a745381b9d
## What changes were proposed in this pull request? I remove the deprecate `ImageSchema.readImages`. Move some useful methods from class `ImageSchema` into class `ImageFileFormat`. In pyspark, I rename `ImageSchema` class to be `ImageUtils`, and keep some useful python methods in it. ## How was this patch tested? UT. Please review https://spark.apache.org/contributing.html before opening a pull request. Closes #25245 from WeichenXu123/remove_image_schema. Authored-by: WeichenXu <weichen.xu@databricks.com> Signed-off-by: HyukjinKwon <gurwls223@apache.org>
236 lines
7.6 KiB
Python
236 lines
7.6 KiB
Python
#
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# Licensed to the Apache Software Foundation (ASF) under one or more
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# contributor license agreements. See the NOTICE file distributed with
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# this work for additional information regarding copyright ownership.
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# The ASF licenses this file to You under the Apache License, Version 2.0
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# (the "License"); you may not use this file except in compliance with
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# the License. You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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"""
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.. attribute:: ImageSchema
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An attribute of this module that contains the instance of :class:`_ImageSchema`.
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.. autoclass:: _ImageSchema
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:members:
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"""
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import sys
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import warnings
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import numpy as np
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from distutils.version import LooseVersion
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from pyspark import SparkContext
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from pyspark.sql.types import Row, _create_row, _parse_datatype_json_string
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from pyspark.sql import DataFrame, SparkSession
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__all__ = ["ImageSchema"]
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class _ImageSchema(object):
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"""
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Internal class for `pyspark.ml.image.ImageSchema` attribute. Meant to be private and
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not to be instantized. Use `pyspark.ml.image.ImageSchema` attribute to access the
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APIs of this class.
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"""
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def __init__(self):
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self._imageSchema = None
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self._ocvTypes = None
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self._columnSchema = None
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self._imageFields = None
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self._undefinedImageType = None
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@property
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def imageSchema(self):
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"""
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Returns the image schema.
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:return: a :class:`StructType` with a single column of images
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named "image" (nullable) and having the same type returned by :meth:`columnSchema`.
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.. versionadded:: 2.3.0
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"""
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if self._imageSchema is None:
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ctx = SparkContext._active_spark_context
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jschema = ctx._jvm.org.apache.spark.ml.image.ImageSchema.imageSchema()
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self._imageSchema = _parse_datatype_json_string(jschema.json())
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return self._imageSchema
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@property
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def ocvTypes(self):
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"""
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Returns the OpenCV type mapping supported.
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:return: a dictionary containing the OpenCV type mapping supported.
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.. versionadded:: 2.3.0
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"""
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if self._ocvTypes is None:
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ctx = SparkContext._active_spark_context
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self._ocvTypes = dict(ctx._jvm.org.apache.spark.ml.image.ImageSchema.javaOcvTypes())
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return self._ocvTypes
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@property
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def columnSchema(self):
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"""
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Returns the schema for the image column.
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:return: a :class:`StructType` for image column,
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``struct<origin:string, height:int, width:int, nChannels:int, mode:int, data:binary>``.
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.. versionadded:: 2.4.0
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"""
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if self._columnSchema is None:
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ctx = SparkContext._active_spark_context
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jschema = ctx._jvm.org.apache.spark.ml.image.ImageSchema.columnSchema()
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self._columnSchema = _parse_datatype_json_string(jschema.json())
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return self._columnSchema
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@property
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def imageFields(self):
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"""
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Returns field names of image columns.
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:return: a list of field names.
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.. versionadded:: 2.3.0
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"""
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if self._imageFields is None:
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ctx = SparkContext._active_spark_context
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self._imageFields = list(ctx._jvm.org.apache.spark.ml.image.ImageSchema.imageFields())
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return self._imageFields
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@property
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def undefinedImageType(self):
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"""
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Returns the name of undefined image type for the invalid image.
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.. versionadded:: 2.3.0
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"""
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if self._undefinedImageType is None:
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ctx = SparkContext._active_spark_context
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self._undefinedImageType = \
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ctx._jvm.org.apache.spark.ml.image.ImageSchema.undefinedImageType()
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return self._undefinedImageType
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def toNDArray(self, image):
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"""
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Converts an image to an array with metadata.
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:param `Row` image: A row that contains the image to be converted. It should
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have the attributes specified in `ImageSchema.imageSchema`.
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:return: a `numpy.ndarray` that is an image.
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.. versionadded:: 2.3.0
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"""
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if not isinstance(image, Row):
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raise TypeError(
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"image argument should be pyspark.sql.types.Row; however, "
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"it got [%s]." % type(image))
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if any(not hasattr(image, f) for f in self.imageFields):
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raise ValueError(
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"image argument should have attributes specified in "
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"ImageSchema.imageSchema [%s]." % ", ".join(self.imageFields))
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height = image.height
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width = image.width
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nChannels = image.nChannels
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return np.ndarray(
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shape=(height, width, nChannels),
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dtype=np.uint8,
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buffer=image.data,
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strides=(width * nChannels, nChannels, 1))
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def toImage(self, array, origin=""):
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"""
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Converts an array with metadata to a two-dimensional image.
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:param `numpy.ndarray` array: The array to convert to image.
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:param str origin: Path to the image, optional.
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:return: a :class:`Row` that is a two dimensional image.
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.. versionadded:: 2.3.0
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"""
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if not isinstance(array, np.ndarray):
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raise TypeError(
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"array argument should be numpy.ndarray; however, it got [%s]." % type(array))
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if array.ndim != 3:
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raise ValueError("Invalid array shape")
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height, width, nChannels = array.shape
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ocvTypes = ImageSchema.ocvTypes
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if nChannels == 1:
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mode = ocvTypes["CV_8UC1"]
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elif nChannels == 3:
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mode = ocvTypes["CV_8UC3"]
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elif nChannels == 4:
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mode = ocvTypes["CV_8UC4"]
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else:
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raise ValueError("Invalid number of channels")
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# Running `bytearray(numpy.array([1]))` fails in specific Python versions
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# with a specific Numpy version, for example in Python 3.6.0 and NumPy 1.13.3.
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# Here, it avoids it by converting it to bytes.
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if LooseVersion(np.__version__) >= LooseVersion('1.9'):
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data = bytearray(array.astype(dtype=np.uint8).ravel().tobytes())
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else:
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# Numpy prior to 1.9 don't have `tobytes` method.
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data = bytearray(array.astype(dtype=np.uint8).ravel())
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# Creating new Row with _create_row(), because Row(name = value, ... )
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# orders fields by name, which conflicts with expected schema order
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# when the new DataFrame is created by UDF
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return _create_row(self.imageFields,
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[origin, height, width, nChannels, mode, data])
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ImageSchema = _ImageSchema()
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# Monkey patch to disallow instantiation of this class.
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def _disallow_instance(_):
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raise RuntimeError("Creating instance of _ImageSchema class is disallowed.")
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_ImageSchema.__init__ = _disallow_instance
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def _test():
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import doctest
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import pyspark.ml.image
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globs = pyspark.ml.image.__dict__.copy()
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spark = SparkSession.builder\
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.master("local[2]")\
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.appName("ml.image tests")\
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.getOrCreate()
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globs['spark'] = spark
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(failure_count, test_count) = doctest.testmod(
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pyspark.ml.image, globs=globs,
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optionflags=doctest.ELLIPSIS | doctest.NORMALIZE_WHITESPACE)
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spark.stop()
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if failure_count:
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sys.exit(-1)
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if __name__ == "__main__":
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_test()
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