * S_H)) ** 2
+        + R_T * (delta_C_prime / (kC * S_C)) * (delta_H_prime / (kH * S_H))
+    )
+
+    return delta_E
+
+
+def calculate_color_accuracy(measured, standard):
+    """计算色差（简化版，欧氏距离 × 1000）"""
+    delta_E = {}
+
+    for color in measured.keys():
+        dx = measured[color][0] - standard[color][0]
+        dy = measured[color][1] - standard[color][1]
+        delta_E[color] = np.sqrt(dx * dx + dy * dy) * 1000
+
+    return delta_E
+
+
+# 29 色 SDR 标准色板（保持与原实现一致）
+_SDR_COLOR_PATTERNS = [
+    ("White", 255, 255, 255),
+    ("Gray 80", 230, 230, 230),
+    ("Gray 65", 209, 209, 209),
+    ("Gray 50", 186, 186, 186),
+    ("Gray 35", 158, 158, 158),
+    ("Dark Skin", 115, 82, 66),
+    ("Light Skin", 194, 150, 130),
+    ("Blue Sky", 94, 122, 156),
+    ("Foliage", 89, 107, 66),
+    ("Blue Flower", 130, 128, 176),
+    ("Bluish Green", 99, 189, 168),
+    ("Orange", 217, 120, 41),
+    ("Purplish Blue", 74, 92, 163),
+    ("Moderate Red", 194, 84, 97),
+    ("Purple", 92, 61, 107),
+    ("Yellow Green", 158, 186, 64),
+    ("Orange Yellow", 230, 161, 46),
+    ("Blue (Legacy)", 51, 61, 150),
+    ("Green (Legacy)", 71, 148, 71),
+    ("Red (Legacy)", 176, 48, 59),
+    ("Yellow (Legacy)", 237, 199, 33),
+    ("Magenta (Legacy)", 186, 84, 145),
+    ("Cyan (Legacy)", 0, 133, 163),
+    ("100% Red", 255, 0, 0),
+    ("100% Green", 0, 255, 0),
+    ("100% Blue", 0, 0, 255),
+    ("100% Cyan", 0, 255, 255),
+    ("100% Magenta", 255, 0, 255),
+    ("100% Yellow", 255, 255, 0),
+]
+
+
+def _rgb_to_xy_srgb(r, g, b):
+    """sRGB (8bit) → CIE 1931 xy"""
+    r, g, b = r / 255.0, g / 255.0, b / 255.0
+
+    def gamma_decode(c):
+        if c <= 0.04045:
+            return c / 12.92
+        else:
+            return ((c + 0.055) / 1.055) ** 2.4
+
+    r_linear = gamma_decode(r)
+    g_linear = gamma_decode(g)
+    b_linear = gamma_decode(b)
+
+    # sRGB → XYZ（D65 白点，IEC 61966-2-1）
+    X = r_linear * 0.4124564 + g_linear * 0.3575761 + b_linear * 0.1804375
+    Y = r_linear * 0.2126729 + g_linear * 0.7151522 + b_linear * 0.0721750
+    Z = r_linear * 0.0193339 + g_linear * 0.1191920 + b_linear * 0.9503041
+
+    total = X + Y + Z
+    if total == 0:
+        return 0.3127, 0.3290  # D65 白点
+
+    return X / total, Y / total
+
+
+def get_accuracy_color_standards(test_type):
+    """
+    获取色准测试的标准 xy 色度坐标（动态计算）
+
+    Args:
+        test_type: 测试类型 ("sdr_movie" 或 "hdr_movie")
+
+    Returns:
+        dict: {color_name: (x, y), ...}
+    """
+    # 注意：原实现对 sdr/hdr 使用同一张色板，这里保持原行为。
+    del test_type  # 参数保留以兼容调用方签名
+    standards = {}
+    for name, r, g, b in _SDR_COLOR_PATTERNS:
+        standards[name] = _rgb_to_xy_srgb(r, g, b)
+    return standards

app/tests/eotf.py

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+"""EOTF（PQ / ST.2084）相关纯算法。"""
+
+import numpy as np
+
+
+def calculate_pq_curve(gray_levels):
+    """计算 PQ (ST.2084) EOTF 理想曲线。
+
+    Args:
+        gray_levels: 灰阶百分比数组 (0-100)
+
+    Returns:
+        numpy.ndarray: 归一化亮度数组 (0-1)
+    """
+    # PQ 曲线参数（ITU-R BT.2100 标准）
+    m1 = 0.1593017578125   # = 2610 / 16384
+    m2 = 78.84375          # = 78.84375
+    c1 = 0.8359375         # = 3424 / 4096
+    c2 = 18.8515625        # = 2413 / 128
+    c3 = 18.6875           # = 2392 / 128
+
+    L_bar = []
+    for gray in gray_levels:
+        V = gray / 100.0
+
+        if V <= 0:
+            L_bar.append(0)
+        else:
+            V_pow = np.power(V, 1 / m2)
+            numerator = max(V_pow - c1, 0)
+            denominator = c2 - c3 * V_pow
+
+            if denominator > 0:
+                L = np.power(numerator / denominator, 1 / m1)
+            else:
+                L = 0
+
+            L_bar.append(L)
+
+    return np.array(L_bar)

app/tests/gamma.py

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+"""Gamma 相关纯算法。"""
+
+import algorithm.pq_algorithm as pq_algorithm
+
+
+def calculate_gamma(results, max_index_fix, pattern_params=None):
+    """计算 Gamma 值，返回 (results_with_gamma_list, L_bar)。
+
+    Args:
+        results: 测量结果列表
+        max_index_fix: 最大灰阶索引
+        pattern_params: 8bit pattern 参数，用于计算 input_level
+                        （与 22293 Gamma 数据对齐）
+    """
+    results_with_gamma_list = pq_algorithm.calculate_gamma(
+        results, max_index_fix, pattern_params
+    )
+    L_bar = pq_algorithm.calculate_L_bar(results)
+    return results_with_gamma_list, L_bar

app/tests/gamut.py

@@ -0,0 +1,9 @@
+"""色域（Gamut）相关纯算法。"""
+
+import algorithm.pq_algorithm as pq_algorithm
+
+
+def calculate_gamut_coverage(results):
+    """计算色域覆盖率（DCI-P3）。"""
+    area, coverage = pq_algorithm.calculate_gamut_coverage_DCIP3(results)
+    return area, coverage