Github user holdenk commented on a diff in the pull request:
https://github.com/apache/spark/pull/19439#discussion_r149486897
--- Diff: python/pyspark/ml/image.py ---
@@ -0,0 +1,192 @@
+#
+# 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
+
+ A singleton-like attribute of :class:`_ImageSchema` in this module.
+
+.. autoclass:: _ImageSchema
+ :members:
+"""
+
+from pyspark import SparkContext
+from pyspark.sql.types import Row, _create_row, _parse_datatype_json_string
+from pyspark.sql import DataFrame, SparkSession
+import numpy as np
+
+
+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._imageFields = None
+ self._undefinedImageType = None
+
+ @property
+ def imageSchema(self):
+ """
+ Returns the image schema.
+
+ :rtype StructType: a DataFrame with a single column of images
+ named "image" (nullable)
+
+ .. 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
+
+ :rtype dict: 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._ocvTypes())
+ return self._ocvTypes
+
+ @property
+ def imageFields(self):
+ """
+ Returns field names of image columns.
+
+ :rtype list: 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 a one-dimensional array.
+
+ :param image: The image to be converted
+ :rtype array: The image as a one-dimensional array
+
+ .. versionadded:: 2.3.0
+ """
+
+ 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 a one-dimensional array to a two-dimensional image.
--- End diff --
I think calling the input a 1-d array is a little confusing perhaps? Maybe
1-d array w/meta data?
e.g. The check of `array.ndim != 3` later seems at odds with this statement
& the toNDArray function above all would not be what I think of as 1-d arrays
but I could be off-base.
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