bugspot 0.3.1

Lightweight insect detection and tracking using motion-based detection

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.

Resolution-independent units

Every pixel-scale parameter is expressed as a fraction of the image dimensions, not as absolute pixels, so the same config works across resolutions. Two reference dimensions are used:

• Length → fraction of image width W (keys: morph_kernel_size, min_displacement, max_frame_jump, revisit_radius)
• Area → fraction of image area W * H (keys: min_area, max_area)

Fractions are resolved to absolute pixels at runtime via resolve_detection_params(params, W, H) once the frame size is known. The 1080 px column below shows the resolved pixel value for a 1080×1080 frame (width = 1080, area = 1 166 400) for intuition.

Parameter	Default	1080 px wide	Description
GMM
gmm_history	500	—	Frames to build background model
gmm_var_threshold	16	—	Foreground variance threshold
Morphological
morph_kernel_size	0.002	3	Kernel size, fraction of image width (rounded to odd int)
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	0.0002	~233 px²	Min contour area, fraction of image area
max_area	0.035	~40 824 px²	Max contour area, fraction of image area
min_density	3.0	—	Min area/perimeter ratio (unitless)
min_solidity	0.55	—	Min convex hull fill ratio
Tracking
min_displacement	0.05	54 px	Min net movement, fraction of image width
min_path_points	10	—	Min points for topology
max_frame_jump	0.1	108 px	Max jump between frames, fraction of image width
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	0.05	54 px	Revisit radius, fraction of image width