from typing import Literal
import numpy as np
from phenotypic.abc_ import PrefabPipeline
from phenotypic.correction import GridAligner
from phenotypic.detect import WatershedDetector
from phenotypic.enhance import CLAHE, GaussianBlur, MedianFilter
from phenotypic.measure import (
MeasureColor,
MeasureIntensity,
MeasureShape,
MeasureTexture,
)
from phenotypic.refine import (
BorderObjectRemover,
MaskFill,
LowCircularityRemover,
GridOversizedObjectRemover,
MinResidualErrorReducer,
)
[docs]
class HeavyWatershedPipeline(PrefabPipeline):
"""
Provides an image processing pipeline with robust preprocessing/post-processing and watershed segmentation.
This class defines a sequence of operations and measurements designed for
image analysis. It includes smoothing, enhancement, segmentation, border
object removal, and various analysis steps. The pipeline is highly customizable
for tasks such as image segmentation and feature extraction, making it suitable
for applications involving image quantification and preprocessing.
Note:
This pipeline uses computationally intensive operations aimed at cases where there is heavy background noise
"""
[docs]
def __init__(
self,
gaussian_sigma: int = 5,
gaussian_mode: str = "reflect",
gaussian_truncate: float = 4.0,
watershed_footprint: Literal["auto"] | np.ndarray | int | None = None,
watershed_min_size: int = 50,
watershed_compactness: float = 0.001,
watershed_connectivity: int = 1,
watershed_relabel: bool = True,
watershed_ignore_zeros: bool = True,
border_remover_size: int = 25,
circularity_cutoff: float = 0.5,
texture_scale: int = 5,
texture_warn: bool = False,
benchmark: bool = False,
**kwargs,
):
"""
Initializes an image processing pipeline for various image analysis tasks such as object detection,
segmentation, and measurement. This pipeline uses a combination of operations, including filtering,
segmentation, and morphological processing, followed by shape, intensity, texture, and color
measurements.
Args:
gaussian_sigma (int, optional): Standard deviation for Gaussian blur filter. Defaults to 5.
gaussian_mode (str, optional): Mode parameter for Gaussian blur filter (e.g., 'reflect').
Defaults to 'reflect'.
gaussian_truncate (float, optional): Truncate value for Gaussian kernel to limit its size.
Defaults to 4.0.
watershed_footprint (Literal['auto'] | np.ndarray | int | None, optional): Footprint size or
structure for the watershed algorithm. Defaults to None.
watershed_min_size (int, optional): Minimum size of the objects to be retained after watershed
segmentation. Defaults to 50.
watershed_compactness (float, optional): Compactness parameter for the watershed algorithm to
control how tightly regions are formed. Defaults to 0.001.
watershed_connectivity (int, optional): Connectivity parameter for region connectivity in
watershed segmentation. Defaults to 1.
watershed_relabel (bool, optional): Whether to relabel the regions after watershed segmentation.
Defaults to True.
watershed_ignore_zeros (bool, optional): Whether to ignore zero-valued regions in the watershed
algorithm. Defaults to True.
border_remover_size (int, optional): Size of the border in pixels to be removed during border
object removal. Defaults to 25.
circularity_cutoff (float, optional): Threshold for object circularity below which objects will
be removed. Defaults to 0.5.
texture_scale (int, optional): Scale parameter for texture measurement. Defaults to 5.
texture_warn (bool, optional): Whether to issue warnings for invalid texture measurements.
Defaults to False.
benchmark (bool, optional): Whether to enable benchmarking of pipeline performance.
Defaults to False.
**kwargs: Additional keyword arguments for parent class initialization.
"""
watershed_detector = WatershedDetector(
footprint=watershed_footprint,
min_size=watershed_min_size,
compactness=watershed_compactness,
connectivity=watershed_connectivity,
relabel=watershed_relabel,
ignore_zeros=watershed_ignore_zeros,
)
border_remover = BorderObjectRemover(border_size=border_remover_size)
min_residual_reducer = MinResidualErrorReducer()
ops = [
GaussianBlur(
sigma=gaussian_sigma, mode=gaussian_mode, truncate=gaussian_truncate
),
CLAHE(),
MedianFilter(),
watershed_detector,
border_remover,
GridOversizedObjectRemover(),
LowCircularityRemover(cutoff=circularity_cutoff),
min_residual_reducer,
GridAligner(),
watershed_detector,
GridOversizedObjectRemover(),
min_residual_reducer,
border_remover,
LowCircularityRemover(cutoff=circularity_cutoff),
MaskFill(),
]
meas = [
MeasureShape(),
MeasureIntensity(),
MeasureTexture(scale=texture_scale, warn=texture_warn),
MeasureColor(),
]
super().__init__(ops=ops, meas=meas, benchmark=benchmark, **kwargs)
