Python ImagesLoader.fromTif方法代码示例

# 需要导入模块: from thunder.rdds.fileio.imagesloader import ImagesLoader [as 别名]
# 或者: from thunder.rdds.fileio.imagesloader.ImagesLoader import fromTif [as 别名]
    def loadImages(self, datapath, dims=None, inputformat='stack', dtype='int16', startidx=None, stopidx=None):
        """
        Loads an Images object from data stored as a binary image stack, tif, tif-stack, or png files.

        Supports single files or multiple files, stored on a local file system, a networked file sytem
        (mounted and available on all nodes), or Amazon S3. HDFS is not currently supported for image file data.

        Parameters
        ----------
        datapath: string
            Path to data files or directory, specified as either a local filesystem path or in a URI-like format,
            including scheme. A datapath argument may include a single '*' wildcard character in the filename. Examples
            of valid datapaths include 'a/local/relative/directory/*.stack", "s3n:///my-s3-bucket/data/mydatafile.tif",
            "/mnt/my/absolute/data/directory/", or "file:///mnt/another/data/directory/".

        dims: tuple of positive int, optional (but required if inputformat is 'stack')
            Dimensions of input image data, similar to a numpy 'shape' parameter, for instance (1024, 1024, 48). Binary
            stack data will be interpreted as coming from a multidimensional array of the specified dimensions. Stack
            data should be stored in row-major order (Fortran or Matlab convention) rather than column-major order (C
            or python/numpy convention), where the first dimension corresponds to that which is changing most rapidly
            on disk. So for instance given dims of (x, y, z), the coordinates of the data in a binary stack file
            should be ordered as [(x0, y0, z0), (x1, y0, zo), ..., (xN, y0, z0), (x0, y1, z0), (x1, y1, z0), ...,
            (xN, yM, z0), (x0, y0, z1), ..., (xN, yM, zP)].
            If inputformat is 'png', 'tif', or'tif-stack', the dims parameter (if any) will be ignored; data dimensions
            will instead be read out from the image file headers.

        inputformat: {'stack', 'png', 'tif', 'tif-stack'}. optional, default 'stack'
            Expected format of the input data. 'stack' indicates flat files of raw binary data. 'png' or 'tif' indicate
            two-dimensional image files of the corresponding formats. 'tif-stack' indicates a sequence of multipage tif
            files, with each page of the tif corresponding to a separate z-plane.
            For all formats, separate files are interpreted as distinct time points, with ordering given by
            lexicographic sorting of file names.
            This method assumes that stack data consists of signed 16-bit integers in native byte order. Data types of
            image file data will be as specified in the file headers.

        dtype: string or numpy dtype. optional, default 'int16'
            Data type of the image files to be loaded, specified as a numpy "dtype" string. If inputformat is
            'tif-stack', the dtype parameter (if any) will be ignored; data type will instead be read out from the
            tif headers.

        startidx: nonnegative int, optional
            startidx and stopidx are convenience parameters to allow only a subset of input files to be read in. These
            parameters give the starting index (inclusive) and final index (exclusive) of the data files to be used
            after lexicographically sorting all input data files matching the datapath argument. For example,
            startidx=None (the default) and stopidx=10 will cause only the first 10 data files in datapath to be read
            in; startidx=2 and stopidx=3 will cause only the third file (zero-based index of 2) to be read in. startidx
            and stopidx use the python slice indexing convention (zero-based indexing with an exclusive final position).

        stopidx: nonnegative int, optional
            See startidx.

        Returns
        -------
        data: thunder.rdds.Images
            A newly-created Images object, wrapping an RDD of <int index, numpy array> key-value pairs.

        """
        checkparams(inputformat, ['stack', 'png', 'tif', 'tif-stack'])

        from thunder.rdds.fileio.imagesloader import ImagesLoader
        loader = ImagesLoader(self._sc)

        if inputformat.lower() == 'stack':
            data = loader.fromStack(datapath, dims, dtype=dtype, startidx=startidx, stopidx=stopidx)
        elif inputformat.lower() == 'tif':
            data = loader.fromTif(datapath, startidx=startidx, stopidx=stopidx)
        elif inputformat.lower() == 'tif-stack':
            data = loader.fromMultipageTif(datapath, startidx=startidx, stopidx=stopidx)
        else:
            data = loader.fromPng(datapath)

        return data

# 需要导入模块: from thunder.rdds.fileio.imagesloader import ImagesLoader [as 别名]
# 或者: from thunder.rdds.fileio.imagesloader.ImagesLoader import fromTif [as 别名]

