[GitHub] spark pull request #22328: [SPARK-22666][ML][SQL] Spark datasource for image...
Github user asfgit closed the pull request at: https://github.com/apache/spark/pull/22328 --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
[GitHub] spark pull request #22328: [SPARK-22666][ML][SQL] Spark datasource for image...
Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/22328#discussion_r215322021
--- Diff:
mllib/src/main/scala/org/apache/spark/ml/source/image/ImageDataSource.scala ---
@@ -0,0 +1,54 @@
+/*
+ * 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.
+ */
+
+package org.apache.spark.ml.source.image
+
+/**
+ * `image` package implements Spark SQL data source API for loading IMAGE
data as `DataFrame`.
+ * The loaded `DataFrame` has one `StructType` column: `image`.
+ * The schema of the `image` column is:
+ * - origin: String (represents the origin of the image.
+ *If loaded from files, then it is the file path)
+ * - height: Int (height of the image)
+ * - width: Int (width of the image)
+ * - nChannels: Int (number of the image channels)
+ * - mode: Int (OpenCV-compatible type)
+ * - data: BinaryType (Image bytes in OpenCV-compatible order: row-wise
BGR in most cases)
+ *
+ * To use IMAGE data source, you need to set "image" as the format in
`DataFrameReader` and
+ * optionally specify the data source options, for example:
+ * {{{
+ * // Scala
+ * val df = spark.read.format("image")
+ * .option("dropImageFailures", true)
+ * .load("data/mllib/images/partitioned")
+ *
+ * // Java
+ * Dataset df = spark.read().format("image")
+ * .option("dropImageFailures", true)
+ * .load("data/mllib/images/partitioned");
+ * }}}
+ *
+ * IMAGE data source supports the following options:
+ * - "dropImageFailures": Whether to drop the files that are not valid
images from the result.
--- End diff --
How about changing `dropImageFailures` to `dropInvalid`?
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[GitHub] spark pull request #22328: [SPARK-22666][ML][SQL] Spark datasource for image...
Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/22328#discussion_r215322673
--- Diff:
mllib/src/main/scala/org/apache/spark/ml/source/image/ImageOptions.scala ---
@@ -0,0 +1,28 @@
+/*
+ * 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.
+ */
+
+package org.apache.spark.ml.source.image
+
+import org.apache.spark.sql.catalyst.util.CaseInsensitiveMap
+
+private[image] class ImageOptions(
+@transient private val parameters: CaseInsensitiveMap[String]) extends
Serializable {
+
+ def this(parameters: Map[String, String]) =
this(CaseInsensitiveMap(parameters))
+
+ val dropImageFailures = parameters.getOrElse("dropImageFailures",
"false").toBoolean
--- End diff --
Should add ScalaDoc.
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[GitHub] spark pull request #22328: [SPARK-22666][ML][SQL] Spark datasource for image...
Github user mengxr commented on a diff in the pull request: https://github.com/apache/spark/pull/22328#discussion_r215320762 --- Diff: mllib/src/main/scala/org/apache/spark/ml/source/image/ImageDataSource.scala --- @@ -0,0 +1,54 @@ +/* + * 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. + */ + +package org.apache.spark.ml.source.image + +/** + * `image` package implements Spark SQL data source API for loading IMAGE data as `DataFrame`. --- End diff -- "IMAGE" doesn't need to be all uppercase. Just say "loading images". --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
[GitHub] spark pull request #22328: [SPARK-22666][ML][SQL] Spark datasource for image...
Github user mengxr commented on a diff in the pull request: https://github.com/apache/spark/pull/22328#discussion_r215321353 --- Diff: mllib/src/main/scala/org/apache/spark/ml/source/image/ImageDataSource.scala --- @@ -0,0 +1,54 @@ +/* + * 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. + */ + +package org.apache.spark.ml.source.image + +/** + * `image` package implements Spark SQL data source API for loading IMAGE data as `DataFrame`. + * The loaded `DataFrame` has one `StructType` column: `image`. + * The schema of the `image` column is: + * - origin: String (represents the origin of the image. + *If loaded from files, then it is the file path) + * - height: Int (height of the image) + * - width: Int (width of the image) + * - nChannels: Int (number of the image channels) + * - mode: Int (OpenCV-compatible type) + * - data: BinaryType (Image bytes in OpenCV-compatible order: row-wise BGR in most cases) + * + * To use IMAGE data source, you need to set "image" as the format in `DataFrameReader` and --- End diff -- ditto on "IMAGE" --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
[GitHub] spark pull request #22328: [SPARK-22666][ML][SQL] Spark datasource for image...
Github user mengxr commented on a diff in the pull request: https://github.com/apache/spark/pull/22328#discussion_r215320923 --- Diff: mllib/src/main/scala/org/apache/spark/ml/source/image/ImageDataSource.scala --- @@ -0,0 +1,54 @@ +/* + * 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. + */ + +package org.apache.spark.ml.source.image + +/** + * `image` package implements Spark SQL data source API for loading IMAGE data as `DataFrame`. + * The loaded `DataFrame` has one `StructType` column: `image`. + * The schema of the `image` column is: + * - origin: String (represents the origin of the image. + *If loaded from files, then it is the file path) --- End diff -- does it always load from files? --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
[GitHub] spark pull request #22328: [SPARK-22666][ML][SQL] Spark datasource for image...
Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/22328#discussion_r215323149
--- Diff:
mllib/src/test/scala/org/apache/spark/ml/source/image/ImageFileFormatSuite.scala
---
@@ -0,0 +1,119 @@
+/*
+ * 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.
+ */
+
+package org.apache.spark.ml.source.image
+
+import java.nio.file.Paths
+
+import org.apache.spark.SparkFunSuite
+import org.apache.spark.ml.image.ImageSchema._
+import org.apache.spark.mllib.util.MLlibTestSparkContext
+import org.apache.spark.sql.Row
+import org.apache.spark.sql.functions.{col, substring_index}
+
+class ImageFileFormatSuite extends SparkFunSuite with
MLlibTestSparkContext {
+
+ // Single column of images named "image"
+ private lazy val imagePath = "../data/mllib/images/imagesWithPartitions"
+
+ test("image datasource count test") {
+val df1 = spark.read.format("image").load(imagePath)
+assert(df1.count === 9)
+
+val df2 = spark.read.format("image").option("dropImageFailures",
"true").load(imagePath)
+assert(df2.count === 8)
+ }
+
+ test("image datasource test: read jpg image") {
+val df = spark.read.format("image").load(imagePath +
"/cls=kittens/date=2018-02/DP153539.jpg")
+assert(df.count() === 1)
+ }
+
+ test("image datasource test: read png image") {
+val df = spark.read.format("image").load(imagePath +
"/cls=multichannel/date=2018-01/BGRA.png")
+assert(df.count() === 1)
+ }
+
+ 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")
+ .load(filePath)
+assert(df.count() === 0)
+
+val df2 = spark.read.format("image").option("dropImageFailures",
"false")
+ .load(filePath)
+assert(df2.count() === 1)
+val result = df2.head()
+assert(result === invalidImageRow(
+ Paths.get(filePath).toAbsolutePath().normalize().toUri().toString))
+ }
+
+ test("image datasource partition test") {
+val result = spark.read.format("image")
+ .option("dropImageFailures", "true").load(imagePath)
+ .select(substring_index(col("image.origin"), "/", -1).as("origin"),
col("cls"), col("date"))
+ .collect()
+
+assert(Set(result: _*) === Set(
+ Row("29.5.a_b_EGDP022204.jpg", "kittens", "2018-01"),
+ Row("54893.jpg", "kittens", "2018-02"),
+ Row("DP153539.jpg", "kittens", "2018-02"),
+ Row("DP802813.jpg", "kittens", "2018-02"),
+ Row("BGRA.png", "multichannel", "2018-01"),
+ Row("BGRA_alpha_60.png", "multichannel", "2018-01"),
+ Row("chr30.4.184.jpg", "multichannel", "2018-02"),
+ Row("grayscale.jpg", "multichannel", "2018-02")
+))
+ }
+
+ // Images with the different number of channels
+ test("readImages pixel values test") {
+
+val images = spark.read.format("image").option("dropImageFailures",
"true")
+ .load(imagePath + "/cls=multichannel/").collect()
+
+val firstBytes20Map = images.map { rrow =>
+ val row = rrow.getAs[Row]("image")
+ val filename = Paths.get(getOrigin(row)).getFileName().toString()
+ val mode = getMode(row)
+ val bytes20 = getData(row).slice(0, 20).toList
+ filename -> Tuple2(mode, bytes20)
--- End diff --
nit: It is useful to leave an inline comment here:)
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[GitHub] spark pull request #22328: [SPARK-22666][ML][SQL] Spark datasource for image...
Github user jaceklaskowski commented on a diff in the pull request:
https://github.com/apache/spark/pull/22328#discussion_r215216011
--- Diff:
mllib/src/main/scala/org/apache/spark/ml/source/image/ImageDataSource.scala ---
@@ -0,0 +1,51 @@
+/*
+ * 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.
+ */
+
+package org.apache.spark.ml.source.image
+
+/**
+ * `image` package implements Spark SQL data source API for loading IMAGE
data as `DataFrame`.
+ * The loaded `DataFrame` has one `StructType` column: `image`.
+ * The schema of the `image` column is:
+ * - origin: String (represent the origin of image. If loaded from file,
then it is file path)
+ * - height: Int (height of image)
+ * - width: Int (width of image)
+ * - nChannels: Int (number of image channels)
+ * - mode: Int (OpenCV-compatible type)
+ * - data: BinaryType (Image bytes in OpenCV-compatible order: row-wise
BGR in most cases)
+ *
+ * To use IMAGE data source, you need to set "image" as the format in
`DataFrameReader` and
+ * optionally specify options, for example:
+ * {{{
+ * // Scala
+ * val df = spark.read.format("image")
+ * .option("dropImageFailures", "true")
--- End diff --
Really? What about `option(key: String, value: Boolean): DataFrameReader`
then? There are more -->
http://spark.apache.org/docs/latest/api/scala/index.html#org.apache.spark.sql.DataFrameReader
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[GitHub] spark pull request #22328: [SPARK-22666][ML][SQL] Spark datasource for image...
