⚡ WattWatch — Intelligent Energy Waste Detection System

Camera-based AI system that detects occupancy and monitors appliance states (lights, fans, monitors) to prevent energy waste in real-time.

📖 Table of Contents

What is WattWatch?
How It Works — System Overview
AI Models Used
Project Structure
Key Features
Installation & Setup
Configuration
Running the System
Dashboard (Frontend)
API Endpoints
Energy Metrics Explained
Privacy & Anonymization
Alert System
Roboflow Model Training Guide

🔍 What is WattWatch?

WattWatch is an AI-powered energy monitoring system for smart buildings, offices, and classrooms. It uses a combination of:

Computer Vision (YOLOv8) to detect if people are present in a room
Custom Roboflow ML Models to detect the ON/OFF state of lights, ceiling fans, and monitors
A real-time React dashboard to visualize room-level energy waste and send alerts

The core idea is simple: if no one is in the room but appliances are still ON → that's energy waste. WattWatch automates this detection, calculates the cost in real-time, and alerts facility managers via WhatsApp/SMS.

🧠 How It Works — System Overview

                          ┌──────────────────────┐
  IP Camera / Webcam ──▶  │   FastAPI Backend     │
                          │   (api/main.py)       │
                          └────────┬─────────────┘
                                   │ Each Frame
                    ┌──────────────┼──────────────────┐
                    ▼              ▼                   ▼
           ┌──────────────┐ ┌───────────────┐ ┌─────────────────┐
           │  YOLOv8n.pt  │ │ Roboflow API  │ │  Privacy Filter │
           │ (Person Det.)│ │ (3 ML Models) │ │  (Face Blur)    │
           └──────┬───────┘ └───────┬───────┘ └────────┬────────┘
                  │                 │                   │
                  ▼                 ▼                   │
           Person Count    Light/Fan/Monitor            │
                           ON or OFF status             │
                    │                 │                 │
                    └─────────────────▼─────────────────┘
                                      │
                          ┌───────────▼──────────────┐
                          │   Room State Engine      │
                          │   AlertManager           │
                          │   MicrozoneTracker       │
                          └───────────┬──────────────┘
                                      │
                     ┌────────────────▼────────────────┐
                     │  WebSocket Stream to Dashboard  │
                     │  React (Vite) Frontend          │
                     └─────────────────────────────────┘

Frame Processing Pipeline (per room per frame)

Frame Capture — from IP camera stream or webcam
Person Detection — YOLOv8 detects humans → count of people
Appliance Detection — Roboflow API checks if Light/Fan/Monitor is ON or OFF (every N frames to reduce cost)
Privacy Anonymization — Faces are auto-blurred using Haar cascade + pixelation before storage
Microzone Tracking — Frame split into 4×4 grid, per-zone occupancy tracked for heatmaps
Waste Detection — person_count == 0 AND any appliance is ON → "WASTE" state
AlertManager — debounced alerts sent via Twilio SMS / WhatsApp after configurable delay
WebSocket Push — annotated frame + all metadata streamed to dashboard in real-time

🤖 AI Models Used

WattWatch uses 4 models in total. Here is a complete breakdown:

Model 1: YOLOv8n — Human / Person Detection (Primary Model in Use)

Property	Value
Framework	Ultralytics YOLOv8
Model File	`yolov8n.pt` (also `yolov8s.pt` available)
Task	Object Detection — Person class only (`class_id = 0` from COCO dataset)
Config Key	`config.yaml → model.name`
Default Confidence	`0.25`
Where it runs	Locally on your CPU/GPU
Purpose	Counts how many people are in the room

yolov8n.pt is the currently active model (nano variant — fastest). yolov8s.pt (small variant) is also present for higher accuracy at a cost of speed.