#.........这里部分代码省略.........
            where the first dimension changes most rapidly. For 'png' or 'tif' data dimensions
            will be read from the image file headers.

        inputFormat: str, optional, default = 'stack'
            Expected format of the input data: 'stack', 'png', or 'tif'. 'stack' indicates flat binary stacks.
            'png' or 'tif' indicate image formats. Page of a multipage tif file will be extend along
            the third dimension. Separate files interpreted as distinct records, with ordering
            given by lexicographic sorting of file names.

        ext: string, optional, default = None
            File extension, default will be "bin" if inputFormat=="stack", "tif" for inputFormat=='tif',
            and 'png' for inputFormat=="png".

        dtype: string or numpy dtype. optional, default 'int16'
            Data type of the image files to be loaded, specified as a numpy "dtype" string.
            Ignored for 'tif' or 'png' (data will be inferred from image formats).

        blockSize: string or positive int, optional, default "150M"
            Requested size of blocks (e.g "64M", "512k", "2G"). If shuffle=True, can also be a
            tuple of int specifying the number of pixels or splits per dimension. Indirectly
            controls the number of Spark partitions, with one partition per block.

        blockSizeUnits: string, either "pixels" or "splits", default "pixels"
            Units for interpreting a tuple passed as blockSize when shuffle=True.

        startIdx: nonnegative int, optional, default = None
            Convenience parameters to read only a subset of input files. Uses python slice conventions
            (zero-based indexing with exclusive final position). These parameters give the starting
            and final index after lexicographic sorting.

        stopIdx: nonnegative int, optional, default = None
            See startIdx.

        shuffle: boolean, optional, default = True
            Controls whether the conversion from Images to Series formats will use of a Spark shuffle-based method.

        overwrite: boolean, optional, default False
            If true, the directory specified by outputDirPath will be deleted (recursively) if it
            already exists. (Use with caution.)

        recursive: boolean, optional, default = False
            If true, will recursively descend directories rooted at dataPath, loading all files
            in the tree with an appropriate extension.

        nplanes: positive integer, optional, default = None
            Subdivide individual image files. Every `nplanes` from each file will be considered a new record.
            With nplanes=None (the default), a single file will be considered as representing a single record.
            If the number of records per file is not the same across all files, then `renumber` should be set
            to True to ensure consistent keys.

        npartitions: positive int, optional, default = None
            Specify number of partitions for the RDD, if unspecified will use 1 partition per image.

        renumber: boolean, optional, default = False
            Recalculate keys for records after images are loading. Only necessary if different files contain
            different number of records (e.g. due to specifying nplanes). See Images.renumber().

        confFilename : string, optional, default = 'conf.json'
            Name of conf file if using to specify parameters for binary stack data

        """
        checkParams(inputFormat, ['stack', 'tif', 'tif-stack'])

        if not overwrite:
            raiseErrorIfPathExists(outputDirPath, awsCredentialsOverride=self._credentials)
            overwrite = True  # prevent additional downstream checks for this path

        if not ext:
            ext = DEFAULT_EXTENSIONS.get(inputFormat.lower(), None)

        if shuffle:
            from thunder.rdds.fileio.imagesloader import ImagesLoader
            loader = ImagesLoader(self._sc)
            if inputFormat.lower() == 'stack':
                images = loader.fromStack(dataPath, dims, ext=ext, dtype=dtype, startIdx=startIdx, stopIdx=stopIdx,
                                          recursive=recursive, nplanes=nplanes, npartitions=npartitions,
                                          confFilename=confFilename)
            else:
                # 'tif' or 'tif-stack'
                images = loader.fromTif(dataPath, ext=ext, startIdx=startIdx, stopIdx=stopIdx,
                                        recursive=recursive, nplanes=nplanes, npartitions=npartitions)
            if renumber:
                images = images.renumber()
            images.toBlocks(blockSize, units=blockSizeUnits).saveAsBinarySeries(outputDirPath, overwrite=overwrite)
        else:
            from thunder.rdds.fileio.seriesloader import SeriesLoader
            if nplanes is not None:
                raise NotImplementedError("nplanes is not supported with shuffle=False")
            if npartitions is not None:
                raise NotImplementedError("npartitions is not supported with shuffle=False")
            loader = SeriesLoader(self._sc)
            if inputFormat.lower() == 'stack':
                loader.saveFromStack(dataPath, outputDirPath, dims, ext=ext, dtype=dtype,
                                     blockSize=blockSize, overwrite=overwrite, startIdx=startIdx,
                                     stopIdx=stopIdx, recursive=recursive)
            else:
                # 'tif' or 'tif-stack'
                loader.saveFromTif(dataPath, outputDirPath, ext=ext, blockSize=blockSize,
                                   startIdx=startIdx, stopIdx=stopIdx, overwrite=overwrite,
                                   recursive=recursive)