Github user WeichenXu123 commented on a diff in the pull request:
https://github.com/apache/spark/pull/22328#discussion_r215200249
--- Diff:
mllib/src/main/scala/org/apache/spark/ml/source/image/ImageOptions.scala ---
@@ -0,0 +1,28 @@
+/*
+ * 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.
+ */
+
+package org.apache.spark.ml.source.image
+
+import org.apache.spark.sql.catalyst.util.CaseInsensitiveMap
+
+private[image] class ImageOptions(
+@transient private val parameters: CaseInsensitiveMap[String]) extends
Serializable {
+
+ def this(parameters: Map[String, String]) =
this(CaseInsensitiveMap(parameters))
+
+ val dropImageFailures = parameters.getOrElse("dropImageFailures",
"false").toBoolean
--- End diff --
because `parameters` is `Map[String, String]` type.
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[GitHub] spark pull request #22328: [SPARK-22666][ML][SQL] Spark datasource for image...
Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/22328#discussion_r215179601
--- Diff:
mllib/src/main/scala/org/apache/spark/ml/source/image/ImageDataSource.scala ---
@@ -0,0 +1,53 @@
+/*
+ * 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.
+ */
+
+package org.apache.spark.ml.source.image
+
+/**
+ * `image` package implements Spark SQL data source API for loading IMAGE
data as `DataFrame`.
+ * The loaded `DataFrame` has one `StructType` column: `image`.
+ * The schema of the `image` column is:
+ * - origin: String (represent the origin of image. If loaded from file,
then it is file path)
+ * - height: Int (height of image)
+ * - width: Int (width of image)
+ * - nChannels: Int (number of image channels)
+ * - mode: Int (OpenCV-compatible type)
+ * - data: BinaryType (Image bytes in OpenCV-compatible order: row-wise
BGR in most cases)
+ *
+ * To use IMAGE data source, you need to set "image" as the format in
`DataFrameReader` and
+ * optionally specify the datasource options, for example:
+ * {{{
+ * // Scala
+ * val df = spark.read.format("image")
+ * .option("dropImageFailures", "true")
+ * .load("data/mllib/images/imagesWithPartitions")
+ *
+ * // Java
+ * Dataset df = spark.read().format("image")
+ * .option("dropImageFailures", "true")
+ * .load("data/mllib/images/imagesWithPartitions");
+ * }}}
+ *
+ * IMAGE data source supports the following options:
+ * - "dropImageFailures": Whether to drop the files that are not valid
images from the result.
+ *
+ * @note This IMAGE data source does not support "write".
+ *
+ * @note This class is public for documentation purpose. Please don't use
this class directly.
+ * Rather, use the data source API as illustrated above.
+ */
+class ImageDataSource private() {}
--- End diff --
Re: @cloud-fan The Scala package doc doesn't work for Java, which requires
a different format.
Re: @HyukjinKwon It would be nice to have some doc in the site, though I
didn't find the list of built-in data sources in the doc site. I think it is
okay to have docs in both locations for IDE users and for people search on the
web.
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[GitHub] spark pull request #22328: [SPARK-22666][ML][SQL] Spark datasource for image...
Github user HyukjinKwon commented on a diff in the pull request:
https://github.com/apache/spark/pull/22328#discussion_r215144730
--- Diff:
mllib/src/main/scala/org/apache/spark/ml/source/image/ImageDataSource.scala ---
@@ -0,0 +1,53 @@
+/*
+ * 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.
+ */
+
+package org.apache.spark.ml.source.image
+
+/**
+ * `image` package implements Spark SQL data source API for loading IMAGE
data as `DataFrame`.
+ * The loaded `DataFrame` has one `StructType` column: `image`.
+ * The schema of the `image` column is:
+ * - origin: String (represent the origin of image. If loaded from file,
then it is file path)
+ * - height: Int (height of image)
+ * - width: Int (width of image)
+ * - nChannels: Int (number of image channels)
+ * - mode: Int (OpenCV-compatible type)
+ * - data: BinaryType (Image bytes in OpenCV-compatible order: row-wise
BGR in most cases)
+ *
+ * To use IMAGE data source, you need to set "image" as the format in
`DataFrameReader` and
+ * optionally specify the datasource options, for example:
+ * {{{
+ * // Scala
+ * val df = spark.read.format("image")
+ * .option("dropImageFailures", "true")
+ * .load("data/mllib/images/imagesWithPartitions")
+ *
+ * // Java
+ * Dataset df = spark.read().format("image")
+ * .option("dropImageFailures", "true")
+ * .load("data/mllib/images/imagesWithPartitions");
+ * }}}
+ *
+ * IMAGE data source supports the following options:
+ * - "dropImageFailures": Whether to drop the files that are not valid
images from the result.
+ *
+ * @note This IMAGE data source does not support "write".
+ *
+ * @note This class is public for documentation purpose. Please don't use
this class directly.
+ * Rather, use the data source API as illustrated above.
+ */
+class ImageDataSource private() {}
--- End diff --
Can we just simply remove this and make a followup for the doc in the site
..
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[GitHub] spark pull request #22328: [SPARK-22666][ML][SQL] Spark datasource for image...
Github user cloud-fan commented on a diff in the pull request:
https://github.com/apache/spark/pull/22328#discussion_r215140040
--- Diff:
mllib/src/main/scala/org/apache/spark/ml/source/image/ImageDataSource.scala ---
@@ -0,0 +1,53 @@
+/*
+ * 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.
+ */
+
+package org.apache.spark.ml.source.image
+
+/**
+ * `image` package implements Spark SQL data source API for loading IMAGE
data as `DataFrame`.
+ * The loaded `DataFrame` has one `StructType` column: `image`.
+ * The schema of the `image` column is:
+ * - origin: String (represent the origin of image. If loaded from file,
then it is file path)
+ * - height: Int (height of image)
+ * - width: Int (width of image)
+ * - nChannels: Int (number of image channels)
+ * - mode: Int (OpenCV-compatible type)
+ * - data: BinaryType (Image bytes in OpenCV-compatible order: row-wise
BGR in most cases)
+ *
+ * To use IMAGE data source, you need to set "image" as the format in
`DataFrameReader` and
+ * optionally specify the datasource options, for example:
+ * {{{
+ * // Scala
+ * val df = spark.read.format("image")
+ * .option("dropImageFailures", "true")
+ * .load("data/mllib/images/imagesWithPartitions")
+ *
+ * // Java
+ * Dataset df = spark.read().format("image")
+ * .option("dropImageFailures", "true")
+ * .load("data/mllib/images/imagesWithPartitions");
+ * }}}
+ *
+ * IMAGE data source supports the following options:
+ * - "dropImageFailures": Whether to drop the files that are not valid
images from the result.
+ *
+ * @note This IMAGE data source does not support "write".
+ *
+ * @note This class is public for documentation purpose. Please don't use
this class directly.
+ * Rather, use the data source API as illustrated above.
+ */
+class ImageDataSource private() {}
--- End diff --
Is this a convention? AFAIK in the scala world we usually put document in
package object.
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[GitHub] spark pull request #22328: [SPARK-22666][ML][SQL] Spark datasource for image...
Github user cloud-fan commented on a diff in the pull request:
https://github.com/apache/spark/pull/22328#discussion_r215139263
--- Diff:
sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/DataSource.scala
---
@@ -567,6 +567,7 @@ object DataSource extends Logging {
val parquet = classOf[ParquetFileFormat].getCanonicalName
val csv = classOf[CSVFileFormat].getCanonicalName
val libsvm = "org.apache.spark.ml.source.libsvm.LibSVMFileFormat"
+val image = "org.apache.spark.ml.source.image.ImageFileFormat"
--- End diff --
We did it for libsvm for historical reasons. Since image is a new source, I
don't think we have compatibility issues.
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[GitHub] spark pull request #22328: [SPARK-22666][ML][SQL] Spark datasource for image...
Github user cloud-fan commented on a diff in the pull request:
https://github.com/apache/spark/pull/22328#discussion_r215139063
--- Diff:
mllib/src/main/scala/org/apache/spark/ml/source/image/ImageFileFormat.scala ---
@@ -0,0 +1,109 @@
+/*
+ * 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.
+ */
+
+package org.apache.spark.ml.source.image
+
+import com.google.common.io.{ByteStreams, Closeables}
+import org.apache.hadoop.conf.Configuration
+import org.apache.hadoop.fs.{FileStatus, Path}
+import org.apache.hadoop.mapreduce.Job
+
+import org.apache.spark.ml.image.ImageSchema
+import org.apache.spark.sql.SparkSession
+import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.catalyst.encoders.RowEncoder
+import org.apache.spark.sql.catalyst.expressions.{AttributeReference,
UnsafeRow}
+import org.apache.spark.sql.catalyst.util.CaseInsensitiveMap
+import org.apache.spark.sql.execution.datasources.{DataSource, FileFormat,
OutputWriterFactory, PartitionedFile}
+import org.apache.spark.sql.sources.{DataSourceRegister, Filter}
+import org.apache.spark.sql.types.StructType
+import org.apache.spark.util.SerializableConfiguration
+
+
+private[image] class ImageFileFormatOptions(
+@transient private val parameters: CaseInsensitiveMap[String]) extends
Serializable {
+
+ def this(parameters: Map[String, String]) =
this(CaseInsensitiveMap(parameters))
+
+ val dropImageFailures = parameters.getOrElse("dropImageFailures",
"false").toBoolean
+}
+
+private[image] class ImageFileFormat extends FileFormat with
DataSourceRegister {
+
+ override def inferSchema(
+ sparkSession: SparkSession,
+ options: Map[String, String],
+ files: Seq[FileStatus]): Option[StructType] =
Some(ImageSchema.imageSchema)
+
+ override def prepareWrite(
+ sparkSession: SparkSession,
+ job: Job, options: Map[String, String],
+ dataSchema: StructType): OutputWriterFactory = {
+throw new UnsupportedOperationException(
+ s"prepareWrite is not supported for image data source")
+ }
+
+ override def shortName(): String = "image"
+
+ override protected def buildReader(
+ sparkSession: SparkSession,
+ dataSchema: StructType,
+ partitionSchema: StructType,
+ requiredSchema: StructType,
+ filters: Seq[Filter],
+ options: Map[String, String],
+ hadoopConf: Configuration): (PartitionedFile) =>
Iterator[InternalRow] = {
--- End diff --
sample pushdown should be supported by data source v2 in the next release,
then we can migrate the image source to data source v2 at that time.