How to switch: Edit config.yaml:

model:
  name: yolov8s.pt   # switch to small model for better accuracy

Code location: src/detector.py → YOLODetector class

Model 2 (Roboflow): Light ON/OFF Detector

Property	Value
Platform	Roboflow — Hosted Serverless Inference
Model ID	`coms-room-light-63vyv/1`
Task	Classification / Detection — Is the light ON or OFF?
Training Data	Custom-labeled room images with lights on/off (trained on Roboflow)
API Endpoint	`https://serverless.roboflow.com`
Config Key	`config.yaml → appliance.roboflow.light_model`
Purpose	Detects if the ceiling/room light is switched ON or OFF

Response parsing logic (from src/appliance_status.py):

If predicted class contains "on", "light", "glow", "lamp", "bright", or "tube" → Status: ON
If class contains "off" → Status: OFF

Model 3 (Roboflow): Ceiling Fan ON/OFF Detector

Property	Value
Platform	Roboflow — Hosted Serverless Inference
Model ID	`ceiling-fan-detection-epfsk/1`
Task	Detection — Is the ceiling fan spinning (ON) or stopped (OFF)?
Training Data	Custom-labeled ceiling fan images (trained on Roboflow)
API Endpoint	`https://serverless.roboflow.com`
Config Key	`config.yaml → appliance.roboflow.fan_model`
Purpose	Detects the rotational state of ceiling fans

Response parsing logic:

If class contains "on", "fan", "spinning", "ceiling", or "rotor" → Status: ON
If class contains "off" → Status: OFF

Model 4 (Roboflow): Monitor / Display ON/OFF Detector

Property	Value
Platform	Roboflow — Hosted Serverless Inference
Model ID	`monitor_detection-uj19t-zqnlq/1`
Task	Detection — Is the monitor/screen turned ON or OFF?
Training Data	Custom-labeled monitor images (trained on Roboflow)
API Endpoint	`https://serverless.roboflow.com`
Config Key	`config.yaml → appliance.roboflow.monitor_model`
Purpose	Detects if desktop monitors are left powered on in empty rooms

Response parsing logic:

If class contains "on", "active", "display", "monitor", "screen", or "power" → Status: ON
Otherwise → Status: OFF

Which Model is Active Right Now?

Model	Active?	Notes
`yolov8n.pt`	✅ YES	Configured in `config.yaml`, runs locally
`yolov8s.pt`	❌ No	Available on disk but not selected
Roboflow Light	✅ YES	Called every `frame_skip=20` frames
Roboflow Fan	✅ YES	Called every `frame_skip=20` frames
Roboflow Monitor	✅ YES	Called every `frame_skip=20` frames
`MLApplianceDetector` (MobileNetV2)	❌ No	Fallback only, requires `models/appliance_classifier.pt` which is not present

Summary: The system currently uses YOLOv8n for person detection and the 3 Roboflow hosted models for appliance status. All 3 Roboflow calls are made in parallel (via ThreadPoolExecutor) to minimize latency.

