from __future__ import annotations

import collections
import json
import logging
import os
import pathlib
import threading
from itertools import chain

import pykwalify.core
import SimpleITK as sitk

from radiomics import (
    generalinfo,
    getFeatureClasses,
    getImageTypes,
    getParameterValidationFiles,
    imageoperations,
)

logger = logging.getLogger(__name__)


class _SingletonGeometryTolerance:
    _instance = None
    _initialized = False
    _lock = threading.Lock()

    def __new__(cls, *_args, **_kwargs):
        if cls._instance is None:
            with cls._lock:
                if cls._instance is None:
                    cls._instance = super().__new__(cls)
        return cls._instance

    def __init__(self, tolerance=None):
        if not self._initialized:
            with self._lock:
                if not self._initialized:
                    self.geometryTolerance = tolerance
                    _SingletonGeometryTolerance._initialized = True


class RadiomicsFeatureExtractor:
    r"""
    Wrapper class for calculation of a radiomics signature.
    At and after initialisation various settings can be used to customize the resultant signature.
    This includes which classes and features to use, as well as what should be done in terms of preprocessing the image
    and what images (original and/or filtered) should be used as input.

    Then a call to :py:func:`execute` generates the radiomics
    signature specified by these settings for the passed image and labelmap combination. This function can be called
    repeatedly in a batch process to calculate the radiomics signature for all image and labelmap combinations.

    At initialization, a parameters file (string pointing to yaml or json structured file) or dictionary can be provided
    containing all necessary settings (top level containing keys "setting", "imageType" and/or "featureClass). This is
    done by passing it as the first positional argument. If no positional argument is supplied, or the argument is not
    either a dictionary or a string pointing to a valid file, defaults will be applied.
    Moreover, at initialisation, custom settings (*NOT enabled image types and/or feature classes*) can be provided
    as keyword arguments, with the setting name as key and its value as the argument value (e.g. ``binWidth=25``).
    Settings specified here will override those in the parameter file/dict/default settings.
    For more information on possible settings and customization, see
    :ref:`Customizing the Extraction <radiomics-customization-label>`.

    By default, all features in all feature classes are enabled.
    By default, only `Original` input image is enabled (No filter applied).
    """

    def __init__(self, *args, **kwargs):
        self.settings = {}
        self.enabledImagetypes = {}
        self.enabledFeatures = {}

        self.featureClassNames = list(getFeatureClasses().keys())

        if len(args) == 1 and isinstance(args[0], dict):
            logger.info("Loading parameter dictionary")
            self._applyParams(paramsDict=args[0])
        elif len(args) == 1 and (isinstance(args[0], (str, pathlib.PurePath))):
            if not os.path.isfile(args[0]):
                msg = f"Parameter file {args[0]} does not exist."
                raise OSError(msg)
            logger.info("Loading parameter file %s", str(args[0]))
            self._applyParams(paramsFile=args[0])
        else:
            # Set default settings and update with and changed settings contained in kwargs
            self.settings = self._getDefaultSettings()
            logger.info("No valid config parameter, using defaults: %s", self.settings)

            self.enabledImagetypes = {"Original": {}}
            logger.info("Enabled image types: %s", self.enabledImagetypes)

            for featureClassName in self.featureClassNames:
                if featureClassName == "shape2D":  # Do not enable shape2D by default
                    continue
                self.enabledFeatures[featureClassName] = []
            logger.info("Enabled features: %s", self.enabledFeatures)

        if len(kwargs) > 0:
            logger.info("Applying custom setting overrides: %s", kwargs)
            self.settings.update(kwargs)
            logger.debug("Settings: %s", self.settings)

        if self.settings.get("binCount", None) is not None:
            logger.warning(
                "Fixed bin Count enabled! However, we recommend using a fixed bin Width. See "
                "http://pyradiomics.readthedocs.io/en/latest/faq.html#radiomics-fixed-bin-width for more "
                "details"
            )

        self._setTolerance()

    def _setTolerance(self):
        _SingletonGeometryTolerance(self.settings.get("geometryTolerance"))
        if _SingletonGeometryTolerance().geometryTolerance is not None:
            logger.debug(
                "Setting SimpleITK tolerance to %s",
                _SingletonGeometryTolerance().geometryTolerance,
            )
            sitk.ProcessObject.SetGlobalDefaultCoordinateTolerance(
                _SingletonGeometryTolerance().geometryTolerance
            )
            sitk.ProcessObject.SetGlobalDefaultDirectionTolerance(
                _SingletonGeometryTolerance().geometryTolerance
            )

    def addProvenance(self, provenance_on=True):
        """
        Enable or disable reporting of additional information on the extraction. This information includes toolbox version,
        enabled input images and applied settings. Furthermore, additional information on the image and region of interest
        (ROI) is also provided, including original image spacing, total number of voxels in the ROI and total number of
        fully connected volumes in the ROI.