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[GitHub] spark pull request #22328: [SPARK-22666][ML][SQL] Spark datasource for image...
Github user WeichenXu123 commented on a diff in the pull request:
https://github.com/apache/spark/pull/22328#discussion_r215138998
--- Diff:
mllib/src/main/scala/org/apache/spark/ml/source/image/ImageDataSource.scala ---
@@ -0,0 +1,53 @@
+/*
+ * 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.
+ */
+
+package org.apache.spark.ml.source.image
+
+/**
+ * `image` package implements Spark SQL data source API for loading IMAGE
data as `DataFrame`.
+ * The loaded `DataFrame` has one `StructType` column: `image`.
+ * The schema of the `image` column is:
+ * - origin: String (represent the origin of image. If loaded from file,
then it is file path)
+ * - height: Int (height of image)
+ * - width: Int (width of image)
+ * - nChannels: Int (number of image channels)
+ * - mode: Int (OpenCV-compatible type)
+ * - data: BinaryType (Image bytes in OpenCV-compatible order: row-wise
BGR in most cases)
+ *
+ * To use IMAGE data source, you need to set "image" as the format in
`DataFrameReader` and
+ * optionally specify the datasource options, for example:
+ * {{{
+ * // Scala
+ * val df = spark.read.format("image")
+ * .option("dropImageFailures", "true")
+ * .load("data/mllib/images/imagesWithPartitions")
+ *
+ * // Java
+ * Dataset df = spark.read().format("image")
+ * .option("dropImageFailures", "true")
+ * .load("data/mllib/images/imagesWithPartitions");
+ * }}}
+ *
+ * IMAGE data source supports the following options:
+ * - "dropImageFailures": Whether to drop the files that are not valid
images from the result.
+ *
+ * @note This IMAGE data source does not support "write".
+ *
+ * @note This class is public for documentation purpose. Please don't use
this class directly.
+ * Rather, use the data source API as illustrated above.
+ */
+class ImageDataSource private() {}
--- End diff --
for doc.
similar to `LibSVMDataSource`
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[GitHub] spark pull request #22328: [SPARK-22666][ML][SQL] Spark datasource for image...
Github user cloud-fan commented on a diff in the pull request:
https://github.com/apache/spark/pull/22328#discussion_r215138931
--- Diff:
mllib/src/main/scala/org/apache/spark/ml/source/image/ImageDataSource.scala ---
@@ -0,0 +1,53 @@
+/*
+ * 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.
+ */
+
+package org.apache.spark.ml.source.image
+
+/**
+ * `image` package implements Spark SQL data source API for loading IMAGE
data as `DataFrame`.
+ * The loaded `DataFrame` has one `StructType` column: `image`.
+ * The schema of the `image` column is:
+ * - origin: String (represent the origin of image. If loaded from file,
then it is file path)
+ * - height: Int (height of image)
+ * - width: Int (width of image)
+ * - nChannels: Int (number of image channels)
+ * - mode: Int (OpenCV-compatible type)
+ * - data: BinaryType (Image bytes in OpenCV-compatible order: row-wise
BGR in most cases)
+ *
+ * To use IMAGE data source, you need to set "image" as the format in
`DataFrameReader` and
+ * optionally specify the datasource options, for example:
+ * {{{
+ * // Scala
+ * val df = spark.read.format("image")
+ * .option("dropImageFailures", "true")
+ * .load("data/mllib/images/imagesWithPartitions")
+ *
+ * // Java
+ * Dataset df = spark.read().format("image")
+ * .option("dropImageFailures", "true")
+ * .load("data/mllib/images/imagesWithPartitions");
+ * }}}
+ *
+ * IMAGE data source supports the following options:
+ * - "dropImageFailures": Whether to drop the files that are not valid
images from the result.
+ *
+ * @note This IMAGE data source does not support "write".
+ *
+ * @note This class is public for documentation purpose. Please don't use
this class directly.
+ * Rather, use the data source API as illustrated above.
+ */
+class ImageDataSource private() {}
--- End diff --
why do we need this class?
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[GitHub] spark pull request #22328: [SPARK-22666][ML][SQL] Spark datasource for image...
Github user WeichenXu123 commented on a diff in the pull request:
https://github.com/apache/spark/pull/22328#discussion_r215138889
--- Diff:
sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/DataSource.scala
---
@@ -567,6 +567,7 @@ object DataSource extends Logging {
val parquet = classOf[ParquetFileFormat].getCanonicalName
val csv = classOf[CSVFileFormat].getCanonicalName
val libsvm = "org.apache.spark.ml.source.libsvm.LibSVMFileFormat"
+val image = "org.apache.spark.ml.source.image.ImageFileFormat"
--- End diff --
similar to `libsvm`.
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[GitHub] spark pull request #22328: [SPARK-22666][ML][SQL] Spark datasource for image...
Github user WeichenXu123 commented on a diff in the pull request:
https://github.com/apache/spark/pull/22328#discussion_r215138862
--- Diff:
mllib/src/test/scala/org/apache/spark/ml/source/image/ImageFileFormatSuite.scala
---
@@ -0,0 +1,119 @@
+/*
+ * 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.
+ */
+
+package org.apache.spark.ml.source.image
+
+import java.nio.file.Paths
+
+import org.apache.spark.SparkFunSuite
+import org.apache.spark.ml.image.ImageSchema._
+import org.apache.spark.mllib.util.MLlibTestSparkContext
+import org.apache.spark.sql.Row
+import org.apache.spark.sql.functions.{col, substring_index}
+
+class ImageFileFormatSuite extends SparkFunSuite with
MLlibTestSparkContext {
+
+ // Single column of images named "image"
+ private lazy val imagePath = "../data/mllib/images/imagesWithPartitions"
+
+ test("image datasource count test") {
+val df1 = spark.read.format("image").load(imagePath)
+assert(df1.count === 9)
+
+val df2 = spark.read.format("image").option("dropImageFailures",
"true").load(imagePath)
+assert(df2.count === 8)
+ }
+
+ test("image datasource test: read jpg image") {
+val df = spark.read.format("image").load(imagePath +
"/cls=kittens/date=2018-02/DP153539.jpg")
+assert(df.count() === 1)
+ }
+
+ test("image datasource test: read png image") {
+val df = spark.read.format("image").load(imagePath +
"/cls=multichannel/date=2018-01/BGRA.png")
+assert(df.count() === 1)
+ }
+
+ 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")
+ .load(filePath)
+assert(df.count() === 0)
+
+val df2 = spark.read.format("image").option("dropImageFailures",
"false")
+ .load(filePath)
+assert(df2.count() === 1)
+val result = df2.head()
+assert(result === invalidImageRow(
+ Paths.get(filePath).toAbsolutePath().normalize().toUri().toString))
+ }
+
+ test("image datasource partition test") {
+val result = spark.read.format("image")
+ .option("dropImageFailures", "true").load(imagePath)
+ .select(substring_index(col("image.origin"), "/", -1).as("origin"),
col("cls"), col("date"))
+ .collect()
+
+assert(Set(result: _*) === Set(
+ Row("29.5.a_b_EGDP022204.jpg", "kittens", "2018-01"),
+ Row("54893.jpg", "kittens", "2018-02"),
+ Row("DP153539.jpg", "kittens", "2018-02"),
+ Row("DP802813.jpg", "kittens", "2018-02"),
+ Row("BGRA.png", "multichannel", "2018-01"),
+ Row("BGRA_alpha_60.png", "multichannel", "2018-01"),
+ Row("chr30.4.184.jpg", "multichannel", "2018-02"),
+ Row("grayscale.jpg", "multichannel", "2018-02")
+))
+ }
+
+ // Images with the different number of channels
+ test("readImages pixel values test") {
+
+val images = spark.read.format("image").option("dropImageFailures",
"true")
+ .load(imagePath + "/cls=multichannel/").collect()
+
+val firstBytes20Map = images.map { rrow =>
+ val row = rrow.getAs[Row]("image")
+ val filename = Paths.get(getOrigin(row)).getFileName().toString()
+ val mode = getMode(row)
+ val bytes20 = getData(row).slice(0, 20).toList
+ filename -> Tuple2(mode, bytes20)
--- End diff --
yea, `(mode, bytes20)` doesn't work, `filename -> (mode, bytes20)` will be
compiled as `filename.->(mode, bytes20)` and `->` receive 2 arguments and
compile error occurs.
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[GitHub] spark pull request #22328: [SPARK-22666][ML][SQL] Spark datasource for image...
Github user WeichenXu123 commented on a diff in the pull request:
https://github.com/apache/spark/pull/22328#discussion_r215138728
--- Diff:
mllib/src/test/scala/org/apache/spark/ml/source/image/ImageFileFormatSuite.scala
---
@@ -0,0 +1,119 @@
+/*
+ * 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.
+ */
+
+package org.apache.spark.ml.source.image
+
+import java.nio.file.Paths
+
+import org.apache.spark.SparkFunSuite
+import org.apache.spark.ml.image.ImageSchema._
+import org.apache.spark.mllib.util.MLlibTestSparkContext
+import org.apache.spark.sql.Row
+import org.apache.spark.sql.functions.{col, substring_index}
+
+class ImageFileFormatSuite extends SparkFunSuite with
MLlibTestSparkContext {
+
+ // Single column of images named "image"
+ private lazy val imagePath = "../data/mllib/images/imagesWithPartitions"
+
+ test("image datasource count test") {
+val df1 = spark.read.format("image").load(imagePath)
+assert(df1.count === 9)
+
+val df2 = spark.read.format("image").option("dropImageFailures",
"true").load(imagePath)
--- End diff --
ditto.
