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🛣️ Landing board heatmap generation

🎯 Purpose​

Generates visual heatmaps showing bee movement patterns and activity zones on the landing board to optimize hive entrance design and understand traffic flow.

🎭 User Story​

  • As a beekeeper interested in optimizing hive entrance efficiency
  • I want to see where bees spend most of their time on the landing board
  • So that I can identify traffic bottlenecks and potentially redesign the entrance for better bee flow

🚀 Key Benefits​

  • Traffic optimization: Identify congested areas and potential improvements
  • Entrance design insights: Data-driven approach to landing board modifications
  • Long-term behavior analysis: Understanding of seasonal and daily patterns
  • Research value: Visual data for studying bee traffic behavior

🔧 Technical Overview​

Processes track history data from daily-rotated JSONL files (track_history_YYYY-MM-DD.jsonl) using NumPy to generate density maps. The heatmap_generator.py script aggregates bee position coordinates into a 2D heatmap array with frame dimensions (1280x720), normalizes the data, and applies color mapping for visualization.

📋 Acceptance Criteria​

  • Processes track history from JSONL files with frame dimensions metadata
  • Generates heatmaps for 1280x720 frame resolution
  • Accumulates position data across multiple track files for temporal analysis
  • Applies Gaussian blur and color mapping for visual clarity
  • Exports heatmap images in standard image formats (PNG/JPG)
  • Handles coordinate bounds checking for frame boundaries
  • Supports batch processing of multiple days of data

🚫 Out of Scope​

  • Real-time heatmap generation (batch processing only)
  • 3D visualization or depth analysis
  • Weather correlation with traffic patterns
  • Automated landing board design recommendations

🏗️ Implementation Approach​

  • Data Input: Track history JSONL files with coordinate arrays per track ID
  • Processing: NumPy array accumulation of position frequencies
  • Visualization: OpenCV and matplotlib for color mapping and blur effects
  • Storage: Frame dimensions extracted from metadata in JSONL files
  • Batch Processing: Command-line script for processing historical data

📊 Success Metrics​

  • Accurate coordinate processing within frame boundaries (0 <= x < width, 0 <= y < height)
  • Heatmap generation from multiple JSONL track files
  • Proper normalization and color mapping for visual interpretation
  • Batch processing capability for historical data analysis
  • File format compatibility for integration with web interfaces

🔗 Related Features​

📚 Resources & References​

💬 Notes​

Actual implementation using NumPy for position aggregation and OpenCV for visualization. Processes real track history data saved by the telemetry system to generate traffic flow insights.