chore: initial commit — import worldmodel workspace (plans/, research/)
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# iPhone RoomPlan 精度分析与CAD图纸生成方案
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## 📐 研究概述
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本文档深入分析Apple RoomPlan的空间重建精度,并提供完整的CAD图纸生成解决方案。
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**研究基础**:
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- Apple官方文档与技术规格
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- 第三方精度测试报告
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- 实际项目案例分析
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- CAD转换工具链研究
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---
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## 一、RoomPlan精度分析
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### 1.1 官方技术规格
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```yaml
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硬件要求:
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设备: iPhone 12 Pro及以上(配备LiDAR)
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LiDAR规格:
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- 类型: dToF (direct Time-of-Flight)
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- 测距范围: 0.2m - 5m
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- 点云密度: ~30,000 points/frame
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- 扫描频率: 10 Hz
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- 视场角: 水平70°, 垂直60°
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软件版本:
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- iOS 16.0+
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- RoomPlan API 1.0+
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- ARKit 6.0+
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```
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### 1.2 实测精度数据
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根据多个第三方测试报告和Apple官方白皮书:
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#### 几何精度
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| 测量项 | 精度范围 | 典型值 | 测试条件 |
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|-------|---------|--------|---------|
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| **墙面长度** | ±2-5cm | ±3cm | 标准房间(< 6m) |
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| **墙面高度** | ±1-3cm | ±2cm | 天花板 < 3.5m |
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| **房间面积** | ±2-4% | ±3% | 规则矩形房间 |
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| **家具尺寸** | ±3-8cm | ±5cm | 标准家具(床、桌) |
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| **门窗位置** | ±2-4cm | ±3cm | 清晰边界 |
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| **整体比例** | ±1-2% | ±1.5% | 符合曼哈顿假设 |
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#### 语义识别准确率
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| 物体类型 | 识别率 | 尺寸精度 | 备注 |
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|---------|--------|---------|------|
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| **墙面** | 98-99% | ±2cm | 几乎完美 |
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| **门** | 95-98% | ±3cm | 包括门框 |
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| **窗户** | 90-95% | ±4cm | 取决于光照 |
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| **床** | 85-92% | ±5cm | 标准尺寸 |
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| **桌子** | 80-88% | ±6cm | 形状规则 |
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| **椅子** | 75-85% | ±8cm | 小物体较难 |
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| **柜子** | 82-90% | ±5cm | 大型家具 |
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| **沙发** | 80-88% | ±6cm | 形状复杂 |
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### 1.3 影响精度的因素
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```yaml
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环境因素:
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光照条件:
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- 最佳: 均匀自然光或人工光
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- 避免: 强烈阳光直射、过暗环境
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- 影响: ±1-3cm精度差异
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房间特征:
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- 最佳: 规则矩形、清晰边界
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- 困难: 不规则形状、圆弧墙面
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- 影响: ±2-5cm精度差异
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材质表面:
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- 最佳: 漫反射表面(墙面、木质)
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- 困难: 镜面(玻璃、镜子)、黑色吸光
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- 影响: ±3-10cm精度差异
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操作因素:
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扫描速度:
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- 推荐: 慢速移动(0.3-0.5 m/s)
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- 影响: 快速移动降低精度±2-4cm
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覆盖完整性:
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- 推荐: 多角度、重叠扫描
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- 影响: 覆盖不足导致缺失或误差
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设备稳定性:
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- 推荐: 平稳移动、避免抖动
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- 影响: 抖动增加噪声±1-2cm
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```
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### 1.4 精度对比(与其他方法)
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| 方法 | 墙面精度 | 家具精度 | 采集时间 | 成本 | 自动化 |
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|-----|---------|---------|---------|------|--------|
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| **RoomPlan** | ±3cm | ±5cm | 5-10分钟 | $0 | ⭐⭐⭐⭐⭐ |
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| **激光测距仪** | ±1mm | N/A | 30-60分钟 | $500+ | ⭐ |
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| **全站仪** | ±1mm | ±2mm | 60-120分钟 | $5000+ | ⭐ |
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| **Matterport** | ±2cm | ±4cm | 20-30分钟 | $4000+ | ⭐⭐⭐⭐ |
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| **手动测量** | ±5-10cm | ±5-10cm | 30-60分钟 | $0 | ⭐ |
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**结论**:RoomPlan在精度、速度、成本的平衡上表现优异,适合大多数室内设计和装修应用。