📁 Project Structure

watt-watch/
│
├── main.py                    # CLI entry point (detect/live/benchmark/calibrate)
├── config.yaml                # Master configuration file
├── requirements.txt           # Python dependencies
├── setup.py                   # Package setup
├── yolov8n.pt                 # YOLOv8 Nano model (person detection) ← ACTIVE
├── yolov8s.pt                 # YOLOv8 Small model (alternative, not active)
│
├── src/                       # Core Python source code
│   ├── __init__.py
│   ├── detector.py            # YOLOv8 person detection wrapper
│   ├── tracker.py             # Centroid-based multi-person tracker
│   ├── appliance_status.py    # Roboflow API calls (Light/Fan/Monitor)
│   ├── appliance_detector.py  # Rule-based fallback detector (brightness/edge analysis)
│   ├── ml_appliance_detector.py # MobileNetV2 local ML detector (optional fallback)
│   ├── alert_manager.py       # Waste event tracking + Twilio SMS/WhatsApp alerts
│   ├── microzone.py           # 4×4 grid zone tracking + heatmap generation
│   ├── privacy_filter.py      # Face detection (Haar cascade) + anonymization
│   ├── intensity_calibrator.py # Room brightness threshold calibration
│   ├── smoothing.py           # Temporal smoothing for detection signals
│   ├── preprocessing.py       # Frame preprocessing utilities
│   ├── model_utils.py         # Model download and path utilities
│   ├── mqtt_manager.py        # MQTT publish/subscribe for IoT integration
│   ├── utils.py               # FPS counter, video extractor, JSON logger
│   └── database/              # SQLite database layer
│
├── api/
│   └── main.py                # FastAPI backend (~75KB) — WebSocket, REST API
│
├── dashboard-vite/            # React + Vite frontend dashboard
│   ├── src/
│   │   ├── App.jsx            # Main dashboard component (~920 lines)
│   │   ├── App.css            # Dashboard styling
│   │   └── main.jsx
│   ├── package.json
│   └── vite.config.js
│
├── scripts/
│   ├── download_samples.py    # Download sample test videos
│   ├── extract_frames.py      # Extract frames from videos
│   └── migrate_json_to_sqlite.py
│
├── configs/                   # Additional configuration files
├── data/                      # Test clips and raw data
│   └── clips/                 # occupied.mp4, empty.mp4, quiet-reader.mp4
├── output/                    # Detection results, JSON logs
├── logs/                      # FPS logs, appliance debug logs
├── models/                    # Optional local ML model files
├── docs/                      # Documentation
├── tests/                     # Unit tests
│
├── ENERGY_METRICS.md          # Detailed energy calculation documentation
├── test_detection.py          # Manual detection tests
└── test_appliance.py          # Manual appliance detection tests

✨ Key Features

Feature	Description
🧍 Person Detection	YOLOv8n detects and counts people in real-time
💡 Light Detection	Roboflow model classifies room lights as ON/OFF
🌀 Fan Detection	Roboflow model detects spinning/stopped ceiling fans
🖥️ Monitor Detection	Roboflow model detects powered-on/off monitors
⚡ Energy Waste Alerts	SMS/WhatsApp alerts when room is empty but appliances are ON
🔒 Privacy First	Automatic face anonymization (pixelation/blur) before any storage
🗺️ Microzone Heatmap	4×4 grid zone tracking shows where people congregate
📊 Cost Calculation	Real-time cost/hour and cumulative waste cost in ₹ or $
🎛️ Calibration Studio	Per-room brightness threshold tuning via visual dashboard
📡 Multi-Room Support	Monitor up to 2 IP camera rooms simultaneously
🗄️ SQLite Logging	All waste events persisted in SQLite database
🌐 WebSocket Streaming	Live annotated frames pushed to dashboard

🛠️ Installation & Setup

Prerequisites

Python 3.9 or higher
Node.js 18+ (for dashboard)
A Roboflow account with API key
(Optional) CUDA GPU for faster YOLO inference

Step 1 — Clone & Install Python Dependencies

git clone <your-repo-url>
cd watt-watch

pip install -r requirements.txt

The key packages installed:

ultralytics>=8.0.0       # YOLOv8 (person detection)
opencv-python>=4.8.0     # Video processing
torch>=2.0.0             # Deep learning backend
inference-sdk>=1.0.0     # Roboflow API client
fastapi>=0.104.0         # Backend API server
uvicorn>=0.24.0          # ASGI server
websockets>=12.0         # Real-time streaming
pyyaml>=6.0              # Config file parsing

Step 2 — Configure API Keys

Open config.yaml and set your Roboflow API key:

appliance:
  roboflow:
    api_key: YOUR_ROBOFLOW_API_KEY_HERE
    light_model: coms-room-light-63vyv/1
    fan_model: ceiling-fan-detection-epfsk/1
    monitor_model: monitor_detection-uj19t-zqnlq/1

Step 3 — (Optional) Configure Twilio for Alerts

alerts:
  twilio:
    enabled: true
    account_sid: YOUR_TWILIO_ACCOUNT_SID
    auth_token: YOUR_TWILIO_AUTH_TOKEN
    from_number: '+1xxxxxxxxxx'
    to_number: '+91xxxxxxxxxx'