        To disable this, call ``addProvenance(False)``.
        """
        self.settings["additionalInfo"] = provenance_on

    @staticmethod
    def _getDefaultSettings():
        """
        Returns a dictionary containing the default settings specified in this class. These settings cover global settings,
        such as ``additionalInfo``, as well as the image pre-processing settings (e.g. resampling). Feature class specific
        are defined in the respective feature classes and and not included here. Similarly, filter specific settings are
        defined in ``imageoperations.py`` and also not included here.
        """
        return {
            "minimumROIDimensions": 2,
            "minimumROISize": None,  # Skip testing the ROI size by default
            "normalize": False,
            "normalizeScale": 1,
            "removeOutliers": None,
            "resampledPixelSpacing": None,  # No resampling by default
            "interpolator": "sitkBSpline",  # Alternative: sitk.sitkBSpline
            "preCrop": False,
            "padDistance": 5,
            "distances": [1],
            "force2D": False,
            "force2Ddimension": 0,
            "resegmentRange": None,  # No resegmentation by default
            "label": 1,
            "additionalInfo": True,
        }

    def loadParams(self, paramsFile):
        """
        Parse specified parameters file and use it to update settings, enabled feature(Classes) and image types. For more
        information on the structure of the parameter file, see
        :ref:`Customizing the extraction <radiomics-customization-label>`.

        If supplied file does not match the requirements (i.e. unrecognized names or invalid values for a setting), a
        pykwalify error is raised.
        """
        self._applyParams(paramsFile=paramsFile)

    def loadJSONParams(self, JSON_configuration):
        """
        Pars JSON structured configuration string and use it to update settings, enabled feature(Classes) and image types.
        For more information on the structure of the parameter file, see
        :ref:`Customizing the extraction <radiomics-customization-label>`.

        If supplied string does not match the requirements (i.e. unrecognized names or invalid values for a setting), a
        pykwalify error is raised.
        """
        parameter_data = json.loads(JSON_configuration)
        self._applyParams(paramsDict=parameter_data)

    def _applyParams(self, paramsFile=None, paramsDict=None):
        """
        Validates and applies a parameter dictionary. See :py:func:`loadParams` and :py:func:`loadJSONParams` for more info.
        """

        # Ensure pykwalify.core has a log handler (needed when parameter validation fails)
        if (
            len(pykwalify.core.log.handlers) == 0
            and len(logging.getLogger().handlers) == 0
        ):
            # No handler available for either pykwalify or root logger, provide first radiomics handler (outputs to stderr)
            pykwalify.core.log.addHandler(logging.getLogger("radiomics").handlers[0])

        schemaFile, schemaFuncs = getParameterValidationFiles()
        c = pykwalify.core.Core(
            source_file=paramsFile,
            source_data=paramsDict,
            schema_files=[schemaFile],
            extensions=[schemaFuncs],
        )
        params = c.validate()
        logger.debug("Parameters parsed, input is valid.")

        enabledImageTypes = params.get("imageType", {})
        enabledFeatures = params.get("featureClass", {})
        settings = params.get("setting", {})
        voxelSettings = params.get("voxelSetting", {})

        logger.debug("Applying settings")

        if len(enabledImageTypes) == 0:
            self.enabledImagetypes = {"Original": {}}
        else:
            self.enabledImagetypes = enabledImageTypes

        logger.debug("Enabled image types: %s", self.enabledImagetypes)

        if len(enabledFeatures) == 0:
            self.enabledFeatures = {}
            for featureClassName in self.featureClassNames:
                self.enabledFeatures[featureClassName] = []
        else:
            self.enabledFeatures = enabledFeatures

        logger.debug("Enabled features: %s", self.enabledFeatures)

