Progress Photo Analyzer
Business Case
Problem Statement
Site photos are underutilized for progress tracking:
- Manual review is time-consuming
- Subjective progress assessment
- No systematic comparison to plans
- Safety issues may be missed
Solution
AI-powered photo analysis system that extracts progress information, detects safety concerns, and compares site conditions to BIM models.
Business Value
- Automation - Reduce manual photo review
- Accuracy - Objective progress measurement
- Safety - Automatic hazard detection
- Documentation - Structured photo records
Technical Implementation
import pandas as pd
from datetime import datetime, date
from typing import Dict, Any, List, Optional, Tuple
from dataclasses import dataclass, field
from enum import Enum
from pathlib import Path
import base64
class PhotoType(Enum):
"""Types of construction photos."""
PROGRESS = "progress"
SAFETY = "safety"
QUALITY = "quality"
GENERAL = "general"
DELIVERY = "delivery"
class AnalysisStatus(Enum):
"""Analysis status."""
PENDING = "pending"
ANALYZING = "analyzing"
COMPLETED = "completed"
FAILED = "failed"
class SafetyIssue(Enum):
"""Detected safety issues."""
MISSING_PPE = "missing_ppe"
FALL_HAZARD = "fall_hazard"
HOUSEKEEPING = "housekeeping"
SCAFFOLDING = "scaffolding"
ELECTRICAL = "electrical"
EXCAVATION = "excavation"
NONE = "none"
class WorkActivity(Enum):
"""Detected work activities."""
EXCAVATION = "excavation"
FOUNDATION = "foundation"
CONCRETE_POUR = "concrete_pour"
STEEL_ERECTION = "steel_erection"
FRAMING = "framing"
ROOFING = "roofing"
MEP_ROUGH = "mep_rough"
DRYWALL = "drywall"
FINISHES = "finishes"
EXTERIOR = "exterior"
UNKNOWN = "unknown"
@dataclass
class PhotoMetadata:
"""Photo metadata."""
photo_id: str
filename: str
capture_date: datetime
location: str
level: str
zone: str
photo_type: PhotoType
photographer: str = ""
gps_coordinates: Optional[Tuple[float, float]] = None
file_path: str = ""
@dataclass
class ProgressDetection:
"""Detected progress information."""
work_activity: WorkActivity
confidence: float
description: str
completion_estimate: float # 0-100%
elements_visible: List[str] = field(default_factory=list)
@dataclass
class SafetyDetection:
"""Detected safety information."""
issue_type: SafetyIssue
confidence: float
description: str
severity: str # low, medium, high
location_in_image: Optional[Tuple[int, int, int, int]] = None # bounding box
@dataclass
class PhotoAnalysisResult:
"""Complete photo analysis result."""
photo_id: str
metadata: PhotoMetadata
analysis_date: datetime
status: AnalysisStatus
progress_detections: List[ProgressDetection]
safety_detections: List[SafetyDetection]
weather_conditions: str
worker_count: int
equipment_visible: List[str]
quality_issues: List[str]
notes: str = ""
bim_comparison: Optional[Dict[str, Any]] = None
class ProgressPhotoAnalyzer:
"""Analyze construction site photos."""
def __init__(self, project_name: str):
self.project_name = project_name
self.photos: Dict[str, PhotoMetadata] = {}
self.results: Dict[str, PhotoAnalysisResult] = {}
self._photo_counter = 0
def register_photo(self,
filename: str,
capture_date: datetime,
location: str,
level: str = "",
zone: str = "",
photo_type: PhotoType = PhotoType.PROGRESS,
photographer: str = "",
file_path: str = "") -> PhotoMetadata:
"""Register a photo for analysis."""
self._photo_counter += 1
photo_id = f"PH-{self._photo_counter:05d}"
metadata = PhotoMetadata(
photo_id=photo_id,
filename=filename,
capture_date=capture_date,
location=location,
level=level,
zone=zone,
photo_type=photo_type,
photographer=photographer,
file_path=file_path
)
self.photos[photo_id] = metadata
return metadata
def analyze_photo(self, photo_id: str,
image_data: bytes = None) -> PhotoAnalysisResult:
"""Analyze a registered photo."""
if photo_id not in self.photos:
raise ValueError(f"Photo {photo_id} not registered")
metadata = self.photos[photo_id]
# Perform analysis (simulated - would use CV/AI models)
progress_detections = self._detect_progress(metadata, image_data)
safety_detections = self._detect_safety(metadata, image_data)
weather = self._detect_weather(metadata, image_data)
worker_count = self._count_workers(image_data)
equipment = self._detect_equipment(image_data)
result = PhotoAnalysisResult(
photo_id=photo_id,
metadata=metadata,
analysis_date=datetime.now(),
status=AnalysisStatus.COMPLETED,
progress_detections=progress_detections,
safety_detections=safety_detections,
weather_conditions=weather,
worker_count=worker_count,
equipment_visible=equipment,
quality_issues=[]
)
self.results[photo_id] = result
return result
def _detect_progress(self, metadata: PhotoMetadata,
image_data: bytes = None) -> List[ProgressDetection]:
"""Detect work progress in photo."""
# Simulated detection based on metadata
detections = []
# In real implementation, this would use computer vision
location_lower = metadata.location.lower()
if 'foundation' in location_lower or 'basement' in location_lower:
detections.append(ProgressDetection(
work_activity=WorkActivity.FOUNDATION,
confidence=0.85,
description="Foundation work visible",
completion_estimate=60.0
))
elif 'steel' in location_lower or 'structure' in location_lower:
detections.append(ProgressDetection(
work_activity=WorkActivity.STEEL_ERECTION,
confidence=0.90,
description="Structural steel installation",
completion_estimate=45.0
))
elif 'roof' in location_lower:
detections.append(ProgressDetection(
work_activity=WorkActivity.ROOFING,
confidence=0.80,
description="Roofing work in progress",
completion_estimate=30.0
))
else:
detections.append(ProgressDetection(
work_activity=WorkActivity.UNKNOWN,
confidence=0.50,
description="General construction activity",
completion_estimate=0.0
))
return detections
def _detect_safety(self, metadata: PhotoMetadata,
image_data: bytes = None) -> List[SafetyDetection]:
"""Detect safety issues in photo."""
# Simulated detection - real implementation would use AI models
detections = []
# In production, this would analyze the actual image
if metadata.photo_type == PhotoType.SAFETY:
# Return empty for demonstration
pass
return detections
def _detect_weather(self, metadata: PhotoMetadata,
image_data: bytes = None) -> str:
"""Detect weather conditions from photo."""
# Simulated - would use image analysis
return "clear"
def _count_workers(self, image_data: bytes = None) -> int:
"""Count workers visible in photo."""
# Simulated