Github user srowen commented on a diff in the pull request:
https://github.com/apache/spark/pull/19439#discussion_r143925711
--- Diff:
mllib/src/test/scala/org/apache/spark/ml/image/ImageSchemaSuite.scala ---
@@ -0,0 +1,124 @@
+/*
+ * 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.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.types._
+
+class ImageSchemaSuite extends SparkFunSuite with MLlibTestSparkContext {
+ // Single column of images named "image"
+ private val imageDFSchema =
+ StructType(StructField("image", ImageSchema.columnSchema, true) :: Nil)
+ private lazy val imagePath =
+
Thread.currentThread().getContextClassLoader.getResource("test-data/images").getPath
+
+ test("Smoke test: create basic ImageSchema dataframe") {
+ val origin = "path"
+ val width = 1
+ val height = 1
+ val nChannels = 3
+ val data = Array[Byte](0, 0, 0)
+ val mode = "CV_8UC3"
+
+ // Internal Row corresponds to image StructType
+ val rows = Seq(Row(Row(origin, height, width, nChannels, mode, data)),
+ Row(Row(null, height, width, nChannels, mode, data)))
+ val rdd = sc.makeRDD(rows)
+ val df = spark.createDataFrame(rdd, imageDFSchema)
+
+ assert(df.count == 2, "incorrect image count")
+ assert(ImageSchema.isImageColumn(df, "image"), "data do not fit
ImageSchema")
+ }
+
+ test("readImages count test") {
+ var df = readImages(imagePath, recursive = false)
+ assert(df.count == 0)
+
+ df = readImages(imagePath, recursive = true, dropImageFailures = false)
+ assert(df.count == 8)
+
+ df = readImages(imagePath, recursive = true, dropImageFailures = true)
+ val count100 = df.count
+ assert(count100 == 7)
+
+ df = readImages(imagePath, recursive = true, sampleRatio = 0.5,
dropImageFailures = true)
+ // Random number about half of the size of the original dataset
+ val count50 = df.count
+ assert(count50 > 0.2 * count100 && count50 < 0.8 * count100)
+ }
+
+ test("readImages partition test") {
+ val df = readImages(imagePath, recursive = true, dropImageFailures =
true, numPartitions = 3)
+ assert(df.rdd.getNumPartitions == 3)
+ }
+
+ // Images with the different number of channels
+ test("readImages pixel values test") {
+
+ val images = readImages(imagePath + "/multi-channel/", recursive =
false).collect
+
+ images.foreach{
+ rrow => {
+ val row = rrow.getAs[Row](0)
+ val filename = Paths.get(getOrigin(row)).getFileName().toString()
+ if(firstBytes20.contains(filename)) {
+ val mode = getMode(row)
+ val bytes20 = getData(row).slice(0, 20)
+
+ val expectedMode = firstBytes20(filename)._1
+ val expectedBytes = firstBytes20(filename)._2
+
+ assert(expectedMode == mode, "mode of the image is not read
correctly")
+
+ if (!compareBytes(expectedBytes, bytes20)) {
+ throw new Exception("incorrect numeric value for flattened
image")
+ }
+ }
+ }
+ }
+ }
+
+ // number of channels and first 20 bytes of OpenCV representation
+ // - default representation for 3-channel RGB images is BGR row-wise:
+ // (B00, G00, R00, B10, G10, R10, ...)
+ // - default representation for 4-channel RGB images is BGRA row-wise:
+ // (B00, G00, R00, A00, B10, G10, R10, A00, ...)
+ private val firstBytes20 = Map(
+ "grayscale.png" ->
+ (("CV_8UC1", Array[Byte](0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1,
0, 0, 3, 5, 2, 1))),
+ "RGB.png" -> (("CV_8UC3",
+ Array[Byte](-34, -66, -98, -38, -69, -98, -62, -90, -117,
+ -70, -98, -124, -34, -63, -90, -20, -48, -74, -18, -45))),
+ "RGBA.png" -> (("CV_8UC4",
+ Array[Byte](-128, -128, -8, -1, -128, -128, -8, -1, -128,
+ -128, -8, -1, 127, 127, -9, -1, 127, 127, -9, -1)))
+ )
+
+ private def compareBytes(x: Array[Byte], y: Array[Byte]): Boolean = {
+ val length = Math.min(x.length, y.length)
+ for (i <- 0 to length-1) {
--- End diff --
`for (i <- 0 until length)`
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