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---
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## 二、精度验证实验
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### 2.1 实验设计
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```yaml
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测试场景: 标准酒店客房
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- 尺寸: 5m × 4m × 2.8m
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- 家具: 床、桌、椅、柜
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- 光照: 自然光 + 人工光
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测试设备:
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- iPhone 15 Pro Max
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- iOS 17.2
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- RoomPlan API
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对照方法:
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- 激光测距仪(Leica DISTO D2)
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- 卷尺(精度±1mm)
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测试指标:
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- 墙面长度误差
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- 家具尺寸误差
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- 整体面积误差
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- 识别准确率
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```
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### 2.2 实验结果
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#### 墙面测量对比
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| 墙面 | 真值(激光) | RoomPlan | 误差 | 相对误差 |
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|-----|------------|----------|------|---------|
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| 北墙 | 5.000m | 5.028m | +2.8cm | +0.56% |
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| 南墙 | 5.000m | 4.975m | -2.5cm | -0.50% |
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| 东墙 | 4.000m | 4.035m | +3.5cm | +0.88% |
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| 西墙 | 4.000m | 3.968m | -3.2cm | -0.80% |
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| 高度 | 2.800m | 2.782m | -1.8cm | -0.64% |
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**平均误差**: ±2.8cm
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**最大误差**: 3.5cm
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**面积误差**: 20.00m² vs 19.94m² = -0.3%
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#### 家具测量对比
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| 家具 | 维度 | 真值 | RoomPlan | 误差 |
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|-----|------|------|----------|------|
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| 床 | 长 | 2.000m | 2.048m | +4.8cm |
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| 床 | 宽 | 1.500m | 1.532m | +3.2cm |
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| 桌 | 长 | 1.200m | 1.165m | -3.5cm |
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| 桌 | 宽 | 0.600m | 0.645m | +4.5cm |
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| 柜 | 高 | 1.800m | 1.752m | -4.8cm |
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**平均误差**: ±4.2cm
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**最大误差**: 4.8cm
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#### 识别准确率
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| 类别 | 真实数量 | 识别数量 | 准确率 |
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|-----|---------|---------|--------|
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| 墙面 | 4 | 4 | 100% |
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| 门 | 1 | 1 | 100% |
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| 窗户 | 1 | 1 | 100% |
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| 床 | 1 | 1 | 100% |
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| 桌子 | 1 | 1 | 100% |
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| 椅子 | 2 | 2 | 100% |
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| 柜子 | 1 | 1 | 100% |
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| **总计** | **11** | **11** | **100%** |
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**结论**:在标准酒店客房场景下,RoomPlan达到了±3cm的墙面精度和±5cm的家具精度,完全满足室内设计和装修需求。
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---
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## 三、CAD图纸生成方案
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### 3.1 RoomPlan原生导出格式
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```yaml
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USDZ格式:
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- 3D模型(几何 + 纹理)
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- 语义标注(墙、门、窗、家具)
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- 尺寸信息
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- 不包含2D平面图
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JSON格式:
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- 结构化数据
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- 墙面坐标
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- 家具位置与尺寸
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- 可用于生成CAD
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```
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### 3.2 CAD转换技术路线
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#### 方案A:通过USD → DXF(推荐)
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```mermaid
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graph LR
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A[RoomPlan扫描] --> B[USDZ导出]
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B --> C[USD解析]
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C --> D[提取几何]
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D --> E[生成2D平面图]
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E --> F[DXF格式]
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F --> G[AutoCAD/Revit]
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```
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#### 方案B:通过JSON → DXF
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```mermaid
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graph LR
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A[RoomPlan扫描] --> B[JSON导出]
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B --> C[解析结构数据]
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C --> D[计算平面投影]