# 需要导入模块: from thunder.rdds.fileio.imagesloader import ImagesLoader [as 别名]
# 或者: from thunder.rdds.fileio.imagesloader.ImagesLoader import fromTif [as 别名]
    def loadImagesAsSeries(self, dataPath, dims=None, inputFormat='stack', ext=None, dtype='int16',
                           blockSize="150M", blockSizeUnits="pixels", startIdx=None, stopIdx=None,
                           shuffle=True, recursive=False, nplanes=None, npartitions=None,
                           renumber=False, confFilename='conf.json'):
        """
        Load Images data as Series data.

        Parameters
        ----------
        dataPath: string
            Path to data files or directory, as either a local filesystem path or a URI.
            May include a single '*' wildcard in the filename. Examples of valid dataPaths include
            'local/directory/*.stack", "s3n:///my-s3-bucket/data/", or "file:///mnt/another/directory/".

        dims: tuple of positive int, optional (required if inputFormat is 'stack')
            Image dimensions. Binary stack data will be interpreted as a multidimensional array
            with the given dimensions, and should be stored in row-major order (Fortran or Matlab convention),
            where the first dimension changes most rapidly. For 'png' or 'tif' data dimensions
            will be read from the image file headers.

        inputFormat: str, optional, default = 'stack'
            Expected format of the input data: 'stack', 'png', or 'tif'. 'stack' indicates flat binary stacks.
            'png' or 'tif' indicate image formats. Page of a multipage tif file will be extend along
            the third dimension. Separate files interpreted as distinct records, with ordering
            given by lexicographic sorting of file names.

        ext: string, optional, default = None
            File extension, default will be "bin" if inputFormat=="stack", "tif" for inputFormat=='tif',
            and 'png' for inputFormat=="png".

        dtype: string or numpy dtype. optional, default 'int16'
            Data type of the image files to be loaded, specified as a numpy "dtype" string.
            Ignored for 'tif' or 'png' (data will be inferred from image formats).

        blockSize: string or positive int, optional, default "150M"
            Requested size of blocks (e.g "64M", "512k", "2G"). If shuffle=True, can also be a
            tuple of int specifying the number of pixels or splits per dimension. Indirectly
            controls the number of Spark partitions, with one partition per block.

        blockSizeUnits: string, either "pixels" or "splits", default "pixels"
            Units for interpreting a tuple passed as blockSize when shuffle=True.

        startIdx: nonnegative int, optional, default = None
            Convenience parameters to read only a subset of input files. Uses python slice conventions
            (zero-based indexing with exclusive final position). These parameters give the starting
            and final index after lexicographic sorting.

        stopIdx: nonnegative int, optional, default = None
            See startIdx.

        shuffle: boolean, optional, default = True
            Controls whether the conversion from Images to Series formats will use of a Spark shuffle-based method.

        recursive: boolean, optional, default = False
            If true, will recursively descend directories rooted at dataPath, loading all files
            in the tree with an appropriate extension.

        nplanes: positive integer, optional, default = None
            Subdivide individual image files. Every `nplanes` from each file will be considered a new record.
            With nplanes=None (the default), a single file will be considered as representing a single record.
            If the number of records per file is not the same across all files, then `renumber` should be set
            to True to ensure consistent keys.

        npartitions: positive int, optional, default = None
            Specify number of partitions for the RDD, if unspecified will use 1 partition per image.

        renumber: boolean, optional, default = False
            Recalculate keys for records after images are loading. Only necessary if different files contain
            different number of records (e.g. due to specifying nplanes). See Images.renumber().

        confFilename : string, optional, default = 'conf.json'
            Name of conf file if using to specify parameters for binary stack data

