dqm-ml-images 2.0.0rc1

Python library designed provide core dqm-ml metrics without huge dependencies, as well as common API shared by metrics

DQM-ML Images

Image feature extraction package for DQM-ML V2. Provides metrics for assessing image dataset quality.

Installation

pip install dqm-ml-images

Note: dqm-ml-images provides metric processors only — no CLI or job orchestration. Use directly via Python or with dqm-ml-job for YAML config execution.

Usage

Using Python Directly

import numpy as np
from pathlib import Path
from dqm_ml_images import VisualFeaturesProcessor
from PIL import Image

# Load or generate sample images
images = [Image.open("path/to/image1.jpg"), Image.open("path/to/image2.jpg")]

# Create and configure the processor
processor = VisualFeaturesProcessor(
    name="image_quality",
    config={
        "input_columns": ["image_bytes"],
        "grayscale": True
    }
)

# Process images to extract features
batch = {"image_bytes": images}
features = processor.compute_features(batch)
print(f"Luminosity: {features['m_luminosity']}")
print(f"Contrast: {features['m_contrast']}")
print(f"Blur: {features['m_blur_level']}")
print(f"Entropy: {features['m_entropy']}")

With dqm-ml-job

For running from a YAML config, install together with dqm-ml-job:

pip install dqm-ml-job dqm-ml-images

Then use this config:

metrics_processor:
  image_quality:
    type: visual_metric
    input_columns: ["image_data"]
    grayscale: true

Features

Feature	Description
Luminosity	Mean gray level — measures overall brightness
Contrast	RMS contrast — measures tonal range
Blur	Variance of Laplacian — estimates sharpness/focus
Entropy	Shannon entropy — measures information content

Output

The processor adds these columns to your data:

• m_luminosity
• m_contrast
• m_blur_level
• m_entropy

Requirements

• opencv-python
• pillow
• numpy

Dependencies

DQM-ML is modular. For visual features:

# Minimal: use as library only
pip install dqm-ml-images

# For YAML config execution
pip install dqm-ml-job dqm-ml-images

# Full stack with all metrics
pip install dqm-ml-job dqm-ml-core dqm-ml-images dqm-ml-pytorch

See Also

• Visual Features Documentation
• Configuration Guide