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PythonReactSEOPDF
OpenClaw

image-to-data

Extract data from construction images using AI Vision. Analyze site photos, scanned documents, drawings.

datadrivenconstruction0 installations selon la source☆ 0 Étoiles du dépôt

Installation

Suivez les instructions du dépôt pour votre agent. Consultez les fichiers et dépendances avant l’installation.

SkillHub CLI

skillhub install datadrivenconstruction/image-to-data

Examinez la source et la compatibilité, puis suivez les étapes d’installation.

Donnez le lien à votre agent et demandez-lui d’examiner instructions et dépendances avant d’expliquer les étapes.

Voir la source ↗Guides

Instructions

Image To Data

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 m...

Informations sur la source

Source
github
Repository
openclaw/skills
Path
skills/datadrivenconstruction/image-to-data
Version
—
Commit
ee612319881c9306a664db29f0bf3372b36f1d92
Source de classification
rules
Statut
active
Visibilité
public

Ces informations proviennent de l’analyse indexée et peuvent être anciennes. Vérifiez les fichiers actuels.

Contenu extrait

SKILL.md
# Image To Data

## 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

```python
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 m...

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