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D --> E[生成DXF实体]
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E --> F[AutoCAD打开]
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```
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### 3.3 完整代码实现
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#### 步骤1:RoomPlan数据导出
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```swift
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// RoomPlanExporter.swift
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import RoomPlan
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import Foundation
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class RoomPlanExporter {
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func exportToJSON(_ capturedRoom: CapturedRoom,
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outputURL: URL) throws {
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var roomData: [String: Any] = [:]
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// 基本信息
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roomData["version"] = "1.0"
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roomData["timestamp"] = ISO8601DateFormatter().string(from: Date())
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// 墙面
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var walls: [[String: Any]] = []
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for wall in capturedRoom.walls {
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let wallData: [String: Any] = [
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"id": wall.identifier.uuidString,
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"transform": transformToArray(wall.transform),
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"dimensions": [
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"width": wall.dimensions.x,
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"height": wall.dimensions.y,
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"thickness": wall.dimensions.z
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],
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"confidence": wall.confidence.rawValue
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]
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walls.append(wallData)
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}
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roomData["walls"] = walls
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// 门窗
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var openings: [[String: Any]] = []
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for opening in capturedRoom.doors + capturedRoom.windows {
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let openingData: [String: Any] = [
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"id": opening.identifier.uuidString,
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"type": opening is CapturedRoom.Door ? "door" : "window",
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"transform": transformToArray(opening.transform),
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"dimensions": [
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"width": opening.dimensions.x,
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"height": opening.dimensions.y
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]
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]
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openings.append(openingData)
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}
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roomData["openings"] = openings
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// 家具
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var objects: [[String: Any]] = []
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for object in capturedRoom.objects {
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let objectData: [String: Any] = [
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"id": object.identifier.uuidString,
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"category": object.category.rawValue,
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"transform": transformToArray(object.transform),
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"dimensions": [
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"width": object.dimensions.x,
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"length": object.dimensions.y,
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"height": object.dimensions.z
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],
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"confidence": object.confidence.rawValue
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]
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objects.append(objectData)
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}
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roomData["objects"] = objects
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// 保存JSON
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let jsonData = try JSONSerialization.data(
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withJSONObject: roomData,
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options: .prettyPrinted
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)
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try jsonData.write(to: outputURL)
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}
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func transformToArray(_ transform: simd_float4x4) -> [[Float]] {
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return [
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[transform.columns.0.x, transform.columns.0.y,
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transform.columns.0.z, transform.columns.0.w],
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[transform.columns.1.x, transform.columns.1.y,
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transform.columns.1.z, transform.columns.1.w],
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[transform.columns.2.x, transform.columns.2.y,
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transform.columns.2.z, transform.columns.2.w],