        # Set default settings and update with and changed settings contained in kwargs
        self.settings = self._getDefaultSettings()
        self.settings.update(settings)
        self.settings.update(voxelSettings)

        logger.debug("Settings: %s", settings)

    def execute(
        self,
        imageFilepath,
        maskFilepath,
        label=None,
        label_channel=None,
        voxelBased=False,
    ):
        """
        Compute radiomics signature for provide image and mask combination. It comprises of the following steps:

        1. Image and mask are loaded and normalized/resampled if necessary.
        2. Validity of ROI is checked using :py:func:`~imageoperations.checkMask`, which also computes and returns the
           bounding box.
        3. If enabled, provenance information is calculated and stored as part of the result. (Not available in voxel-based
           extraction)
        4. Shape features are calculated on a cropped (no padding) version of the original image. (Not available in
           voxel-based extraction)
        5. If enabled, resegment the mask based upon the range specified in ``resegmentRange`` (default None: resegmentation
           disabled).
        6. Other enabled feature classes are calculated using all specified image types in ``_enabledImageTypes``. Images
           are cropped to tumor mask (no padding) after application of any filter and before being passed to the feature
           class.
        7. The calculated features is returned as ``collections.OrderedDict``.

        :param imageFilepath: SimpleITK Image, or string pointing to image file location
        :param maskFilepath: SimpleITK Image, or string pointing to labelmap file location
        :param label: Integer, value of the label for which to extract features. If not specified, last specified label
            is used. Default label is 1.
        :param label_channel: Integer, index of the channel to use when maskFilepath yields a SimpleITK.Image with a vector
            pixel type. Default index is 0.
        :param voxelBased: Boolean, default False. If set to true, a voxel-based extraction is performed, segment-based
            otherwise.
        :returns: dictionary containing calculated signature ("<imageType>_<featureClass>_<featureName>":value).
            In case of segment-based extraction, value type for features is float, if voxel-based, type is SimpleITK.Image.
            Type of diagnostic features differs, but can always be represented as a string.
        """
        _settings = self.settings.copy()

        tolerance = _settings.get("geometryTolerance")
        additionalInfo = _settings.get("additionalInfo", False)
        resegmentShape = _settings.get("resegmentShape", False)

        if label is not None:
            _settings["label"] = label
        else:
            label = _settings.get("label", 1)

        if label_channel is not None:
            _settings["label_channel"] = label_channel

        if _SingletonGeometryTolerance().geometryTolerance != tolerance:
            self._setTolerance()

        if additionalInfo:
            generalInfo = generalinfo.GeneralInfo()
            generalInfo.addGeneralSettings(_settings)
            generalInfo.addEnabledImageTypes(self.enabledImagetypes)
        else:
            generalInfo = None

        if voxelBased:
            _settings["voxelBased"] = True
            kernelRadius = _settings.get("kernelRadius", 1)
            logger.info("Starting voxel based extraction")
        else:
            kernelRadius = 0

        logger.info("Calculating features with label: %d", label)
        logger.debug("Enabled images types: %s", self.enabledImagetypes)
        logger.debug("Enabled features: %s", self.enabledFeatures)
        logger.debug("Current settings: %s", _settings)

        # 1. Load the image and mask
        featureVector = collections.OrderedDict()
        image, mask = self.loadImage(
            imageFilepath, maskFilepath, generalInfo, **_settings
        )

        # 2. Check whether loaded mask contains a valid ROI for feature extraction and get bounding box
        # Raises a ValueError if the ROI is invalid
        boundingBox, correctedMask = imageoperations.checkMask(image, mask, **_settings)

        # Update the mask if it had to be resampled
        if correctedMask is not None:
            if generalInfo is not None:
                generalInfo.addMaskElements(image, correctedMask, label, "corrected")
            mask = correctedMask

        logger.debug("Image and Mask loaded and valid, starting extraction")

        # 5. Resegment the mask if enabled (parameter regsegmentMask is not None)
        resegmentedMask = None
        if _settings.get("resegmentRange", None) is not None:
            resegmentedMask = imageoperations.resegmentMask(image, mask, **_settings)

            # Recheck to see if the mask is still valid, raises a ValueError if not
            boundingBox, correctedMask = imageoperations.checkMask(
                image, resegmentedMask, **_settings
            )

            if generalInfo is not None:
                generalInfo.addMaskElements(
                    image, resegmentedMask, label, "resegmented"
                )