        Returns
        -------
        data: thunder.rdds.Series
            A Series object, wrapping an RDD, with (n-tuples of ints) : (numpy array) pairs.
            Keys will be n-tuples of int, with n given by dimensionality of the images, and correspond
            to indexes into the image arrays. Value will have length equal to the number of image files.
            With each image contributing one point to this value array, with ordering given by
            the lexicographic ordering of image file names.
        """
        checkParams(inputFormat, ['stack', 'tif', 'tif-stack'])

        if not ext:
            ext = DEFAULT_EXTENSIONS.get(inputFormat.lower(), None)

        if shuffle:
            from thunder.rdds.fileio.imagesloader import ImagesLoader
            loader = ImagesLoader(self._sc)
            if inputFormat.lower() == 'stack':
                images = loader.fromStack(dataPath, dims, dtype=dtype, ext=ext, startIdx=startIdx, stopIdx=stopIdx,
                                          recursive=recursive, nplanes=nplanes, npartitions=npartitions,
                                          confFilename=confFilename)
            else:
                # tif / tif stack
                images = loader.fromTif(dataPath, ext=ext, startIdx=startIdx, stopIdx=stopIdx,
                                        recursive=recursive, nplanes=nplanes, npartitions=npartitions)
            if renumber:
                images = images.renumber()
#.........这里部分代码省略.........

# 需要导入模块: from thunder.rdds.fileio.imagesloader import ImagesLoader [as 别名]
# 或者: from thunder.rdds.fileio.imagesloader.ImagesLoader import fromTif [as 别名]
    def loadImages(self, dataPath, dims=None, dtype=None, inputFormat='stack', ext=None,
                   startIdx=None, stopIdx=None, recursive=False, nplanes=None, npartitions=None,
                   renumber=False, confFilename='conf.json'):
        """
        Loads an Images object from data stored as a binary image stack, tif, or png files.

        Supports single files or multiple files, stored on a local file system, a networked file sytem
        (mounted and available on all nodes), or Amazon S3. HDFS is not currently supported for image file data.

        Parameters
        ----------
        dataPath: string
            Path to data files or directory, as either a local filesystem path or a URI.
            May include a single '*' wildcard in the filename. Examples of valid dataPaths include
            'local/directory/*.stack", "s3n:///my-s3-bucket/data/", or "file:///mnt/another/directory/".

        dims: tuple of positive int, optional (required if inputFormat is 'stack')
            Image dimensions. Binary stack data will be interpreted as a multidimensional array
            with the given dimensions, and should be stored in row-major order (Fortran or Matlab convention),
            where the first dimension changes most rapidly. For 'png' or 'tif' data dimensions
            will be read from the image file headers.

        inputFormat: str, optional, default = 'stack'
            Expected format of the input data: 'stack', 'png', or 'tif'. 'stack' indicates flat binary stacks.
            'png' or 'tif' indicate image format. Page of a multipage tif file will be extend along
            the third dimension. Separate files interpreted as distinct records, with ordering
            given by lexicographic sorting of file names.

        ext: string, optional, default = None
            File extension, default will be "bin" if inputFormat=="stack", "tif" for inputFormat=='tif',
            and 'png' for inputFormat=="png".

        dtype: string or numpy dtype, optional, default = 'int16'
            Data type of the image files to be loaded, specified as a numpy "dtype" string.
            Ignored for 'tif' or 'png' (data will be inferred from image formats).

        startIdx: nonnegative int, optional, default = None
            Convenience parameters to read only a subset of input files. Uses python slice conventions
            (zero-based indexing with exclusive final position). These parameters give the starting
            and final index after lexicographic sorting.

        stopIdx: nonnegative int, optional, default = None
            See startIdx.

        recursive: boolean, optional, default = False
            If true, will recursively descend directories rooted at dataPath, loading all files
            in the tree with an appropriate extension.

        nplanes: positive integer, optional, default = None
            Subdivide individual image files. Every `nplanes` from each file will be considered a new record.
            With nplanes=None (the default), a single file will be considered as representing a single record.
            If the number of records per file is not the same across all files, then `renumber` should be set
            to True to ensure consistent keys.

        npartitions: positive int, optional, default = None
            Specify number of partitions for the RDD, if unspecified will use 1 partition per image.

        renumber: boolean, optional, default = False
            Recalculate keys for records after images are loading. Only necessary if different files contain
            different number of records (e.g. due to specifying nplanes). See Images.renumber().

        confFilename : string, optional, default = 'conf.json'
            Name of conf file if using to specify parameters for binary stack data