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[GitHub] spark pull request #22328: [SPARK-22666][ML][SQL] Spark datasource for image...
Github user WeichenXu123 commented on a diff in the pull request:
https://github.com/apache/spark/pull/22328#discussion_r215138711
--- Diff:
mllib/src/main/scala/org/apache/spark/ml/source/image/ImageDataSource.scala ---
@@ -0,0 +1,51 @@
+/*
+ * 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.
+ */
+
+package org.apache.spark.ml.source.image
+
+/**
+ * `image` package implements Spark SQL data source API for loading IMAGE
data as `DataFrame`.
+ * The loaded `DataFrame` has one `StructType` column: `image`.
+ * The schema of the `image` column is:
+ * - origin: String (represent the origin of image. If loaded from file,
then it is file path)
+ * - height: Int (height of image)
+ * - width: Int (width of image)
+ * - nChannels: Int (number of image channels)
+ * - mode: Int (OpenCV-compatible type)
+ * - data: BinaryType (Image bytes in OpenCV-compatible order: row-wise
BGR in most cases)
+ *
+ * To use IMAGE data source, you need to set "image" as the format in
`DataFrameReader` and
+ * optionally specify options, for example:
+ * {{{
+ * // Scala
+ * val df = spark.read.format("image")
+ * .option("dropImageFailures", "true")
--- End diff --
option API require (k: String, v:String) parameters.
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[GitHub] spark pull request #22328: [SPARK-22666][ML][SQL] Spark datasource for image...
Github user jaceklaskowski commented on a diff in the pull request: https://github.com/apache/spark/pull/22328#discussion_r215138305 --- Diff: mllib/src/main/scala/org/apache/spark/ml/source/image/ImageDataSource.scala --- @@ -29,7 +29,7 @@ package org.apache.spark.ml.source.image * - data: BinaryType (Image bytes in OpenCV-compatible order: row-wise BGR in most cases) * * To use IMAGE data source, you need to set "image" as the format in `DataFrameReader` and - * optionally specify options, for example: + * optionally specify the datasource options, for example: --- End diff -- s/datasource/data source --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
[GitHub] spark pull request #22328: [SPARK-22666][ML][SQL] Spark datasource for image...
Github user jaceklaskowski commented on a diff in the pull request:
https://github.com/apache/spark/pull/22328#discussion_r215138635
--- Diff:
mllib/src/main/scala/org/apache/spark/ml/source/image/ImageOptions.scala ---
@@ -0,0 +1,28 @@
+/*
+ * 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.
+ */
+
+package org.apache.spark.ml.source.image
+
+import org.apache.spark.sql.catalyst.util.CaseInsensitiveMap
+
+private[image] class ImageOptions(
+@transient private val parameters: CaseInsensitiveMap[String]) extends
Serializable {
+
+ def this(parameters: Map[String, String]) =
this(CaseInsensitiveMap(parameters))
+
+ val dropImageFailures = parameters.getOrElse("dropImageFailures",
"false").toBoolean
--- End diff --
Why `false` is a String not a boolean?
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[GitHub] spark pull request #22328: [SPARK-22666][ML][SQL] Spark datasource for image...
Github user jaceklaskowski commented on a diff in the pull request: https://github.com/apache/spark/pull/22328#discussion_r215138476 --- Diff: mllib/src/main/scala/org/apache/spark/ml/source/image/ImageDataSource.scala --- @@ -45,6 +45,8 @@ package org.apache.spark.ml.source.image * IMAGE data source supports the following options: * - "dropImageFailures": Whether to drop the files that are not valid images from the result. * + * @note This IMAGE data source does not support "write". --- End diff -- s/"write"/saving images to a file(s)/ ? --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
[GitHub] spark pull request #22328: [SPARK-22666][ML][SQL] Spark datasource for image...
Github user WeichenXu123 commented on a diff in the pull request: https://github.com/apache/spark/pull/22328#discussion_r215138174 --- Diff: data/mllib/images/images/license.txt --- @@ -0,0 +1,13 @@ +The images in the folder "kittens" are under the creative commons CC0 license, or no rights reserved: --- End diff -- No worry. Only very few images. And keep old testcase not changed will help this PR get merged ASAP. --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
[GitHub] spark pull request #22328: [SPARK-22666][ML][SQL] Spark datasource for image...
Github user WeichenXu123 commented on a diff in the pull request: https://github.com/apache/spark/pull/22328#discussion_r215135665 --- Diff: mllib/src/main/scala/org/apache/spark/ml/source/image/ImageDataSource.scala --- @@ -0,0 +1,51 @@ +/* + * 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. + */ + +package org.apache.spark.ml.source.image + +/** + * `image` package implements Spark SQL data source API for loading IMAGE data as `DataFrame`. + * The loaded `DataFrame` has one `StructType` column: `image`. + * The schema of the `image` column is: + * - origin: String (represent the origin of image. If loaded from file, then it is file path) + * - height: Int (height of image) + * - width: Int (width of image) + * - nChannels: Int (number of image channels) + * - mode: Int (OpenCV-compatible type) + * - data: BinaryType (Image bytes in OpenCV-compatible order: row-wise BGR in most cases) + * + * To use IMAGE data source, you need to set "image" as the format in `DataFrameReader` and + * optionally specify options, for example: --- End diff -- The latter "options" is "datasource options", it is the widely used term. So I prefer to change to "optionally specify the datasource options" --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
[GitHub] spark pull request #22328: [SPARK-22666][ML][SQL] Spark datasource for image...
Github user jaceklaskowski commented on a diff in the pull request:
https://github.com/apache/spark/pull/22328#discussion_r215038606
--- Diff:
mllib/src/test/scala/org/apache/spark/ml/source/image/ImageFileFormatSuite.scala
---
@@ -0,0 +1,119 @@
+/*
+ * 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.
+ */
+
+package org.apache.spark.ml.source.image
+
+import java.nio.file.Paths
+
+import org.apache.spark.SparkFunSuite
+import org.apache.spark.ml.image.ImageSchema._
+import org.apache.spark.mllib.util.MLlibTestSparkContext
+import org.apache.spark.sql.Row
+import org.apache.spark.sql.functions.{col, substring_index}
+
+class ImageFileFormatSuite extends SparkFunSuite with
MLlibTestSparkContext {
+
+ // Single column of images named "image"
+ private lazy val imagePath = "../data/mllib/images/imagesWithPartitions"
+
+ test("image datasource count test") {
+val df1 = spark.read.format("image").load(imagePath)
+assert(df1.count === 9)
+
+val df2 = spark.read.format("image").option("dropImageFailures",
"true").load(imagePath)
+assert(df2.count === 8)
+ }
+
+ test("image datasource test: read jpg image") {
+val df = spark.read.format("image").load(imagePath +
"/cls=kittens/date=2018-02/DP153539.jpg")
+assert(df.count() === 1)
+ }
+
+ test("image datasource test: read png image") {
+val df = spark.read.format("image").load(imagePath +
"/cls=multichannel/date=2018-01/BGRA.png")
+assert(df.count() === 1)
+ }
+
+ 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")
+ .load(filePath)
+assert(df.count() === 0)
+
+val df2 = spark.read.format("image").option("dropImageFailures",
"false")
+ .load(filePath)
+assert(df2.count() === 1)
+val result = df2.head()
+assert(result === invalidImageRow(
+ Paths.get(filePath).toAbsolutePath().normalize().toUri().toString))
+ }
+
+ test("image datasource partition test") {
+val result = spark.read.format("image")
+ .option("dropImageFailures", "true").load(imagePath)
+ .select(substring_index(col("image.origin"), "/", -1).as("origin"),
col("cls"), col("date"))
+ .collect()
+
+assert(Set(result: _*) === Set(
+ Row("29.5.a_b_EGDP022204.jpg", "kittens", "2018-01"),
+ Row("54893.jpg", "kittens", "2018-02"),
+ Row("DP153539.jpg", "kittens", "2018-02"),
+ Row("DP802813.jpg", "kittens", "2018-02"),
+ Row("BGRA.png", "multichannel", "2018-01"),
+ Row("BGRA_alpha_60.png", "multichannel", "2018-01"),
+ Row("chr30.4.184.jpg", "multichannel", "2018-02"),
+ Row("grayscale.jpg", "multichannel", "2018-02")
+))
+ }
+
+ // Images with the different number of channels
+ test("readImages pixel values test") {
+
+val images = spark.read.format("image").option("dropImageFailures",
"true")
+ .load(imagePath + "/cls=multichannel/").collect()
+
+val firstBytes20Map = images.map { rrow =>
+ val row = rrow.getAs[Row]("image")
+ val filename = Paths.get(getOrigin(row)).getFileName().toString()
+ val mode = getMode(row)
+ val bytes20 = getData(row).slice(0, 20).toList
+ filename -> Tuple2(mode, bytes20)
--- End diff --
Why is `Tuple2` required here? Wouldn't `(mode, bytes20)` work here?
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[GitHub] spark pull request #22328: [SPARK-22666][ML][SQL] Spark datasource for image...
Github user jaceklaskowski commented on a diff in the pull request:
https://github.com/apache/spark/pull/22328#discussion_r215037240
--- Diff:
mllib/src/main/scala/org/apache/spark/ml/source/image/ImageFileFormat.scala ---
@@ -0,0 +1,109 @@
+/*
+ * 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.
