Source code for viqa.utils.loading

"""Module for utility functions for data loading.

Examples
--------
    .. doctest-skip::

        >>> from viqa import load_data
        >>> img_path = "path/to/image.mhd"
        >>> img = load_data(img_path)

        >>> import numpy as np
        >>> from viqa import ImageArray, crop_image, normalize_data
        >>> img = np.random.rand(128, 128)
        >>> img.dtype
        dtype('float64')
        >>> type(img)
        <class 'numpy.ndarray'>
        >>> img = ImageArray(img)
        >>> type(img)
        <class 'viqa.utils.loading.ImageArray'>
        >>> img = normalize_data(img, data_range_output=(0, 255))
        >>> img.dtype
        dtype('uint8')
        >>> img = crop_image(img, (0, 64), (0, 64))
        >>> img.shape
        (64, 64)
"""

# Authors
# -------
# Author: Lukas Behammer
# Research Center Wels
# University of Applied Sciences Upper Austria, 2023
# CT Research Group
#
# Modifications
# -------------
# Original code, 2024, Lukas Behammer
#
# License
# -------
# BSD-3-Clause License

import csv
import glob
import os
import re
from typing import Any, Tuple, Union
from warnings import warn

import nibabel as nib
import numpy as np
import skimage as ski
from scipy.stats import kurtosis, skew
from torch import Tensor
from tqdm.autonotebook import tqdm

from .deprecation import RemovedInFutureVersionWarning
from .misc import _to_grayscale
from .visualization import visualize_2d, visualize_3d


[docs] class ImageArray(np.ndarray): """ Class for image arrays. This class is a subclass of :py:class:`numpy.ndarray` and adds attributes for image statistics. Attributes ---------- mean_value : float Mean of the image array median : float Median of the image array variance : float Variance of the image array standarddev : float Standard deviation of the image array skewness : float Skewness of the image array kurtosis : float Kurtosis of the image array histogram : tuple Histogram of the image array minimum : float Minimum value of the image array maximum : float Maximum value of the image array Parameters ---------- input_array : np.ndarray Numpy array containing the data Returns ------- obj : ImageArray New instance of the ImageArray class """ def __new__(cls, input_array): """ Create a new instance of the ImageArray class. Parameters ---------- input_array : np.ndarray Numpy array containing the data Returns ------- obj : ImageArray New instance of the ImageArray class """ # Input array is an already formed ndarray instance obj = np.asarray(input_array).view(cls) return obj def __array_finalize__(self, obj): """ Finalize the array. Parameters ---------- obj : object Object to finalize """ if obj is None: return # Add attributes self.mean_value = getattr( obj, "mean_value", "Not set. Run calculate_statistics() first." ) self.median = getattr( obj, "median", "Not set. Run calculate_statistics() first." ) self.variance = getattr( obj, "variance", "Not set. Run calculate_statistics() first." ) self.standarddev = getattr( obj, "standarddev", "Not set. Run calculate_statistics() first." ) self.skewness = getattr( obj, "skewness", "Not set. Run calculate_statistics() first." ) self.kurtosis = getattr( obj, "kurtosis", "Not set. Run calculate_statistics() first." ) self.histogram = getattr( obj, "histogram", "Not set. Run calculate_statistics() first." ) self.minimum = getattr( obj, "minimum", "Not set. Run calculate_statistics() first." ) self.maximum = getattr( obj, "maximum", "Not set. Run calculate_statistics() first." ) def __array_ufunc__(self, ufunc, method, *inputs, **kwargs): """Make sure that an ImageArray is returned when a ufunc operation is performed on the ImageArray class. """ # Adapted code by @Thawn from # https://stackoverflow.com/questions/51520630/subclassing-numpy-array-propagate-attributes # convert inputs and outputs of class ImageArray to np.ndarray to prevent # infinite recursion args = ( (i.view(np.ndarray) if isinstance(i, ImageArray) else i) for i in inputs ) outputs = kwargs.pop("out", []) if outputs: kwargs["out"] = tuple( (o.view(np.ndarray) if isinstance(o, ImageArray) else o) for o in outputs ) else: outputs = (None,) * ufunc.nout # call numpys implementation of __array_ufunc__ results = super().