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BirdBox Web Interface - Pre-Research Document

1. Core Features Overview

1.1 Storage & Statistics

  • Local storage on Raspberry Pi
  • Default 10GB storage limit
  • Warning at 85% capacity
  • Hourly statistics
  • CSV export capability
class StorageManager:
    DEFAULT_STORAGE_LIMIT = 10 * 1024 * 1024 * 1024  # 10GB
    WARNING_THRESHOLD = 0.85

    def check_storage(self):
        total, used, free = shutil.disk_usage("/")
        return {
            "total": total,
            "used": used,
            "free": free,
            "warning": used / total > self.WARNING_THRESHOLD
        }

1.2 Configuration Profiles

  • Persistent storage
  • Multiple profiles support
  • Configurable parameters:
    • Motor frequencies
    • Sensor sensitivity
    • Feeding delays
class ConfigProfile:
    def __init__(self):
        self.config = {
            "name": "Default",
            "motor_frequency": {
                "min": 10,
                "max": 100,
                "default": 50
            },
            "sensor_sensitivity": 0.75,
            "feeding_delay": 5  # seconds
        }

1.3 Maintenance Functions

  • Food level estimation based on motor runtime
  • Statistical optimization
  • Manual reset with backdating capability
  • Basic maintenance logging
class FoodLevelEstimator:
    def __init__(self):
        self.motor_runtime = 0
        self.last_refill = datetime.now()
        self.food_per_second = 0.5  # g/s

1.4 Notifications (Telegram)

  • Internet connectivity check
  • Configuration via UI
  • Visual status indication
  • Priority levels
class NotificationManager:
    def __init__(self):
        self.telegram_token = None
        self.chat_id = None

    def check_connection(self):
        try:
            return requests.get('https://api.telegram.org',
                              timeout=5).status_code == 200
        except:
            return False

1.5 Camera Integration

  • Buffer: 10s before trigger
  • Post-event: 20s after motor stop
  • Configurable resolution
  • Storage management
  • Email warnings before deletion
class CameraManager:
    def __init__(self):
        self.buffer_time = 10
        self.post_time = 20
        self.resolution = (1280, 720)
        self.format = 'h264'

1.6 Research Data

  • CSV export functionality
  • Configurable data points
  • Offline availability
  • Storage monitoring
class ResearchData:
    data_points = {
        "timestamp": [],
        "sensor_triggers": [],
        "motor_runtime": [],
        "estimated_food": [],
        "temperature": [],
        "video_references": []
    }

2. UI Design

2.1 Dashboard Layout

<div class="dashboard">
    <div class="stats-cards">
        <!-- Statistics Cards -->
    </div>
    <div class="charts">
        <!-- Interactive Charts -->
    </div>
    <div class="config-panel">
        <!-- Configuration Interface -->
    </div>
</div>

2.2 Responsive Design

  • Mobile-first approach
  • Flexible grid system
  • Touch-friendly controls

3. Future Extensions

3.1 Bird Species Recognition

  • TensorFlow Lite implementation
  • Pre-trained MobileNet model
  • Transfer learning capability
  • Local inference
  • Movement pre-filtering

3.2 Advanced Analytics

  • Pattern recognition
  • Behavior analysis
  • Weather correlation
  • Feeding optimization

4. Technical Requirements

4.1 Hardware

  • Raspberry Pi
  • Camera module
  • GPIO sensors
  • Motors
  • SD card (min. 16GB recommended)

4.2 Software

  • Python 3.7+
  • Flask
  • OpenCV
  • TensorFlow Lite (optional)
  • SQLite/JSON storage