python/pyspark/ml/image.py

#
# Licensed to the Apache Software Foundation (ASF) under one or more
# contributor license agreements.  See the NOTICE file distributed with
# this work for additional information regarding copyright ownership.
# The ASF licenses this file to You under the Apache License, Version 2.0
# (the "License"); you may not use this file except in compliance with
# the License.  You may obtain a copy of the License at
#
#    http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#

"""
.. attribute:: ImageSchema

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

.. autoclass:: _ImageSchema
   :members:
"""

import sys
import warnings

import numpy as np
from distutils.version import LooseVersion

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

__all__ = ["ImageSchema"]


class _ImageSchema(object):
    """
    Internal class for `pyspark.ml.image.ImageSchema` attribute. Meant to be private and
    not to be instantized. Use `pyspark.ml.image.ImageSchema` attribute to access the
    APIs of this class.
    """

    def __init__(self):
        self._imageSchema = None
        self._ocvTypes = None
        self._columnSchema = None
        self._imageFields = None
        self._undefinedImageType = None

    @property
    def imageSchema(self):
        """
        Returns the image schema.

        :return: a :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
            jschema = ctx._jvm.org.apache.spark.ml.image.ImageSchema.imageSchema()
            self._imageSchema = _parse_datatype_json_string(jschema.json())
        return self._imageSchema

    @property
    def ocvTypes(self):
        """
        Returns the OpenCV type mapping supported.

        :return: a dictionary containing the OpenCV type mapping supported.

        .. versionadded:: 2.3.0
        """

        if self._ocvTypes is None:
            ctx = SparkContext._active_spark_context
            self._ocvTypes = dict(ctx._jvm.org.apache.spark.ml.image.ImageSchema.javaOcvTypes())
        return self._ocvTypes

    @property
    def columnSchema(self):
        """
        Returns the schema for the image column.

        :return: a :class:`StructType` 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
            jschema = ctx._jvm.org.apache.spark.ml.image.ImageSchema.columnSchema()
            self._columnSchema = _parse_datatype_json_string(jschema.json())
        return self._columnSchema

    @property
    def imageFields(self):
        """
        Returns field names of image columns.

        :return: a list of field names.

        .. versionadded:: 2.3.0
        """

        if self._imageFields is None:
            ctx = SparkContext._active_spark_context
            self._imageFields = list(ctx._jvm.org.apache.spark.ml.image.ImageSchema.imageFields())
        return self._imageFields

    @property
    def undefinedImageType(self):
        """
        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
            self._undefinedImageType = \
                ctx._jvm.org.apache.spark.ml.image.ImageSchema.undefinedImageType()
        return self._undefinedImageType

    def toNDArray(self, image):
        """
        Converts an image to an array with metadata.

        :param `Row` image: A row that contains the image to be converted. It should
            have the attributes specified in `ImageSchema.imageSchema`.
        :return: a `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,
            buffer=image.data,
            strides=(width * nChannels, nChannels, 1))

    def toImage(self, array, origin=""):
        """
        Converts an array with metadata to a two-dimensional image.

        :param `numpy.ndarray` array: The array to convert to image.
        :param str origin: Path to the image, optional.
        :return: a :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])

    def readImages(self, path, recursive=False, numPartitions=-1,
                   dropImageFailures=False, sampleRatio=1.0, seed=0):
        """
        Reads the directory of images from the local or remote source.

        .. note:: If multiple jobs are run in parallel with different sampleRatio or recursive flag,
            there may be a race condition where one job overwrites the hadoop configs of another.

        .. note:: If sample ratio is less than 1, sampling uses a PathFilter that is efficient but
            potentially non-deterministic.

        .. note:: Deprecated in 2.4.0. Use `spark.read.format("image").load(path)` instead and
            this `readImages` will be removed in 3.0.0.

        :param str path: Path to the image directory.
        :param bool recursive: Recursive search flag.
        :param int numPartitions: Number of DataFrame partitions.
        :param bool dropImageFailures: Drop the files that are not valid images.
        :param float sampleRatio: Fraction of the images loaded.
        :param int seed: Random number seed.
        :return: a :class:`DataFrame` with a single column of "images",
               see ImageSchema for details.

        >>> df = ImageSchema.readImages('data/mllib/images/origin/kittens', recursive=True)
        >>> df.count()
        5

        .. versionadded:: 2.3.0
        """
        warnings.warn("`ImageSchema.readImage` is deprecated. " +
                      "Use `spark.read.format(\"image\").load(path)` instead.", DeprecationWarning)
        spark = SparkSession.builder.getOrCreate()
        image_schema = spark._jvm.org.apache.spark.ml.image.ImageSchema
        jsession = spark._jsparkSession
        jresult = image_schema.readImages(path, jsession, recursive, numPartitions,
                                          dropImageFailures, float(sampleRatio), seed)
        return DataFrame(jresult, spark._wrapped)


ImageSchema = _ImageSchema()


# Monkey patch to disallow instantiation of this class.
def _disallow_instance(_):
    raise RuntimeError("Creating instance of _ImageSchema class is disallowed.")
_ImageSchema.__init__ = _disallow_instance


def _test():
    import doctest
    import pyspark.ml.image
    globs = pyspark.ml.image.__dict__.copy()
    spark = SparkSession.builder\
        .master("local[2]")\
        .appName("ml.image tests")\
        .getOrCreate()
    globs['spark'] = spark

    (failure_count, test_count) = doctest.testmod(
        pyspark.ml.image, globs=globs,
        optionflags=doctest.ELLIPSIS | doctest.NORMALIZE_WHITESPACE)
    spark.stop()
    if failure_count:
        sys.exit(-1)


if __name__ == "__main__":
    _test()