Field Progress Photo Analyzer

Business Case

Site photos document progress but are often poorly organized. This skill provides systematic photo cataloging and analysis.

Technical Implementation

import pandas as pd
from datetime import datetime, date
from typing import Dict, Any, List, Optional
from dataclasses import dataclass, field
from enum import Enum


class PhotoCategory(Enum):
    PROGRESS = "progress"
    QUALITY = "quality"
    SAFETY = "safety"
    DELIVERY = "delivery"
    ISSUE = "issue"
    GENERAL = "general"


@dataclass
class SitePhoto:
    photo_id: str
    filename: str
    captured_date: datetime
    category: PhotoCategory
    location: str
    level: str
    zone: str
    captured_by: str
    description: str
    tags: List[str] = field(default_factory=list)
    activity_code: str = ""
    file_path: str = ""


class ProgressPhotoAnalyzer:
    def __init__(self, project_name: str):
        self.project_name = project_name
        self.photos: Dict[str, SitePhoto] = {}
        self._counter = 0

    def catalog_photo(self, filename: str, captured_date: datetime,
                     category: PhotoCategory, location: str, level: str,
                     captured_by: str, description: str = "",
                     zone: str = "", tags: List[str] = None) -> SitePhoto:
        self._counter += 1
        photo_id = f"PH-{self._counter:05d}"

        photo = SitePhoto(
            photo_id=photo_id,
            filename=filename,
            captured_date=captured_date,
            category=category,
            location=location,
            level=level,
            zone=zone,
            captured_by=captured_by,
            description=description,
            tags=tags or []
        )
        self.photos[photo_id] = photo
        return photo

    def get_photos_by_date(self, target_date: date) -> List[SitePhoto]:
        return [p for p in self.photos.values()
                if p.captured_date.date() == target_date]

    def get_photos_by_location(self, level: str, zone: str = None) -> List[SitePhoto]:
        photos = [p for p in self.photos.values() if p.level == level]
        if zone:
            photos = [p for p in photos if p.zone == zone]
        return photos

    def search_by_tag(self, tag: str) -> List[SitePhoto]:
        tag_lower = tag.lower()
        return [p for p in self.photos.values()
                if any(tag_lower in t.lower() for t in p.tags)]

    def get_summary(self) -> Dict[str, Any]:
        by_category = {}
        by_level = {}
        for p in self.photos.values():
            cat = p.category.value
            by_category[cat] = by_category.get(cat, 0) + 1
            by_level[p.level] = by_level.get(p.level, 0) + 1

        return {
            'total_photos': len(self.photos),
            'by_category': by_category,
            'by_level': by_level
        }

    def export_catalog(self, output_path: str):
        data = [{
            'ID': p.photo_id,
            'Filename': p.filename,
            'Date': p.captured_date,
            'Category': p.category.value,
            'Level': p.level,
            'Zone': p.zone,
            'Location': p.location,
            'By': p.captured_by,
            'Tags': ', '.join(p.tags)
        } for p in self.photos.values()]
        pd.DataFrame(data).to_excel(output_path, index=False)

Quick Start

analyzer = ProgressPhotoAnalyzer("Office Tower")

photo = analyzer.catalog_photo(
    filename="IMG_001.jpg",
    captured_date=datetime.now(),
    category=PhotoCategory.PROGRESS,
    location="Column Grid B-3",
    level="Level 5",
    captured_by="Site Super",
    tags=["concrete", "forming"]
)

Resources

• DDC Book: Chapter 4.1 - Site Documentation