visual-patch-audit 0.1.0

Compare and audit image patches for VLM and computer vision workflows.

visual-patch-audit

Compare and audit image patches for VLM and computer vision workflows.

Installation

pip install visual-patch-audit

For local development:

pip install -e ".[dev]"

Usage

from visual_patch_audit import compare_patches

report = compare_patches(
    reference_patches="reference_patches",
    candidate_patches="candidate_patches",
)

print(report)

Visual example

This example compares a selected mountain/forest patch from one landscape image against a selected patch from another landscape image.

	Image	Patch
Reference
Image to compare

from visual_patch_audit import compare_patch

result = compare_patch(
    "assets/readme/reference_patch.png",
    "assets/readme/candidate_patch.png",
)

print(result)

Actual result:

{
    "similarity": {
        "histogram_similarity": 0.869569,
        "brightness_similarity": 0.988622,
        "contrast_similarity": 0.989545,
        "texture_similarity": 0.999121,
        "overall_similarity": 0.950974,
    },
    "issues": [],
}

The score is interpretable: the patches have similar brightness, contrast, texture, and color distribution.

Generate the README images and result again:

python examples/readme_visual_example.py

Output

{
    "reference_count": 20,
    "candidate_count": 5,
    "score": 82,
    "similarity": {
        "mean_histogram_similarity": 0.86,
        "mean_brightness_similarity": 0.91,
        "mean_contrast_similarity": 0.77,
        "mean_edge_density_similarity": 0.74,
        "mean_texture_similarity": 0.79,
    },
    "issues": [
        {
            "patch": "candidate_patches/patch_04.png",
            "type": "low_similarity",
            "severity": "medium",
            "message": "Patch is visually different from the reference set.",
        }
    ],
}

Inspect one patch

from visual_patch_audit import inspect_patch

features = inspect_patch("patch.png")
print(features)

Compare two patches

from visual_patch_audit import compare_patch

result = compare_patch("reference.png", "candidate.png")
print(result)

Find outliers

from visual_patch_audit import find_outlier_patches

outliers = find_outlier_patches("patches")
print(outliers)

Overview

visual-patch-audit is a Python utility for comparing image patches using simple visual similarity metrics.

It is useful when building:

• VLM pipelines
• segmentation workflows
• object detection workflows
• visual dataset validation systems
• model output review tools
• image patch quality checks
• computer vision evaluation pipelines

Features

• Compares one patch against another
• Compares candidate patches against reference patches
• Extracts interpretable patch features
• Detects visually unusual patches
• Reports similarity metrics and potential issues
• Supports JPEG, PNG, WEBP, TIFF, and BMP images
• Uses Pillow and numpy
• Simple API

Limitations

visual-patch-audit uses deterministic visual similarity metrics. It does not determine semantic correctness, medical truth, diagnosis, object identity, or ground-truth validity. It does not replace expert review, model evaluation, annotation review, or safety-critical validation.

Use it as one inspection layer in a broader VLM or computer vision evaluation workflow. Outlier detection is intentionally simple and uses O(n^2) pairwise comparisons.

Issues

Report issues at: https://github.com/edujbarrios/visual-patch-audit

Author

Eduardo J. Barrios
[e-mail]

License

Mozilla Public License 2.0