Github user imatiach-msft commented on a diff in the pull request: https://github.com/apache/spark/pull/19439#discussion_r144742476 --- 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 -- done
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