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AnimalRecognitionDemo | using Redis Streams , RedisGears and RedisAI for Realtime

 by   RedisGears Python Version: Current License: Apache-2.0

 by   RedisGears Python Version: Current License: Apache-2.0

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kandi X-RAY | AnimalRecognitionDemo Summary

AnimalRecognitionDemo is a Python library typically used in Big Data, Redis applications. AnimalRecognitionDemo has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. However AnimalRecognitionDemo build file is not available. You can download it from GitHub.
This demo combines several Redis data structures and Redis Modules to process a AnimalRecognitionDemostream of images and filter out the images that contain cats. It forwards the images that contain cats to a stream: cats.
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Quality
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kandi-support Support

  • AnimalRecognitionDemo has a low active ecosystem.
  • It has 30 star(s) with 15 fork(s). There are 4 watchers for this library.
  • It had no major release in the last 12 months.
  • There are 3 open issues and 4 have been closed. On average issues are closed in 37 days. There are 10 open pull requests and 0 closed requests.
  • It has a neutral sentiment in the developer community.
  • The latest version of AnimalRecognitionDemo is current.
This Library - Support
Best in #Python
Average in #Python
This Library - Support
Best in #Python
Average in #Python

quality kandi Quality

  • AnimalRecognitionDemo has 0 bugs and 0 code smells.
This Library - Quality
Best in #Python
Average in #Python
This Library - Quality
Best in #Python
Average in #Python

securitySecurity

  • AnimalRecognitionDemo has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
  • AnimalRecognitionDemo code analysis shows 0 unresolved vulnerabilities.
  • There are 0 security hotspots that need review.
This Library - Security
Best in #Python
Average in #Python
This Library - Security
Best in #Python
Average in #Python

license License

  • AnimalRecognitionDemo is licensed under the Apache-2.0 License. This license is Permissive.
  • Permissive licenses have the least restrictions, and you can use them in most projects.
This Library - License
Best in #Python
Average in #Python
This Library - License
Best in #Python
Average in #Python

buildReuse

  • AnimalRecognitionDemo releases are not available. You will need to build from source code and install.
  • AnimalRecognitionDemo has no build file. You will be need to create the build yourself to build the component from source.
  • Installation instructions are not available. Examples and code snippets are available.
  • AnimalRecognitionDemo saves you 94 person hours of effort in developing the same functionality from scratch.
  • It has 241 lines of code, 11 functions and 6 files.
  • It has high code complexity. Code complexity directly impacts maintainability of the code.
This Library - Reuse
Best in #Python
Average in #Python
This Library - Reuse
Best in #Python
Average in #Python
Top functions reviewed by kandi - BETA

kandi has reviewed AnimalRecognitionDemo and discovered the below as its top functions. This is intended to give you an instant insight into AnimalRecognitionDemo implemented functionality, and help decide if they suit your requirements.

  • Adds a graph to the graph runner
    • Log a message
  • Determine whether the value should be taken
    • Add animals to a stream
      • Wait until the RDB load is loaded
        • Pass all images

          Get all kandi verified functions for this library.

          Get all kandi verified functions for this library.

          AnimalRecognitionDemo Key Features

          Redis Streams to capture the input video stream: all

          RedisGears to process this stream

          RedisAI to classify the images with MobilenetV2

          AnimalRecognitionDemo Examples and Code Snippets

          See all related Code Snippets

          Running the Demo

          copy iconCopydownload iconDownload
          $ git clone https://github.com/RedisGears/AnimalRecognitionDemo.git
          $ cd AnimalRecognitionDemo
          # If you don't have it already, install https://git-lfs.github.com/ (On OSX: brew install git-lfs)
          $ git lfs install && git lfs fetch && git lfs checkout
          $ docker-compose up
          

          See all related Code Snippets

          Community Discussions

          Trending Discussions on Big Data
          • How to group unassociated content
          • Using Spark window with more than one partition when there is no obvious partitioning column
          • What is the best way to store +3 millions records in Firestore?
          • spark-shell throws java.lang.reflect.InvocationTargetException on running
          • For function over multiple rows (i+1)?
          • Filling up shuffle buffer (this may take a while)
          • Designing Twitter Search - How to sort large datasets?
          • Unnest Query optimisation for singular record
          • handling million of rows for lookup operation using python
          • split function does not return any observations with large dataset
          Trending Discussions on Big Data

          QUESTION

          How to group unassociated content

          Asked 2022-Apr-15 at 12:43

          I have a hive table that records user behavior

          like this

          userid behavior timestamp url
          1 view 1650022601 url1
          1 click 1650022602 url2
          1 click 1650022614 url3
          1 view 1650022617 url4
          1 click 1650022622 url5
          1 view 1650022626 url7
          2 view 1650022628 url8
          2 view 1650022631 url9

          About 400GB is added to the table every day.

