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# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file.
"""
Image I/O registry
"""
from __future__ import annotations
import abc
import os.path as osp
from typing import Sequence
import numpy as np
from sigima.config import _
from sigima.io.base import BaseIORegistry, FormatBase
from sigima.objects.image import ImageObj, create_image
from sigima.worker import CallbackWorkerProtocol
class ImageIORegistry(BaseIORegistry):
"""Metaclass for registering image I/O handler classes"""
REGISTRY_INFO: str = _("Image I/O formats")
_io_format_instances: list[ImageFormatBase] = []
class ImageFormatBaseMeta(ImageIORegistry, abc.ABCMeta):
"""Mixed metaclass to avoid conflicts"""
class ImageFormatBase(abc.ABC, FormatBase, metaclass=ImageFormatBaseMeta):
"""Base image format object.
This class is used to define the interface for image I/O formats.
It is an abstract base class that defines the methods that must be
implemented by any image format class.
"""
@abc.abstractmethod
def read(
self, filename: str, worker: CallbackWorkerProtocol | None = None
) -> Sequence[ImageObj]:
"""Read list of image objects from file
Args:
filename: File name
worker: Callback worker object
Returns:
List of image objects
"""
@abc.abstractmethod
def write(self, filename: str, obj: ImageObj) -> None:
"""Write data to file
Args:
filename: file name
obj: native object (signal or image)
Raises:
NotImplementedError: if format is not supported
"""
class SingleImageFormatBase(ImageFormatBase):
"""Base image format object for single image (e.g., TIFF, PNG, etc.)."""
@staticmethod
def create_object(filename: str, index: int | None = None) -> ImageObj:
"""Create empty object
Args:
filename: File name
index: Index of object in file
Returns:
Image object
"""
name = osp.basename(filename)
if index is not None:
name += f" {index:02d}"
return create_image(name, metadata={"source": filename})
def read(
self, filename: str, worker: CallbackWorkerProtocol | None = None
) -> list[ImageObj]:
"""Read list of image objects from file
Args:
filename: File name
worker: Callback worker object
Returns:
List of image objects
"""
# Default implementation covers the case of a single image:
obj = self.create_object(filename)
obj.data = self.read_data(filename)
unique_values = np.unique(obj.data)
if len(unique_values) == 2:
# Binary image: set LUT range to unique values
obj.zscalemin, obj.zscalemax = unique_values.tolist()
return [obj]
@staticmethod
@abc.abstractmethod
def read_data(filename: str) -> np.ndarray:
"""Read data and return it
Args:
filename: File name
Returns:
Image array data
"""
def write(self, filename: str, obj: ImageObj) -> None:
"""Write data to file
Args:
filename: file name
obj: native object (signal or image)
Raises:
NotImplementedError: if format is not supported
"""
data = obj.data
self.write_data(filename, data)
@staticmethod
def write_data(filename: str, data: np.ndarray) -> None:
"""Write data to file
Args:
filename: File name
data: Image array data
"""
raise NotImplementedError(f"Writing to {filename} is not supported")
class MultipleImagesFormatBase(SingleImageFormatBase):
"""Base image format object for multiple images (e.g., SIF or SPE).
Works with read function that returns a NumPy array of 3 dimensions, where
the first dimension is the number of images.
"""
def read(
self, filename: str, worker: CallbackWorkerProtocol | None = None
) -> list[ImageObj]:
"""Read list of image objects from file
Args:
filename: File name
worker: Callback worker object
Returns:
List of image objects
"""
data = self.read_data(filename)
if len(data.shape) == 3:
objlist = []
for idx in range(data.shape[0]):
obj = self.create_object(filename, index=idx)
obj.data = data[idx, ::]
objlist.append(obj)
if worker is not None:
worker.set_progress((idx + 1) / data.shape[0])
if worker.was_canceled():
break
return objlist
obj = self.create_object(filename)
obj.data = data
return [obj]
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