        Returns
        -------
        data: thunder.rdds.Images
            An Images object, wrapping an RDD of with (int) : (numpy array) pairs

        """
        checkParams(inputFormat, ['stack', 'png', 'tif', 'tif-stack'])

        from thunder.rdds.fileio.imagesloader import ImagesLoader
        loader = ImagesLoader(self._sc)

        # Checking StartIdx is smaller or equal to StopIdx
        if startIdx is not None and stopIdx is not None and startIdx > stopIdx:
            raise Exception("Error. startIdx {} is larger than stopIdx {}".inputFormat(startIdx, stopIdx))

        if not ext:
            ext = DEFAULT_EXTENSIONS.get(inputFormat.lower(), None)

        if inputFormat.lower() == 'stack':
            data = loader.fromStack(dataPath, dims=dims, dtype=dtype, ext=ext, startIdx=startIdx, stopIdx=stopIdx,
                                    recursive=recursive, nplanes=nplanes, npartitions=npartitions,
                                    confFilename=confFilename)
        elif inputFormat.lower().startswith('tif'):
            data = loader.fromTif(dataPath, ext=ext, startIdx=startIdx, stopIdx=stopIdx, recursive=recursive,
                                  nplanes=nplanes, npartitions=npartitions)
        else:
            if nplanes:
                raise NotImplementedError("nplanes argument is not supported for png files")
            data = loader.fromPng(dataPath, ext=ext, startIdx=startIdx, stopIdx=stopIdx, recursive=recursive,
                                  npartitions=npartitions)

        if not renumber:
            return data
        else:
            return data.renumber()

# 需要导入模块: from thunder.rdds.fileio.imagesloader import ImagesLoader [as 别名]
# 或者: from thunder.rdds.fileio.imagesloader.ImagesLoader import fromTif [as 别名]
    def loadImages(self, dataPath, dims=None, inputFormat='stack', ext=None, dtype='int16',
                   startIdx=None, stopIdx=None, recursive=False, nplanes=None, npartitions=None,
                   renumber=False):
        """
        Loads an Images object from data stored as a binary image stack, tif, or png files.

        Supports single files or multiple files, stored on a local file system, a networked file sytem
        (mounted and available on all nodes), or Amazon S3. HDFS is not currently supported for image file data.

        Parameters
        ----------
        dataPath: string
            Path to data files or directory, specified as either a local filesystem path or in a URI-like format,
            including scheme. A dataPath argument may include a single '*' wildcard character in the filename. Examples
            of valid dataPaths include 'a/local/relative/directory/*.stack", "s3n:///my-s3-bucket/data/mydatafile.tif",
            "/mnt/my/absolute/data/directory/", or "file:///mnt/another/data/directory/".

        dims: tuple of positive int, optional (but required if inputFormat is 'stack')
            Dimensions of input image data, similar to a numpy 'shape' parameter, for instance (1024, 1024, 48). Binary
            stack data will be interpreted as coming from a multidimensional array of the specified dimensions. Stack
            data should be stored in row-major order (Fortran or Matlab convention) rather than column-major order (C
            or python/numpy convention), where the first dimension corresponds to that which is changing most rapidly
            on disk. So for instance given dims of (x, y, z), the coordinates of the data in a binary stack file
            should be ordered as [(x0, y0, z0), (x1, y0, zo), ..., (xN, y0, z0), (x0, y1, z0), (x1, y1, z0), ...,
            (xN, yM, z0), (x0, y0, z1), ..., (xN, yM, zP)].
            If inputFormat is 'png' or 'tif', the dims parameter (if any) will be ignored; data dimensions
            will instead be read out from the image file headers.

        inputFormat: {'stack', 'png', 'tif'}. optional, default 'stack'
            Expected format of the input data. 'stack' indicates flat files of raw binary data. 'png' or 'tif' indicate
            image files of the corresponding formats. Each page of a multipage tif file will be interpreted as a
            separate z-plane. For all formats, separate files are interpreted as distinct time points, with ordering
            given by lexicographic sorting of file names.

        ext: string, optional, default None
            Extension required on data files to be loaded. By default will be "stack" if inputFormat=="stack", "tif" for
            inputFormat=='tif', and 'png' for inputFormat="png".