          I want to order by timestamp asc, then one 'view' is in a group between another 'view' like this table, the first 3 lines belong to a same group , then subtract the timestamps, like 1650022614 - 1650022601 as the view time.

          How to do this?

          i try lag and lead function, or scala like this

                  val pairRDD: RDD[(Int, String)] = record.map(x => {
                      if (StringUtil.isDateString(x.split("\\s+")(0))) {
                          partition = partition + 1
                          (partition, x)
                      } else {
                          (partition, x)
                      }
                  })
          

          or java like this

                  LongAccumulator part = spark.sparkContext().longAccumulator("part");
          
                  JavaPairRDD<Long, Row> pairRDD = spark.sql(sql).coalesce(1).javaRDD().mapToPair((PairFunction<Row, Long, Row>) row -> {
                      if (row.getAs("event") == "pageview") {
                          part.add(1L);
                      }
                  return new Tuple2<>(part.value(), row);
                  });
          

          but when a dataset is very large, this code just stupid.

          save me plz

          ANSWER

          Answered 2022-Apr-15 at 12:43

          If you use dataframe, you can build partition by using window that sum a column whose value is 1 when you change partition and 0 if you don't change partition.

          You can transform a RDD to a dataframe with sparkSession.createDataframe() method as explained in this answer

          Back to your problem. In you case, you change partition every time column behavior is equal to "view". So we can start with this condition:

          import org.apache.spark.sql.functions.col
          
          val df1 = df.withColumn("is_view", (col("behavior") === "view").cast("integer"))
          

          You get the following dataframe:

          +------+--------+----------+----+-------+
          |userid|behavior|timestamp |url |is_view|
          +------+--------+----------+----+-------+
          |1     |view    |1650022601|url1|1      |
          |1     |click   |1650022602|url2|0      |
          |1     |click   |1650022614|url3|0      |
          |1     |view    |1650022617|url4|1      |
          |1     |click   |1650022622|url5|0      |
          |1     |view    |1650022626|url7|1      |
          |2     |view    |1650022628|url8|1      |
          |2     |view    |1650022631|url9|1      |
          +------+--------+----------+----+-------+
          

          Then you use a window ordered by timestamp to sum over the is_view column:

          import org.apache.spark.sql.expressions.Window
          import org.apache.spark.sql.functions.sum
          
          val df2 = df1.withColumn("partition", sum("is_view").over(Window.partitionBy("userid").orderBy("timestamp")))
          

          Which get you the following dataframe:

          +------+--------+----------+----+-------+---------+
          |userid|behavior|timestamp |url |is_view|partition|
          +------+--------+----------+----+-------+---------+
          |1     |view    |1650022601|url1|1      |1        |
          |1     |click   |1650022602|url2|0      |1        |
          |1     |click   |1650022614|url3|0      |1        |
          |1     |view    |1650022617|url4|1      |2        |
          |1     |click   |1650022622|url5|0      |2        |
          |1     |view    |1650022626|url7|1      |3        |
          |2     |view    |1650022628|url8|1      |1        |
          |2     |view    |1650022631|url9|1      |2        |
          +------+--------+----------+----+-------+---------+
          

          Then, you just have to aggregate per userid and partition:

          import org.apache.spark.sql.functions.{max, min}
          
          val result = df2.groupBy("userid", "partition")
            .agg((max("timestamp") - min("timestamp")).as("duration"))
          

          And you get the following results:

          +------+---------+--------+
          |userid|partition|duration|
          +------+---------+--------+
          |1     |1        |13      |
          |1     |2        |5       |
          |1     |3        |0       |
          |2     |1        |0       |
          |2     |2        |0       |
          +------+---------+--------+
          

          The complete scala code:

          import org.apache.spark.sql.expressions.Window
          import org.apache.spark.sql.functions.{col, max, min, sum}
          
          val result = df
            .withColumn("is_view", (col("behavior") === "view").cast("integer"))
            .withColumn("partition", sum("is_view").over(Window.partitionBy("userid").orderBy("timestamp")))
            .groupBy("userid", "partition")
            .agg((max("timestamp") - min("timestamp")).as("duration"))
          

          Source https://stackoverflow.com/questions/71883786

          Community Discussions, Code Snippets contain sources that include Stack Exchange Network

          Vulnerabilities

          No vulnerabilities reported

          Install AnimalRecognitionDemo

          You can download it from GitHub.
          You can use AnimalRecognitionDemo like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.

          Support

          For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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