Github user viirya commented on a diff in the pull request:
https://github.com/apache/spark/pull/19439#discussion_r143689831
--- Diff: mllib/src/main/scala/org/apache/spark/ml/image/ImageSchema.scala
---
@@ -0,0 +1,217 @@
+/*
+ * 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.image
+
+import java.awt.Color
+import java.awt.color.ColorSpace
+import java.io.ByteArrayInputStream
+import javax.imageio.ImageIO
+
+import org.apache.spark.annotation.Since
+import org.apache.spark.sql.{DataFrame, Row, SparkSession}
+import org.apache.spark.sql.types._
+
+@Since("2.3.0")
+object ImageSchema {
+
+ val undefinedImageType = "Undefined"
+
+ val ocvTypes = Map(
+ undefinedImageType -> -1,
+ "CV_8U" -> 0, "CV_8UC1" -> 0, "CV_8UC2" -> 8, "CV_8UC3" -> 16,
"CV_8UC4" -> 24,
+ "CV_8S" -> 1, "CV_8SC1" -> 1, "CV_8SC2" -> 9, "CV_8SC3" -> 17,
"CV_8SC4" -> 25,
+ "CV_16U" -> 2, "CV_16UC1" -> 2, "CV_16UC2" -> 10, "CV_16UC3" -> 18,
"CV_16UC4" -> 26,
+ "CV_16S" -> 3, "CV_16SC1" -> 3, "CV_16SC2" -> 11, "CV_16SC3" -> 19,
"CV_16SC4" -> 27,
+ "CV_32S" -> 4, "CV_32SC1" -> 4, "CV_32SC2" -> 12, "CV_32SC3" -> 20,
"CV_32SC4" -> 28,
+ "CV_32F" -> 5, "CV_32FC1" -> 5, "CV_32FC2" -> 13, "CV_32FC3" -> 21,
"CV_32FC4" -> 29,
+ "CV_64F" -> 6, "CV_64FC1" -> 6, "CV_64FC2" -> 14, "CV_64FC3" -> 22,
"CV_64FC4" -> 30
+ )
+
+ /** Schema for the image column: Row(String, Int, Int, Int, Array[Byte])
*/
+ val columnSchema = StructType(
+ StructField("origin", StringType, true) ::
+ StructField("height", IntegerType, false) ::
+ StructField("width", IntegerType, false) ::
+ StructField("nChannels", IntegerType, false) ::
+ // OpenCV-compatible type: CV_8UC3 in most cases
+ StructField("mode", StringType, false) ::
+ // Bytes in OpenCV-compatible order: row-wise BGR in most cases
+ StructField("data", BinaryType, false) :: Nil)
+
+ // Dataframe with a single column of images named "image" (nullable)
+ private val imageDFSchema = StructType(StructField("image",
columnSchema, true) :: Nil)
+
+ @Since("2.3.0")
+ def getOrigin(row: Row): String = row.getString(0)
+
+ @Since("2.3.0")
+ def getHeight(row: Row): Int = row.getInt(1)
+
+ @Since("2.3.0")
+ def getWidth(row: Row): Int = row.getInt(2)
+
+ @Since("2.3.0")
+ def getNChannels(row: Row): Int = row.getInt(3)
+
+ @Since("2.3.0")
+ def getMode(row: Row): String = row.getString(4)
+
+ @Since("2.3.0")
+ def getData(row: Row): Array[Byte] = row.getAs[Array[Byte]](5)
+
+ /** Check if the dataframe column contains images (i.e. has ImageSchema)
+ *
+ * @param df Dataframe
+ * @param column Column name
+ * @return True if the given column matches the image schema
+ */
+ @Since("2.3.0")
+ def isImageColumn(df: DataFrame, column: String): Boolean =
+ df.schema(column).dataType == columnSchema
+
+ /** Default values for the invalid image
+ *
+ * @param origin Origin of the invalid image
+ * @return Row with the default values
+ */
+ private def invalidImageRow(origin: String): Row = Row(Row(origin, -1,
-1, -1, undefinedImageType,
+ Array.ofDim[Byte](0)))
+
+ /** Convert the compressed image (jpeg, png, etc.) into OpenCV
+ * representation and store it in dataframe Row
+ *
+ * @param origin Arbitrary string that identifies the image
