Перейти к содержанию

ImageTransformer

ImageTransformer is a special type of action for image transformation.

This type allows you to consistently describe the necessary changes for the image, so that when performing a step, you can get an already modified image at the output.

The conversion can be single or multiple.

ImageTransformer does not implement transforms itself — it combines torchvision.transforms and OpenCV. Medical-specific helpers include circle_crop, ellipse_crop, and min_area_rect_crop for fundus-camera images.

from hyppopipe.data.image import Image
from hyppopipe.pipeline.image.transform import ImageTransformer
from hyppopipe.pipeline import Pipeline, Step

from torchvision.transforms import v2 as transforms


image_data_processing = Pipeline(
    {
        "GetResizedImage": Step(
            ImageTransformer().resize(224),
            inputs=["__input__"],
        ),
        "GetSharpenImage": Step(
            ImageTransformer().sharpen(2.0),
            inputs=["GetResizedImage"],
        ),
        "GetBluredImage": Step(
            ImageTransformer().gaussian_blur(5, sigma=(0.1, 2.0)),
            inputs=["GetResizedImage"],
        ),
        # Or we can use fluent transformations:
        "FluentTransformations": Step(
            ImageTransformer().resize(224).gaussian_blur(5, sigma=(0.1, 2.0)).rotate(90),
            inputs=["__input__"],
        ),
        # Or composed transformations:
        "ComposedTransformations": Step(
            ImageTransformer.from_compose(
                transforms.Compose([
                    transforms.Resize(224),
                    transforms.GaussianBlur(5, sigma=(0.1, 2.0)),
                    transforms.RandomRotation((90, 90)),
                ])
            ),
            inputs=["__input__"],
        )

    },
    shift_result=False,
)

original_image = Image.from_path("images/image74prime.tif")
pipeline_result = image_data_processing.predict(original_image)

Documentation

Composable torchvision and OpenCV transforms for :class:~hyppopipe.data.image.Image.

CircleCropConfig dataclass

Bases: ContourSelectConfig

:class:ContourSelectConfig plus circle-specific filters for :meth:ImageTransformer.circle_crop.

Attributes:

Name Type Description
min_radius_ratio float

Minimum enclosing-circle radius vs min(H, W).
max_radius_ratio float

Maximum radius vs min(H, W) (limits full-frame squares).
min_circularity float

4π·area/perimeter²; 0 disables the check.

ContourSelectConfig dataclass

Contour detection and filtering for OpenCV contour-based steps.

Used by :meth:ImageTransformer.ellipse_crop, :meth:ImageTransformer.min_area_rect_crop, :meth:ImageTransformer.convex_hull_mask, and :meth:ImageTransformer.contour_mask.

Attributes:

Name Type Description
threshold int

Grayscale binarization level (higher → fewer faint regions).
min_contour_area_ratio float

Ignore contours smaller than this fraction of the image.
max_contour_area_ratio float

Ignore contours larger than this (often the image frame).
reject_frame_contour bool

Skip contours whose bounding box touches all four edges.
frame_margin_px int

Edge tolerance when detecting a frame-filling contour.

FloodFillConfig dataclass

Tuning for :meth:ImageTransformer.flood_fill.

ImageTransformer

Fluent builder that applies transforms in the order they were added.

Torchvision steps run on CHW tensors; OpenCV steps receive BGR HWC uint8 arrays (standard OpenCV layout) and are converted automatically.

OpenCV steps skip themselves on invalid input or failed callables (with a warning) and pass the tensor through to the next step.

__call__(image)

Apply all configured transforms in registration order.

__init__()

Start with an empty transform list.

add(step)

Append a custom CHW tensor → CHW tensor step.

center_crop(size)

Append torchvision.transforms.v2.CenterCrop.

circle_crop(config=None, **kwargs)

Append OpenCV crop around a filtered contour's enclosing circle.

Parameters:

Name Type Description Default
config CircleCropConfig | None

Full tuning object; overrides keyword arguments when set.

 None
**kwargs Any

Fields for :class:CircleCropConfig (e.g. threshold=40).

 {}

Example::

ImageTransformer().circle_crop(
    threshold=40,
    max_contour_area_ratio=0.75,
    min_circularity=0.5,
)

clear()

Remove all transforms from the recipe.

contour_mask(config=None, *, background=None, **kwargs)

Zero (or fill) pixels outside the selected contour polygon.

convex_hull_mask(config=None, *, background=None, **kwargs)

Zero (or fill) pixels outside the convex hull of the selected contour.

ellipse_crop(config=None, **kwargs)

Crop axis-aligned square around :func:cv2.fitEllipse of the selected contour.

flood_fill(config=None, **kwargs)

Region growing from seed_xy (image center when omitted).

from_compose(compose) classmethod

Create a transformer from a torchvision.transforms.v2.Compose.

Each child transform becomes its own sequential step (order preserved).

from_transforms(transformations) classmethod

Create a transformer from torchvision v2 transforms (one step each).

gaussian_blur(kernel_size, sigma)

Append torchvision.transforms.v2.GaussianBlur.

min_area_rect_crop(config=None, **kwargs)

Perspective-crop the oriented minimum-area rectangle of the selected contour.

morphology(config=None, **kwargs)

Apply :func:cv2.morphologyEx on grayscale (optionally binarized first).

normalize(mean, std)

Append torchvision.transforms.v2.Normalize.

opencv(fn, *, step_name='opencv')

Append an OpenCV callable (BGR HWC uint8 in/out).

remove_small_components(config=None, *, background=None, **kwargs)

Drop connected components smaller than min_area after thresholding.

resize(size, *args, **kwargs)

Append torchvision.transforms.v2.Resize.

rotate(degrees)

Append torchvision.transforms.v2.RandomRotation.

sharpen(factor=2.0)

Append torchvision.transforms.v2.RandomAdjustSharpness with p=1.

torchvision(transform)

Append a torchvision.transforms.v2 transform (runs on CHW tensor).

MaskBackgroundConfig dataclass

Background applied outside a contour or component mask.

Attributes:

Name Type Description
value tuple[int, int, int] | int

(B, G, R) for color images, or a single intensity for grayscale.

MorphologyConfig dataclass

Tuning for :meth:ImageTransformer.morphology.

RemoveSmallComponentsConfig dataclass

Tuning for :meth:ImageTransformer.remove_small_components.