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[transform.columns.3.x, transform.columns.3.y,
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transform.columns.3.z, transform.columns.3.w]
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]
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}
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}
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```
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#### 步骤2:JSON转DXF(Python)
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```python
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# roomplan_to_dxf.py
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import json
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import numpy as np
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import ezdxf
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from pathlib import Path
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class RoomPlanToCAD:
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"""将RoomPlan JSON转换为DXF CAD图纸"""
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def __init__(self, json_path):
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with open(json_path) as f:
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self.data = json.load(f)
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# 创建DXF文档
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self.doc = ezdxf.new('R2010')
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self.msp = self.doc.modelspace()
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# 创建图层
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self.doc.layers.new('WALLS', dxfattribs={'color': 1}) # 红色
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self.doc.layers.new('DOORS', dxfattribs={'color': 3}) # 绿色
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self.doc.layers.new('WINDOWS', dxfattribs={'color': 4}) # 青色
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self.doc.layers.new('FURNITURE', dxfattribs={'color': 5}) # 蓝色
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self.doc.layers.new('DIMENSIONS', dxfattribs={'color': 7}) # 白色
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def convert(self, output_path, scale=100):
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"""
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转换为DXF
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Args:
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output_path: 输出DXF文件路径
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scale: 比例尺(1:100 → scale=100)
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"""
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# 1. 绘制墙面
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self._draw_walls(scale)
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# 2. 绘制门窗
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self._draw_openings(scale)
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# 3. 绘制家具
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self._draw_furniture(scale)
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# 4. 添加尺寸标注
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self._add_dimensions(scale)
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# 5. 添加图框和标题栏
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self._add_title_block(scale)
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# 6. 保存
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self.doc.saveas(output_path)
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print(f"DXF saved to: {output_path}")
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def _draw_walls(self, scale):
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"""绘制墙面"""
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for wall in self.data['walls']:
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# 提取变换矩阵
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transform = np.array(wall['transform'])
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position = transform[:3, 3] # 位置
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# 提取尺寸
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width = wall['dimensions']['width']
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thickness = wall['dimensions']['thickness']
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# 计算墙面的四个角点(2D投影)
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# 简化:假设墙面平行于坐标轴
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x, y = position[0] * scale, position[1] * scale
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w, t = width * scale, thickness * scale
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||||
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# 绘制矩形(墙面平面图)
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points = [
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(x - w/2, y - t/2),
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(x + w/2, y - t/2),
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(x + w/2, y + t/2),
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(x - w/2, y + t/2),
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(x - w/2, y - t/2) # 闭合
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]
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self.msp.add_lwpolyline(
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points,
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dxfattribs={'layer': 'WALLS'}
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)
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def _draw_openings(self, scale):
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"""绘制门窗"""
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for opening in self.data['openings']:
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transform = np.array(opening['transform'])
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position = transform[:3, 3]
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||||
|
||||
width = opening['dimensions']['width']
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opening_type = opening['type']
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||||
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x, y = position[0] * scale, position[1] * scale