+ */
+
+package org.apache.spark.ml.source.image
+
+import com.google.common.io.{ByteStreams, Closeables}
+import org.apache.hadoop.conf.Configuration
+import org.apache.hadoop.fs.{FileStatus, Path}
+import org.apache.hadoop.mapreduce.Job
+
+import org.apache.spark.ml.image.ImageSchema
+import org.apache.spark.sql.SparkSession
+import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.catalyst.encoders.RowEncoder
+import org.apache.spark.sql.catalyst.expressions.{AttributeReference,
UnsafeRow}
+import org.apache.spark.sql.catalyst.util.CaseInsensitiveMap
+import org.apache.spark.sql.execution.datasources.{DataSource, FileFormat,
OutputWriterFactory, PartitionedFile}
+import org.apache.spark.sql.sources.{DataSourceRegister, Filter}
+import org.apache.spark.sql.types.StructType
+import org.apache.spark.util.SerializableConfiguration
+
+
+private[image] class ImageFileFormatOptions(
+@transient private val parameters: CaseInsensitiveMap[String]) extends
Serializable {
+
+ def this(parameters: Map[String, String]) =
this(CaseInsensitiveMap(parameters))
+
+ val dropImageFailures = parameters.getOrElse("dropImageFailures",
"false").toBoolean
+}
+
+private[image] class ImageFileFormat extends FileFormat with
DataSourceRegister {
+
+ override def inferSchema(
+ sparkSession: SparkSession,
+ options: Map[String, String],
+ files: Seq[FileStatus]): Option[StructType] =
Some(ImageSchema.imageSchema)
+
+ override def prepareWrite(
+ sparkSession: SparkSession,
+ job: Job, options: Map[String, String],
--- End diff --
New line after `job: Job`
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[GitHub] spark pull request #22328: [SPARK-22666][ML][SQL] Spark datasource for image...
Github user jaceklaskowski commented on a diff in the pull request:
https://github.com/apache/spark/pull/22328#discussion_r215039097
--- Diff:
sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/DataSource.scala
---
@@ -567,6 +567,7 @@ object DataSource extends Logging {
val parquet = classOf[ParquetFileFormat].getCanonicalName
val csv = classOf[CSVFileFormat].getCanonicalName
val libsvm = "org.apache.spark.ml.source.libsvm.LibSVMFileFormat"
+val image = "org.apache.spark.ml.source.image.ImageFileFormat"
--- End diff --
Why is this needed?
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[GitHub] spark pull request #22328: [SPARK-22666][ML][SQL] Spark datasource for image...
Github user jaceklaskowski commented on a diff in the pull request: https://github.com/apache/spark/pull/22328#discussion_r215036263 --- Diff: mllib/src/main/scala/org/apache/spark/ml/source/image/ImageDataSource.scala --- @@ -0,0 +1,51 @@ +/* + * 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. + */ + +package org.apache.spark.ml.source.image + +/** + * `image` package implements Spark SQL data source API for loading IMAGE data as `DataFrame`. + * The loaded `DataFrame` has one `StructType` column: `image`. + * The schema of the `image` column is: + * - origin: String (represent the origin of image. If loaded from file, then it is file path) --- End diff -- "represents" + "the image". I can see many missing `a`s and `the`s in the description :( --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
[GitHub] spark pull request #22328: [SPARK-22666][ML][SQL] Spark datasource for image...
Github user jaceklaskowski commented on a diff in the pull request:
https://github.com/apache/spark/pull/22328#discussion_r215037968
--- Diff:
mllib/src/test/scala/org/apache/spark/ml/source/image/ImageFileFormatSuite.scala
---
@@ -0,0 +1,119 @@
+/*
+ * 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.
+ */
+
+package org.apache.spark.ml.source.image
+
+import java.nio.file.Paths
+
+import org.apache.spark.SparkFunSuite
+import org.apache.spark.ml.image.ImageSchema._
+import org.apache.spark.mllib.util.MLlibTestSparkContext
+import org.apache.spark.sql.Row
+import org.apache.spark.sql.functions.{col, substring_index}
+
+class ImageFileFormatSuite extends SparkFunSuite with
MLlibTestSparkContext {
+
+ // Single column of images named "image"
+ private lazy val imagePath = "../data/mllib/images/imagesWithPartitions"
+
+ test("image datasource count test") {
+val df1 = spark.read.format("image").load(imagePath)
+assert(df1.count === 9)
+
+val df2 = spark.read.format("image").option("dropImageFailures",
"true").load(imagePath)
--- End diff --
`true` as a boolean value, please.
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[GitHub] spark pull request #22328: [SPARK-22666][ML][SQL] Spark datasource for image...
Github user jaceklaskowski commented on a diff in the pull request:
https://github.com/apache/spark/pull/22328#discussion_r215036643
--- Diff:
mllib/src/main/scala/org/apache/spark/ml/source/image/ImageDataSource.scala ---
@@ -0,0 +1,51 @@
+/*
+ * 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.
+ */
+
+package org.apache.spark.ml.source.image
+
+/**
+ * `image` package implements Spark SQL data source API for loading IMAGE
data as `DataFrame`.
+ * The loaded `DataFrame` has one `StructType` column: `image`.
+ * The schema of the `image` column is:
+ * - origin: String (represent the origin of image. If loaded from file,
then it is file path)
+ * - height: Int (height of image)
+ * - width: Int (width of image)
+ * - nChannels: Int (number of image channels)
+ * - mode: Int (OpenCV-compatible type)
+ * - data: BinaryType (Image bytes in OpenCV-compatible order: row-wise
BGR in most cases)
+ *
+ * To use IMAGE data source, you need to set "image" as the format in
`DataFrameReader` and
+ * optionally specify options, for example:
+ * {{{
+ * // Scala
+ * val df = spark.read.format("image")
+ * .option("dropImageFailures", "true")
--- End diff --
`true` as a boolean value, please.
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[GitHub] spark pull request #22328: [SPARK-22666][ML][SQL] Spark datasource for image...
Github user imatiach-msft commented on a diff in the pull request: https://github.com/apache/spark/pull/22328#discussion_r215000161 --- Diff: data/mllib/images/images/license.txt --- @@ -0,0 +1,13 @@ +The images in the folder "kittens" are under the creative commons CC0 license, or no rights reserved: --- End diff -- update to comment above: I don't think you need duplicate images in the PR - in the old tests you can just specify the path up to the cls/date folder. There might be a few minor changes to the tests but I think that would be a better strategy than to have duplicate images in source. --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
[GitHub] spark pull request #22328: [SPARK-22666][ML][SQL] Spark datasource for image...
Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/22328#discussion_r214981991
--- Diff:
mllib/src/main/scala/org/apache/spark/ml/source/image/ImageFileFormat.scala ---
@@ -0,0 +1,109 @@
+/*
+ * 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.
+ */
+
+package org.apache.spark.ml.source.image
+
+import com.google.common.io.{ByteStreams, Closeables}
+import org.apache.hadoop.conf.Configuration
+import org.apache.hadoop.fs.{FileStatus, Path}
+import org.apache.hadoop.mapreduce.Job
+
+import org.apache.spark.ml.image.ImageSchema
+import org.apache.spark.sql.SparkSession
+import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.catalyst.encoders.RowEncoder
+import org.apache.spark.sql.catalyst.expressions.{AttributeReference,
UnsafeRow}
+import org.apache.spark.sql.catalyst.util.CaseInsensitiveMap
+import org.apache.spark.sql.execution.datasources.{DataSource, FileFormat,
OutputWriterFactory, PartitionedFile}
+import org.apache.spark.sql.sources.{DataSourceRegister, Filter}
+import org.apache.spark.sql.types.StructType
+import org.apache.spark.util.SerializableConfiguration
+
+
+private[image] class ImageFileFormatOptions(
+@transient private val parameters: CaseInsensitiveMap[String]) extends
Serializable {
+
+ def this(parameters: Map[String, String]) =
this(CaseInsensitiveMap(parameters))
+
+ val dropImageFailures = parameters.getOrElse("dropImageFailures",
"false").toBoolean
+}
+
+private[image] class ImageFileFormat extends FileFormat with
DataSourceRegister {
+
+ override def inferSchema(
+ sparkSession: SparkSession,
+ options: Map[String, String],
+ files: Seq[FileStatus]): Option[StructType] =
Some(ImageSchema.imageSchema)
+
+ override def prepareWrite(
+ sparkSession: SparkSession,
+ job: Job, options: Map[String, String],
+ dataSchema: StructType): OutputWriterFactory = {
+throw new UnsupportedOperationException(
+ s"prepareWrite is not supported for image data source")
+ }
+
+ override def shortName(): String = "image"
+
+ override protected def buildReader(
+ sparkSession: SparkSession,
+ dataSchema: StructType,
+ partitionSchema: StructType,
+ requiredSchema: StructType,
+ filters: Seq[Filter],
+ options: Map[String, String],
+ hadoopConf: Configuration): (PartitionedFile) =>
Iterator[InternalRow] = {
--- End diff --
It won't be addressed in this PR. The best way to support it is to allow
data source handle sampling operation. cc @cloud-fan
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[GitHub] spark pull request #22328: [SPARK-22666][ML][SQL] Spark datasource for image...
Github user imatiach-msft commented on a diff in the pull request:
https://github.com/apache/spark/pull/22328#discussion_r214975055
--- Diff:
mllib/src/main/scala/org/apache/spark/ml/source/image/ImageFileFormat.scala ---
@@ -0,0 +1,109 @@
+/*
+ * 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.
