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layout: global title: Data sources displayTitle: Data sources

In this section, we introduce how to use data source in ML to load data. Beside some general data sources such as Parquet, CSV, JSON and JDBC, we also provide some specific data sources for ML.

Table of Contents

• This will become a table of contents (this text will be scraped). {:toc}

Image data source

This image data source is used to load image files from a directory, it can load compressed image (jpeg, png, etc.) into raw image representation via ImageIO in Java library. The loaded DataFrame has one StructType column: “image”, containing image data stored as image schema. The schema of the image column is:

• origin: StringType (represents the file path of the image)
• height: IntegerType (height of the image)
• width: IntegerType (width of the image)
• nChannels: IntegerType (number of image channels)
• mode: IntegerType (OpenCV-compatible type)
• data: BinaryType (Image bytes in OpenCV-compatible order: row-wise BGR in most cases)

{% highlight scala %} scala> val df = spark.read.format(“image”).option(“dropInvalid”, true).load(“data/mllib/images/origin/kittens”) df: org.apache.spark.sql.DataFrame = [image: struct<origin: string, height: int ... 4 more fields>]

scala> df.select(“image.origin”, “image.width”, “image.height”).show(truncate=false) +-----------------------------------------------------------------------+-----+------+ |origin |width|height| +-----------------------------------------------------------------------+-----+------+ |file:///spark/data/mllib/images/origin/kittens/54893.jpg |300 |311 | |file:///spark/data/mllib/images/origin/kittens/DP802813.jpg |199 |313 | |file:///spark/data/mllib/images/origin/kittens/29.5.a_b_EGDP022204.jpg |300 |200 | |file:///spark/data/mllib/images/origin/kittens/DP153539.jpg |300 |296 | +-----------------------------------------------------------------------+-----+------+ {% endhighlight %}

{% highlight java %} Dataset imagesDF = spark.read().format(“image”).option(“dropInvalid”, true).load(“data/mllib/images/origin/kittens”); imageDF.select(“image.origin”, “image.width”, “image.height”).show(false); /* Will output: +-----------------------------------------------------------------------+-----+------+ |origin |width|height| +-----------------------------------------------------------------------+-----+------+ |file:///spark/data/mllib/images/origin/kittens/54893.jpg |300 |311 | |file:///spark/data/mllib/images/origin/kittens/DP802813.jpg |199 |313 | |file:///spark/data/mllib/images/origin/kittens/29.5.a_b_EGDP022204.jpg |300 |200 | |file:///spark/data/mllib/images/origin/kittens/DP153539.jpg |300 |296 | +-----------------------------------------------------------------------+-----+------+ */ {% endhighlight %}

{% highlight python %}

df = spark.read.format(“image”).option(“dropInvalid”, True).load(“data/mllib/images/origin/kittens”) df.select(“image.origin”, “image.width”, “image.height”).show(truncate=False) +-----------------------------------------------------------------------+-----+------+ |origin |width|height| +-----------------------------------------------------------------------+-----+------+ |file:///spark/data/mllib/images/origin/kittens/54893.jpg |300 |311 | |file:///spark/data/mllib/images/origin/kittens/DP802813.jpg |199 |313 | |file:///spark/data/mllib/images/origin/kittens/29.5.a_b_EGDP022204.jpg |300 |200 | |file:///spark/data/mllib/images/origin/kittens/DP153539.jpg |300 |296 | +-----------------------------------------------------------------------+-----+------+ {% endhighlight %}

{% highlight r %}

df = read.df(“data/mllib/images/origin/kittens”, “image”) head(select(df, df$image.origin, df$image.width, df$image.height))

1 file:///spark/data/mllib/images/origin/kittens/54893.jpg 2 file:///spark/data/mllib/images/origin/kittens/DP802813.jpg 3 file:///spark/data/mllib/images/origin/kittens/29.5.a_b_EGDP022204.jpg 4 file:///spark/data/mllib/images/origin/kittens/DP153539.jpg width height 1 300 311 2 199 313 3 300 200 4 300 296

{% endhighlight %}