Source code for

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.. attribute:: ImageSchema

    An attribute of this module that contains the instance of :class:`_ImageSchema`.

.. autoclass:: _ImageSchema

import sys
from typing import Any, Dict, List, NoReturn, Optional, cast

import numpy as np
from distutils.version import LooseVersion

from pyspark import SparkContext
from pyspark.sql.types import Row, StructType, _create_row, _parse_datatype_json_string
from pyspark.sql import SparkSession

__all__ = ["ImageSchema"]

[docs]class _ImageSchema: """ Internal class for `` attribute. Meant to be private and not to be instantized. Use `` attribute to access the APIs of this class. """ def __init__(self) -> None: self._imageSchema: Optional[StructType] = None self._ocvTypes: Optional[Dict[str, int]] = None self._columnSchema: Optional[StructType] = None self._imageFields: Optional[List[str]] = None self._undefinedImageType: Optional[str] = None @property def imageSchema(self) -> StructType: """ Returns the image schema. Returns ------- :class:`StructType` with a single column of images named "image" (nullable) and having the same type returned by :meth:`columnSchema`. .. versionadded:: 2.3.0 """ if self._imageSchema is None: ctx = SparkContext._active_spark_context assert ctx is not None and ctx._jvm is not None jschema = self._imageSchema = cast(StructType, _parse_datatype_json_string(jschema.json())) return self._imageSchema @property def ocvTypes(self) -> Dict[str, int]: """ Returns the OpenCV type mapping supported. Returns ------- dict a dictionary containing the OpenCV type mapping supported. .. versionadded:: 2.3.0 """ if self._ocvTypes is None: ctx = SparkContext._active_spark_context assert ctx is not None and ctx._jvm is not None self._ocvTypes = dict( return self._ocvTypes @property def columnSchema(self) -> StructType: """ Returns the schema for the image column. Returns ------- :class:`StructType` a schema for image column, ``struct<origin:string, height:int, width:int, nChannels:int, mode:int, data:binary>``. .. versionadded:: 2.4.0 """ if self._columnSchema is None: ctx = SparkContext._active_spark_context assert ctx is not None and ctx._jvm is not None jschema = self._columnSchema = cast(StructType, _parse_datatype_json_string(jschema.json())) return self._columnSchema @property def imageFields(self) -> List[str]: """ Returns field names of image columns. Returns ------- list a list of field names. .. versionadded:: 2.3.0 """ if self._imageFields is None: ctx = SparkContext._active_spark_context assert ctx is not None and ctx._jvm is not None self._imageFields = list( return self._imageFields @property def undefinedImageType(self) -> str: """ Returns the name of undefined image type for the invalid image. .. versionadded:: 2.3.0 """ if self._undefinedImageType is None: ctx = SparkContext._active_spark_context assert ctx is not None and ctx._jvm is not None self._undefinedImageType = ( ) return self._undefinedImageType
[docs] def toNDArray(self, image: Row) -> np.ndarray: """ Converts an image to an array with metadata. Parameters ---------- image : :class:`Row` image: A row that contains the image to be converted. It should have the attributes specified in `ImageSchema.imageSchema`. Returns ------- :class:`numpy.ndarray` that is an image. .. versionadded:: 2.3.0 """ if not isinstance(image, Row): raise TypeError( "image argument should be pyspark.sql.types.Row; however, " "it got [%s]." % type(image) ) if any(not hasattr(image, f) for f in self.imageFields): raise ValueError( "image argument should have attributes specified in " "ImageSchema.imageSchema [%s]." % ", ".join(self.imageFields) ) height = image.height width = image.width nChannels = image.nChannels return np.ndarray( shape=(height, width, nChannels), dtype=np.uint8,, strides=(width * nChannels, nChannels, 1), )
[docs] def toImage(self, array: np.ndarray, origin: str = "") -> Row: """ Converts an array with metadata to a two-dimensional image. Parameters ---------- array : :class:`numpy.ndarray` The array to convert to image. origin : str Path to the image, optional. Returns ------- :class:`Row` that is a two dimensional image. .. versionadded:: 2.3.0 """ if not isinstance(array, np.ndarray): raise TypeError( "array argument should be numpy.ndarray; however, it got [%s]." % type(array) ) if array.ndim != 3: raise ValueError("Invalid array shape") height, width, nChannels = array.shape ocvTypes = ImageSchema.ocvTypes if nChannels == 1: mode = ocvTypes["CV_8UC1"] elif nChannels == 3: mode = ocvTypes["CV_8UC3"] elif nChannels == 4: mode = ocvTypes["CV_8UC4"] else: raise ValueError("Invalid number of channels") # Running `bytearray(numpy.array([1]))` fails in specific Python versions # with a specific Numpy version, for example in Python 3.6.0 and NumPy 1.13.3. # Here, it avoids it by converting it to bytes. if LooseVersion(np.__version__) >= LooseVersion("1.9"): data = bytearray(array.astype(dtype=np.uint8).ravel().tobytes()) else: # Numpy prior to 1.9 don't have `tobytes` method. data = bytearray(array.astype(dtype=np.uint8).ravel()) # Creating new Row with _create_row(), because Row(name = value, ... ) # orders fields by name, which conflicts with expected schema order # when the new DataFrame is created by UDF return _create_row(self.imageFields, [origin, height, width, nChannels, mode, data])
ImageSchema = _ImageSchema() # Monkey patch to disallow instantiation of this class. def _disallow_instance(_: Any) -> NoReturn: raise RuntimeError("Creating instance of _ImageSchema class is disallowed.") _ImageSchema.__init__ = _disallow_instance # type: ignore[assignment] def _test() -> None: import doctest import globs = spark = SparkSession.builder.master("local[2]").appName("ml.image tests").getOrCreate() globs["spark"] = spark (failure_count, test_count) = doctest.testmod(, globs=globs, optionflags=doctest.ELLIPSIS | doctest.NORMALIZE_WHITESPACE ) spark.stop() if failure_count: sys.exit(-1) if __name__ == "__main__": _test()