__array_ufunc__(ufunc, method, *args, **kwargs) if results is NotImplemented: return NotImplemented if method == "at": # method == 'at' means that the operation is performed in-place. Therefore, # we are done. return # now we need to make sure that outputs that where specified with the 'out' # argument are handled correctly: if ufunc.nout == 1: results = (results,) results = tuple( (result.view(ImageArray) if output is None else output) for result, output in zip(results, outputs, strict=False) ) return results[0] if len(results) == 1 else results
[docs] def calculate_statistics(self): """Calculate statistics of the image array. .. admonition:: The following statistics are calculated: * mean * median * variance * standard deviation * skewness * kurtosis * histogram * minimum * maximum """ # Add attributes self.mean_value = np.mean(self) self.median = np.median(self.view()) self.variance = np.var(self.view()) self.standarddev = np.std(self.view()) self.skewness = skew(self.view(), axis=None) self.kurtosis = kurtosis(self.view(), axis=None) if self.view().dtype.kind in ["u", "i"]: self.view().histogram = np.histogram( self.view(), bins=np.iinfo(self.view().dtype).max ) else: self.histogram = np.histogram(self.view(), bins=255) self.minimum = np.min(self.view()) self.maximum = np.max(self.view())
[docs] def describe( self, path: Union[str, os.PathLike, None] = None, filename: Union[str, None] = None, ) -> dict: """ Export image statistics to a csv file. Parameters ---------- path : str or os.PathLike, optional Path to the directory where the csv file should be saved filename : str, optional Name of the csv file Returns ------- stats : dict Dictionary containing the image statistics Warns ----- RuntimeWarning If no path or filename is provided. Statistics are not exported. Examples -------- >>> import numpy as np >>> from viqa import ImageArray >>> img = np.random.rand(128, 128) >>> img = ImageArray(img) >>> img.describe(path="path/to", filename="image_statistics") """ stats = { "mean": self.mean_value, "median": self.median, "variance": self.variance, "standarddev": self.standarddev, "skewness": self.skewness, "kurtosis": self.kurtosis, "minimum": self.minimum, "maximum": self.maximum, } if path and filename: # export to csv if not filename.lower().endswith(".csv"): filename += ".csv" # Create file path file_path = os.path.join(path, filename) with open(file_path, mode="w", newline="") as f: # Open file writer = csv.DictWriter(f, stats.keys()) writer.writeheader() writer.writerow(stats) print(f"Statistics exported to {file_path}") else: warn( "No path or filename provided. Statistics not exported.", RuntimeWarning ) return stats
[docs] def visualize( self, slices: Tuple[int, int, int], export_path=None, **kwargs ) -> None: """ Visualize the image array. If export_path is provided, the visualization is saved to the directory. Parameters ---------- slices : Tuple[int, int, int] Slices for the x, y and z axis export_path : str or os.PathLike, optional Path to the directory where the visualization should be saved **kwargs : dict Additional keyword arguments for visualization. See :py:func:`.viqa.utils.visualize_3d`. Raises ------ ValueError If the image is not 2D or 3D. Warns ----- UserWarning If the image is 2D, the parameter slices will be ignored. Examples -------- >>> import numpy as np >>> from viqa import ImageArray >>> img = np.random.rand(128, 128, 128) >>> img = ImageArray(img) >>> img.visualize(slices=(64, 64, 64)) """ if self.ndim == 3: visualize_3d(self, slices, export_path, **kwargs) elif self.ndim == 2: warn("Image is 2D. Parameter slices will be ignored.", RuntimeWarning) visualize_2d(self, export_path, **kwargs) else: raise ValueError("Image must be 2D or 3D.")