+ */
+
+package org.apache.spark.ml.source.image
+
+import com.google.common.io.{ByteStreams, Closeables}
+import org.apache.hadoop.conf.Configuration
+import org.apache.hadoop.fs.{FileStatus, Path}
+import org.apache.hadoop.mapreduce.Job
+
+import org.apache.spark.ml.image.ImageSchema
+import org.apache.spark.sql.SparkSession
+import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.catalyst.encoders.RowEncoder
+import org.apache.spark.sql.catalyst.expressions.{AttributeReference,
UnsafeRow}
+import org.apache.spark.sql.catalyst.util.CaseInsensitiveMap
+import org.apache.spark.sql.execution.datasources.{DataSource, FileFormat,
OutputWriterFactory, PartitionedFile}
+import org.apache.spark.sql.sources.{DataSourceRegister, Filter}
+import org.apache.spark.sql.types.StructType
+import org.apache.spark.util.SerializableConfiguration
+
+
+private[image] class ImageFileFormatOptions(
+@transient private val parameters: CaseInsensitiveMap[String]) extends
Serializable {
+
+ def this(parameters: Map[String, String]) =
this(CaseInsensitiveMap(parameters))
+
+ val dropImageFailures = parameters.getOrElse("dropImageFailures",
"false").toBoolean
+}
+
+private[image] class ImageFileFormat extends FileFormat with
DataSourceRegister {
+
+ override def inferSchema(
+ sparkSession: SparkSession,
+ options: Map[String, String],
+ files: Seq[FileStatus]): Option[StructType] =
Some(ImageSchema.imageSchema)
+
+ override def prepareWrite(
+ sparkSession: SparkSession,
+ job: Job, options: Map[String, String],
+ dataSchema: StructType): OutputWriterFactory = {
+throw new UnsupportedOperationException(
+ s"prepareWrite is not supported for image data source")
+ }
+
+ override def shortName(): String = "image"
+
+ override protected def buildReader(
+ sparkSession: SparkSession,
+ dataSchema: StructType,
+ partitionSchema: StructType,
+ requiredSchema: StructType,
+ filters: Seq[Filter],
+ options: Map[String, String],
+ hadoopConf: Configuration): (PartitionedFile) =>
Iterator[InternalRow] = {
--- End diff --
should the sampling option be ported as well? It seemed like an important
option in case users didn't want to load all images.
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[GitHub] spark pull request #22328: [SPARK-22666][ML][SQL] Spark datasource for image...
Github user imatiach-msft commented on a diff in the pull request:
https://github.com/apache/spark/pull/22328#discussion_r214973718
--- Diff:
mllib/src/test/scala/org/apache/spark/ml/source/image/ImageFileFormatSuite.scala
---
@@ -0,0 +1,119 @@
+/*
+ * 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.
+ */
+
+package org.apache.spark.ml.source.image
+
+import java.nio.file.Paths
+
+import org.apache.spark.SparkFunSuite
+import org.apache.spark.ml.image.ImageSchema._
+import org.apache.spark.mllib.util.MLlibTestSparkContext
+import org.apache.spark.sql.Row
+import org.apache.spark.sql.functions.{col, substring_index}
+
+class ImageFileFormatSuite extends SparkFunSuite with
MLlibTestSparkContext {
+
+ // Single column of images named "image"
+ private lazy val imagePath = "../data/mllib/images/imagesWithPartitions"
+
+ test("image datasource count test") {
+val df1 = spark.read.format("image").load(imagePath)
+assert(df1.count === 9)
+
+val df2 = spark.read.format("image").option("dropImageFailures",
"true").load(imagePath)
+assert(df2.count === 8)
+ }
+
+ test("image datasource test: read jpg image") {
+val df = spark.read.format("image").load(imagePath +
"/cls=kittens/date=2018-02/DP153539.jpg")
+assert(df.count() === 1)
+ }
+
+ test("image datasource test: read png image") {
+val df = spark.read.format("image").load(imagePath +
"/cls=multichannel/date=2018-01/BGRA.png")
+assert(df.count() === 1)
+ }
+
+ 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")
+ .load(filePath)
+assert(df.count() === 0)
+
+val df2 = spark.read.format("image").option("dropImageFailures",
"false")
+ .load(filePath)
+assert(df2.count() === 1)
+val result = df2.head()
+assert(result === invalidImageRow(
+ Paths.get(filePath).toAbsolutePath().normalize().toUri().toString))
+ }
+
+ test("image datasource partition test") {
+val result = spark.read.format("image")
+ .option("dropImageFailures", "true").load(imagePath)
+ .select(substring_index(col("image.origin"), "/", -1).as("origin"),
col("cls"), col("date"))
+ .collect()
+
+assert(Set(result: _*) === Set(
+ Row("29.5.a_b_EGDP022204.jpg", "kittens", "2018-01"),
+ Row("54893.jpg", "kittens", "2018-02"),
+ Row("DP153539.jpg", "kittens", "2018-02"),
+ Row("DP802813.jpg", "kittens", "2018-02"),
+ Row("BGRA.png", "multichannel", "2018-01"),
+ Row("BGRA_alpha_60.png", "multichannel", "2018-01"),
+ Row("chr30.4.184.jpg", "multichannel", "2018-02"),
+ Row("grayscale.jpg", "multichannel", "2018-02")
+))
+ }
+
+ // Images with the different number of channels
+ test("readImages pixel values test") {
+
--- End diff --
tiny nit: remove newline (or make all tests consistent in terms of
formatting)
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[GitHub] spark pull request #22328: [SPARK-22666][ML][SQL] Spark datasource for image...
Github user imatiach-msft commented on a diff in the pull request: https://github.com/apache/spark/pull/22328#discussion_r214970271 --- Diff: mllib/src/main/scala/org/apache/spark/ml/source/image/ImageDataSource.scala --- @@ -0,0 +1,51 @@ +/* + * 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. + */ + +package org.apache.spark.ml.source.image + +/** + * `image` package implements Spark SQL data source API for loading IMAGE data as `DataFrame`. + * The loaded `DataFrame` has one `StructType` column: `image`. + * The schema of the `image` column is: + * - origin: String (represent the origin of image. If loaded from file, then it is file path) + * - height: Int (height of image) + * - width: Int (width of image) + * - nChannels: Int (number of image channels) + * - mode: Int (OpenCV-compatible type) + * - data: BinaryType (Image bytes in OpenCV-compatible order: row-wise BGR in most cases) + * + * To use IMAGE data source, you need to set "image" as the format in `DataFrameReader` and + * optionally specify options, for example: --- End diff -- tiny nit: although it makes sense, "optionally specify options" is a bit confusing, maybe "optionally specify arguments" or just "specify options"? --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
[GitHub] spark pull request #22328: [SPARK-22666][ML][SQL] Spark datasource for image...
Github user imatiach-msft commented on a diff in the pull request:
https://github.com/apache/spark/pull/22328#discussion_r214973111
--- Diff:
mllib/src/main/scala/org/apache/spark/ml/source/image/ImageFileFormat.scala ---
@@ -0,0 +1,109 @@
+/*
+ * 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.
+ */
+
+package org.apache.spark.ml.source.image
+
+import com.google.common.io.{ByteStreams, Closeables}
+import org.apache.hadoop.conf.Configuration
+import org.apache.hadoop.fs.{FileStatus, Path}
+import org.apache.hadoop.mapreduce.Job
+
+import org.apache.spark.ml.image.ImageSchema
+import org.apache.spark.sql.SparkSession
+import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.catalyst.encoders.RowEncoder
+import org.apache.spark.sql.catalyst.expressions.{AttributeReference,
UnsafeRow}
+import org.apache.spark.sql.catalyst.util.CaseInsensitiveMap
+import org.apache.spark.sql.execution.datasources.{DataSource, FileFormat,
OutputWriterFactory, PartitionedFile}
+import org.apache.spark.sql.sources.{DataSourceRegister, Filter}
+import org.apache.spark.sql.types.StructType
+import org.apache.spark.util.SerializableConfiguration
+
+
+private[image] class ImageFileFormatOptions(
+@transient private val parameters: CaseInsensitiveMap[String]) extends
Serializable {
+
+ def this(parameters: Map[String, String]) =
this(CaseInsensitiveMap(parameters))
+
+ val dropImageFailures = parameters.getOrElse("dropImageFailures",
"false").toBoolean
+}
+
+private[image] class ImageFileFormat extends FileFormat with
DataSourceRegister {
+
+ override def inferSchema(
+ sparkSession: SparkSession,
+ options: Map[String, String],
+ files: Seq[FileStatus]): Option[StructType] =
Some(ImageSchema.imageSchema)
+
+ override def prepareWrite(
+ sparkSession: SparkSession,
+ job: Job, options: Map[String, String],
+ dataSchema: StructType): OutputWriterFactory = {
+throw new UnsupportedOperationException(
+ s"prepareWrite is not supported for image data source")
+ }
+
+ override def shortName(): String = "image"
+
+ override protected def buildReader(
--- End diff --
hmm, is there any way we could combine the two apis? I don't like having
to support two different implementations. Or, what is the issue that is
blocking us from combining them?
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[GitHub] spark pull request #22328: [SPARK-22666][ML][SQL] Spark datasource for image...
Github user imatiach-msft commented on a diff in the pull request: https://github.com/apache/spark/pull/22328#discussion_r214969396 --- Diff: data/mllib/images/images/license.txt --- @@ -0,0 +1,13 @@ +The images in the folder "kittens" are under the creative commons CC0 license, or no rights reserved: --- End diff -- not sure why this was added to images/images folder, also strange that it wasn't moved --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
[GitHub] spark pull request #22328: [SPARK-22666][ML][SQL] Spark datasource for image...
Github user imatiach-msft commented on a diff in the pull request:
https://github.com/apache/spark/pull/22328#discussion_r214971542
--- Diff:
mllib/src/main/scala/org/apache/spark/ml/source/image/ImageDataSource.scala ---
@@ -0,0 +1,51 @@
+/*
+ * 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.
+ */
+
+package org.apache.spark.ml.source.image
+
+/**
+ * `image` package implements Spark SQL data source API for loading IMAGE
data as `DataFrame`.
+ * The loaded `DataFrame` has one `StructType` column: `image`.
+ * The schema of the `image` column is:
+ * - origin: String (represent the origin of image. If loaded from file,
then it is file path)
+ * - height: Int (height of image)
+ * - width: Int (width of image)
+ * - nChannels: Int (number of image channels)
+ * - mode: Int (OpenCV-compatible type)
+ * - data: BinaryType (Image bytes in OpenCV-compatible order: row-wise
BGR in most cases)
+ *
+ * To use IMAGE data source, you need to set "image" as the format in
`DataFrameReader` and
+ * optionally specify options, for example:
+ * {{{
+ * // Scala
+ * val df = spark.read.format("image")
--- End diff --
this is very cool!
