[
https://issues.apache.org/jira/browse/SPARK-21866?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Denny Lee updated SPARK-21866:
------------------------------
Description:
h2. Background and motivation
As Apache Spark is being used more and more in the industry, some new use cases
are emerging for different data formats beyond the traditional SQL types or the
numerical types (vectors and matrices). Deep Learning applications commonly
deal with image processing. A number of projects add some Deep Learning
capabilities to Spark (see list below), but they struggle to communicate with
each other or with MLlib pipelines because there is no standard way to
represent an image in Spark DataFrames. We propose to federate efforts for
representing images in Spark by defining a representation that caters to the
most common needs of users and library developers.
This SPIP proposes a specification to represent images in Spark DataFrames and
Datasets (based on existing industrial standards), and an interface for loading
sources of images. It is not meant to be a full-fledged image processing
library, but rather the core description that other libraries and users can
rely on. Several packages already offer various processing facilities for
transforming images or doing more complex operations, and each has various
design tradeoffs that make them better as standalone solutions.
This project is a joint collaboration between Microsoft and Databricks, which
have been testing this design in two open source packages: MMLSpark and Deep
Learning Pipelines.
The proposed image format is an in-memory, decompressed representation that
targets low-level applications. It is significantly more liberal in memory
usage than compressed image representations such as JPEG, PNG, etc., but it
allows easy communication with popular image processing libraries and has no
decoding overhead.
h2. Targets users and personas:
Data scientists, data engineers, library developers.
The following libraries define primitives for loading and representing images,
and will gain from a common interchange format (in alphabetical order):
* BigDL
* DeepLearning4J
* Deep Learning Pipelines
* MMLSpark
* TensorFlow (Spark connector)
* TensorFlowOnSpark
* TensorFrames
* Thunder
h2. Goals:
* Simple representation of images in Spark DataFrames, based on pre-existing
industrial standards (OpenCV)
* This format should eventually allow the development of high-performance
integration points with image processing libraries such as libOpenCV, Google
TensorFlow, CNTK, and other C libraries.
* The reader should be able to read popular formats of images from distributed
sources.
h2. Non-Goals:
Images are a versatile medium and encompass a very wide range of formats and
representations. This SPIP explicitly aims at the most common use case in the
industry currently: multi-channel matrices of binary, int32, int64, float or
double data that can fit comfortably in the heap of the JVM:
* the total size of an image should be restricted to less than 2GB (roughly)
* the meaning of color channels is application-specific and is not mandated by
the standard (in line with the OpenCV standard)
* specialized formats used in meteorology, the medical field, etc. are not
supported
* this format is specialized to images and does not attempt to solve the more
general problem of representing n-dimensional tensors in Spark
h2. Proposed API changes
We propose to add a new package in the package structure, under the MLlib
project:
{{org.apache.spark.image}}
h3. Data format
We propose to add the following structure:
imageSchema = StructType([
* StructField("mode", StringType(), False),
** The exact representation of the data.
** The values are described in the following OpenCV convention. Basically, the
type has both "depth" and "number of channels" info: in particular, type
"CV_8UC3" means "3 channel unsigned bytes". BGRA format would be CV_8UC4 (value
32 in the table) with the channel order specified by convention.
** The exact channel ordering and meaning of each channel is dictated by
convention. By default, the order is RGB (3 channels) and BGRA (4 channels).
If the image failed to load, the value is the empty string "".
* StructField("origin", StringType(), True),
** Some information about the origin of the image. The content of this is
application-specific.
** When the image is loaded from files, users should expect to find the file
name in this field.
* StructField("height", IntegerType(), False),
** the height of the image, pixels
** If the image fails to load, the value is -1.
* StructField("width", IntegerType(), False),
** the width of the image, pixels
** If the image fails to load, the value is -1.
* StructField("nChannels", IntegerType(), False),
** The number of channels in this image: it is typically a value of 1 (B&W), 3
(RGB), or 4 (BGRA)
** If the image fails to load, the value is -1.
* StructField("data", BinaryType(), False)
** packed array content. Due to implementation limitation, it cannot currently
store more than 2 billions of pixels.
** The data is stored in a pixel-by-pixel BGR row-wise order. This follows the
OpenCV convention.