def _load_data_from_disk(file_dir: str | os.PathLike, file_name: str) -> ImageArray: """ Load data from a file. Parameters ---------- file_dir : str or os.PathLike Directory of the file file_name : str or os.PathLike Name of the file with extension Returns ------- img_arr : ImageArray Numpy array containing the data Raises ------ ValueError If the file extension is not supported \n If the bit depth is not supported \n If no bit depth was found \n If no dimension was found """ # Create file path components file_name_split = os.path.splitext(file_name) # Split file name and extension file_ext = file_name_split[-1] file_path = os.path.join(file_dir, file_name) # Complete file path # Check file extension if file_ext == ".mhd": # If file is a .mhd file img_arr = load_mhd(file_dir, file_name) return ImageArray(img_arr) elif file_ext == ".raw": # If file is a .raw file img_arr = load_raw(file_dir, file_name) return ImageArray(img_arr) elif file_ext == ".nii": img_arr = load_nifti(file_path) return ImageArray(img_arr) elif file_ext == ".gz": if re.search(".nii", file_name): img_arr = load_nifti(file_path) return ImageArray(img_arr) else: raise ValueError("File extension not supported") elif file_ext in [".png", ".jpg", ".jpeg", ".bmp", ".tiff"]: img_arr = ski.io.imread(file_path) return ImageArray(img_arr) else: raise ValueError( "File extension not supported" ) # Raise exception if file extension is not supported
[docs] def load_mhd(file_dir: str | os.PathLike, file_name: str | os.PathLike) -> np.ndarray: """ Load data from a ``.mhd`` file. Parameters ---------- file_dir : str or os.PathLike Directory of the file file_name : str or os.PathLike Name of the file with extension Returns ------- img_arr : np.ndarray Numpy array containing the data Raises ------ ValueError If the bit depth is not supported Examples -------- >>> from viqa.utils import load_mhd # doctest: +SKIP >>> img = load_raw("path/to/image.mhd") # doctest: +SKIP """ file_path = os.path.join(file_dir, file_name) # Complete file path f = open(file=file_path) # Open header file file_header_txt = f.read().split("\n") # Extract header lines # Create dictionary from lines file_header = { key: value for line in file_header_txt[0:-1] for key, value in [line.split(" = ")] } data_file_path = os.path.join( file_dir, file_header["ElementDataFile"] ) # Get data file path from header # Extract dimension # Get DimSize from header and change to type int dim_size = [int(val) for val in file_header["DimSize"].split()] # Check bit depth bit_depth = file_header["ElementType"] # Get ElementType from header data_type: type[Union[np.floating[Any] | np.integer[Any] | np.unsignedinteger[Any]]] # Set data type according to bit depth if bit_depth == "MET_USHORT": data_type = np.ushort # Set data type to unsigned short elif bit_depth == "MET_UCHAR": data_type = np.ubyte # Set data type to unsigned byte elif bit_depth == "MET_FLOAT": data_type = np.float32 # Set data type to float32 else: raise ValueError( "Bit depth not supported" ) # Raise exception if the bit depth is not supported img_arr = _load_binary(data_file_path, data_type, dim_size) return img_arr
[docs] def load_raw(file_dir: str | os.PathLike, file_name: str | os.PathLike) -> np.ndarray: """ Load data from a ``.raw`` file. Parameters ---------- file_dir : str or os.PathLike Directory of the file file_name : str or os.PathLike Name of the file with extension Returns ------- img_arr : np.ndarray Numpy array containing the data Raises ------ ValueError If the bit depth is not supported. \n If no bit depth was found. \n If no dimension was found. Examples -------- >>> from viqa.utils import load_raw # doctest: +SKIP >>> img = load_raw("path/to/image.raw") # doctest: +SKIP """ # Create file path components file_name_split = os.path.splitext(file_name) # Split file name and extension file_name_head = file_name_split[0] # File name without extension # Check dimension dim_search_result = re.search( r"(\d+(x)\d+(x)\d+)", file_name_head ) # Search for dimension in file name if dim_search_result is not None: # If dimension was found dim = dim_search_result.group(1) # Get dimension from file name else: raise ValueError( "No dimension found" ) # Raise exception if no dimension was found # Extract dimension dim_size = re.split("x", dim) # Split dimension string into list dim_size = [int(val) for val in dim_size] # Change DimSize to type int # Check bit depth bit_depth_search_result = re.search( r"(\d{1,2}bit)", file_name_head ) # Search for the bit