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Image quality analysis provider.
Delegates all metric computation to cores.vision.quality — no duplicated
brightness/contrast/sharpness/noise logic.
"""
from __future__ import annotations
import numpy as np
from config.settings import Settings, settings as _default_settings
from cores.vision import brightness, contrast, sharpness, noise_level, quality_score
from pipeline.feature_extraction import PipelineOutput
from providers.base import BaseProvider, ProviderCapability
class ImageQualityProvider(BaseProvider):
name = "image_quality"
capability = ProviderCapability.IMAGE_ANALYSIS
def __init__(self, settings: Settings | None = None) -> None:
super().__init__(settings=settings or _default_settings)
def is_available(self) -> bool:
return True
def _run(self, pipeline_output: PipelineOutput) -> tuple[dict, dict]:
img: np.ndarray = pipeline_output.image
b = brightness(img)
c = contrast(img)
s = sharpness(img)
n = noise_level(img)
q = quality_score(img)
raw = {
"brightness": b,
"contrast": c,
"sharpness": s,
"noise_level": n,
"quality_score": q,
"image_size": {"width": img.shape[1], "height": img.shape[0]},
}
normalized = {
"quality_score": round(q, 4),
"brightness": round(b, 4),
"contrast": round(c, 4),
"sharpness": round(s, 4),
"noise_level": round(n, 4),
"width": int(img.shape[1]),
"height": int(img.shape[0]),
"channels": int(img.shape[2]) if img.ndim == 3 else 1,
"color_profile": None,
"dominant_colors": [],
"aspects": {
"method": "variance_of_laplacian",
"noise_method": "median_absolute_deviation",
},
}
return raw, normalized
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