tessl/pypi-scikit-image

Comprehensive image processing and computer vision library for Python with algorithms for filtering, morphology, segmentation, and feature detection

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Overview

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Exposure

Name: tessl/pypi-scikit-image
Author: tessl

Image intensity and exposure adjustment operations for enhancing image contrast, brightness, and visibility. Includes histogram operations, gamma correction, and adaptive enhancement methods.

Capabilities

Histogram Operations

def histogram(image, nbins=256, source_range='image', normalize=False):
    """Calculate image histogram."""

def equalize_hist(image, nbins=256, mask=None):
    """Global histogram equalization."""

def equalize_adapthist(image, kernel_size=None, clip_limit=0.01, nbins=256):
    """Adaptive histogram equalization (CLAHE)."""

def match_histograms(image, reference, multichannel=False, channel_axis=None):
    """Match image histogram to reference."""

def cumulative_distribution(image, nbins=256):
    """Compute cumulative distribution function."""

Intensity Adjustment

def rescale_intensity(image, in_range='image', out_range='dtype'):
    """Rescale image intensity values."""

def adjust_gamma(image, gamma=1, gain=1):
    """Apply gamma correction."""

def adjust_sigmoid(image, cutoff=0.5, gain=10, inv=False):
    """Apply sigmoid intensity adjustment."""

def adjust_log(image, gain=1, inv=False):
    """Apply logarithmic intensity adjustment."""

Quality Assessment

def is_low_contrast(image, fraction_threshold=0.05, lower_percentile=1, upper_percentile=99, method='linear'):
    """Check if image has low contrast."""

Types

from numpy.typing import NDArray
import numpy as np

Histogram = NDArray[np.integer]
IntensityRange = tuple
GammaValue = float

Install with Tessl CLI

npx tessl i tessl/pypi-scikit-image

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