** If the image fails to load, this array is empty.
For more information about image types, here is an OpenCV guide on types:
[http://docs.opencv.org/2.4/modules/core/doc/intro.html#fixed-pixel-types-limited-use-of-templates]
The reference implementation provides some functions to convert popular formats
(JPEG, PNG, etc.) to the image specification above, and some functions to
verify if an image is valid.
h2. Image ingest API
We propose the following function to load images from a remote distributed
source as a DataFrame. Here is the signature in Scala. The python interface is
similar. For compatibility with java, this function should be made available
through a builder pattern or through the DataSource API. The exact mechanics
can be discussed during implementation; the goal of the proposal below is to
propose a specification of the behavior.
{code:java}
def readImages(
path: String,
session: SparkSession = null,
recursive: Boolean = false,
numPartitions: Int = 0,
dropImageFailures: Boolean = false,
// Experimental options
sampleRatio: Double = 1.0): DataFrame
{code}
The type of the returned DataFrame should be the structure type above, with the
expectation that all the file names be filled.
Mandatory parameters:
* *path*: a directory for a file system that contains images
Optional parameters:
* *session* (SparkSession, default null): the Spark Session to use to create
the dataframe. If not provided, it will use the current default Spark session
via SparkSession.getOrCreate().
* *recursive* (bool, default false): take the top-level images or look into
directory recursively
* *numPartitions* (int, default null): the number of partitions of the final
dataframe. By default uses the default number of partitions from Spark.
* *dropImageFailures* (bool, default false): drops the files that failed to
load. If false (do not drop), some invalid images are kept.
Parameters that are experimental/may be quickly deprecated. These would be
useful to have but are not critical for a first cut:
* *sampleRatio* (float, in (0,1), default 1): if less than 1, returns a
fraction of the data. There is no statistical guarantee about how the sampling
is performed. This proved to be very helpful for fast prototyping. Marked as
experimental since it should be pushed to the Spark core.
The implementation is expected to be in Scala for performance, with a wrapper
for python.
This function should be lazy to the extent possible: it should not trigger
access to the data when called. Ideally, any file system supported by Spark
should be supported when loading images. There may be restrictions for some
options such as zip files, etc.
The reference implementation has also some experimental options (undocumented
here).
h2. Reference implementation
A reference implementation is available as an open-source Spark package in this
repository (Apache 2.0 license):
[https://github.com/Microsoft/spark-images]
This Spark package will also be published in a binary form on
spark-packages.org .
Comments about the API should be addressed in this ticket.
h2. Optional Rejected Designs
The use of User-Defined Types was considered. It adds some burden to the
implementation of various languages and does not provide significant advantages.
was:
h2. Background and motivation
As Apache Spark is being used more and more in the industry, some new use cases
are emerging for different data formats beyond the traditional SQL types or the
numerical types (vectors and matrices). Deep Learning applications commonly
deal with image processing. A number of projects add some Deep Learning
capabilities to Spark (see list below), but they struggle to communicate with
each other or with MLlib pipelines because there is no standard way to
represent an image in Spark DataFrames. We propose to federate efforts for
representing images in Spark by defining a representation that caters to the
most common needs of users and library developers.
This SPIP proposes a specification to represent images in Spark DataFrames and
Datasets (based on existing industrial standards), and an interface for loading
sources of images. It is not meant to be a full-fledged image processing
library, but rather the core description that other libraries and users can
rely on. Several packages already offer various processing facilities for
transforming images or doing more complex operations, and each has various
design tradeoffs that make them better as standalone solutions.
This project is a joint collaboration between Microsoft and Databricks, which
have been testing this design in two open source packages: MMLSpark and Deep
Learning Pipelines.
The proposed image format is an in-memory, decompressed representation that
targets low-level applications. It is significantly more liberal in memory
usage than compressed image representations such as JPEG, PNG, etc., but it
allows easy communication with popular image processing libraries and has no
decoding overhead.
h2. Targets users and personas:
Data scientists, data engineers, library developers.
The following libraries define primitives for loading and representing images,
and will gain from a common interchange format (in alphabetical order):
* BigDL
* DeepLearning4J
* Deep Learning Pipelines
* MMLSpark
* TensorFlow (Spark connector)
* TensorFlowOnSpark
* TensorFrames
* Thunder
h2. Goals:
* Simple representation of images in Spark DataFrames, based on pre-existing
industrial standards (OpenCV)
* This format should eventually allow the development of high-performance
integration points with image processing libraries such as libOpenCV, Google
TensorFlow, CNTK, and other C libraries.