depth in file name if bit_depth_search_result is not None: # If the bit depth was found bit_depth = bit_depth_search_result.group(1) # Get the bit depth from file name else: raise ValueError( "No bit depth found" ) # Raise exception if no bit depth was found # Set data type according to bit depth if bit_depth == "16bit": data_type = np.ushort # Set data type to unsigned short elif bit_depth == "8bit": data_type = np.ubyte # Set data type to unsigned byte else: raise ValueError( "Bit depth not supported" ) # Raise exception if the bit depth is not supported data_file_path = os.path.join(file_dir, file_name) # Get data file path img_arr = _load_binary(data_file_path, data_type, dim_size) return img_arr
[docs] def load_nifti(file_path: str | os.PathLike) -> np.ndarray: """ Load data from a ``.nii`` file. Parameters ---------- file_path : str or os.PathLike File path Returns ------- img_arr : np.ndarray Numpy array containing the data Examples -------- >>> from viqa.utils import load_nifti # doctest: +SKIP >>> img = load_nifti("path/to/image.nii.gz") # doctest: +SKIP Notes ----- This function wraps the nibabel function :py:func:`nibabel.loadsave.load`. """ img = nib.load(file_path) img_arr = img.get_fdata() # type: ignore[attr-defined] return img_arr
def _load_binary(data_file_path, data_type, dim_size): # Load data with open(file=data_file_path, mode="rb") as f: # Open data file img_arr_orig = np.fromfile( file=f, dtype=data_type ) # Read data file into numpy array according to data type if img_arr_orig.size != np.prod(dim_size): raise ValueError( "Size of data file (" + data_file_path + ") does not match dimensions (" + str(dim_size) + ")" ) # Reshape numpy array according to DimSize img_arr = img_arr_orig.reshape(*dim_size[::-1]) # Rotate and flip image img_arr = np.rot90(img_arr, axes=(0, 2)) img_arr = np.flip(img_arr, 0) return img_arr
[docs] def load_data( img: np.ndarray | ImageArray | Tensor | str | os.PathLike, data_range: int | None = None, normalize: bool = False, batch: bool = False, roi: Union[list[Tuple[int, int]], None] = None, ) -> list[ImageArray] | ImageArray: """ Load data from a numpy array, a pytorch tensor or a file path. Parameters ---------- img : np.ndarray, viqa.ImageArray, torch.Tensor, str or os.PathLike Numpy array, ImageArray, tensor or file path data_range : int, optional, default=None Maximum value of the returned data. Passed to :py:func:`viqa.utils.normalize_data`. normalize : bool, default False If True, data is normalized to (0, ``data_range``) based on min and max of img. batch : bool, default False If True, img is a file path and all files in the directory are loaded. .. deprecated:: 4.0.0 This will be deprecated in version 4.0.0. .. todo:: Deprecate in version 4.0.0 roi : list[Tuple[int, int]], optional, default=None Region of interest for cropping the image. The format is a list of tuples with the ranges for the x, y (and z) axis. First value in the tuple denotes the start and the second value the end of the range. If not set, the whole image is loaded. Returns ------- img_arr : ImageArray or list[ImageArray] :py:class:`viqa.utils.ImageArray` or list of :py:class:`viqa.utils.ImageArray` containing the data Raises ------ ValueError If input type is not supported \n If ``data_range=None`` and ``normalize=True`` Warns ----- RuntimeWarning If ``data_range`` is set but ``normalize=False``. ``data_range`` will be ignored. Warnings -------- ``batch`` will be deprecated in version 4.0.0. .. todo:: Deprecate in version 4.0.0 Examples -------- .. doctest-skip:: >>> from viqa import load_data >>> path_r = "path/to/reference/image.mhd" >>> path_m = "path/to/modified/image.mhd" >>> img_r = load_data(path_r) >>> img_m = load_data(path_m) >>> from viqa import load_data >>> img_r = np.random.rand(128, 128) >>> img_r = load_data(img_r, data_range=255, normalize=True) """ # TODO: Deprecate in version 4.0.0 if batch: raise RemovedInFutureVersionWarning( "Batch loading is deprecated and will be removed in vIQA 4.0.x." ) # exceptions and warning for data_range and normalize if normalize and data_range is None: raise ValueError("Parameter data_range must be set if normalize is True.") if not normalize and data_range is not None: warn( "Parameter data_range is set but normalize is False. Parameter " "data_range will be ignored.", RuntimeWarning, ) img_arr: list[np.ndarray] | np.ndarray # TODO: list can be removed in version 4.0.0 # Check input type match img: case str() | os.PathLike(): # If input is a file path # Check if batch if batch: # TODO: Deprecate in version 4.0.0 # Get all files in directory files = glob.glob(img) # type: ignore[type-var] img_arr = [] # Initialize list for numpy arrays # Load data from disk for each file for file in tqdm(files): img_arr.append( _load_data_from_disk( file_dir=os.path.dirname(file), file_name=os.path.basename(file), ) ) else: file_dir = os.path.dirname(img) file_name = os.path.basename(img) img_arr = _load_data_from_disk( file_dir, file_name ) # Load data from disk case ImageArray(): # If input is an ImageArray img_arr = img # Use input as ImageArray case np.ndarray(): # If input is a numpy array img_arr = img # Use input as numpy array case Tensor(): # If input is a pytorch tensor img_arr = img.cpu().numpy() # Convert tensor to numpy array case [np.ndarray()]: # If input is a list # TODO: Deprecate in version 4.0.0 img_arr = img # Use input as list of ImageArrays batch = True # Set batch to True to normalize list of numpy arrays case _: raise ValueError( "Input type not supported" ) # Raise exception if input type is not supported # Normalize data if normalize and data_range: # TODO: Deprecate in version 4.0.0 if batch: img_arr = [ normalize_data(img=img, data_range_output=(0, data_range)) for img in img_arr ] elif not isinstance(img_arr, list): img_arr = normalize_data(img=img_arr, data_range_output=(0, data_range)) if roi: # Crop image if batch: # TODO: Deprecate in version 4.0.0 img_arr = [crop_image(img, *roi) for img in img_arr] elif not isinstance(img_arr, list): img_arr = crop_image(img_arr, *roi) img_final: list[ImageArray] | ImageArray if isinstance(img_arr, ImageArray): img_final = img_arr elif isinstance(img_arr, list): # TODO: Deprecate in version 4.0.0 img_final = [ImageArray(img) for img in img_arr] else: img_final = ImageArray(img_arr) return img_final
[docs] def normalize_data( img: np.ndarray | ImageArray, data_range_output: Tuple[int, int], data_range_input: Union[Tuple[int, int], None] = None, automatic_data_range: bool = True, ) -> np.ndarray | ImageArray: """Normalize an image to a given data range. Parameters ---------- img : np.ndarray or ImageArray Input image. data_range_output : Tuple[int] Data range of the returned data. data_range_input : Tuple[int], default=None Data range of the input data. Needs to be set if ``automatic_data_range`` is False. automatic_data_range : bool, default=True Automatically determine the input data range. Returns ------- img_arr : np.ndarray or ImageArray Input image normalized to data_range. Raises ------ ValueError If data type is not supported. If ``data_range_output`` is not supported. If ``automatic_data_range`` is False and ``data_range_input`` is not set. Warns ----- RuntimeWarning If data is already normalized. Notes ----- Currently only 8 bit int (0-255), 16 bit int (0-65535) and 32 bit float (0-1) data ranges are supported. Examples -------- >>> import numpy as np >>> from viqa import normalize_data >>> img = np.random.rand(128, 128) >>> img_norm = normalize_data( >>> img, >>> data_range_output=(0, 255), >>> automatic_data_range=True, >>> ) >>> np.max(img_norm) 255 """ info: Union[np.iinfo, np.finfo] # Check data type if np.issubdtype(img.dtype, np.integer): # If data type is integer info = np.iinfo(img.dtype) elif np.issubdtype(img.dtype, np.floating): # If data type is float info = np.finfo(img.dtype) else: raise ValueError("Data type not supported") # Check if data is already normalized if info.max is not data_range_output[1] or info.min is not data_range_output[0]: # Normalize data if automatic_data_range: img_min = np.min(img) # Get minimum value of numpy array img_max = np.max(img) # Get maximum value of numpy array else: if data_range_input is None: raise ValueError( "If automatic_data_range is False, data_range_input must be set." ) else: img_min = data_range_input[0] img_max = data_range_input[1] # Normalize numpy array img = ( (img - img_min) * (data_range_output[1] - data_range_output[0]) / (img_max - img_min) ) + data_range_output[0] # Change data type # If data range is 255 (8 bit) if data_range_output[1] == 2**8 - 1 and data_range_output[0] == 0: img = img.astype(np.uint8) # Change data type to unsigned byte # If data range is 65535 (16 bit) elif data_range_output[1] == 2**16 - 1 and data_range_output[0] == 0: img = img.astype(np.uint16) # Change data type to unsigned short # If data range is 1 elif data_range_output[1] == 1 and data_range_output[0] == 0: img = img.astype(np.float32) # Change data type to float32 else: raise ValueError( "Data range not supported. Please use (0, 1), (0, 255) or " "(0, 65535) as data_range_output." ) else: warn("Data is already normalized.", RuntimeWarning) return img