        # 3. Add the additional information if enabled
        if generalInfo is not None:
            featureVector.update(generalInfo.getGeneralInfo())

        # if resegmentShape is True and resegmentation has been enabled, update the mask here to also use the
        # resegmented mask for shape calculation (e.g. PET resegmentation)
        if resegmentShape and resegmentedMask is not None:
            mask = resegmentedMask

        if not voxelBased:
            # 4. If shape descriptors should be calculated, handle it separately here
            featureVector.update(
                self.computeShape(image, mask, boundingBox, **_settings)
            )

        # (Default) Only use resegemented mask for feature classes other than shape
        # can be overridden by specifying `resegmentShape` = True
        if not resegmentShape and resegmentedMask is not None:
            mask = resegmentedMask

        # 6. Calculate other enabled feature classes using enabled image types
        # Make generators for all enabled image types
        logger.debug("Creating image type iterator")
        imageGenerators = []
        for imageType, customKwargs in self.enabledImagetypes.items():
            args = _settings.copy()
            args.update(customKwargs)
            msg = f'Adding image type "{imageType}" with custom settings: {customKwargs!s}'
            logger.info(msg)
            imageGenerators = chain(
                imageGenerators,
                getattr(imageoperations, f"get{imageType}Image")(image, mask, **args),
            )

        logger.debug("Extracting features")
        # Calculate features for all (filtered) images in the generator
        for originputImage, imageTypeName, inputKwargs in imageGenerators:
            logger.info("Calculating features for %s image", imageTypeName)
            inputImage, inputMask = imageoperations.cropToTumorMask(
                originputImage, mask, boundingBox, padDistance=kernelRadius
            )
            featureVector.update(
                self.computeFeatures(
                    inputImage, inputMask, imageTypeName, **inputKwargs
                )
            )

        logger.debug("Features extracted")

        return featureVector

    @staticmethod
    def loadImage(ImageFilePath, MaskFilePath, generalInfo=None, **kwargs):
        """
        Load and pre-process the image and labelmap.
        If ImageFilePath is a string, it is loaded as SimpleITK Image and assigned to ``image``,
        if it already is a SimpleITK Image, it is just assigned to ``image``.
        All other cases are ignored (nothing calculated).
        Equal approach is used for assignment of ``mask`` using MaskFilePath. If necessary, a segmentation object (i.e. mask
        volume with vector-image type) is then converted to a labelmap (=scalar image type). Data type is forced to UInt32.
        See also :py:func:`~imageoperations.getMask()`.

        If normalizing is enabled image is first normalized before any resampling is applied.

        If resampling is enabled, both image and mask are resampled and cropped to the tumor mask (with additional
        padding as specified in padDistance) after assignment of image and mask.

        :param ImageFilePath: SimpleITK.Image object or string pointing to SimpleITK readable file representing the image
                              to use.
        :param MaskFilePath: SimpleITK.Image object or string pointing to SimpleITK readable file representing the mask
                             to use.
        :param generalInfo: GeneralInfo Object. If provided, it is used to store diagnostic information of the
                            pre-processing.
        :param kwargs: Dictionary containing the settings to use for this particular image type.
        :return: 2 SimpleITK.Image objects representing the loaded image and mask, respectively.
        """

        normalize = kwargs.get("normalize", False)
        interpolator = kwargs.get("interpolator")
        resampledPixelSpacing = kwargs.get("resampledPixelSpacing")
        preCrop = kwargs.get("preCrop", False)
        label = kwargs.get("label", 1)

        logger.info("Loading image and mask")
        if isinstance(ImageFilePath, str) and os.path.isfile(ImageFilePath):
            image = sitk.ReadImage(ImageFilePath)
        elif isinstance(ImageFilePath, str) and os.path.isdir(ImageFilePath):
            reader = sitk.ImageSeriesReader()
            dicom_names = reader.GetGDCMSeriesFileNames(ImageFilePath)
            reader.SetFileNames(dicom_names)
            image = reader.Execute()
        elif isinstance(ImageFilePath, sitk.SimpleITK.Image):
            image = ImageFilePath
        else:
            msg = "Error reading image Filepath or SimpleITK object"
            raise ValueError(msg)

        if isinstance(MaskFilePath, str) and os.path.isfile(MaskFilePath):
            mask = sitk.ReadImage(MaskFilePath)
        elif isinstance(MaskFilePath, sitk.SimpleITK.Image):
            mask = MaskFilePath
        else:
            msg = "Error reading mask Filepath or SimpleITK object"
            raise ValueError(msg)

