10-of-my-most-popular-text-to-image-series-prompts-78b0897e
generate a bunch of images, then you curate the results to handpick the best ones
Analyze construction drawings to extract dimensions, annotations, symbols, and metadata. Support quantity takeoff and design review automation.
请根据来源仓库和你的 AI 工具说明安装此技能。先阅读技能文件和依赖要求,再添加到工作流。
SkillHub CLI
skillhub install datadrivenconstruction/drawing-analyzer打开来源仓库,查看说明和兼容环境后,按来源提供的命令或步骤安装。
将来源链接提供给你的 Agent,让它先读取说明、核对依赖,并向你说明将执行的安装步骤。
Analyze construction drawings (PDF, DWG) to extract dimensions, annotations, symbols, title block data, and support automated quantity takeoff and design review.
Drawing analysis automation enables:
from dataclasses import dataclass, field
from typing import List, Dict, Any, Optional, Tuple
import re
import pdfplumber
from pathlib import Path
@dataclass
class TitleBlockData:
project_name: str
project_number: str
sheet_number: str
sheet_title: str
discipline: str
scale: str
date: str
revision: str
drawn_by: str
checked_by: str
approved_by: str
@dataclass
class Dimension:
value: float
unit: str
dimension_type: str # linear, angular, radial
location: Tuple[float, float]
associated_text: str
@dataclass
class Annotation:
text: str
annotation_type: str # note, callout, tag, keynote
location: Tuple[float, float]
references: List[str]
@dataclass
class Symbol:
symbol_type: str # door, window, equipment, etc.
tag: str
location: Tuple[float, float]
properties: Dict[str, Any]
@dataclass
class DrawingAnalysisResult:
file_name: str
title_block: Optional[TitleBlockData]
dimensions: List[Dimension]
annotations: List[Annotation]
symbols: List[Symbol]
scale_factor: float
drawing_area: Tuple[float, float]
quality_issues: List[str]
class DrawingAnalyzer:
"""Analyze construction drawings for data extraction."""
# Common dimension patterns
DIMENSION_PATTERNS = [
r"(\d+'-\s*\d+(?:\s*\d+/\d+)?\"?)", # Feet-inches: 10'-6", 10' - 6 1/2"...
这些信息来自收录时的来源分析,可能滞后;使用前请核对最新来源文件。
# Drawing Analyzer for Construction
## Overview
Analyze construction drawings (PDF, DWG) to extract dimensions, annotations, symbols, title block data, and support automated quantity takeoff and design review.
## Business Case
Drawing analysis automation enables:
- **Faster Takeoffs**: Extract quantities from drawings
- **Quality Control**: Verify drawing completeness
- **Data Extraction**: Pull metadata for project systems
- **Design Review**: Automated checking against standards
## Technical Implementation
```python
from dataclasses import dataclass, field
from typing import List, Dict, Any, Optional, Tuple
import re
import pdfplumber
from pathlib import Path
@dataclass
class TitleBlockData:
project_name: str
project_number: str
sheet_number: str
sheet_title: str
discipline: str
scale: str
date: str
revision: str
drawn_by: str
checked_by: str
approved_by: str
@dataclass
class Dimension:
value: float
unit: str
dimension_type: str # linear, angular, radial
location: Tuple[float, float]
associated_text: str
@dataclass
class Annotation:
text: str
annotation_type: str # note, callout, tag, keynote
location: Tuple[float, float]
references: List[str]
@dataclass
class Symbol:
symbol_type: str # door, window, equipment, etc.
tag: str
location: Tuple[float, float]
properties: Dict[str, Any]
@dataclass
class DrawingAnalysisResult:
file_name: str
title_block: Optional[TitleBlockData]
dimensions: List[Dimension]
annotations: List[Annotation]
symbols: List[Symbol]
scale_factor: float
drawing_area: Tuple[float, float]
quality_issues: List[str]
class DrawingAnalyzer:
"""Analyze construction drawings for data extraction."""
# Common dimension patterns
DIMENSION_PATTERNS = [
r"(\d+'-\s*\d+(?:\s*\d+/\d+)?\"?)", # Feet-inches: 10'-6", 10' - 6 1/2"...generate a bunch of images, then you curate the results to handpick the best ones
generate a bunch of images, then you curate the results to handpick the best ones
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