修改色准测试结果显示

tools/demo_accuracy_plot.py

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+"""离线色准图 Demo。
+
+运行后会在 tools/demo_outputs/ 下生成一张 PNG，
+用于在没有 UCD 设备时预览当前色准图表的 Calman 风格布局。
+"""
+
+from __future__ import annotations
+
+import argparse
+import math
+import sys
+from pathlib import Path
+
+import matplotlib
+
+matplotlib.use("Agg")
+
+import matplotlib.pyplot as plt
+import numpy as np
+
+plt.rcParams["font.family"] = ["sans-serif"]
+plt.rcParams["font.sans-serif"] = ["Microsoft YaHei", "SimHei", "DejaVu Sans"]
+plt.rcParams["axes.unicode_minus"] = False
+
+REPO_ROOT = Path(__file__).resolve().parents[1]
+if str(REPO_ROOT) not in sys.path:
+    sys.path.insert(0, str(REPO_ROOT))
+
+from app.plots.plot_accuracy import plot_accuracy
+from app.tests.color_accuracy import (
+    calculate_delta_e_2000,
+    get_accuracy_color_standards,
+)
+
+
+COLOR_NAMES = [
+    "White",
+    "Gray 80",
+    "Gray 65",
+    "Gray 50",
+    "Gray 35",
+    "Dark Skin",
+    "Light Skin",
+    "Blue Sky",
+    "Foliage",
+    "Blue Flower",
+    "Bluish Green",
+    "Orange",
+    "Purplish Blue",
+    "Moderate Red",
+    "Purple",
+    "Yellow Green",
+    "Orange Yellow",
+    "Blue (Legacy)",
+    "Green (Legacy)",
+    "Red (Legacy)",
+    "Yellow (Legacy)",
+    "Magenta (Legacy)",
+    "Cyan (Legacy)",
+    "100% Red",
+    "100% Green",
+    "100% Blue",
+    "100% Cyan",
+    "100% Magenta",
+    "100% Yellow",
+]
+
+
+class _DummyNotebook:
+    def select(self, *_args, **_kwargs):
+        return None
+
+
+class _DummyCanvas:
+    def draw(self):
+        return None
+
+
+class _DemoApp:
+    def __init__(self, fig):
+        self.accuracy_fig = fig
+        self.accuracy_canvas = _DummyCanvas()
+        self.chart_notebook = _DummyNotebook()
+        self.accuracy_chart_frame = object()
+
+    def get_test_type_name(self, test_type):
+        mapping = {
+            "sdr_movie": "SDR Movie",
+            "hdr_movie": "HDR Movie",
+            "screen_module": "屏模组",
+        }
+        return mapping.get(test_type, str(test_type))
+
+
+def _build_demo_data(test_type: str = "sdr_movie"):
+    standards = get_accuracy_color_standards(test_type)
+    rng = np.random.default_rng(20260527)
+
+    measured = []
+    color_patches = []
+    delta_e_values = []
+
+    for idx, name in enumerate(COLOR_NAMES):
+        sx, sy = standards[name]
+
+        # 构造一些“看起来像真实测量”的偏移：
+        # 大部分点轻微偏移，少数点更明显，便于看出方向和等级差异。
+        if idx < 5:
+            offset_scale = 0.0012
+        elif idx < 23:
+            offset_scale = 0.0028
+        else:
+            offset_scale = 0.0045
+
+        angle = rng.uniform(0, 2 * math.pi)
+        radius = offset_scale * (0.55 + 0.85 * rng.random())
+        dx = math.cos(angle) * radius
+        dy = math.sin(angle) * radius
+
+        # 为了让图上连线不完全随机，给部分饱和色再加一点定向偏移。
+        if idx >= 23:
+            dx += 0.002 * (1 if idx % 2 == 0 else -1)
+            dy += 0.0015 * (1 if idx % 3 == 0 else -1)
+
+        mx = min(max(sx + dx, 0.0), 0.8)
+        my = min(max(sy + dy, 0.0), 0.9)
+
+        # 亮度也做一点微小变化，避免所有点完全同一层。
+        measured_lv = 70.0 + rng.normal(0, 4.0)
+        measured_lv = max(measured_lv, 1.0)
+
+        delta_e = calculate_delta_e_2000(mx, my, measured_lv, sx, sy)
+
+        measured.append((mx, my, measured_lv))
+        color_patches.append(name)
+        delta_e_values.append(delta_e)
+
+    avg_delta_e = float(np.mean(delta_e_values))
+    max_delta_e = float(np.max(delta_e_values))
+    min_delta_e = float(np.min(delta_e_values))
+
+    return {
+        "color_patches": color_patches,
+        "delta_e_values": delta_e_values,
+        "color_measurements": measured,
+        "avg_delta_e": avg_delta_e,
+        "max_delta_e": max_delta_e,
+        "min_delta_e": min_delta_e,
+        "excellent_count": sum(1 for value in delta_e_values if value < 3),
+        "good_count": sum(1 for value in delta_e_values if 3 <= value < 5),
+        "poor_count": sum(1 for value in delta_e_values if value >= 5),
+        "avg_delta_e_gray": float(np.mean(delta_e_values[0:5])),
+        "avg_delta_e_colorchecker": float(np.mean(delta_e_values[5:23])),
+        "avg_delta_e_saturated": float(np.mean(delta_e_values[23:29])),
+        "target_gamma": 2.2,
+    }
+
+
+def main():
+    parser = argparse.ArgumentParser(description="Generate an offline color accuracy demo PNG.")
+    parser.add_argument(
+        "--output",
+        type=Path,
+        default=Path(__file__).resolve().parent / "demo_outputs" / "accuracy_demo.png",
+        help="Output PNG path.",
+    )
+    parser.add_argument(
+        "--test-type",
+        choices=["sdr_movie", "hdr_movie", "screen_module"],
+        default="sdr_movie",
+        help="Test type used for the title and standard color set.",
+    )
+    args = parser.parse_args()
+
+    args.output.parent.mkdir(parents=True, exist_ok=True)
+
+    fig = plt.Figure(figsize=(14, 8), dpi=120, tight_layout=False)
+    app = _DemoApp(fig)
+    accuracy_data = _build_demo_data(args.test_type)
+
+    plot_accuracy(app, accuracy_data, args.test_type)
+    fig.savefig(args.output, dpi=220)
+
+    print(f"Saved demo image to: {args.output}")
+
+
+if __name__ == "__main__":
+    main()