BugSpot

Lightweight insect detection and tracking using motion-based GMM background subtraction, Hungarian algorithm tracking, and path topology analysis. Core library for B++ and Sensing Garden.

No ML framework dependencies — only requires OpenCV, NumPy, and SciPy.

Installation

pip install bugspot

Quick Start

Command Line

# Run with defaults
bugspot video.mp4

# Run with a custom config
bugspot video.mp4 --config detection_config.yaml --output results/

Python API

from bugspot import DetectionPipeline

pipeline = DetectionPipeline()
result = pipeline.process_video("video.mp4")

print(f"Confirmed: {len(result.confirmed_tracks)} tracks")

for track_id, track in result.confirmed_tracks.items():
    print(f"  Track {track_id[:8]}: {track.num_detections} detections, {track.duration:.1f}s")

    for frame_num, crop in track.crops:
        pass  # feed crop to your classifier

    if track.composite is not None:
        import cv2
        cv2.imwrite(f"track_{track_id[:8]}.jpg", track.composite)

Save Outputs to Disk

result = pipeline.process_video(
    "video.mp4",
    save_crops_dir="output/crops",
    save_composites_dir="output/composites",
)

Continuous Operation (Multi-Chunk)

For processing video chunks where tracks persist across boundaries:

pipeline = DetectionPipeline(config)

for video_chunk in video_queue:
    result = pipeline.process_video(video_chunk)

    # Process results...

    pipeline.clear()  # Keep tracker state, clear detections

Single Video (Stateless)

For one-off processing without persistent state:

pipeline = DetectionPipeline(config)
result = pipeline.process_video("video.mp4")

pipeline.reset()  # Full reset — clear everything including tracker

Pipeline

1. Detection — GMM background subtraction → morphological filtering → shape filters → cohesiveness check
2. Tracking — Hungarian algorithm matching with lost track recovery
3. Topology Analysis — Path analysis confirms insect-like movement (vs plants/noise)
4. Crop Extraction — Re-reads video to extract crop images for confirmed tracks
5. Composite Rendering — Lighten blend on darkened background showing temporal trail

Configuration

See detection_config.yaml for all parameters with descriptions.

Parameter	Default	Description
GMM
gmm_history	500	Frames to build background model
gmm_var_threshold	16	Foreground variance threshold
Morphological
morph_kernel_size	3	Kernel size (NxN)
Cohesiveness
min_largest_blob_ratio	0.80	Min largest blob / total motion
max_num_blobs	5	Max blobs in detection
min_motion_ratio	0.15	Min motion pixels / bbox area
Shape
min_area	200	Min contour area (px²)
max_area	40000	Max contour area (px²)
min_density	3.0	Min area/perimeter ratio
min_solidity	0.55	Min convex hull fill ratio
Tracking
min_displacement	50	Min net movement (px)
min_path_points	10	Min points for topology
max_frame_jump	100	Max jump between frames (px)
max_lost_frames	45	Frames before track deleted
max_area_change_ratio	3.0	Max area change ratio
Tracker Matching
tracker_w_dist	0.6	Distance weight (0-1)
tracker_w_area	0.4	Area weight (0-1)
tracker_cost_threshold	0.3	Max cost for match
Topology
max_revisit_ratio	0.30	Max revisited positions
min_progression_ratio	0.70	Min forward progression
max_directional_variance	0.90	Max heading variance
revisit_radius	50	Revisit radius (px)