167 lines
5.0 KiB
Python
167 lines
5.0 KiB
Python
"""CIE 色度图底图渲染与缓存。
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将"重型图像渲染"（colour-science 的谱迹颜色填充）与"轻量框架数据层"
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（参考/实测三角形、标签、覆盖率）解耦。
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底图：
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- 仅在首次调用或缓存失效时通过 colour-science 渲染一次；
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- 渲染结果保存为 numpy RGBA 数组，同时落盘到 settings/cache/，
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下次启动直接 imread 加载，避免重新跑色彩科学计算。
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调用方在每次绘图时只需 `ax.imshow(bg, extent=bbox)`，再叠加自己的矢量层。
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"""
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from __future__ import annotations
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import hashlib
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import os
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import threading
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from typing import Tuple
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import numpy as np
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# 谱迹底图分辨率（边长，单位像素）。1024 对于 14 inch 画布足够细腻，
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# 文件大小 ~1-2MB，单次渲染 ~0.5-1 s，缓存后毫秒级加载。
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_DIAGRAM_RES = 1024
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# 缓存版本号：当渲染参数或风格调整时递增，强制重新生成。
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_CACHE_VERSION = "v1"
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_BBox = Tuple[float, float, float, float] # (xmin, xmax, ymin, ymax)
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_CIE1931_BBOX: _BBox = (0.0, 0.8, 0.0, 0.9)
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_CIE1976_BBOX: _BBox = (0.0, 0.65, 0.0, 0.6)
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_memory_cache: dict[str, np.ndarray] = {}
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_lock = threading.Lock()
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def _cache_dir() -> str:
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# 项目根目录通过本文件位置反推：app/plots/ -> 项目根
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here = os.path.dirname(os.path.abspath(__file__))
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root = os.path.abspath(os.path.join(here, "..", ".."))
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d = os.path.join(root, "settings", "cache")
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os.makedirs(d, exist_ok=True)
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return d
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def _cache_key(kind: str, bbox: _BBox) -> str:
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sig = f"{kind}|{bbox}|{_DIAGRAM_RES}|{_CACHE_VERSION}"
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h = hashlib.md5(sig.encode("utf-8")).hexdigest()[:10]
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return f"chromaticity_{kind}_{h}.npy"
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def _cache_path(kind: str, bbox: _BBox) -> str:
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return os.path.join(_cache_dir(), _cache_key(kind, bbox))
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def _render_chromaticity(kind: str, bbox: _BBox) -> np.ndarray:
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"""通过 colour-science 离屏渲染谱迹底图，返回 RGBA float 数组。"""
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# 延迟导入：仅在缓存未命中时支付 colour.plotting 的加载开销。
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import matplotlib
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prev_backend = matplotlib.get_backend()
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try:
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matplotlib.use("Agg", force=True)
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except Exception:
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pass
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import matplotlib.pyplot as plt
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from colour.plotting import (
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plot_chromaticity_diagram_CIE1931,
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plot_chromaticity_diagram_CIE1976UCS,
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)
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xmin, xmax, ymin, ymax = bbox
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aspect = (xmax - xmin) / (ymax - ymin)
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height = _DIAGRAM_RES
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width = int(round(height * aspect))
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fig = plt.figure(figsize=(width / 100.0, height / 100.0), dpi=100)
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ax = fig.add_axes([0.0, 0.0, 1.0, 1.0])
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if kind == "cie1931":
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plot_chromaticity_diagram_CIE1931(
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axes=ax, show=False, title=False,
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tight_layout=False, transparent_background=True,
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bounding_box=bbox,
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)
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elif kind == "cie1976":
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plot_chromaticity_diagram_CIE1976UCS(
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axes=ax, show=False, title=False,
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tight_layout=False, transparent_background=True,
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bounding_box=bbox,
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)
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else:
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plt.close(fig)
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raise ValueError(f"unknown diagram kind: {kind!r}")
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ax.set_xlim(xmin, xmax)
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ax.set_ylim(ymin, ymax)
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ax.set_axis_off()
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ax.set_position([0.0, 0.0, 1.0, 1.0])
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fig.canvas.draw()
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# 从 canvas 抓取 RGBA 数组
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buf = np.asarray(fig.canvas.buffer_rgba()).copy()
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buf = np.flipud(buf)
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plt.close(fig)
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try:
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matplotlib.use(prev_backend, force=True)
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except Exception:
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pass
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return buf
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def _load_or_render(kind: str, bbox: _BBox) -> np.ndarray:
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key = _cache_key(kind, bbox)
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with _lock:
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if key in _memory_cache:
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return _memory_cache[key]
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disk = _cache_path(kind, bbox)
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if os.path.isfile(disk):
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try:
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arr = np.load(disk)
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_memory_cache[key] = arr
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return arr
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except Exception:
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# 缓存损坏则重新渲染
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try:
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os.remove(disk)
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except OSError:
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pass
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arr = _render_chromaticity(kind, bbox)
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_memory_cache[key] = arr
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try:
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np.save(disk, arr)
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except Exception:
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pass
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return arr
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def get_cie1931_background() -> Tuple[np.ndarray, _BBox]:
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"""返回 (RGBA 数组, bbox)，可直接 ax.imshow(arr, extent=[*bbox])。"""
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return _load_or_render("cie1931", _CIE1931_BBOX), _CIE1931_BBOX
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def get_cie1976_background() -> Tuple[np.ndarray, _BBox]:
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return _load_or_render("cie1976", _CIE1976_BBOX), _CIE1976_BBOX
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def clear_cache(*, disk: bool = False) -> None:
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"""清空内存缓存（可选连同磁盘）。供调试/样式调整时使用。"""
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with _lock:
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_memory_cache.clear()
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if disk:
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d = _cache_dir()
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for name in os.listdir(d):
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if name.startswith("chromaticity_") and name.endswith(".npy"):
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try:
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os.remove(os.path.join(d, name))
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except OSError:
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pass
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