        dtype: string or numpy dtype. optional, default 'int16'
            Data type of the image files to be loaded, specified as a numpy "dtype" string. If inputFormat is
            'tif' or 'png', the dtype parameter (if any) will be ignored; data type will instead be read out from the
            tif headers.

        startIdx: nonnegative int, optional
            startIdx and stopIdx are convenience parameters to allow only a subset of input files to be read in. These
            parameters give the starting index (inclusive) and final index (exclusive) of the data files to be used
            after lexicographically sorting all input data files matching the dataPath argument. For example,
            startIdx=None (the default) and stopIdx=10 will cause only the first 10 data files in dataPath to be read
            in; startIdx=2 and stopIdx=3 will cause only the third file (zero-based index of 2) to be read in. startIdx
            and stopIdx use the python slice indexing convention (zero-based indexing with an exclusive final position).

        stopIdx: nonnegative int, optional
            See startIdx.

        recursive: boolean, default False
            If true, will recursively descend directories rooted at dataPath, loading all files in the tree that
            have an appropriate extension. Recursive loading is currently only implemented for local filesystems
            (not s3).

        nplanes: positive integer, default None
            If passed, will cause a single image file to be subdivided into multiple records. Every
            `nplanes` z-planes (or multipage tif pages) in the file will be taken as a new record, with the
            first nplane planes of the first file being record 0, the second nplane planes being record 1, etc,
            until the first file is exhausted and record ordering continues with the first nplane planes of the
            second file, and so on. With nplanes=None (the default), a single file will be considered as
            representing a single record. Keys are calculated assuming that all input files contain the same
            number of records; if the number of records per file is not the same across all files,
            then `renumber` should be set to True to ensure consistent keys.

        npartitions: positive int, optional
            If specified, request a certain number of partitions for the underlying Spark RDD. Default is 1
            partition per image file.

        renumber: boolean, optional, default False
            If renumber evaluates to True, then the keys for each record will be explicitly recalculated after
            all images are loaded. This should only be necessary at load time when different files contain
            different number of records. See Images.renumber().

        Returns
        -------
        data: thunder.rdds.Images
            A newly-created Images object, wrapping an RDD of <int index, numpy array> key-value pairs.

        """
        checkParams(inputFormat, ['stack', 'png', 'tif', 'tif-stack'])

        from thunder.rdds.fileio.imagesloader import ImagesLoader
        loader = ImagesLoader(self._sc)

        if not ext:
            ext = DEFAULT_EXTENSIONS.get(inputFormat.lower(), None)

        if inputFormat.lower() == 'stack':
            data = loader.fromStack(dataPath, dims, dtype=dtype, ext=ext, startIdx=startIdx, stopIdx=stopIdx,
                                    recursive=recursive, nplanes=nplanes, npartitions=npartitions)
        elif inputFormat.lower().startswith('tif'):
            data = loader.fromTif(dataPath, ext=ext, startIdx=startIdx, stopIdx=stopIdx, recursive=recursive,
                                  nplanes=nplanes, npartitions=npartitions)
        else:
            if nplanes:
#.........这里部分代码省略.........

# 需要导入模块: from thunder.rdds.fileio.imagesloader import ImagesLoader [as 别名]
# 或者: from thunder.rdds.fileio.imagesloader.ImagesLoader import fromTif [as 别名]

#.........这里部分代码省略.........
        blockSize: string formatted as e.g. "64M", "512k", "2G", or positive int, tuple of positive int, or instance of
                   BlockingStrategy. optional, default "150M"
            Requested size of individual output files in bytes (or kilobytes, megabytes, gigabytes). blockSize can also
            be an instance of blockingStrategy, or a tuple of int specifying either the number of pixels or of splits
            per dimension to apply to the loaded images. Whether a tuple of int is interpreted as pixels or as splits
            depends on the value of the blockSizeUnits parameter.  This parameter also indirectly controls the number
            of Spark partitions to be used, with one partition used per block created.

        blockSizeUnits: string, either "pixels" or "splits" (or unique prefix of each, such as "s"), default "pixels"
            Specifies units to be used in interpreting a tuple passed as blockSizeSpec when shuffle=True. If a string
            or a BlockingStrategy instance is passed as blockSizeSpec, or if shuffle=False, this parameter has no
            effect.