[docs] def crop_image( img: np.ndarray | ImageArray, x: Tuple[int, int], y: Tuple[int, int], z: Union[Tuple[int, int], None], ) -> np.ndarray | ImageArray: """ Crop the image array. Parameters ---------- img : np.ndarray or ImageArray Input image x : Tuple[int, int] Range for the x-axis y : Tuple[int, int] Range for the y-axis z : Tuple[int, int] or None Range for the z-axis Returns ------- img_crop : np.ndarray or ImageArray Cropped image array Raises ------ ValueError If the image is not 2D or 3D. If the cropped image shape is larger than the original image shape. Warns ----- RuntimeWarning If the image is 2D, the parameter z will be ignored. """ # Get original shape to check if image is already cropped img_shape = np.array(img.shape) if img.ndim == 2 or (img.ndim == 3 and img.shape[-1] == 3): # If image is 2D crop_shape = np.array((x[1] - x[0], y[1] - y[0])) if (crop_shape < img_shape).all(): # If cropping is smaller than original image if z is not None: warn("Image is 2D. Parameter z will be ignored.", RuntimeWarning) img_crop = img[x[0] : x[1], y[0] : y[1]] elif crop_shape == img_shape: # If image is already cropped warn("Image is already cropped.", RuntimeWarning) img_crop = img else: # If cropping is larger than original image raise ValueError("Cropped image shape must be smaller than original image.") elif img.ndim == 3 and z is not None: # If image is 3D crop_shape = np.array((x[1] - x[0], y[1] - y[0], z[1] - z[0])) if (crop_shape < img_shape).all(): # If cropping is smaller than original image img_crop = img[x[0] : x[1], y[0] : y[1], z[0] : z[1]] elif crop_shape == img_shape: # If image is already cropped warn("Image is already cropped.", RuntimeWarning) img_crop = img else: # If cropping is larger than original image raise ValueError("Cropped image shape must be smaller than original image.") else: # If image is not 2D or 3D raise ValueError("Image must be 2D or 3D.") return img_crop
def _check_imgs( img_r: np.ndarray | Tensor | str | os.PathLike, img_m: np.ndarray | Tensor | str | os.PathLike, **kwargs, ) -> Tuple[list | np.ndarray, list | np.ndarray]: """Check if two images are of the same type and shape.""" chromatic = kwargs.pop("chromatic", False) # load images img_r_loaded = load_data(img_r, **kwargs) img_m_loaded = load_data(img_m, **kwargs) if isinstance(img_r_loaded, np.ndarray) and isinstance( img_m_loaded, np.ndarray ): # If both images are numpy arrays # Check if images are of the same type and shape if img_r_loaded.dtype != img_m_loaded.dtype: # If image types do not match raise ValueError("Image types do not match") if img_r_loaded.shape != img_m_loaded.shape: # If image shapes do not match raise ValueError("Image shapes do not match") elif type(img_r_loaded) is not type(img_m_loaded): # If image types do not match raise ValueError( "Image types do not match. img_r is of type {type(img_r_loaded)} and img_m " "is of type {type(" "img_m_loaded)}" ) elif isinstance(img_r, list) and isinstance( img_m, list ): # If both images are lists or else if len(img_r_loaded) != len(img_m_loaded): # If number of images do not match raise ValueError( "Number of images do not match. img_r has {len(img_r_loaded)} images " "and img_m has {len(img_m_loaded)} images" ) for img_a, img_b in zip( img_r_loaded, img_m_loaded, strict=False ): # For each image in the list if img_a.dtype != img_b.dtype: # If image types do not match raise ValueError("Image types do not match") if img_a.dtype != img_b.shape: # If image shapes do not match raise ValueError("Image shapes do not match") else: raise ValueError("Image format not supported.") if not isinstance(img_r_loaded, list): # Check if images are chromatic if chromatic is False and img_r_loaded.shape[-1] == 3: # Convert to grayscale as backup if falsely claimed to be non-chromatic warn("Images are chromatic. Converting to grayscale.") img_r_loaded = _to_grayscale(img_r_loaded) img_m_loaded = _to_grayscale(img_m_loaded) elif chromatic is True and img_r_loaded.shape[-1] != 3: raise ValueError("Images are not chromatic.") return img_r_loaded, img_m_loaded def _resize_image(img_r, img_m, scaling_order=1): # Resize image if shapes unequal if img_r.shape != img_m.shape: img_m = ski.transform.resize( img_m, img_r.shape, preserve_range=True, order=scaling_order ) img_m = img_m.astype(img_r.dtype) return img_m