* The reader should be able to read popular formats of images from distributed
sources.
h2. Non-Goals:
Images are a versatile medium and encompass a very wide range of formats and
representations. This SPIP explicitly aims at the most common use case in the
industry currently: multi-channel matrices of binary, int32, int64, float or
double data that can fit comfortably in the heap of the JVM:
* the total size of an image should be restricted to less than 2GB (roughly)
* the meaning of color channels is application-specific and is not mandated by
the standard (in line with the OpenCV standard)
* specialized formats used in meteorology, the medical field, etc. are not
supported
* this format is specialized to images and does not attempt to solve the more
general problem of representing n-dimensional tensors in Spark
h2. Proposed API changes
We propose to add a new package in the package structure, under the MLlib
project:
{{org.apache.spark.image}}
h3. Data format
We propose to add the following structure:
imageSchema = StructType([
* StructField("mode", StringType(), False),
** The exact representation of the data.
** The values are described in the following OpenCV convention. Basically, the
type has both "depth" and "number of channels" info: in particular, type
"CV_8UC3" means "3 channel unsigned bytes". BGRA format would be CV_8UC4 (value
32 in the table) with the channel order specified by convention.
** The exact channel ordering and meaning of each channel is dictated by
convention. By default, the order is RGB (3 channels) and BGRA (4 channels).
If the image failed to load, the value is the empty string "".
* StructField("origin", StringType(), True),
** Some information about the origin of the image. The content of this is
application-specific.
** When the image is loaded from files, users should expect to find the file
name in this field.
* StructField("height", IntegerType(), False),
** the height of the image, pixels
** If the image fails to load, the value is -1.
* StructField("width", IntegerType(), False),
** the width of the image, pixels
** If the image fails to load, the value is -1.
* StructField("nChannels", IntegerType(), False),
** The number of channels in this image: it is typically a value of 1 (B&W), 3
(RGB), or 4 (BGRA)
** If the image fails to load, the value is -1.
* StructField("data", BinaryType(), False)
** packed array content. Due to implementation limitation, it cannot currently
store more than 2 billions of pixels.
** The data is stored in a pixel-by-pixel BGR row-wise order. This follows the
OpenCV convention.
** If the image fails to load, this array is empty.
For more information about image types, here is an OpenCV guide on types:
http://docs.opencv.org/2.4/modules/core/doc/intro.html#fixed-pixel-types-limited-use-of-templates
The reference implementation provides some functions to convert popular formats
(JPEG, PNG, etc.) to the image specification above, and some functions to
verify if an image is valid.
h2. Image ingest API
We propose the following function to load images from a remote distributed
source as a DataFrame. Here is the signature in Scala. The python interface is
similar. For compatibility with java, this function should be made available
through a builder pattern or through the DataSource API. The exact mechanics
can be discussed during implementation; the goal of the proposal below is to
propose a specification of the behavior.
{code}
def readImages(
path: String,
session: SparkSession = null,
recursive: Boolean = false,
numPartitions: Int = 0,
dropImageFailures: Boolean = false,
// Experimental options
sampleRatio: Double = 1.0): DataFrame
{code}
The type of the returned DataFrame should be the structure type above, with the
expectation that all the file names be filled.
Mandatory parameters:
* *path*: a directory for a file system that contains images
Optional parameters:
* *session* (SparkSession, default null): the Spark Session to use to create
the dataframe. If not provided, it will use the current default Spark session
via SparkSession.getOrCreate().
* *recursive* (bool, default false): take the top-level images or look into
directory recursively
* *numPartitions* (int, default null): the number of partitions of the final
dataframe. By default uses the default number of partitions from Spark.
* *dropImageFailures* (bool, default false): drops the files that failed to
load. If false (do not drop), some invalid images are kept.
Parameters that are experimental/may be quickly deprecated. These would be
useful to have but are not critical for a first cut:
* *sampleRatio* (float, in (0,1), default 1): if less than 1, returns a
fraction of the data. There is no statistical guarantee about how the sampling
is performed. This proved to be very helpful for fast prototyping. Marked as
experimental since it should be pushed to the Spark core.
