🏪 Store Signage Illumination Compliance Audit Agent

A production-grade Python AI agent that automatically audits store signage for night-time illumination compliance using computer vision and GPT-4 Vision API.

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📋 Problem Statement

Retail chains with hundreds of stores need to ensure all storefront signage lights are properly illuminated during night hours for visibility and brand compliance. Manual audits are:

• Time-consuming: Requires field visits or manual image review
• Inconsistent: Human reviewers may miss issues or have bias
• Expensive: Significant operational overhead
• Error-prone: Difficult to track compliance across many locations

The Solution: An automated AI agent that processes signage images and provides standardized compliance reports in seconds.

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🎯 Solution Overview

This mini AI agent performs automated compliance audits on store signage images:

1. Image Validation: OpenCV-based checks for image quality (readability, resolution, blur)
2. AI Inspection: GPT-4 Vision API analyzes signage illumination status
3. Compliance Logic: Rule-based evaluation determines Pass/Fail/Attention Needed
4. Excel Reporting: Professional audit report with color-coded compliance status

Key Benefits:

• ✅ Processes ~50 images in minutes (not hours)
• ✅ Consistent, objective analysis using AI
• ✅ Detailed compliance reasoning for manual review
• ✅ Professional Excel reports for stakeholders
• ✅ Cost-effective automation

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🔄 AI Agent Workflow

Execution Pipeline

Input Images (data/images/)
           ↓
    [Stage 1: Discovery]
    Find all image files (*.jpg, *.png)
           ↓
    [Stage 2: Image Validation]
    ├─ Check if image is readable (OpenCV)
    ├─ Verify resolution ≥ 500x300 pixels
    └─ Detect blur (Laplacian variance > 100)
           ↓
    [Stage 3: AI Inspection] (Only if validation passes)
    ├─ Encode image to base64
    ├─ Send structured prompt to GPT-4 Vision
    ├─ Receive JSON analysis result
    └─ Parse: signage_visible, light_status, partial_illumination, confidence
           ↓
    [Stage 4: Compliance Evaluation]
    ├─ Apply rule-based decision logic (8 phases)
    ├─ Determine: PASS / FAIL / ATTENTION NEEDED
    └─ Generate detailed remarks for review
           ↓
    [Stage 5: Report Generation]
    ├─ Transform results to DataFrame
    ├─ Create Excel with formatting
    ├─ Apply color coding (Green/Red/Yellow)
    └─ Output: signage_audit_report.xlsx
           ↓
Output Report (output/signage_audit_report.xlsx)

Decision Logic (Simplified)

Condition	Outcome
Image unreadable or too small	FAIL
Signage not visible in image	FAIL
Light is OFF	FAIL
Partial letter illumination	ATTENTION NEEDED
Light ON + High quality + High confidence	PASS

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🛠 Tools & Technologies

Component	Technology	Purpose
Image Processing	OpenCV (cv2)	Blur detection, dimension validation
AI Vision	GPT-4 Vision API	Signage illumination analysis
Data Processing	Pandas	Result transformation & aggregation
Excel Export	openpyxl	Professional report formatting
Language	Python 3.8+	Core implementation
API Integration	Requests	HTTP calls to OpenAI

Dependencies

opencv-python==4.8.0
pandas==2.0.0
openpyxl==3.1.0
requests==2.31.0

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📂 Project Structure

ai_agent/
├── src/
│   ├── main.py                      # Main orchestrator & agent logic
│   ├── config.py                    # Configuration (paths, API keys)
│   ├── image_validator.py           # OpenCV image quality checks
│   ├── ai_inspector.py              # GPT-4 Vision integration
│   ├── compliance_evaluator.py      # Rule-based compliance logic
│   └── report_generator.py          # Excel report creation & formatting
├── data/
│   └── images/                      # Input: Store signage images
├── output/                          # Output: Excel audit reports
├── requirements.txt                 # Python package dependencies
└── README.md                        # This file

File Responsibilities

File	Responsibility
main.py	Orchestrates entire pipeline; manages workflow and logging
config.py	Environment setup (paths, API keys)
image_validator.py	OpenCV: readability, resolution, blur detection
ai_inspector.py	GPT-4 Vision API integration with structured prompts
compliance_evaluator.py	8-phase rule-based compliance decision logic
report_generator.py	DataFrame transformation & professional Excel formatting

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🚀 How to Run the Project

Prerequisites

• Python 3.8 or higher
• OpenAI API key (for GPT-4 Vision)
• ~50 store signage images in data/images/

Installation & Setup

# 1. Navigate to project directory
cd ai_agent

# 2. Install dependencies
pip install -r requirements.txt

# 3. Set OpenAI API key (Windows PowerShell)
$env:OPENAI_API_KEY = "sk-your-api-key-here"

# Or (bash/Linux/Mac)
export OPENAI_API_KEY="sk-your-api-key-here"

# 4. Place images in data/images/ folder
# Expected naming: STORE_CODE.jpg (e.g., LK_1023.jpg, SM_5067.jpg)

# 5. Run the agent
python src/main.py

Expected Output

======================================================================
STORE SIGNAGE ILLUMINATION COMPLIANCE AUDIT
======================================================================

Step 1: Discovering images in 'data/images'...
✓ Found 42 image(s) to process

Step 2: Processing images through validation and AI inspection...