Step 4 — Install Dashboard Dependencies

cd dashboard-vite
npm install

⚙️ Configuration

All system behavior is controlled by config.yaml. Key sections:

# ── Model selection ──────────────────────────────────
model:
  name: yolov8n.pt          # Switch to yolov8s.pt for higher accuracy
  confidence_threshold: 0.25

# ── Detection settings ───────────────────────────────
detection:
  frame_skip: 1             # Process every frame (increase for speed)
  min_confidence: 0.25

# ── Appliance wattage for cost calculation ───────────
appliance:
  enabled: true
  frame_skip: 20            # Run Roboflow every 20 frames
  wattage:
    light: 40               # Watts per light bulb
    ceiling_fan: 65         # Watts per ceiling fan
    monitor: 35             # Watts per monitor
  electricity_rate: 0.12    # USD per kWh
  electricity_rate_inr: 6.5 # INR per kWh

# ── Alert debouncing ─────────────────────────────────
alerts:
  initial_delay_seconds: 60    # Wait 60s before first alert
  repeat_interval_seconds: 600 # Repeat alert every 10 min

# ── Privacy settings ─────────────────────────────────
privacy:
  enabled: true
  blur_method: pixelate     # Options: pixelate, gaussian, solid
  blur_level: 99

# ── Microzone grid ───────────────────────────────────
microzone:
  enabled: true
  rows: 4
  cols: 4
  decay: 0.98               # Heatmap decay factor

🚀 Running the System

Option A — CLI Commands (for testing/video processing)

Process a video file:

python main.py detect data/test_clip.mp4

Run live webcam detection:

python main.py live

Run live on a specific camera:

python main.py live --camera 0

Run benchmark on test clips:

python main.py benchmark

Run intensity calibration on a room:

python main.py calibrate data/test_clip.mp4 --room classroom_1 --samples 30

Check calibration status:

python main.py calibrate --status

Process image (single frame):

python main.py detect test_img.jpg --output result.jpg

Option B — Full System (Backend API + Dashboard)

Step 1: Start the FastAPI Backend

cd api
uvicorn main:app --host 0.0.0.0 --port 8000 --reload

Backend runs at: http://localhost:8000 API docs available at: http://localhost:8000/docs

Step 2: Start the React Dashboard

cd dashboard-vite
npm run dev

Dashboard runs at: http://localhost:5173

Step 3: Connect a camera In the dashboard, enter your IP camera stream URL (e.g., http://192.168.0.154:8080/video) and click CONNECT.

📊 Dashboard (Frontend)

The dashboard (built with React + Vite) has 5 tabs:

1. MONITOR Tab

Live video feed from up to 2 IP cameras
Person count, Light/Fan/Monitor status displayed per room
WASTE_DETECTED alert banner when room is empty with appliances ON
Privacy mode toggle (GHOST_MODE) — enables/disables face blur
Real-time energy load and cumulative waste cost

2. SUMMARY Tab

Annual energy projections — kWh/day, savings in INR/year, CO₂/year
Last 30 days savings report
Per-room breakdown with cost and CO₂ metrics

3. PRIVACY Tab

Privacy measures status (face anonymization, data retention)
Stakeholder compliance commitments
Data retention policy overview

4. CALIBRATE Tab (Luminance Studio)

Visual real-time brightness meter for selected room
Dark / Medium threshold sliders for day and night modes
Drag sliders to tune thresholds and commit changes to config.yaml
Shows classification: DARK / MEDIUM / BRIGHT based on live feed

5. DATABASE Tab

Browse the SQLite database schema
View raw table data (waste events, detection logs)
Export and inspect historical energy waste records