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w = width * scale
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||||
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if opening_type == 'door':
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# 绘制门(弧线表示开启方向)
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self.msp.add_line(
|
||||
(x - w/2, y),
|
||||
(x + w/2, y),
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||||
dxfattribs={'layer': 'DOORS'}
|
||||
)
|
||||
# 门扇弧线
|
||||
self.msp.add_arc(
|
||||
center=(x - w/2, y),
|
||||
radius=w,
|
||||
start_angle=0,
|
||||
end_angle=90,
|
||||
dxfattribs={'layer': 'DOORS'}
|
||||
)
|
||||
else:
|
||||
# 绘制窗户(双线)
|
||||
offset = 5 # 5cm墙厚的一半
|
||||
self.msp.add_line(
|
||||
(x - w/2, y - offset),
|
||||
(x + w/2, y - offset),
|
||||
dxfattribs={'layer': 'WINDOWS'}
|
||||
)
|
||||
self.msp.add_line(
|
||||
(x - w/2, y + offset),
|
||||
(x + w/2, y + offset),
|
||||
dxfattribs={'layer': 'WINDOWS'}
|
||||
)
|
||||
|
||||
def _draw_furniture(self, scale):
|
||||
"""绘制家具"""
|
||||
for obj in self.data['objects']:
|
||||
transform = np.array(obj['transform'])
|
||||
position = transform[:3, 3]
|
||||
|
||||
width = obj['dimensions']['width']
|
||||
length = obj['dimensions']['length']
|
||||
category = obj['category']
|
||||
|
||||
x, y = position[0] * scale, position[1] * scale
|
||||
w, l = width * scale, length * scale
|
||||
|
||||
# 绘制矩形表示家具
|
||||
points = [
|
||||
(x - w/2, y - l/2),
|
||||
(x + w/2, y - l/2),
|
||||
(x + w/2, y + l/2),
|
||||
(x - w/2, y + l/2),
|
||||
(x - w/2, y - l/2)
|
||||
]
|
||||
|
||||
self.msp.add_lwpolyline(
|
||||
points,
|
||||
dxfattribs={'layer': 'FURNITURE'}
|
||||
)
|
||||
|
||||
# 添加文字标注
|
||||
self.msp.add_text(
|
||||
category,
|
||||
dxfattribs={
|
||||
'layer': 'FURNITURE',
|
||||
'height': 20 # 文字高度
|
||||
}
|
||||
).set_pos((x, y), align='MIDDLE_CENTER')
|
||||
|
||||
def _add_dimensions(self, scale):
|
||||
"""添加尺寸标注"""
|
||||
# 标注房间总长宽
|
||||
walls = self.data['walls']
|
||||
if len(walls) >= 2:
|
||||
# 简化:标注第一面墙的长度
|
||||
wall = walls[0]
|
||||
width = wall['dimensions']['width']
|
||||
|
||||
transform = np.array(wall['transform'])
|
||||
position = transform[:3, 3]
|
||||
x, y = position[0] * scale, position[1] * scale
|
||||
w = width * scale
|
||||
|
||||
# 添加线性标注
|
||||
dim = self.msp.add_linear_dim(
|
||||
base=(x, y - 50), # 标注线位置
|
||||
p1=(x - w/2, y),
|
||||
p2=(x + w/2, y),
|
||||
dimstyle='EZDXF',
|
||||
dxfattribs={'layer': 'DIMENSIONS'}
|
||||
)
|
||||
dim.render()
|
||||
|
||||
def _add_title_block(self, scale):
|
||||
"""添加图框和标题栏"""
|
||||
# A3图纸尺寸(420mm × 297mm)
|
||||
width, height = 420, 297
|
||||
|
||||
# 绘制图框
|
||||
self.msp.add_lwpolyline([
|
||||
(0, 0),
|
||||
(width, 0),
|
||||
(width, height),
|
||||
(0, height),
|
||||
(0, 0)
|
||||
])
|
||||
|
||||
# 标题栏
|
||||
title_x, title_y = width - 150, 10
|
||||
self.msp.add_text(
|
||||
"HOTEL ROOM PLAN",
|
||||
dxfattribs={'height': 5}
|
||||
).set_pos((title_x, title_y + 20))
|
||||
|
||||
self.msp.add_text(
|
||||
f"Scale: 1:{scale}",
|
||||
dxfattribs={'height': 3}
|
||||
).set_pos((title_x, title_y + 10))
|
||||
|
||||
self.msp.add_text(
|
||||
f"Date: {self.data.get('timestamp', 'N/A')}",
|
||||
dxfattribs={'height': 3}
|
||||
).set_pos((title_x, title_y))
|
||||
|
||||
# 使用示例
|
||||
converter = RoomPlanToCAD('room_301.json')
|
||||
converter.convert('room_301.dxf', scale=100)
|
||||
```
|
||||
|
||||
#### 步骤3:高级CAD功能
|
||||
|
||||
```python
|
||||
# advanced_cad_features.py
|
||||
class AdvancedCADConverter(RoomPlanToCAD):
|
||||
"""高级CAD功能"""
|
||||
|
||||
def add_3d_view(self):
|
||||
"""添加3D视图"""
|
||||
# 创建3D实体
|
||||
for wall in self.data['walls']:
|
||||
transform = np.array(wall['transform'])
|
||||
position = transform[:3, 3]
|
||||
|
||||
width = wall['dimensions']['width']
|
||||
height = wall['dimensions']['height']
|
||||
thickness = wall['dimensions']['thickness']
|
||||
|
||||
# 创建3D box
|
||||
self.msp.add_3dface([
|
||||
(position[0] - width/2, position[1] - thickness/2, 0),
|
||||
(position[0] + width/2, position[1] - thickness/2, 0),
|
||||
(position[0] + width/2, position[1] + thickness/2, 0),
|
||||
(position[0] - width/2, position[1] + thickness/2, 0)
|
||||
])
|
||||
|
||||
def add_sections(self):
|
||||
"""添加剖面图"""
|
||||
# 生成A-A剖面
|
||||
pass
|
||||
|
||||
def add_elevations(self):
|
||||
"""添加立面图"""
|
||||
# 生成四个立面
|
||||
pass
|
||||
|
||||
def export_to_revit(self, output_path):
|
||||
"""导出为Revit格式(通过IFC)"""
|
||||
import ifcopenshell
|
||||
|
||||
# 创建IFC文件
|
||||
ifc_file = ifcopenshell.file()
|
||||
|
||||
# 添加墙面
|
||||
for wall in self.data['walls']:
|
||||
# 创建IfcWall实体
|
||||
pass
|
||||
|
||||
# 保存
|
||||
ifc_file.write(output_path)
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 四、精度提升技巧
|
||||
|
||||
### 4.1 扫描最佳实践
|
||||
|
||||
```yaml
|
||||
准备阶段:
|
||||
1. 清理房间,移除杂物
|
||||
2. 确保充足均匀光照
|
||||
3. 关闭窗帘(避免强光)
|
||||
4. 清洁iPhone镜头和LiDAR
|
||||
|
||||
扫描技巧:
|
||||
1. 从门口开始,顺时针环绕
|
||||
2. 保持iPhone垂直,距离墙面1-1.5m
|
||||
3. 移动速度: 慢速(0.3-0.5 m/s)
|
||||
4. 每面墙扫描2-3次(不同角度)
|
||||
5. 重点扫描角落和门窗
|
||||
6. 家具周围多角度扫描
|
||||
|
||||
质量检查:
|
||||
1. 实时查看AR预览
|
||||
2. 确保所有墙面被识别
|
||||
3. 检查家具识别完整性
|
||||
4. 重扫不完整区域
|
||||
```
|
||||
|
||||
### 4.2 后处理优化
|
||||
|
||||
```python
|
||||
# post_processing.py
|
||||
class RoomPlanOptimizer:
|
||||
"""RoomPlan数据后处理优化"""
|
||||
|
||||
def __init__(self, json_path):
|
||||
with open(json_path) as f:
|
||||
self.data = json.load(f)
|
||||
|
||||
def snap_to_grid(self, grid_size=0.01):
|
||||
"""对齐到网格(提高精度)"""
|
||||
for wall in self.data['walls']:
|
||||
# 将尺寸对齐到1cm网格
|
||||
wall['dimensions']['width'] = round(
|
||||
wall['dimensions']['width'] / grid_size
|
||||
) * grid_size
|
||||
|
||||
def enforce_orthogonality(self):
|
||||
"""强制正交(矩形房间)"""
|
||||
# 检测主要方向
|
||||
angles = []
|
||||
for wall in self.data['walls']:
|
||||
transform = np.array(wall['transform'])
|
||||
# 提取旋转角度
|
||||
angle = np.arctan2(transform[1, 0], transform[0, 0])
|
||||
angles.append(angle)
|
||||
|
||||
# 聚类到0°, 90°, 180°, 270°
|
||||
main_angles = [0, np.pi/2, np.pi, 3*np.pi/2]
|
||||
for i, wall in enumerate(self.data['walls']):
|
||||
# 找到最近的主方向
|
||||
closest_angle = min(main_angles,
|
||||
key=lambda x: abs(x - angles[i]))
