kbarbel640-del 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 - would use person detection
return 0
def _detect_equipment(self, image_data: bytes = None) -> List[str]:
"""Detect equipment visible in photo."""
# Simulated - would use object detection
return []
def compare_to_bim(self, photo_id: str,
bim_render: bytes = None) -> Dict[str, Any]:
"""Compare photo to BIM model render."""
if photo_id not in self.results:
return {'error': 'Photo not analyzed'}
# Simulated comparison
comparison = {
'similarity_score': 0.75,
'alignment_quality': 'good',
'discrepancies': [],
'notes': 'Photo roughly matches BIM model'
}
self.results[photo_id].bim_comparison = comparison
return comparison
def get_progress_summary(self,
from_date: date = None,
to_date: date = None) -> Dict[str, Any]:
"""Generate progress summary from analyzed photos."""
filtered_results = list(self.results.values())
if from_date:
filtered_results = [r for r in filtered_results
if r.metadata.capture_date.date() >= from_date]
if to_date:
filtered_results = [r for r in filtered_results
if r.metadata.capture_date.date() <= to_date]
# Aggregate by activity
by_activity = {}
for result in filtered_results:
for detection in result.progress_detections:
activity = detection.work_activity.value
if activity not in by_activity:
by_activity[activity] = {
'count': 0,
'avg_completion': 0,
'photos': []
}
by_activity[activity]['count'] += 1
by_activity[activity]['avg_completion'] += detection.completion_estimate
by_activity[activity]['photos'].append(result.photo_id)
# Calculate averages
for activity in by_activity:
count = by_activity[activity]['count']
if count > 0:
by_activity[activity]['avg_completion'] /= count
# Safety summary
total_safety_issues = sum(len(r.safety_detections) for r in filtered_results)
return {
'total_photos': len(filtered_results),
'date_range': {
'from': from_date.isoformat() if from_date else None,
'to': to_date.isoformat() if to_date else None
},
'by_activity': by_activity,
'safety_issues_detected': total_safety_issues,
'average_worker_count': sum(r.worker_count for r in filtered_results) / len(filtered_results) if filtered_results else 0
}
def export_report(self, output_path: str):
"""Export analysis results to Excel."""
with pd.ExcelWriter(output_path, engine='openpyxl') as writer:
# Photos list
photos_data = []
for result in self.results.values():
photos_data.append({
'Photo ID': result.photo_id,
'Filename': result.metadata.filename,
'Date': result.metadata.capture_date,
'Location': result.metadata.location,
'Level': result.metadata.level,
'Type': result.metadata.photo_type.value,
'Status': result.status.value,
'Worker Count': result.worker_count,
'Weather': result.weather_conditions
})
pd.DataFrame(photos_data).to_excel(writer, sheet_name='Photos', index=False)
# Progress detections
progress_data = []
for result in self.results.values():
for detection in result.progress_detections:
progress_data.append({
'Photo ID': result.photo_id,
'Activity': detection.work_activity.value,
'Confidence': detection.confidence,
'Completion %': detection.completion_estimate,
'Description': detection.description
})
if progress_data:
pd.DataFrame(progress_data).to_excel(writer, sheet_name='Progress', index=False)
# Safety detections
safety_data = []
for result in self.results.values():
for detection in result.safety_detections:
safety_data.append({
'Photo ID': result.photo_id,
'Issue': detection.issue_type.value,
'Severity': detection.severity,
'Confidence': detection.confidence,
'Description': detection.description
})
if safety_data:
pd.DataFrame(safety_data).to_excel(writer, sheet_name='Safety', index=False)
return output_path
def analyze_site_photos(photo_files: List[str],
project_name: str,
output_path: str = None) -> Dict[str, Any]:
"""Quick function to analyze multiple photos."""
analyzer = ProgressPhotoAnalyzer(project_name)
for file_path in photo_files:
path = Path(file_path)
metadata = analyzer.register_photo(
filename=path.name,
capture_date=datetime.now(),
location="Site",
photo_type=PhotoType.PROGRESS,
file_path=file_path
)
analyzer.analyze_photo(metadata.photo_id)
summary = analyzer.get_progress_summary()
if output_path:
analyzer.export_report(output_path)
return summary
Quick Start
# Initialize analyzer
analyzer = ProgressPhotoAnalyzer("Office Tower Project")
# Register and analyze photos
metadata = analyzer.register_photo(
filename="site_photo_001.jpg",
capture_date=datetime.now(),
location="Level 3 - Core",
level="Level 3",
zone="Zone A",
photo_type=PhotoType.PROGRESS,
photographer="John Smith"
)
result = analyzer.analyze_photo(metadata.photo_id)
print(f"Detected activity: {result.progress_detections[0].work_activity.value}")
print(f"Completion estimate: {result.progress_detections[0].completion_estimate}%")
Common Use Cases
1. Daily Progress Report
from datetime import date
summary = analyzer.get_progress_summary(
from_date=date.today(),
to_date=date.today()
)
print(f"Photos analyzed today: {summary['total_photos']}")
2. Safety Monitoring
safety_photos = [r for r in analyzer.results.values() if r.safety_detections]
for result in safety_photos:
for issue in result.safety_detections:
print(f"Safety issue: {issue.issue_type.value} - {issue.severity}")
3. Export Analysis
analyzer.export_report("photo_analysis_report.xlsx")
Resources
- DDC Book: Chapter 4.1 - Site Data Collection
- Reference: Computer Vision for Construction