The implementation is expected to be in Scala for performance, with a wrapper
for python.
This function should be lazy to the extent possible: it should not trigger
access to the data when called. Ideally, any file system supported by Spark
should be supported when loading images. There may be restrictions for some
options such as zip files, etc.
The reference implementation has also some experimental options (undocumented
here).
h2. Reference implementation
A reference implementation is available as an open-source Spark package in this
repository (Apache 2.0 license):
https://github.com/Microsoft/spark-images
This Spark package will also be published in a binary form on
spark-packages.org .
Comments about the API should be addressed in this ticket.
h2. Optional Rejected Designs
The use of User-Defined Types was considered. It adds some burden to the
implementation of various languages and does not provide significant advantages.
> SPIP: Image support in Spark
> ----------------------------
>
> Key: SPARK-21866
> URL: https://issues.apache.org/jira/browse/SPARK-21866
> Project: Spark
> Issue Type: Improvement
> Components: ML
> Affects Versions: 2.2.0
> Reporter: Timothy Hunter
> Assignee: Ilya Matiach
> Priority: Major
> Labels: SPIP
> Fix For: 2.3.0
>
> Attachments: SPIP - Image support for Apache Spark V1.1.pdf
>
>
> h2. Background and motivation
> As Apache Spark is being used more and more in the industry, some new use
> cases are emerging for different data formats beyond the traditional SQL
> types or the numerical types (vectors and matrices). Deep Learning
> applications commonly deal with image processing. A number of projects add
> some Deep Learning capabilities to Spark (see list below), but they struggle
> to communicate with each other or with MLlib pipelines because there is no
> standard way to represent an image in Spark DataFrames. We propose to
> federate efforts for representing images in Spark by defining a
> representation that caters to the most common needs of users and library
> developers.
> This SPIP proposes a specification to represent images in Spark DataFrames
> and Datasets (based on existing industrial standards), and an interface for
> loading sources of images. It is not meant to be a full-fledged image
> processing library, but rather the core description that other libraries and
> users can rely on. Several packages already offer various processing
> facilities for transforming images or doing more complex operations, and each
> has various design tradeoffs that make them better as standalone solutions.
> This project is a joint collaboration between Microsoft and Databricks, which
> have been testing this design in two open source packages: MMLSpark and Deep
> Learning Pipelines.
> The proposed image format is an in-memory, decompressed representation that
> targets low-level applications. It is significantly more liberal in memory
> usage than compressed image representations such as JPEG, PNG, etc., but it
> allows easy communication with popular image processing libraries and has no
> decoding overhead.
> h2. Targets users and personas:
> Data scientists, data engineers, library developers.
> The following libraries define primitives for loading and representing
> images, and will gain from a common interchange format (in alphabetical
> order):
> * BigDL
> * DeepLearning4J
> * Deep Learning Pipelines
> * MMLSpark
> * TensorFlow (Spark connector)
> * TensorFlowOnSpark
> * TensorFrames
> * Thunder
> h2. Goals:
> * Simple representation of images in Spark DataFrames, based on pre-existing
> industrial standards (OpenCV)
> * This format should eventually allow the development of high-performance
> integration points with image processing libraries such as libOpenCV, Google
> TensorFlow, CNTK, and other C libraries.
> * The reader should be able to read popular formats of images from
> distributed sources.
> h2. Non-Goals:
> Images are a versatile medium and encompass a very wide range of formats and
> representations. This SPIP explicitly aims at the most common use case in the
> industry currently: multi-channel matrices of binary, int32, int64, float or
> double data that can fit comfortably in the heap of the JVM:
> * the total size of an image should be restricted to less than 2GB (roughly)
> * the meaning of color channels is application-specific and is not mandated
> by the standard (in line with the OpenCV standard)
> * specialized formats used in meteorology, the medical field, etc. are not
> supported
> * this format is specialized to images and does not attempt to solve the
> more general problem of representing n-dimensional tensors in Spark
> h2. Proposed API changes
> We propose to add a new package in the package structure, under the MLlib
> project:
> {{org.apache.spark.image}}
> h3. Data format
> We propose to add the following structure:
> imageSchema = StructType([
> * StructField("mode", StringType(), False),
> ** The exact representation of the data.