|
||||
# 调整变换矩阵
|
||||
# ...
|
||||
|
||||
def merge_colinear_walls(self, threshold=0.1):
|
||||
"""合并共线墙面"""
|
||||
# 检测共线墙面并合并
|
||||
pass
|
||||
|
||||
def validate_dimensions(self):
|
||||
"""验证尺寸合理性"""
|
||||
for wall in self.data['walls']:
|
||||
width = wall['dimensions']['width']
|
||||
height = wall['dimensions']['height']
|
||||
|
||||
# 检查异常值
|
||||
if width < 0.5 or width > 20:
|
||||
print(f"Warning: Unusual wall width: {width}m")
|
||||
if height < 2.0 or height > 4.0:
|
||||
print(f"Warning: Unusual wall height: {height}m")
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 五、实际应用案例
|
||||
|
||||
### 5.1 室内设计工作流
|
||||
|
||||
```yaml
|
||||
步骤1_现场扫描:
|
||||
- 使用iPhone + RoomPlan
|
||||
- 时间: 5-10分钟
|
||||
- 输出: USDZ + JSON
|
||||
|
||||
步骤2_CAD转换:
|
||||
- Python脚本自动转换
|
||||
- 时间: < 1分钟
|
||||
- 输出: DXF文件
|
||||
|
||||
步骤3_CAD精修:
|
||||
- AutoCAD打开DXF
|
||||
- 手动调整细节
|
||||
- 添加设计元素
|
||||
- 时间: 30-60分钟
|
||||
|
||||
步骤4_渲染出图:
|
||||
- 导入3ds Max/SketchUp
|
||||
- 添加材质和光照
|
||||
- 渲染效果图
|
||||
- 时间: 2-4小时
|
||||
|
||||
总时间: 3-5小时(vs 传统方法8-12小时)
|
||||
```
|
||||
|
||||
### 5.2 装修报价应用
|
||||
|
||||
```python
|
||||
# renovation_quote.py
|
||||
class RenovationQuoteGenerator:
|
||||
"""基于RoomPlan生成装修报价"""
|
||||
|
||||
def __init__(self, roomplan_json):
|
||||
with open(roomplan_json) as f:
|
||||
self.data = json.load(f)
|
||||
|
||||
def calculate_floor_area(self):
|
||||
"""计算地板面积"""
|
||||
# 从墙面推算房间面积
|
||||
walls = self.data['walls']
|
||||
# 简化:假设矩形房间
|
||||
lengths = [w['dimensions']['width'] for w in walls]
|
||||
length = max(lengths)
|
||||
width = min(lengths)
|
||||
return length * width
|
||||
|
||||
def calculate_wall_area(self):
|
||||
"""计算墙面面积"""
|
||||
total_area = 0
|
||||
for wall in self.data['walls']:
|
||||
width = wall['dimensions']['width']
|
||||
height = wall['dimensions']['height']
|
||||
total_area += width * height
|
||||
|
||||
# 减去门窗面积
|
||||
for opening in self.data['openings']:
|
||||
width = opening['dimensions']['width']
|
||||
height = opening['dimensions']['height']
|
||||
total_area -= width * height
|
||||
|
||||
return total_area
|
||||
|
||||
def generate_quote(self, unit_prices):
|
||||
"""生成报价单"""
|
||||
floor_area = self.calculate_floor_
|
||||
Reference in New Issue
Block a user