+1 On Sat, Sep 23, 2017 at 7:08 PM, Noman Khan <nomanbp...@live.com> wrote:
> +1 > > Regards > Noman > ------------------------------ > *From:* Denny Lee <denny.g....@gmail.com> > *Sent:* Friday, September 22, 2017 2:59:33 AM > *To:* Apache Spark Dev; Sean Owen; Tim Hunter > *Cc:* Danil Kirsanov; Joseph Bradley; Reynold Xin; Sudarshan Sudarshan > *Subject:* Re: [VOTE][SPIP] SPARK-21866 Image support in Apache Spark > > +1 > > On Thu, Sep 21, 2017 at 11:15 Sean Owen <so...@cloudera.com> wrote: > >> Am I right that this doesn't mean other packages would use this >> representation, but that they could? >> >> The representation looked fine to me w.r.t. what DL frameworks need. >> >> My previous comment was that this is actually quite lightweight. It's >> kind of like how I/O support is provided for CSV and JSON, so makes enough >> sense to add to Spark. It doesn't really preclude other solutions. >> >> For those reasons I think it's fine. +1 >> >> On Thu, Sep 21, 2017 at 6:32 PM Tim Hunter <timhun...@databricks.com> >> wrote: >> >>> Hello community, >>> >>> I would like to call for a vote on SPARK-21866. It is a short proposal >>> that has important applications for image processing and deep learning. >>> Joseph Bradley has offered to be the shepherd. >>> >>> JIRA ticket: https://issues.apache.org/jira/browse/SPARK-21866 >>> PDF version: https://issues.apache.org/jira/secure/attachment/12884792/ >>> SPIP%20-%20Image%20support%20for%20Apache%20Spark%20V1.1.pdf >>> >>> 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. >>> 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 >>> >>> 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. >>> >>> 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 >>> >>> Proposed API changes >>> >>> We propose to add a new package in the package structure, under the >>> MLlib project: >>> org.apache.spark.image >>> 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. >>> 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 and of the options: >>> >>> def readImages( >>> path: String, >>> session: SparkSession = null, >>> recursive: Boolean = false, >>> numPartitions: Int = 0, >>> dropImageFailures: Boolean = false, >>> // Experimental options sampleRatio: Double = 1.0): DataFrame >>> >>> 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). >>> 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. >>> 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. >>> >>