  [1/42] Processing: LK_1023.jpg
    → Running image validation...
      ✓ Image readable: 1920x1080
      ✓ Resolution OK: True
      ✓ Blur check: SHARP
    → Running AI signage inspection...
      ✓ Signage visible: True
      ✓ Light status: ON
      ✓ Partial illumination: False
      ✓ AI Confidence: 0.95
    → Evaluating compliance...
      ✓ Status: PASS
      ✓ Reason: Compliant with illumination requirements
      ✓ Manual review needed: False

  [2/42] Processing: SM_5067.jpg
    ... (continues for all images)

Step 3: Generating audit report...
✓ Signage audit report generated successfully: output/signage_audit_report.xlsx
  Total stores analyzed: 42
  Compliant (PASS): 38
  Non-compliant (FAIL): 2
  Attention Needed: 2

Step 4: Audit Complete

Summary Statistics:
  • Total stores analyzed: 42
  • Compliant (PASS): 38 (90.5%)
  • Non-compliant (FAIL): 2 (4.8%)
  • Attention Needed: 2 (4.8%)
  • Require manual review: 4

======================================================================

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📊 Sample Output

Excel Report: signage_audit_report.xlsx

Store Code	Image Quality	Signage Visible	Light On	Partial Letters	Compliance Status	Remarks
LK_1023	Valid	Yes	Yes	No	PASS ✓	Compliant with illumination requirements...
SM_5067	Valid	Yes	No	No	FAIL ✗	Signage light is OFF (non-compliant)...
BR_2341	Valid	Yes	Yes	Yes	ATTENTION NEEDED ⚠	Some letters show partial illumination...
DL_8901	Invalid	No	No	No	FAIL ✗	Image resolution below minimum...

Features:

• ✅ Color-coded rows (Green=Pass, Red=Fail, Yellow=Attention)
• ✅ Frozen header for easy scrolling
• ✅ Wrapped remarks for full visibility
• ✅ Professional formatting

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🔧 Customization

Adjust Image Validation Thresholds

Edit src/image_validator.py:

class ImageValidator:
    MIN_WIDTH = 500      # Change minimum width
    MIN_HEIGHT = 300     # Change minimum height
    BLUR_THRESHOLD = 100 # Change blur sensitivity (higher = more lenient)

Change AI Prompt

Edit src/ai_inspector.py:

ANALYSIS_PROMPT = """
Your custom prompt here...
Respond in JSON format with fields: signage_visible, light_status, etc.
"""

Use Mock Mode (Testing without API)

The agent automatically uses mock mode if OPENAI_API_KEY is not set. For testing:

# No API key needed - returns realistic test scenarios
python src/main.py

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📈 Future Improvements

Phase 2 Enhancements

• Batch Processing: Support cloud storage (AWS S3, Azure Blob)
• Database Integration: Store results in PostgreSQL/MongoDB
• Dashboard: Web UI for monitoring compliance metrics
• Email Alerts: Automated notifications for FAIL/ATTENTION cases
• Image Archival: Store processed images with metadata
• API Endpoint: REST API for real-time single-image analysis

Phase 3 Advanced Features

• Multi-region Support: Analyze different signage types per region
• Trend Analysis: Track compliance over time
• Store Comparison: Benchmark against similar stores
• Custom ML Model: Fine-tune on retail signage for better accuracy
• OCR Integration: Read actual text from signage for brand verification
• Mobile App: Field agents can submit images in real-time

Technical Debt

• Unit tests (pytest)
• Integration tests with mock API responses
• Logging framework (Python logging)
• Configuration file (YAML/JSON instead of hardcoded)
• CI/CD pipeline (GitHub Actions)
• Docker containerization

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📝 License

This project is provided as-is for educational and commercial use.

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👤 Author

Developed as a mini AI agent demonstration for retail operations automation.

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❓ FAQ

Q: What if I don't have an OpenAI API key?
A: The agent will automatically use mock mode with realistic test data.

Q: How long does it take to process 50 images?
A: ~5-10 minutes depending on API response times (2-3 sec per image).

Q: Can I use images from URLs instead of local files?
A: Currently supports local files. Future version will support cloud storage.

Q: What image formats are supported?
A: JPG, PNG (extensible in SUPPORTED_EXTENSIONS).

Q: How accurate is the AI analysis?
A: GPT-4 Vision is ~95% accurate for illumination detection. Confidence scores are provided for uncertain cases.

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📞 Support

For issues or questions:

1. Check the console output for detailed error messages
2. Verify image files are in data/images/
3. Ensure OpenAI API key is valid
4. Check internet connectivity for API calls