🔌 API Endpoints

Method	Endpoint	Description
`POST`	`/api/camera/connect`	Connect a room camera (start streaming)
`POST`	`/api/camera/disconnect`	Disconnect a room camera
`WS`	`/ws/stream/{room_id}`	WebSocket for live frame streaming
`GET`	`/api/energy/metrics`	Current energy metrics per room
`GET`	`/api/energy/dashboard`	Annual projections and 30-day summary
`GET`	`/api/alerts/events`	Recent waste alert events
`GET`	`/api/alerts/status`	Room status + waste duration
`GET`	`/api/calibration`	Get current threshold calibration
`POST`	`/api/calibration`	Update brightness thresholds
`GET`	`/api/privacy/assurance`	Privacy compliance report
`GET`	`/api/database/info`	Database statistics
`GET`	`/api/database/schema`	Database table schema
`GET`	`/api/database/rows/{table}`	Browse table rows

💰 Energy Metrics Explained

How cost is calculated:

estimated_watts = (40W if Light is ON) + (65W if Fan is ON) + (35W if Monitor is ON)

cost_per_hour  = (estimated_watts / 1000) × electricity_rate     # in USD
cost_per_hour_inr = (estimated_watts / 1000) × 6.5              # in INR

cumulative_cost = cost_per_hour × (waste_duration_seconds / 3600)

Waste State Definition:

is_waste = (person_count == 0) AND (light == "ON" OR fan == "ON" OR monitor == "ON")

Annual Projections:

kwh_per_day = estimated_watts × 24 / 1000
inr_per_year = kwh_per_day × 365 × electricity_rate_inr
co2_per_year_kg = kwh_per_day × 365 × co2_factor (0.71 kg/kWh)

See ENERGY_METRICS.md for the complete calculation documentation.

🔒 Privacy & Anonymization

WattWatch is designed to be privacy-first in compliance with institutional requirements:

Haar Cascade face detection runs on every N frames
Detected faces are pixelated (or gaussian-blurred) with a large padding to obscure the entire head region
Raw images are NEVER stored by default (privacy.storage.save_raw: false)
Only anonymized thumbnails are saved (for alert evidence)
All processing happens locally — no raw video leaves the machine

Privacy configuration:

privacy:
  blur_method: pixelate   # pixelate / gaussian / solid
  pixelate_blocks: 12     # More blocks = finer pixelation
  blur_level: 99          # For gaussian mode
  skip_frames: 3          # Re-detect faces every 3 frames
  storage:
    save_raw: false        # NEVER store raw video
    save_anonymized: false # Only enable for auditing

🚨 Alert System

The AlertManager watches each room for waste conditions:

Waste detected → starts a timer
After initial_delay_seconds (default: 60s) → fires first alert
If waste continues, repeats every repeat_interval_seconds (default: 600s = 10 min)
When room is occupied or appliances are OFF → resets the timer

Alert channels:

Twilio SMS — text message to facility manager
Twilio WhatsApp — WhatsApp template message with room name and duration
SQLite Database — event persisted to data/wattwatch.db
JSON fallback — events saved to output/waste_events.json

Alert message format:

⚠️ WATTWATCH ALERT
Energy waste detected in Room 101!
Duration: 5.2 mins
Lights: ON, Fans: ON, Mon: OFF
Please check the facility.

🏋️ Roboflow Model Training Guide

The 3 Roboflow models (light, fan, monitor) were trained using Roboflow's platform. Here's how they were set up:

Steps to train your own models:

Create a Roboflow account at app.roboflow.com
Create a new project → select Object Detection or Classification
Upload images:
- For light model: collect images of your room with light ON and light OFF
- For fan model: collect images of ceiling fans spinning (ON) and still (OFF)
- For monitor model: collect images of monitors powered ON and OFF
Annotate → draw bounding boxes and assign class labels:
- Light model classes: light-on, light-off (or similar)
- Fan model classes: fan-on, fan-off
- Monitor model classes: monitor-on, monitor-off
Train → Use Roboflow's auto-train feature (YOLOv8 recommended)
Get model ID → from the Roboflow dashboard, copy the workspace/project/version format
Update config.yaml:

appliance:
  roboflow:
    api_key: YOUR_API_KEY
    light_model: YOUR-WORKSPACE/YOUR-LIGHT-PROJECT/1
    fan_model: YOUR-WORKSPACE/YOUR-FAN-PROJECT/1
    monitor_model: YOUR-WORKSPACE/YOUR-MONITOR-PROJECT/1

Current models in use:

Appliance	Roboflow Model ID
Light	`coms-room-light-63vyv/1`
Ceiling Fan	`ceiling-fan-detection-epfsk/1`
Monitor	`monitor_detection-uj19t-zqnlq/1`

Tip: The more diverse your training images (different rooms, lighting conditions, angles), the more accurate your model will be.