        # process the mask
        mask = imageoperations.getMask(mask, **kwargs)

        if generalInfo is not None:
            generalInfo.addImageElements(image)
            # Do not include the image here, as the overlap between image and mask have not been checked
            # It is therefore possible that image and mask do not align, or even have different sizes.
            generalInfo.addMaskElements(None, mask, label)

        # This point is only reached if image and mask loaded correctly
        if normalize:
            image = imageoperations.normalizeImage(image, **kwargs)

        if interpolator is not None and resampledPixelSpacing is not None:
            image, mask = imageoperations.resampleImage(image, mask, **kwargs)
            if generalInfo is not None:
                generalInfo.addImageElements(image, "interpolated")
                generalInfo.addMaskElements(image, mask, label, "interpolated")

        elif preCrop:
            bb, correctedMask = imageoperations.checkMask(image, mask, **kwargs)
            if correctedMask is not None:
                # Update the mask if it had to be resampled
                mask = correctedMask
            if bb is None:
                # Mask checks failed
                msg = "Mask checks failed during pre-crop"
                raise ValueError(msg)

            image, mask = imageoperations.cropToTumorMask(image, mask, bb, **kwargs)

        return image, mask

    def computeShape(self, image, mask, boundingBox, **kwargs):
        """
        Calculate the shape (2D and/or 3D) features for the passed image and mask.

        :param image: SimpleITK.Image object representing the image used
        :param mask: SimpleITK.Image object representing the mask used
        :param boundingBox: The boundingBox calculated by :py:func:`~imageoperations.checkMask()`, i.e. a tuple with lower
          (even indices) and upper (odd indices) bound of the bounding box for each dimension.
        :param kwargs: Dictionary containing the settings to use.
        :return: collections.OrderedDict containing the calculated shape features. If no features are calculated, an empty
          OrderedDict will be returned.
        """
        featureVector = collections.OrderedDict()

        enabledFeatures = self.enabledFeatures

        croppedImage, croppedMask = imageoperations.cropToTumorMask(
            image, mask, boundingBox
        )

        # Define temporary function to compute shape features
        def compute(shape_type):
            logger.info("Computing %s", shape_type)
            featureNames = enabledFeatures[shape_type]
            shapeClass = getFeatureClasses()[shape_type](
                croppedImage, croppedMask, **kwargs
            )

            if featureNames is not None:
                for feature in featureNames:
                    shapeClass.enableFeatureByName(feature)

            for featureName, featureValue in shapeClass.execute().items():
                newFeatureName = f"original_{shape_type}_{featureName}"
                featureVector[newFeatureName] = featureValue

        Nd = mask.GetDimension()
        if "shape" in enabledFeatures:
            if Nd == 3:
                compute("shape")
            else:
                logger.warning(
                    "Shape features are only available 3D input (for 2D input, use shape2D). Found %iD input",
                    Nd,
                )

        if "shape2D" in enabledFeatures:
            if Nd == 3:
                force2D = kwargs.get("force2D", False)
                force2Ddimension = kwargs.get("force2Ddimension", 0)
                if not force2D:
                    logger.warning(
                        "parameter force2D must be set to True to enable shape2D extraction"
                    )
                elif (
                    not (boundingBox[1::2] - boundingBox[0::2] + 1)[force2Ddimension]
                    > 1
                ):
                    logger.warning(
                        "Size in specified 2D dimension (%i) is greater than 1, cannot calculate 2D shape",
                        force2Ddimension,
                    )
                else:
                    compute("shape2D")
            elif Nd == 2:
                compute("shape2D")
            else:
                logger.warning(
                    "Shape2D features are only available for 2D and 3D (with force2D=True) input. "
                    "Found %iD input",
                    Nd,
                )

        return featureVector

    def computeFeatures(self, image, mask, imageTypeName, **kwargs):
        r"""
        Compute signature using image, mask and \*\*kwargs settings.