+ * @param bytes Image bytes (for example, jpeg)
+ * @return Dataframe Row or None (if the decompression fails)
+ */
+ private[spark] def decode(origin: String, bytes: Array[Byte]):
Option[Row] = {
+
+ val img = ImageIO.read(new ByteArrayInputStream(bytes))
+
+ if (img == null) {
+ None
+ } else {
+ val is_gray = img.getColorModel.getColorSpace.getType ==
ColorSpace.TYPE_GRAY
+ val has_alpha = img.getColorModel.hasAlpha
+
+ val height = img.getHeight
+ val width = img.getWidth
+ val (nChannels, mode) =
+ if (is_gray) (1, "CV_8UC1")
+ else if (has_alpha) (4, "CV_8UC4")
+ else (3, "CV_8UC3")
+
+ assert(height*width*nChannels < 1e9, "image is too large")
+ val decoded = Array.ofDim[Byte](height*width*nChannels)
+
+ // Grayscale images in Java require special handling to get the
correct intensity
+ if (is_gray) {
+ var offset = 0
+ val raster = img.getRaster
+ for(h <- 0 until height) {
+ for (w <- 0 until width) {
+ decoded(offset) = raster.getSample(w, h, 0).toByte
+ offset += 1
+ }
+ }
+ }
+ else {
+ var offset = 0
+ for (h <- 0 until height) {
+ for (w <- 0 until width) {
+ val color = new Color(img.getRGB(w, h))
+
+ decoded(offset) = color.getBlue.toByte
+ decoded(offset + 1) = color.getGreen.toByte
+ decoded(offset + 2) = color.getRed.toByte
+ if (nChannels == 4) {
+ decoded(offset + 3) = color.getAlpha.toByte
+ }
+ offset += nChannels
+ }
+ }
+ }
+
+ // the internal "Row" is needed, because the image is a single
dataframe column
+ Some(Row(Row(origin, height, width, nChannels, mode, decoded)))
+ }
+ }
+
+ /** Read the directory of images from the local or remote source
+ *
+ * @param path Path to the image directory
+ * @param sparkSession Spark Session
+ * @param recursive Recursive path search flag
+ * @param numPartitions Number of the dataframe partitions
+ * @param dropImageFailures Drop the files that are not valid images
from the result
+ * @param sampleRatio Fraction of the files loaded
+ * @return Dataframe with a single column "image" of
images;
+ * see ImageSchema for the details
+ */
+ @Since("2.3.0")
+ def readImages(path: String,
+ sparkSession: SparkSession = null,
+ recursive: Boolean = false,
+ numPartitions: Int = 0,
+ dropImageFailures: Boolean = false,
+ sampleRatio: Double = 1.0): DataFrame = {
+ require(sampleRatio <= 1.0 && sampleRatio >= 0, "sampleRatio should be
between 0 and 1")
+
+ val session = if (sparkSession != null) sparkSession else
SparkSession.builder().getOrCreate
+ val partitions =
+ if (numPartitions > 0) numPartitions
+ else session.sparkContext.defaultParallelism
+
+ val oldRecursiveFlag =
RecursiveFlag.setRecursiveFlag(Some(recursive.toString), session)
+ val oldPathFilter: Option[Class[_]] =
+ if (sampleRatio < 1) {
+ SamplePathFilter.setPathFilter(Some(classOf[SamplePathFilter]),
Some(sampleRatio), session)
+ }
+ else {
+ None
+ }
+
+ var result: DataFrame = null
+ try {
+ val streams = session.sparkContext.binaryFiles(path, partitions)
+ .repartition(partitions)
+
+ val images = if (dropImageFailures) {
+ streams.flatMap{
--- End diff --
`flatMap{` -> `flatMap {`
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