Github user imatiach-msft commented on a diff in the pull request: https://github.com/apache/spark/pull/19439#discussion_r144855218 --- 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) --- End diff -- done, used Arrays.equals()
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