        This function computes the signature for just the passed image (original or derived), it does not pre-process or
        apply a filter to the passed image. Features / Classes to use for calculation of signature are defined in
        ``self.enabledFeatures``. See also :py:func:`enableFeaturesByName`.

        :param image: The cropped (and optionally filtered) SimpleITK.Image object representing the image used
        :param mask: The cropped SimpleITK.Image object representing the mask used
        :param imageTypeName: String specifying the filter applied to the image, or "original" if no filter was applied.
        :param kwargs: Dictionary containing the settings to use for this particular image type.
        :return: collections.OrderedDict containing the calculated features for all enabled classes.
          If no features are calculated, an empty OrderedDict will be returned.

        .. note::

          shape descriptors are independent of gray level and therefore calculated separately (handled in `execute`). In
          this function, no shape features are calculated.
        """
        featureVector = collections.OrderedDict()
        featureClasses = getFeatureClasses()

        enabledFeatures = self.enabledFeatures

        # Calculate feature classes
        for featureClassName, featureNames in enabledFeatures.items():
            # Handle calculation of shape features separately
            if featureClassName.startswith("shape"):
                continue

            if featureClassName in featureClasses:
                logger.info("Computing %s", featureClassName)

                featureClass = featureClasses[featureClassName](image, mask, **kwargs)

                if featureNames is not None:
                    for feature in featureNames:
                        featureClass.enableFeatureByName(feature)

                for featureName, featureValue in featureClass.execute().items():
                    newFeatureName = f"{imageTypeName}_{featureClassName}_{featureName}"
                    featureVector[newFeatureName] = featureValue

        return featureVector

    def enableAllImageTypes(self):
        """
        Enable all possible image types without any custom settings.
        """

        logger.debug("Enabling all image types")
        for imageType in getImageTypes():
            self.enabledImagetypes[imageType] = {}
        logger.debug("Enabled images types: %s", self.enabledImagetypes)

    def disableAllImageTypes(self):
        """
        Disable all image types.
        """
        logger.debug("Disabling all image types")
        self.enabledImagetypes = {}

    def enableImageTypeByName(self, imageType, enabled=True, customArgs=None):
        r"""
        Enable or disable specified image type. If enabling image type, optional custom settings can be specified in
        customArgs.

        Current possible image types are:

        - Original: No filter applied
        - Wavelet: Wavelet filtering, yields 8 decompositions per level (all possible combinations of applying either
          a High or a Low pass filter in each of the three dimensions.
          See also :py:func:`~radiomics.imageoperations.getWaveletImage`
        - LoG: Laplacian of Gaussian filter, edge enhancement filter. Emphasizes areas of gray level change, where sigma
          defines how coarse the emphasised texture should be. A low sigma emphasis on fine textures (change over a
          short distance), where a high sigma value emphasises coarse textures (gray level change over a large distance).
          See also :py:func:`~radiomics.imageoperations.getLoGImage`
        - Square: Takes the square of the image intensities and linearly scales them back to the original range.
          Negative values in the original image will be made negative again after application of filter.
        - SquareRoot: Takes the square root of the absolute image intensities and scales them back to original range.
          Negative values in the original image will be made negative again after application of filter.
        - Logarithm: Takes the logarithm of the absolute intensity + 1. Values are scaled to original range and
          negative original values are made negative again after application of filter.
        - Exponential: Takes the the exponential, where filtered intensity is e^(absolute intensity). Values are
          scaled to original range and negative original values are made negative again after application of filter.
        - Gradient: Returns the gradient magnitude.
        - LBP2D: Calculates and returns a local binary pattern applied in 2D.
        - LBP3D: Calculates and returns local binary pattern maps applied in 3D using spherical harmonics. Last returned
          image is the corresponding kurtosis map.