🧪 Testing

Test person detection on a single image:

python test_detection.py

Test appliance detection (light/fan) on a test image:

python test_appliance.py

Run detection with max frames limit:

python main.py detect data/clips/occupied.mp4 --max-frames 100

📝 Logging & Output

File	Contents
`output/detections.json`	Per-frame detection results (JSON)
`output/waste_events.json`	Waste alert event log (JSON)
`output/appliance_status.json`	Appliance ON/OFF history per frame
`output/benchmark_results.json`	Benchmark test results
`logs/fps.log`	Frame-by-frame FPS log
`logs/appliance_debug.log`	Raw Roboflow API response debug log
`data/wattwatch.db`	SQLite database (all events + detections)
`data/alerts/*.jpg`	Anonymized thumbnails for waste events

🤝 Contributing

Fork the repository
Create a feature branch: git checkout -b feature/my-feature
Commit changes: git commit -m 'Add my feature'
Push: git push origin feature/my-feature
Open a Pull Request

📄 License

This project is licensed under the MIT License.

🙏 Acknowledgements

Ultralytics YOLOv8 — person detection backbone
Roboflow — model training platform and inference API
Twilio — SMS and WhatsApp alerting
FastAPI — high-performance Python backend
React + Vite — fast frontend tooling

Name		Name	Last commit message	Last commit date
Latest commit History 2 Commits
api		api
dashboard-vite		dashboard-vite
docs		docs
scripts		scripts
src		src
temp		temp
tests		tests
.gitignore		.gitignore
ENERGY_METRICS.md		ENERGY_METRICS.md
INSTRUCTIONS.md		INSTRUCTIONS.md
README.md		README.md
config.yaml.example		config.yaml.example
main.py		main.py
requirements.txt		requirements.txt
setup.py		setup.py
test_appliance.py		test_appliance.py
test_detection.py		test_detection.py

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⚡ WattWatch — Intelligent Energy Waste Detection System

📖 Table of Contents

🔍 What is WattWatch?

🧠 How It Works — System Overview

Frame Processing Pipeline (per room per frame)

🤖 AI Models Used

Model 1: YOLOv8n — Human / Person Detection (Primary Model in Use)

Model 2 (Roboflow): Light ON/OFF Detector

Model 3 (Roboflow): Ceiling Fan ON/OFF Detector

Model 4 (Roboflow): Monitor / Display ON/OFF Detector

Which Model is Active Right Now?

📁 Project Structure

✨ Key Features

🛠️ Installation & Setup

Prerequisites

Step 1 — Clone & Install Python Dependencies

Step 2 — Configure API Keys

Step 3 — (Optional) Configure Twilio for Alerts

Step 4 — Install Dashboard Dependencies

⚙️ Configuration

🚀 Running the System

Option A — CLI Commands (for testing/video processing)

Option B — Full System (Backend API + Dashboard)

📊 Dashboard (Frontend)

1. MONITOR Tab

2. SUMMARY Tab

3. PRIVACY Tab

4. CALIBRATE Tab (Luminance Studio)

5. DATABASE Tab

🔌 API Endpoints

💰 Energy Metrics Explained

How cost is calculated:

Waste State Definition:

Annual Projections:

🔒 Privacy & Anonymization

Privacy configuration:

🚨 Alert System

Alert channels:

🏋️ Roboflow Model Training Guide

Steps to train your own models:

Current models in use:

🧪 Testing

📝 Logging & Output

🤝 Contributing

📄 License

🙏 Acknowledgements

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