        startIdx: nonnegative int, optional
            startIdx and stopIdx are convenience parameters to allow only a subset of input files to be read in. These
            parameters give the starting index (inclusive) and final index (exclusive) of the data files to be used
            after lexicographically sorting all input data files matching the dataPath argument. For example,
            startIdx=None (the default) and stopIdx=10 will cause only the first 10 data files in dataPath to be read
            in; startIdx=2 and stopIdx=3 will cause only the third file (zero-based index of 2) to be read in. startIdx
            and stopIdx use the python slice indexing convention (zero-based indexing with an exclusive final position).

        stopIdx: nonnegative int, optional
            See startIdx.

        shuffle: boolean, optional, default True
            Controls whether the conversion from Images to Series formats will make use of a Spark shuffle-based method.

        overwrite: boolean, optional, default False
            If true, the directory specified by outputDirPath will first be deleted, along with all its contents, if it
            already exists. (Use with caution.) If false, a ValueError will be thrown if outputDirPath is found to
            already exist.

        recursive: boolean, default False
            If true, will recursively descend directories rooted at dataPath, loading all files in the tree that
            have an appropriate extension. Recursive loading is currently only implemented for local filesystems
            (not s3), and only with shuffle=True.

        nplanes: positive integer, default None
            If passed, will cause a single image file to be subdivided into multiple records. Every
            `nplanes` z-planes (or multipage tif pages) in the file will be taken as a new record, with the
            first nplane planes of the first file being record 0, the second nplane planes being record 1, etc,
            until the first file is exhausted and record ordering continues with the first nplane planes of the
            second file, and so on. With nplanes=None (the default), a single file will be considered as
            representing a single record. Keys are calculated assuming that all input files contain the same
            number of records; if the number of records per file is not the same across all files,
            then `renumber` should be set to True to ensure consistent keys. nplanes is only supported for
            shuffle=True (the default).

        npartitions: positive int, optional
            If specified, request a certain number of partitions for the underlying Spark RDD. Default is 1
            partition per image file. Only applies when shuffle=True.

        renumber: boolean, optional, default False
            If renumber evaluates to True, then the keys for each record will be explicitly recalculated after
            all images are loaded. This should only be necessary at load time when different files contain
            different number of records. renumber is only supported for shuffle=True (the default). See
            Images.renumber().
        """
        checkParams(inputFormat, ['stack', 'tif', 'tif-stack'])

        if inputFormat.lower() == 'stack' and not dims:
            raise ValueError("Dimensions ('dims' parameter) must be specified if loading from binary image stack" +
                             " ('stack' value for 'inputFormat' parameter)")

        if not overwrite:
            raiseErrorIfPathExists(outputDirPath, awsCredentialsOverride=self.awsCredentials)
            overwrite = True  # prevent additional downstream checks for this path

        if not ext:
            ext = DEFAULT_EXTENSIONS.get(inputFormat.lower(), None)

        if shuffle:
            from thunder.rdds.fileio.imagesloader import ImagesLoader
            loader = ImagesLoader(self._sc)
            if inputFormat.lower() == 'stack':
                images = loader.fromStack(dataPath, dims, ext=ext, dtype=dtype, startIdx=startIdx, stopIdx=stopIdx,
                                          recursive=recursive, nplanes=nplanes, npartitions=npartitions)
            else:
                # 'tif' or 'tif-stack'
                images = loader.fromTif(dataPath, ext=ext, startIdx=startIdx, stopIdx=stopIdx,
                                        recursive=recursive, nplanes=nplanes, npartitions=npartitions)
            if renumber:
                images = images.renumber()
            images.toBlocks(blockSize, units=blockSizeUnits).saveAsBinarySeries(outputDirPath, overwrite=overwrite)
        else:
            from thunder.rdds.fileio.seriesloader import SeriesLoader
            if nplanes is not None:
                raise NotImplementedError("nplanes is not supported with shuffle=False")
            if npartitions is not None:
                raise NotImplementedError("npartitions is not supported with shuffle=False")
            loader = SeriesLoader(self._sc)
            if inputFormat.lower() == 'stack':
                loader.saveFromStack(dataPath, outputDirPath, dims, ext=ext, dtype=dtype,
                                     blockSize=blockSize, overwrite=overwrite, startIdx=startIdx,
                                     stopIdx=stopIdx, recursive=recursive)
            else:
                # 'tif' or 'tif-stack'
                loader.saveFromTif(dataPath, outputDirPath, ext=ext, blockSize=blockSize,
                                   startIdx=startIdx, stopIdx=stopIdx, overwrite=overwrite,
                                   recursive=recursive)