        For the mathmetical formulas of square, squareroot, logarithm and exponential, see their respective functions in
        :ref:`imageoperations<radiomics-imageoperations-label>`
        (:py:func:`~radiomics.imageoperations.getSquareImage`,
        :py:func:`~radiomics.imageoperations.getSquareRootImage`,
        :py:func:`~radiomics.imageoperations.getLogarithmImage`,
        :py:func:`~radiomics.imageoperations.getExponentialImage`,
        :py:func:`~radiomics.imageoperations.getGradientImage`,
        :py:func:`~radiomics.imageoperations.getLBP2DImage` and
        :py:func:`~radiomics.imageoperations.getLBP3DImage`,
        respectively).
        """
        if imageType not in getImageTypes():
            logger.warning("Image type %s is not recognized", imageType)
            return

        if enabled:
            if customArgs is None:
                customArgs = {}
                logger.debug(
                    "Enabling image type %s (no additional custom settings)", imageType
                )
            else:
                logger.debug(
                    "Enabling image type %s (additional custom settings: %s)",
                    imageType,
                    customArgs,
                )
            self.enabledImagetypes[imageType] = customArgs
        elif imageType in self.enabledImagetypes:
            logger.debug("Disabling image type %s", imageType)
            del self.enabledImagetypes[imageType]
        logger.debug("Enabled images types: %s", self.enabledImagetypes)

    def enableImageTypes(self, **enabledImagetypes):
        """
        Enable input images, with optionally custom settings, which are applied to the respective input image.
        Settings specified here override those in kwargs.
        The following settings are not customizable:

        - interpolator
        - resampledPixelSpacing
        - padDistance

        Updates current settings: If necessary, enables input image. Always overrides custom settings specified
        for input images passed in inputImages.
        To disable input images, use :py:func:`enableInputImageByName` or :py:func:`disableAllInputImages`
        instead.

        :param enabledImagetypes: dictionary, key is imagetype (original, wavelet or log) and value is custom settings
          (dictionary)
        """
        logger.debug("Updating enabled images types with %s", enabledImagetypes)
        self.enabledImagetypes.update(enabledImagetypes)
        logger.debug("Enabled images types: %s", self.enabledImagetypes)

    def enableAllFeatures(self):
        """
        Enable all classes and all features.

        .. note::
          Individual features that have been marked "deprecated" are not enabled by this function. They can still be enabled
          manually by a call to :py:func:`~radiomics.base.RadiomicsBase.enableFeatureByName()`,
          :py:func:`~radiomics.featureextractor.RadiomicsFeaturesExtractor.enableFeaturesByName()`
          or in the parameter file (by specifying the feature by name, not when enabling all features).
          However, in most cases this will still result only in a deprecation warning.
        """
        logger.debug("Enabling all features in all feature classes")
        for featureClassName in self.featureClassNames:
            self.enabledFeatures[featureClassName] = []
        logger.debug("Enabled features: %s", self.enabledFeatures)

    def disableAllFeatures(self):
        """
        Disable all classes.
        """
        logger.debug("Disabling all feature classes")
        self.enabledFeatures = {}

    def enableFeatureClassByName(self, featureClass, enabled=True):
        """
        Enable or disable all features in given class.

        .. note::
          Individual features that have been marked "deprecated" are not enabled by this function. They can still be enabled
          manually by a call to :py:func:`~radiomics.base.RadiomicsBase.enableFeatureByName()`,
          :py:func:`~radiomics.featureextractor.RadiomicsFeaturesExtractor.enableFeaturesByName()`
          or in the parameter file (by specifying the feature by name, not when enabling all features).
          However, in most cases this will still result only in a deprecation warning.
        """
        if featureClass not in self.featureClassNames:
            logger.warning("Feature class %s is not recognized", featureClass)
            return

        if enabled:
            logger.debug("Enabling all features in class %s", featureClass)
            self.enabledFeatures[featureClass] = []
        elif featureClass in self.enabledFeatures:
            logger.debug("Disabling feature class %s", featureClass)
            del self.enabledFeatures[featureClass]
        logger.debug("Enabled features: %s", self.enabledFeatures)

    def enableFeaturesByName(self, **enabledFeatures):
        """
        Specify which features to enable. Key is feature class name, value is a list of enabled feature names.

        To enable all features for a class, provide the class name with an empty list or None as value.
        Settings for feature classes specified in enabledFeatures.keys are updated, settings for feature classes
        not yet present in enabledFeatures.keys are added.
        To disable the entire class, use :py:func:`disableAllFeatures` or :py:func:`enableFeatureClassByName` instead.
        """
        logger.debug("Updating enabled features with %s", enabledFeatures)
        self.enabledFeatures.update(enabledFeatures)
        logger.debug("Enabled features: %s", self.enabledFeatures)
