Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/22328#discussion_r215322021
--- Diff:
mllib/src/main/scala/org/apache/spark/ml/source/image/ImageDataSource.scala ---
@@ -0,0 +1,54 @@
+/*
+ * 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.source.image
+
+/**
+ * `image` package implements Spark SQL data source API for loading IMAGE
data as `DataFrame`.
+ * The loaded `DataFrame` has one `StructType` column: `image`.
+ * The schema of the `image` column is:
+ * - origin: String (represents the origin of the image.
+ * If loaded from files, then it is the file path)
+ * - height: Int (height of the image)
+ * - width: Int (width of the image)
+ * - nChannels: Int (number of the image channels)
+ * - mode: Int (OpenCV-compatible type)
+ * - data: BinaryType (Image bytes in OpenCV-compatible order: row-wise
BGR in most cases)
+ *
+ * To use IMAGE data source, you need to set "image" as the format in
`DataFrameReader` and
+ * optionally specify the data source options, for example:
+ * {{{
+ * // Scala
+ * val df = spark.read.format("image")
+ * .option("dropImageFailures", true)
+ * .load("data/mllib/images/partitioned")
+ *
+ * // Java
+ * Dataset<Row> df = spark.read().format("image")
+ * .option("dropImageFailures", true)
+ * .load("data/mllib/images/partitioned");
+ * }}}
+ *
+ * IMAGE data source supports the following options:
+ * - "dropImageFailures": Whether to drop the files that are not valid
images from the result.
--- End diff --
How about changing `dropImageFailures` to `dropInvalid`?
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