VisionSentinel Interface

The primary class for executing heuristic validations. It encapsulates SIMD-accelerated C-bindings and provides a zero-overhead Python interface.

Parameters

Parameter	Type	Default	Description
`blur_threshold`	`float`	`110.0`	Laplacian variance threshold for blur detection.
`entropy_min`	`float`	`3.85`	Minimum Shannon entropy required to pass validation.
`specular_clip_limit`	`float`	`0.35`	Maximum allowed ratio of clipped pixels.
`enable_fast_spectral`	`bool`	`True`	Accelerate FFT via CPU vectorization.

Methods

audit_stream(buffer: np.ndarray) -> SentinelMetrics
audit(image: Image.Image | np.ndarray) -> SentinelMetrics

Batch Auditor

Assess massive datasets on disk with parallelized DriftEngine operations.

from vigilcv.core.drift import DriftEngine

engine = DriftEngine()
engine.fit(reference_directory="dataset/train/")
engine.save("baseline.pkl")

report = engine.detect(query_directory="dataset/new_batch/")

if report.is_drifted:
    print(f"DRIFT DETECTED: {report.wasserstein_distance:.4f} W1")

CLI Reference

VigilCV exposes a fully-featured POSIX CLI for bash scripting and CI/CD pipelines.

# Inspect a single image
vigilcv inspect photo.jpg --blur-threshold 150

# Audit a massive directory in parallel
vigilcv audit dataset/train/ --workers 16 --report quality_report.html

# Detect distribution drift against baseline
vigilcv drift dataset/new/ --baseline baselines/prod_v1.pkl --threshold 0.20