Github user srowen commented on a diff in the pull request:
https://github.com/apache/spark/pull/19439#discussion_r143925461
--- 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{
--- End diff --
Just `images.foreach { rrow =>`; minor but avoids extra braces
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