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[GitHub] spark pull request #22328: [SPARK-22666][ML][SQL] Spark datasource for image...
Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/22328#discussion_r214969542
--- Diff:
mllib/src/test/scala/org/apache/spark/ml/image/ImageSchemaSuite.scala ---
@@ -28,7 +28,7 @@ import org.apache.spark.sql.types._
class ImageSchemaSuite extends SparkFunSuite with MLlibTestSparkContext {
// Single column of images named "image"
- private lazy val imagePath = "../data/mllib/images"
+ private lazy val imagePath = "../data/mllib/images/images"
--- End diff --
"images/images" is confusing. Call it `images/origin` and
`images/partitioned`
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[GitHub] spark pull request #22328: [SPARK-22666][ML][SQL] Spark datasource for image...
Github user mengxr commented on a diff in the pull request: https://github.com/apache/spark/pull/22328#discussion_r214967994 --- Diff: mllib/src/main/scala/org/apache/spark/ml/source/image/ImageDataSource.scala --- @@ -0,0 +1,51 @@ +/* + * 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. + */ + +package org.apache.spark.ml.source.image + +/** + * `image` package implements Spark SQL data source API for loading IMAGE data as `DataFrame`. --- End diff -- Should mention it doesn't support write. --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
[GitHub] spark pull request #22328: [SPARK-22666][ML][SQL] Spark datasource for image...
Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/22328#discussion_r214969782
--- Diff:
mllib/src/test/scala/org/apache/spark/ml/source/image/ImageFileFormatSuite.scala
---
@@ -0,0 +1,119 @@
+/*
+ * 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.
+ */
+
+package org.apache.spark.ml.source.image
+
+import java.nio.file.Paths
+
+import org.apache.spark.SparkFunSuite
+import org.apache.spark.ml.image.ImageSchema._
+import org.apache.spark.mllib.util.MLlibTestSparkContext
+import org.apache.spark.sql.Row
+import org.apache.spark.sql.functions.{col, substring_index}
+
+class ImageFileFormatSuite extends SparkFunSuite with
MLlibTestSparkContext {
+
+ // Single column of images named "image"
+ private lazy val imagePath = "../data/mllib/images/imagesWithPartitions"
+
+ test("image datasource count test") {
+val df1 = spark.read.format("image").load(imagePath)
+assert(df1.count === 9)
+
+val df2 = spark.read.format("image").option("dropImageFailures",
"true").load(imagePath)
+assert(df2.count === 8)
+ }
+
+ test("image datasource test: read jpg image") {
+val df = spark.read.format("image").load(imagePath +
"/cls=kittens/date=2018-02/DP153539.jpg")
+assert(df.count() === 1)
+ }
+
+ test("image datasource test: read png image") {
+val df = spark.read.format("image").load(imagePath +
"/cls=multichannel/date=2018-01/BGRA.png")
+assert(df.count() === 1)
+ }
+
+ 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")
+ .load(filePath)
+assert(df.count() === 0)
+
+val df2 = spark.read.format("image").option("dropImageFailures",
"false")
+ .load(filePath)
+assert(df2.count() === 1)
+val result = df2.head()
+assert(result === invalidImageRow(
+ Paths.get(filePath).toAbsolutePath().normalize().toUri().toString))
+ }
+
+ test("image datasource partition test") {
+val result = spark.read.format("image")
+ .option("dropImageFailures", "true").load(imagePath)
+ .select(substring_index(col("image.origin"), "/", -1).as("origin"),
col("cls"), col("date"))
+ .collect()
+
+assert(Set(result: _*) === Set(
+ Row("29.5.a_b_EGDP022204.jpg", "kittens", "2018-01"),
+ Row("54893.jpg", "kittens", "2018-02"),
+ Row("DP153539.jpg", "kittens", "2018-02"),
+ Row("DP802813.jpg", "kittens", "2018-02"),
+ Row("BGRA.png", "multichannel", "2018-01"),
+ Row("BGRA_alpha_60.png", "multichannel", "2018-01"),
+ Row("chr30.4.184.jpg", "multichannel", "2018-02"),
+ Row("grayscale.jpg", "multichannel", "2018-02")
+))
+ }
+
+ // Images with the different number of channels
+ test("readImages pixel values test") {
+
+val images = spark.read.format("image").option("dropImageFailures",
"true")
+ .load(imagePath + "/cls=multichannel/").collect()
+
+val firstBytes20Map = images.map { rrow =>
+ val row = rrow.getAs[Row]("image")
+ val filename = Paths.get(getOrigin(row)).getFileName().toString()
+ val mode = getMode(row)
+ val bytes20 = getData(row).slice(0, 20).toList
+ filename -> Tuple2(mode, bytes20)
+}.toMap
--- End diff --
use Set instead of Map
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[GitHub] spark pull request #22328: [SPARK-22666][ML][SQL] Spark datasource for image...
Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/22328#discussion_r214967452
--- Diff:
mllib/src/main/scala/org/apache/spark/ml/source/image/ImageDataSource.scala ---
@@ -0,0 +1,51 @@
+/*
+ * 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.
+ */
+
+package org.apache.spark.ml.source.image
+
+/**
+ * `image` package implements Spark SQL data source API for loading IMAGE
data as `DataFrame`.
+ * The loaded `DataFrame` has one `StructType` column: `image`.
+ * The schema of the `image` column is:
+ * - origin: String (represent the origin of image. If loaded from file,
then it is file path)
+ * - height: Int (height of image)
+ * - width: Int (width of image)
+ * - nChannels: Int (number of image channels)
+ * - mode: Int (OpenCV-compatible type)
+ * - data: BinaryType (Image bytes in OpenCV-compatible order: row-wise
BGR in most cases)
+ *
+ * To use IMAGE data source, you need to set "image" as the format in
`DataFrameReader` and
+ * optionally specify options, for example:
+ * {{{
+ * // Scala
+ * val df = spark.read.format("image")
+ * .option("dropImageFailures", "true")
+ * .load("data/mllib/images/imagesWithPartitions")
+ *
+ * // Java
+ * Dataset df = spark.read().format("image")
+ * .option("dropImageFailures", "true")
+ * .load("data/mllib/images/imagesWithPartitions");
+ * }}}
+ *
+ * IMAGE data source supports the following options:
+ * - "dropImageFailures": Whether to drop the files that are not valid
images from the result.
+ *
+ * @note This class is public for documentation purpose. Please don't use
this class directly.
+ * Rather, use the data source API as illustrated above.
--- End diff --
I didn't see a section in the doc that lists all built-in data sources. It
would be nice if we create a section and link it to this API doc. I think we
can do it with a follow-up PR. I want to see if we can get this PR merged
before branch cut:)
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[GitHub] spark pull request #22328: [SPARK-22666][ML][SQL] Spark datasource for image...
Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/22328#discussion_r214968664
--- Diff:
mllib/src/main/scala/org/apache/spark/ml/source/image/ImageFileFormat.scala ---
@@ -0,0 +1,109 @@
+/*
+ * 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.
+ */
+
+package org.apache.spark.ml.source.image
+
+import com.google.common.io.{ByteStreams, Closeables}
+import org.apache.hadoop.conf.Configuration
+import org.apache.hadoop.fs.{FileStatus, Path}
+import org.apache.hadoop.mapreduce.Job
+
+import org.apache.spark.ml.image.ImageSchema
+import org.apache.spark.sql.SparkSession
+import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.catalyst.encoders.RowEncoder
+import org.apache.spark.sql.catalyst.expressions.{AttributeReference,
UnsafeRow}
+import org.apache.spark.sql.catalyst.util.CaseInsensitiveMap
+import org.apache.spark.sql.execution.datasources.{DataSource, FileFormat,
OutputWriterFactory, PartitionedFile}
+import org.apache.spark.sql.sources.{DataSourceRegister, Filter}
+import org.apache.spark.sql.types.StructType
+import org.apache.spark.util.SerializableConfiguration
+
+
+private[image] class ImageFileFormatOptions(
+@transient private val parameters: CaseInsensitiveMap[String]) extends
Serializable {
+
+ def this(parameters: Map[String, String]) =
this(CaseInsensitiveMap(parameters))
+
+ val dropImageFailures = parameters.getOrElse("dropImageFailures",
"false").toBoolean
+}
+
+private[image] class ImageFileFormat extends FileFormat with
DataSourceRegister {
+
+ override def inferSchema(
+ sparkSession: SparkSession,
+ options: Map[String, String],
+ files: Seq[FileStatus]): Option[StructType] =
Some(ImageSchema.imageSchema)
+
+ override def prepareWrite(
+ sparkSession: SparkSession,
+ job: Job, options: Map[String, String],
+ dataSchema: StructType): OutputWriterFactory = {
+throw new UnsupportedOperationException(
+ s"prepareWrite is not supported for image data source")
--- End diff --
The error message is user-facing and users do not know `prepareWrite`. So
just say "Write is not supported"
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[GitHub] spark pull request #22328: [SPARK-22666][ML][SQL] Spark datasource for image...
Github user HyukjinKwon commented on a diff in the pull request:
https://github.com/apache/spark/pull/22328#discussion_r214861273
--- Diff:
mllib/src/main/scala/org/apache/spark/ml/source/image/ImageDataSource.scala ---
@@ -0,0 +1,51 @@
+/*
+ * 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.
+ */
+
+package org.apache.spark.ml.source.image
+
+/**
+ * `image` package implements Spark SQL data source API for loading IMAGE
data as `DataFrame`.
+ * The loaded `DataFrame` has one `StructType` column: `image`.
