Github user imatiach-msft commented on a diff in the pull request:
https://github.com/apache/spark/pull/19439#discussion_r148031857
--- Diff: python/pyspark/ml/image.py ---
@@ -0,0 +1,139 @@
+#
+# 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.
+#
+
+from pyspark.ml.util import *
+from pyspark.ml.param.shared import *
+from pyspark.sql.types import *
+from pyspark.sql.types import Row, _create_row
+from pyspark.sql import DataFrame, SparkSession, SQLContext
+import numpy as np
+
+undefinedImageType = "Undefined"
+
+imageFields = ["origin", "height", "width", "nChannels", "mode", "data"]
+
+
+def getOcvTypes(spark=None):
+ """
+ Returns the OpenCV type mapping supported
+
+ :param sparkSession (SparkSession): The current spark session
+ :rtype dict: The OpenCV type mapping supported
+
+ .. versionadded:: 2.3.0
+ """
+ spark = spark or SparkSession.builder.getOrCreate()
+ ctx = spark.sparkContext
+ return ctx._jvm.org.apache.spark.ml.image.ImageSchema.ocvTypes
+
+
+# DataFrame with a single column of images named "image" (nullable)
+def getImageSchema(spark=None):
+ """
+ Returns the image schema
+
+ :param spark (SparkSession): The current spark session
+ :rtype StructType: The image schema
+
+ .. versionadded:: 2.3.0
+ """
+ spark = spark or SparkSession.builder.getOrCreate()
+ ctx = spark.sparkContext
+ return ctx._jvm.org.apache.spark.ml.image.ImageSchema.imageSchema
+
+
+def toNDArray(image):
+ """
+ Converts an image to a one-dimensional array.
+
+ :param image (object): 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(array, origin="", spark=None):
+ """
+ Converts a one-dimensional array to a two-dimensional image.
+
+ :param array (array): The array to convert to image
+ :param origin (str): Path to the image
+ :param spark (SparkSession): The current spark session
+ :rtype object: Two dimensional image
+
+ .. versionadded:: 2.3.0
+ """
+ spark = spark or SparkSession.builder.getOrCreate()
+ if array.ndim != 3:
+ raise
+ height, width, nChannels = array.shape
+ ocvTypes = getOcvTypes(spark)
+ if nChannels == 1:
+ mode = ocvTypes["CV_8UC1"]
+ elif nChannels == 3:
+ mode = ocvTypes["CV_8UC3"]
+ elif nChannels == 4:
+ mode = ocvTypes["CV_8UC4"]
+ else:
+ raise
+ 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(imageFields,
+ [origin, height, width, nChannels, mode, data])
+
+
+def readImages(path, spark=None, recursive=False, numPartitions=0,
+ dropImageFailures=False, sampleRatio=1.0):
+ """
+ Reads the directory of images from the local or remote source.
+
+ :param path (str): Path to the image directory
+ :param spark (SparkSession): The current spark session
+ :param recursive (bool): Recursive search flag
+ :param numPartitions (int): Number of DataFrame partitions
+ :param dropImageFailures (bool): Drop the files that are not valid
images
+ :param sampleRatio (double): Fraction of the images loaded
+ :rtype DataFrame: DataFrame with a single column of "images",
+ see ImageSchema for details
+
+ Examples:
+
+ >>> df = readImages('python/test_support/image/kittens',
recursive=True)
+ >>> df.count
+ 4
+
+ .. versionadded:: 2.3.0
+ """
+ spark = spark or SparkSession.builder.getOrCreate()
+ ctx = spark.sparkContext
+ image_schema = ctx._jvm.org.apache.spark.ml.image.ImageSchema
+ sql_ctx = SQLContext(ctx)
+ jsession = spark._jsparkSession
+ jresult = image_schema.readImages(path, jsession, recursive,
numPartitions,
+ dropImageFailures,
float(sampleRatio))
+ return DataFrame(jresult, sql_ctx)
--- End diff --
done
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