67 lines
2.7 KiB
Markdown
67 lines
2.7 KiB
Markdown
# EfficientWordCloud Core / 核心引擎
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(English below)
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## 🇨🇳 中文说明
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**EfficientWordCloud** 是本项目的高性能 C++ 核心扩展库。它负责处理最耗时的碰撞检测和空间搜索任务，是生成 4K/8K 超高清词云的基础。
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### 核心技术
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1. **积分图 (Integral Image)**：
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使用 O(1) 时间复杂度计算任意矩形区域的像素和。这意味着无论单词多大，碰撞检测的耗时都是恒定的，实现了瞬时检测。
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2. **螺旋搜索 (Spiral Search)**：
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替代传统的随机尝试算法，采用“从中心向外”的螺旋扫描策略。这不仅大幅提高了填充率，还将大图生成速度提升了 **50-100 倍**。
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3. **空间索引 (Spatial Indexing)**：
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内部维护一个层级网格，快速剔除无效区域。
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### 依赖说明
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- 核心扩展包依赖：`numpy`、`pillow`、`matplotlib`
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- 主脚本额外依赖：`pandas`（用于 `wordcloud_generate_hybrid.py` 读取 Excel）
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安装示例：
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```bash
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pip install numpy pillow matplotlib
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# 如果你要运行仓库根目录主脚本，再额外安装：
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pip install pandas
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```
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### 编译安装
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在当前目录下运行以下命令，将在 `efficient_wordcloud` 文件夹中生成编译好的扩展文件（`.so` 或 `.pyd`）：
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```bash
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python3 setup.py build_ext --inplace
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```
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### 性能对比
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| 分辨率 | 原版 wordcloud | EfficientWordCloud (Spiral) | 提速 |
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| :--- | :--- | :--- | :--- |
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| 1920x1080 | ~2.5s | ~0.05s | **50x** |
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| 8000x4000 | ~60s+ | ~0.5s | **120x+** |
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---
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## 🇺🇸 English Description
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**EfficientWordCloud** is the high-performance C++ backend for this project. It handles the computationally expensive collision detection and spatial queries, enabling the generation of 4K/8K ultra-HD word clouds.
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### Key Technologies
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1. **Integral Images**:
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Calculates the sum of pixels in any rectangular area in O(1) time. This allows for instantaneous collision checks regardless of the word size.
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2. **Spiral Search**:
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Replaces the brute-force random sampling with a "Center-Out" spiral heuristic. This significantly improves packing density and boosts performance by **50-100x** on large canvases.
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3. **Spatial Indexing**:
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Maintains an internal hierarchical grid to quickly cull invalid regions.
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### Build & Install
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Run the following command in this directory to build the extension in-place (generates `.so` or `.pyd` inside `efficient_wordcloud` folder):
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```bash
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python3 setup.py build_ext --inplace
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```
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### Performance Comparison
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| Resolution | Original Library | EfficientWordCloud (Spiral) | Speedup |
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| :--- | :--- | :--- | :--- |
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| 1920x1080 | ~2.5s | ~0.05s | **50x** |
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| 8000x4000 | ~60s+ | ~0.5s | **120x+** |
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