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[SPARK-22666][ML][SQL] Spark datasource for image format #22328

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[SPARK-22666][ML][SQL] Spark datasource for image format #22328

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5b5aee6
init pr
WeichenXu123 Sep 4, 2018
5164d19
fix scala style
WeichenXu123 Sep 4, 2018
bd6178c
address comments
WeichenXu123 Sep 5, 2018
4d52754
address comments
WeichenXu123 Sep 5, 2018
3fffd7e
nit update
WeichenXu123 Sep 5, 2018
3c8863c
more nit updates
WeichenXu123 Sep 5, 2018
218ce4c
address comments
WeichenXu123 Sep 5, 2018
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@WeichenXu123
WeichenXu123 committed Sep 5, 2018
commit 3c8863c2bd7a4789dd96e849a72eb0aac708abcf
4 changes: 2 additions & 2 deletions mllib/src/main/scala/org/apache/spark/ml/source/image/ImageDataSource.scala
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Expand Up @@ -34,12 +34,12 @@ package org.apache.spark.ml.source.image
* {{{
* // Scala
* val df = spark.read.format("image")
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@imatiach-msft imatiach-msft Sep 4, 2018

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this is very cool!

* .option("dropImageFailures", "true")
* .option("dropImageFailures", true)
* .load("data/mllib/images/partitioned")
*
* // Java
* Dataset<Row> df = spark.read().format("image")
* .option("dropImageFailures", "true")
* .option("dropImageFailures", true)
* .load("data/mllib/images/partitioned");
* }}}
*
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10 changes: 5 additions & 5 deletions mllib/src/test/scala/org/apache/spark/ml/source/image/ImageFileFormatSuite.scala
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Expand Up @@ -34,7 +34,7 @@ class ImageFileFormatSuite extends SparkFunSuite with MLlibTestSparkContext {
val df1 = spark.read.format("image").load(imagePath)
assert(df1.count === 9)

val df2 = spark.read.format("image").option("dropImageFailures", "true").load(imagePath)
val df2 = spark.read.format("image").option("dropImageFailures", true).load(imagePath)
assert(df2.count === 8)
}

Expand All @@ -50,11 +50,11 @@ class ImageFileFormatSuite extends SparkFunSuite with MLlibTestSparkContext {

test("image datasource test: read non image") {
val filePath = imagePath + "/cls=kittens/date=2018-01/not-image.txt"
val df = spark.read.format("image").option("dropImageFailures", "true")
val df = spark.read.format("image").option("dropImageFailures", true)
.load(filePath)
assert(df.count() === 0)

val df2 = spark.read.format("image").option("dropImageFailures", "false")
val df2 = spark.read.format("image").option("dropImageFailures", false)
.load(filePath)
assert(df2.count() === 1)
val result = df2.head()
Expand All @@ -64,7 +64,7 @@ class ImageFileFormatSuite extends SparkFunSuite with MLlibTestSparkContext {

test("image datasource partition test") {
val result = spark.read.format("image")
.option("dropImageFailures", "true").load(imagePath)
.option("dropImageFailures", true).load(imagePath)
.select(substring_index(col("image.origin"), "/", -1).as("origin"), col("cls"), col("date"))
.collect()

Expand All @@ -82,7 +82,7 @@ class ImageFileFormatSuite extends SparkFunSuite with MLlibTestSparkContext {

// Images with the different number of channels
test("readImages pixel values test") {
val images = spark.read.format("image").option("dropImageFailures", "true")
val images = spark.read.format("image").option("dropImageFailures", true)
.load(imagePath + "/cls=multichannel/").collect()

val firstBytes20Set = images.map { rrow =>
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2 changes: 1 addition & 1 deletion python/pyspark/ml/image.py
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Expand Up @@ -216,7 +216,7 @@ def readImages(self, path, recursive=False, numPartitions=-1,
:return: a :class:`DataFrame` with a single column of "images",
see ImageSchema for details.

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

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