+ * The schema of the `image` column is:
+ * - origin: String (represent the origin of image. If loaded from file,
then it is file path)
+ * - height: Int (height of image)
+ * - width: Int (width of image)
+ * - nChannels: Int (number of image channels)
+ * - mode: Int (OpenCV-compatible type)
+ * - data: BinaryType (Image bytes in OpenCV-compatible order: row-wise
BGR in most cases)
+ *
+ * To use IMAGE data source, you need to set "image" as the format in
`DataFrameReader` and
+ * optionally specify options, for example:
+ * {{{
+ * // Scala
+ * val df = spark.read.format("image")
+ * .option("dropImageFailures", "true")
+ * .load("data/mllib/images/imagesWithPartitions")
+ *
+ * // Java
+ * Dataset df = spark.read().format("image")
+ * .option("dropImageFailures", "true")
+ * .load("data/mllib/images/imagesWithPartitions");
+ * }}}
+ *
+ * IMAGE data source supports the following options:
+ * - "dropImageFailures": Whether to drop the files that are not valid
images from the result.
+ *
+ * @note This class is public for documentation purpose. Please don't use
this class directly.
+ * Rather, use the data source API as illustrated above.
--- End diff --
@WeichenXu123, don't we plan to make a documentation in the site?
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[GitHub] spark pull request #22328: [SPARK-22666][ML][SQL] Spark datasource for image...
Github user HyukjinKwon commented on a diff in the pull request:
https://github.com/apache/spark/pull/22328#discussion_r214860618
--- Diff:
mllib/src/main/scala/org/apache/spark/ml/source/image/ImageFileFormat.scala ---
@@ -0,0 +1,109 @@
+/*
+ * 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.
+ */
+
+package org.apache.spark.ml.source.image
+
+import com.google.common.io.{ByteStreams, Closeables}
+import org.apache.hadoop.conf.Configuration
+import org.apache.hadoop.fs.{FileStatus, Path}
+import org.apache.hadoop.mapreduce.Job
+
+import org.apache.spark.ml.image.ImageSchema
+import org.apache.spark.sql.SparkSession
+import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.catalyst.encoders.RowEncoder
+import org.apache.spark.sql.catalyst.expressions.{AttributeReference,
UnsafeRow}
+import org.apache.spark.sql.catalyst.util.CaseInsensitiveMap
+import org.apache.spark.sql.execution.datasources.{DataSource, FileFormat,
OutputWriterFactory, PartitionedFile}
+import org.apache.spark.sql.sources.{DataSourceRegister, Filter}
+import org.apache.spark.sql.types.StructType
+import org.apache.spark.util.SerializableConfiguration
+
+
+private[image] class ImageFileFormatOptions(
+@transient private val parameters: CaseInsensitiveMap[String]) extends
Serializable {
+
+ def this(parameters: Map[String, String]) =
this(CaseInsensitiveMap(parameters))
+
+ val dropImageFailures = parameters.getOrElse("dropImageFailures",
"false").toBoolean
+}
+
+private[image] class ImageFileFormat extends FileFormat with
DataSourceRegister {
+
+ override def inferSchema(
+ sparkSession: SparkSession,
+ options: Map[String, String],
+ files: Seq[FileStatus]): Option[StructType] =
Some(ImageSchema.imageSchema)
+
+ override def prepareWrite(
+ sparkSession: SparkSession,
+ job: Job, options: Map[String, String],
+ dataSchema: StructType): OutputWriterFactory = {
+throw new UnsupportedOperationException(
+ s"prepareWrite is not supported for image data source")
--- End diff --
tiny nit: `s` can be removed
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[GitHub] spark pull request #22328: [SPARK-22666][ML][SQL] Spark datasource for image...
Github user HyukjinKwon commented on a diff in the pull request:
https://github.com/apache/spark/pull/22328#discussion_r214859773
--- Diff:
sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/DataSource.scala
---
@@ -591,6 +592,8 @@ object DataSource extends Logging {
"org.apache.spark.sql.execution.datasources.orc" -> nativeOrc,
"org.apache.spark.ml.source.libsvm.DefaultSource" -> libsvm,
"org.apache.spark.ml.source.libsvm" -> libsvm,
+ "org.apache.spark.ml.source.image.ImageFileFormat.DefaultSource" ->
image,
--- End diff --
Since this is new datasource, I think we wouldn't probably need this in
backword compatibility map.
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[GitHub] spark pull request #22328: [SPARK-22666][ML][SQL] Spark datasource for image...
Github user HyukjinKwon commented on a diff in the pull request:
https://github.com/apache/spark/pull/22328#discussion_r214859244
--- Diff:
mllib/src/main/scala/org/apache/spark/ml/source/image/ImageFileFormat.scala ---
@@ -0,0 +1,109 @@
+/*
+ * 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.
+ */
+
+package org.apache.spark.ml.source.image
+
+import com.google.common.io.{ByteStreams, Closeables}
+import org.apache.hadoop.conf.Configuration
+import org.apache.hadoop.fs.{FileStatus, Path}
+import org.apache.hadoop.mapreduce.Job
+
+import org.apache.spark.ml.image.ImageSchema
+import org.apache.spark.sql.SparkSession
+import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.catalyst.encoders.RowEncoder
+import org.apache.spark.sql.catalyst.expressions.{AttributeReference,
UnsafeRow}
+import org.apache.spark.sql.catalyst.util.CaseInsensitiveMap
+import org.apache.spark.sql.execution.datasources.{DataSource, FileFormat,
OutputWriterFactory, PartitionedFile}
+import org.apache.spark.sql.sources.{DataSourceRegister, Filter}
+import org.apache.spark.sql.types.StructType
+import org.apache.spark.util.SerializableConfiguration
+
+
+private[image] class ImageFileFormatOptions(
--- End diff --
Shall we make another file for this for consistency?
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[GitHub] spark pull request #22328: [SPARK-22666][ML][SQL] Spark datasource for image...
Github user HyukjinKwon commented on a diff in the pull request:
https://github.com/apache/spark/pull/22328#discussion_r214859128
--- Diff:
mllib/src/main/scala/org/apache/spark/ml/source/image/ImageDataSource.scala ---
@@ -0,0 +1,51 @@
+/*
+ * 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.
+ */
+
+package org.apache.spark.ml.source.image
+
+/**
+ * `image` package implements Spark SQL data source API for loading IMAGE
data as `DataFrame`.
+ * The loaded `DataFrame` has one `StructType` column: `image`.
+ * The schema of the `image` column is:
+ * - origin: String (represent the origin of image. If loaded from file,
then it is file path)
+ * - height: Int (height of image)
+ * - width: Int (width of image)
+ * - nChannels: Int (number of image channels)
+ * - mode: Int (OpenCV-compatible type)
+ * - data: BinaryType (Image bytes in OpenCV-compatible order: row-wise
BGR in most cases)
+ *
+ * To use IMAGE data source, you need to set "image" as the format in
`DataFrameReader` and
+ * optionally specify options, for example:
+ * {{{
+ * // Scala
+ * val df = spark.read.format("image")
+ * .option("dropImageFailures", "true")
+ * .load("data/mllib/images/imagesWithPartitions")
+ *
+ * // Java
+ * Dataset df = spark.read().format("image")
+ * .option("dropImageFailures", "true")
+ * .load("data/mllib/images/imagesWithPartitions");
+ * }}}
+ *
+ * IMAGE data source supports the following options:
+ * - "dropImageFailures": Whether to drop the files that are not valid
images from the result.
+ *
+ * @note This class is public for documentation purpose. Please don't use
this class directly.
+ * Rather, use the data source API as illustrated above.
+ */
+class ImageDataSource private() {}
--- End diff --
newline at the end - technically this should be caught by scalastyle.
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[GitHub] spark pull request #22328: [SPARK-22666][ML][SQL] Spark datasource for image...
Github user HyukjinKwon commented on a diff in the pull request: https://github.com/apache/spark/pull/22328#discussion_r214858990 --- Diff: mllib/src/main/resources/META-INF/services/org.apache.spark.sql.sources.DataSourceRegister --- @@ -1 +1,2 @@ org.apache.spark.ml.source.libsvm.LibSVMFileFormat +org.apache.spark.ml.source.image.ImageFileFormat --- End diff -- tiny nit: newline at the end - this might be caught by maven's checkstyle (https://github.com/apache/spark/pull/21801) --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
[GitHub] spark pull request #22328: [SPARK-22666][ML][SQL] Spark datasource for image...
GitHub user WeichenXu123 opened a pull request: https://github.com/apache/spark/pull/22328 [SPARK-22666][ML][SQL] Spark datasource for image format ## What changes were proposed in this pull request? Implement an image schema datasource. This image datasource support: - partition discovery (loading partitioned images) - dropImageFailures (the same behavior with `ImageSchema.readImage`) - path wildcard matching (the same behavior with `ImageSchema.readImage`) - loading recursively from directory (different from `ImageSchema.readImage`, but use such path: `/path/to/dir/**`) This datasource **NOT** support: - specify `numPartitions` (it will be determined by datasource automatically) - sampling (you can use `df.sample` later but the sampling operator won't be pushdown to datasource) ## How was this patch tested? Unit tests. ## Benchmark I benchmark and compare the cost time between old `ImageSchema.read` API and my image datasource. **cluster**: 4 nodes, each with 64GB memory, 8 cores CPU **test dataset**: Flickr8k_Dataset (about 8091 images) **time cost**: My image datasource time (automatically generate 258 partitions): 38.04s `ImageSchema.read` time (set 16 partitions): 68.4s `ImageSchema.read` time (set 258 partitions): 90.6s **time cost when increase image number by double (clone Flickr8k_Dataset and loads double number images): My image datasource time (automatically generate 515 partitions): 95.4s `ImageSchema.read` (set 32 partitions): 109s `ImageSchema.read` (set 515 partitions): 105s So we can see that my image datasource implementation (this PR) bring some performance improvement compared against old`ImageSchema.read` API. You can merge this pull request into a Git repository by running: $ git pull https://github.com/WeichenXu123/spark image_datasource Alternatively you can review and apply these changes as the patch at: https://github.com/apache/spark/pull/22328.patch To close this pull request, make a commit to your master/trunk branch with (at least) the following in the commit message: This closes #22328 commit 5b5aee66b2ea819341b624164298f0700ee07ddf Author: WeichenXu Date: 2018-09-04T09:48:50Z init pr --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