> ** The values are described in the following OpenCV convention. Basically,
> the type has both "depth" and "number of channels" info: in particular, type
> "CV_8UC3" means "3 channel unsigned bytes". BGRA format would be CV_8UC4
> (value 32 in the table) with the channel order specified by convention.
> ** The exact channel ordering and meaning of each channel is dictated by
> convention. By default, the order is RGB (3 channels) and BGRA (4 channels).
> If the image failed to load, the value is the empty string "".
> * StructField("origin", StringType(), True),
> ** Some information about the origin of the image. The content of this is
> application-specific.
> ** When the image is loaded from files, users should expect to find the file
> name in this field.
> * StructField("height", IntegerType(), False),
> ** the height of the image, pixels
> ** If the image fails to load, the value is -1.
> * StructField("width", IntegerType(), False),
> ** the width of the image, pixels
> ** If the image fails to load, the value is -1.
> * StructField("nChannels", IntegerType(), False),
> ** The number of channels in this image: it is typically a value of 1 (B&W),
> 3 (RGB), or 4 (BGRA)
> ** If the image fails to load, the value is -1.
> * StructField("data", BinaryType(), False)
> ** packed array content. Due to implementation limitation, it cannot
> currently store more than 2 billions of pixels.
> ** The data is stored in a pixel-by-pixel BGR row-wise order. This follows
> the OpenCV convention.
> ** If the image fails to load, this array is empty.
> For more information about image types, here is an OpenCV guide on types:
> [http://docs.opencv.org/2.4/modules/core/doc/intro.html#fixed-pixel-types-limited-use-of-templates]
> The reference implementation provides some functions to convert popular
> formats (JPEG, PNG, etc.) to the image specification above, and some
> functions to verify if an image is valid.
> h2. Image ingest API
> We propose the following function to load images from a remote distributed
> source as a DataFrame. Here is the signature in Scala. The python interface
> is similar. For compatibility with java, this function should be made
> available through a builder pattern or through the DataSource API. The exact
> mechanics can be discussed during implementation; the goal of the proposal
> below is to propose a specification of the behavior.
> {code:java}
> def readImages(
> path: String,
> session: SparkSession = null,
> recursive: Boolean = false,
> numPartitions: Int = 0,
> dropImageFailures: Boolean = false,
> // Experimental options
> sampleRatio: Double = 1.0): DataFrame
> {code}
> The type of the returned DataFrame should be the structure type above, with
> the expectation that all the file names be filled.
> Mandatory parameters:
> * *path*: a directory for a file system that contains images
> Optional parameters:
> * *session* (SparkSession, default null): the Spark Session to use to create
> the dataframe. If not provided, it will use the current default Spark session
> via SparkSession.getOrCreate().
> * *recursive* (bool, default false): take the top-level images or look into
> directory recursively
> * *numPartitions* (int, default null): the number of partitions of the final
> dataframe. By default uses the default number of partitions from Spark.
> * *dropImageFailures* (bool, default false): drops the files that failed to
> load. If false (do not drop), some invalid images are kept.
> Parameters that are experimental/may be quickly deprecated. These would be
> useful to have but are not critical for a first cut:
> * *sampleRatio* (float, in (0,1), default 1): if less than 1, returns a
> fraction of the data. There is no statistical guarantee about how the
> sampling is performed. This proved to be very helpful for fast prototyping.
> Marked as experimental since it should be pushed to the Spark core.
> The implementation is expected to be in Scala for performance, with a wrapper
> for python.
> This function should be lazy to the extent possible: it should not trigger
> access to the data when called. Ideally, any file system supported by Spark
> should be supported when loading images. There may be restrictions for some
> options such as zip files, etc.
> The reference implementation has also some experimental options (undocumented
> here).
> h2. Reference implementation
> A reference implementation is available as an open-source Spark package in
> this repository (Apache 2.0 license):
> [https://github.com/Microsoft/spark-images]
> This Spark package will also be published in a binary form on
> spark-packages.org .
> Comments about the API should be addressed in this ticket.
> h2. Optional Rejected Designs
> The use of User-Defined Types was considered. It adds some burden to the
> implementation of various languages and does not provide significant
> advantages.
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