Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Tencent SkillHub · AI
Image To Data
Extract data from construction images using AI Vision. Analyze site photos, scanned documents, drawings.
skill openclawclawhub Free
Extract data from construction images using AI Vision. Analyze site photos, scanned documents, drawings.
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- Tencent SkillHub
- What's included
- claw.json, instructions.md, SKILL.md
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Trust & source
Release facts
- Source
- Tencent SkillHub
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- Version
- 2.0.0
Provenance
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- datadrivenconstruction
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Documentation
Overview
Based on DDC methodology (Chapter 2.4), this skill extracts structured data from construction images using computer vision, OCR, and AI models to analyze site photos, scanned documents, and drawings. Book Reference: "Преобразование данных в структурированную форму" / "Data Transformation to Structured Form"
Quick Start
from dataclasses import dataclass, field from enum import Enum from typing import List, Dict, Optional, Any, Tuple from datetime import datetime import json import base64 class ImageType(Enum): """Types of construction images""" SITE_PHOTO = "site_photo" SCANNED_DOCUMENT = "scanned_document" FLOOR_PLAN = "floor_plan" ELEVATION = "elevation" DETAIL_DRAWING = "detail_drawing" PROGRESS_PHOTO = "progress_photo" SAFETY_PHOTO = "safety_photo" DEFECT_PHOTO = "defect_photo" MATERIAL_PHOTO = "material_photo" EQUIPMENT_PHOTO = "equipment_photo" class ExtractionType(Enum): """Types of data extraction""" OCR_TEXT = "ocr_text" TABLE = "table" OBJECT_DETECTION = "object_detection" MEASUREMENT = "measurement" CLASSIFICATION = "classification" PROGRESS = "progress" @dataclass class BoundingBox: """Bounding box for detected region""" x: int y: int width: int height: int confidence: float = 1.0 @dataclass class TextRegion: """Extracted text region from image""" text: str bbox: BoundingBox confidence: float language: str = "en" @dataclass class DetectedObject: """Detected object in image""" label: str bbox: BoundingBox confidence: float attributes: Dict[str, Any] = field(default_factory=dict) @dataclass class ExtractedTable: """Extracted table from image""" headers: List[str] rows: List[List[str]] bbox: BoundingBox confidence: float @dataclass class ProgressMeasurement: """Progress measurement from image""" element_type: str total_count: int completed_count: int percent_complete: float area_sqft: Optional[float] = None volume_cuft: Optional[float] = None @dataclass class ImageAnalysisResult: """Complete image analysis result""" image_id: str image_type: ImageType text_regions: List[TextRegion] detected_objects: List[DetectedObject] tables: List[ExtractedTable] progress: Optional[ProgressMeasurement] = None metadata: Dict[str, Any] = field(default_factory=dict) processing_time: float = 0.0 class OCREngine: """OCR engine for text extraction""" def __init__(self, engine: str = "tesseract"): self.engine = engine self.supported_languages = ["en", "ru", "de", "fr", "es"] def extract_text( self, image_data: bytes, language: str = "en" ) -> List[TextRegion]: """Extract text from image""" # Simulated OCR extraction (use actual OCR library in production) # In production: pytesseract, EasyOCR, or cloud OCR services regions = [] # Simulate detecting title block in drawing regions.append(TextRegion( text="PROJECT: OFFICE BUILDING", bbox=BoundingBox(x=100, y=50, width=300, height=30, confidence=0.95), confidence=0.95, language=language )) regions.append(TextRegion( text="DRAWING: A-101", bbox=BoundingBox(x=100, y=90, width=200, height=25, confidence=0.92), confidence=0.92, language=language )) regions.append(TextRegion( text="SCALE: 1:100", bbox=BoundingBox(x=100, y=120, width=150, height=20, confidence=0.88), confidence=0.88, language=language )) return regions def extract_structured_text( self, image_data: bytes, template: Optional[Dict] = None ) -> Dict[str, str]: """Extract structured text using template matching""" # Extract text regions regions = self.extract_text(image_data) # Match to template fields structured = {} if template: for field_name, field_config in template.items(): # Find matching region for region in regions: if field_config.get("keyword") in region.text.lower(): structured[field_name] = region.text break else: # Default extraction for region in regions: if "PROJECT:" in region.text: structured["project_name"] = region.text.split(":")[-1].strip() elif "DRAWING:" in region.text: structured["drawing_number"] = region.text.split(":")[-1].strip() elif "SCALE:" in region.text: structured["scale"] = region.text.split(":")[-1].strip() return structured class ObjectDetector: """Object detection for construction images""" def __init__(self, model: str = "yolov8"): self.model = model self.construction_classes = self._load_construction_classes() def _load_construction_classes(self) -> Dict[str, Dict]: """Load construction-specific object classes""" return { # Equipment "excavator": {"category": "equipment", "safety_zone": 20}, "crane": {"category": "equipment", "safety_zone": 30}, "forklift": {"category": "equipment", "safety_zone": 10}, "concrete_mixer": {"category": "equipment", "safety_zone": 5}, "scaffolding": {"category": "equipment", "safety_zone": 5}, # Safety "hard_hat": {"category": "ppe", "required": True}, "safety_vest": {"category": "ppe", "required": True}, "safety_glasses": {"category": "ppe", "required": False}, "harness": {"category": "ppe", "required": False}, # Materials "rebar_bundle": {"category": "material", "unit": "bundle"}, "concrete_block": {"category": "material", "unit": "pallet"}, "lumber_stack": {"category": "material", "unit": "bundle"}, "pipe_stack": {"category": "material", "unit": "bundle"}, # Workers "worker": {"category": "person", "track": True}, # Building elements "column": {"category": "structure"}, "beam": {"category": "structure"}, "slab": {"category": "structure"}, "wall": {"category": "structure"}, } def detect( self, image_data: bytes, confidence_threshold: float = 0.5 ) -> List[DetectedObject]: """Detect objects in image""" # Simulated detection (use actual model in production) # In production: YOLO, Faster R-CNN, etc. detected = [] # Simulate detected objects sample_detections = [ ("worker", 0.92, BoundingBox(200, 300, 80, 180, 0.92)), ("hard_hat", 0.88, BoundingBox(210, 300, 30, 25, 0.88)), ("safety_vest", 0.85, BoundingBox(210, 340, 60, 80, 0.85)), ("scaffolding", 0.78, BoundingBox(400, 100, 200, 400, 0.78)), ("concrete_block", 0.72, BoundingBox(50, 450, 100, 50, 0.72)), ] for label, conf, bbox in sample_detections: if conf >= confidence_threshold: class_info = self.construction_classes.get(label, {}) detected.append(DetectedObject( label=label, bbox=bbox, confidence=conf, attributes=class_info )) return detected def detect_safety_compliance( self, image_data: bytes ) -> Dict: """Detect safety compliance in image""" objects = self.detect(image_data) workers = [o for o in objects if o.label == "worker"] hard_hats = [o for o in objects if o.label == "hard_hat"] vests = [o for o in objects if o.label == "safety_vest"] compliance = { "workers_detected": len(workers), "hard_hats_detected": len(hard_hats), "vests_detected": len(vests), "hard_hat_compliance": len(hard_hats) / len(workers) if workers else 1.0, "vest_compliance": len(vests) / len(workers) if workers else 1.0, "overall_compliance": "compliant" if len(hard_hats) >= len(workers) else "non-compliant", "violations": [] } if len(hard_hats) < len(workers): compliance["violations"].append({ "type": "missing_hard_hat", "count": len(workers) - len(hard_hats) }) return compliance class TableExtractor: """Extract tables from images""" def extract_tables( self, image_data: bytes, detect_headers: bool = True ) -> List[ExtractedTable]: """Extract tables from image""" # Simulated table extraction # In production: Camelot, Tabula, or custom CNN tables = [] # Simulate a schedule table tables.append(ExtractedTable( headers=["Activity", "Start", "End", "Duration"], rows=[ ["Foundation", "2024-01-01", "2024-01-15", "14 days"], ["Framing", "2024-01-16", "2024-02-28", "44 days"], ["MEP Rough-in", "2024-03-01", "2024-03-31", "31 days"] ], bbox=BoundingBox(50, 200, 500, 200, 0.85), confidence=0.85 )) return tables def table_to_dataframe(self, table: ExtractedTable) -> Dict: """Convert table to dictionary (DataFrame-like)""" return { "columns": table.headers, "data": table.rows, "records": [ dict(zip(table.headers, row)) for row in table.rows ] } class ProgressAnalyzer: """Analyze construction progress from images""" def __init__(self): self.reference_models = {} def analyze_progress( self, current_image: bytes, reference_image: Optional[bytes] = None, element_type: str = "general" ) -> ProgressMeasurement: """Analyze progress by comparing images""" # Simulated progress analysis # In production: Use semantic segmentation + comparison # Simulate progress detection return ProgressMeasurement( element_type=element_type, total_count=100, completed_count=65, percent_complete=65.0, area_sqft=15000.0, volume_cuft=None ) def compare_with_plan( self, site_photo: bytes, plan_image: bytes ) -> Dict: """Compare site photo with plan""" return { "match_score": 0.78, "deviations": [], "completion_estimate": 65.0, "areas_of_concern": [] } class ConstructionImageAnalyzer: """ Main class for construction image analysis. Based on DDC methodology Chapter 2.4. """ def __init__(self): self.ocr = OCREngine() self.detector = ObjectDetector() self.table_extractor = TableExtractor() self.progress_analyzer = ProgressAnalyzer() def analyze_image( self, image_data: bytes, image_type: ImageType, image_id: str = "img_001", extract_types: Optional[List[ExtractionType]] = None ) -> ImageAnalysisResult: """ Analyze a construction image. Args: image_data: Image data as bytes image_type: Type of image image_id: Unique image identifier extract_types: Types of extraction to perform Returns: Complete analysis result """ start_time = datetime.now() if extract_types is None: extract_types = [ExtractionType.OCR_TEXT, ExtractionType.OBJECT_DETECTION] text_regions = [] detected_objects = [] tables = [] progress = None # OCR extraction if ExtractionType.OCR_TEXT in extract_types: text_regions = self.ocr.extract_text(image_data) # Object detection if ExtractionType.OBJECT_DETECTION in extract_types: detected_objects = self.detector.detect(image_data) # Table extraction if ExtractionType.TABLE in extract_types: tables = self.table_extractor.extract_tables(image_data) # Progress analysis if ExtractionType.PROGRESS in extract_types: progress = self.progress_analyzer.analyze_progress(image_data) processing_time = (datetime.now() - start_time).total_seconds() return ImageAnalysisResult( image_id=image_id, image_type=image_type, text_regions=text_regions, detected_objects=detected_objects, tables=tables, progress=progress, metadata={"extraction_types": [e.value for e in extract_types]}, processing_time=processing_time ) def analyze_site_photo( self, image_data: bytes, image_id: str = "site_001" ) -> Dict: """Analyze site photo for progress and safety""" result = self.analyze_image( image_data, ImageType.SITE_PHOTO, image_id, [ExtractionType.OBJECT_DETECTION, ExtractionType.PROGRESS] ) safety = self.detector.detect_safety_compliance(image_data) return { "image_id": result.image_id, "objects_detected": len(result.detected_objects), "progress": result.progress, "safety_compliance": safety, "equipment": [o.label for o in result.detected_objects if o.attributes.get("category") == "equipment"], "materials": [o.label for o in result.detected_objects if o.attributes.get("category") == "material"] } def extract_drawing_data( self, image_data: bytes, image_id: str = "dwg_001" ) -> Dict: """Extract data from scanned drawing""" result = self.analyze_image( image_data, ImageType.FLOOR_PLAN, image_id, [ExtractionType.OCR_TEXT, ExtractionType.TABLE] ) # Extract title block info title_block = self.ocr.extract_structured_text(image_data) return { "image_id": result.image_id, "title_block": title_block, "text_regions": len(result.text_regions), "tables": [ self.table_extractor.table_to_dataframe(t) for t in result.tables ], "all_text": [r.text for r in result.text_regions] } def batch_analyze( self, images: List[Tuple[bytes, ImageType, str]] ) -> List[ImageAnalysisResult]: """Analyze multiple images""" results = [] for image_data, image_type, image_id in images: result = self.analyze_image(image_data, image_type, image_id) results.append(result) return results def export_results( self, result: ImageAnalysisResult, format: str = "json" ) -> str: """Export analysis results""" data = { "image_id": result.image_id, "image_type": result.image_type.value, "text_count": len(result.text_regions), "object_count": len(result.detected_objects), "table_count": len(result.tables), "texts": [ {"text": r.text, "confidence": r.confidence} for r in result.text_regions ], "objects": [ {"label": o.label, "confidence": o.confidence} for o in result.detected_objects ], "processing_time": result.processing_time } if format == "json": return json.dumps(data, indent=2) else: raise ValueError(f"Unsupported format: {format}")
Analyze Site Photo
analyzer = ConstructionImageAnalyzer() # Load image (in production, read from file) with open("site_photo.jpg", "rb") as f: image_data = f.read() result = analyzer.analyze_site_photo(image_data) print(f"Objects detected: {result['objects_detected']}") print(f"Safety compliance: {result['safety_compliance']['overall_compliance']}") print(f"Progress: {result['progress'].percent_complete}%")
Extract Drawing Data
with open("floor_plan.png", "rb") as f: drawing_data = f.read() data = analyzer.extract_drawing_data(drawing_data) print(f"Drawing: {data['title_block'].get('drawing_number')}") print(f"Project: {data['title_block'].get('project_name')}") for table in data['tables']: print(f"Table with {len(table['records'])} rows")
Detect Safety Violations
detector = ObjectDetector() with open("site_photo.jpg", "rb") as f: image_data = f.read() safety = detector.detect_safety_compliance(image_data) if safety['overall_compliance'] == 'non-compliant': for violation in safety['violations']: print(f"Violation: {violation['type']} - Count: {violation['count']}")
Quick Reference
ComponentPurposeConstructionImageAnalyzerMain analysis engineOCREngineText extractionObjectDetectorObject detectionTableExtractorTable extractionProgressAnalyzerProgress analysisImageAnalysisResultComplete analysis result
Resources
Book: "Data-Driven Construction" by Artem Boiko, Chapter 2.4 Website: https://datadrivenconstruction.io
Next Steps
Use cad-to-data for CAD/BIM extraction Use defect-detection-ai for defects Use safety-compliance-checker for safety
Category context
Agent frameworks, memory systems, reasoning layers, and model-native orchestration.
Source: Tencent SkillHub
Largest current source with strong distribution and engagement signals.
Package contents
Included in package
2 Docs1 Config
- SKILL.md
- instructions.md
- claw.json