TrainVehicleModel

Trains HPE Media Server to recognize a vehicle model in one action.

Running this action is equivalent to running the following actions in the following order:

This action is atomic, so that any interruption to the server does not leave the database in an inconsistent state. If you stop the action using the QueueInfo action, or the action returns an error, the database is not modified. If all of the training images fail, HPE Media Server does not add the vehicle model to the database.

Type: asynchronous

Parameter Description Required
database The name of the database to add the vehicle model to. The database must already exist. Yes
identifier A unique identifier for the vehicle model (maximum 254 bytes). If you do not set this parameter, HPE Media Server generates an identifier automatically. No
imagedata The training images to add. Files must be uploaded as multipart/form-data. For more information about sending data to HPE Media Server, refer to the HPE Media Server Administration Guide. Set this or imagepath
imagelabels A comma-separated list of labels to identify the images that you are adding (maximum 254 bytes for each label). Every image added to the same vehicle model must have a unique label, so the number of labels must match the number of images provided using either imagedata or imagepath. If you do not set this parameter, HPE Media Server generates labels automatically. No
imagepath A comma-separated list of paths to the training images to add. The paths must be absolute, or relative to the HPE Media Server executable file. Set this or imagedata
metadata A comma-separated list of metadata key-value pairs to add to the vehicle model. Separate keys from values using a colon (:). To include a comma or colon in a key name or value, you must enclose the key name or value in quotation marks (") and escape any quotation marks that occur within the string with a backslash (\). No
nullimagedata A Boolean value, default false, that specifies whether to remove the images from the database after training is complete. Setting this to true is equivalent to running the action NullVehicleModelImageData on the vehicle model. Be aware that if you remove the images, the training cannot be updated in future. No
numparallel The maximum number of threads that the action can use (default 1). No
vehiclemake The vehicle make to associate with the model. You must specify one of the vehicle makes returned by the action ListVehicleMakes, or Unknown. Yes

Example

The following example trains HPE Media Server by sending the paths of the training images using the imagepath parameter:

curl http://localhost:14000 -F action=TrainVehicleModel
                            -F database=Cars
                            -F identifier=Mustang
                            -F imagepath=image1.jpg,image2.jpg
                            -F imagelabels=image1,image2
                            -F vehiclemake=Ford

Alternatively, you can train HPE Media Server by sending the image data, for example:

curl http://localhost:14000 -F action=TrainVehicleModel
                            -F database=Cars
                            -F identifier=Mustang
                            -F imagedata=@image1.jpg,image2.jpg
                            -F imagelabels=image1,image2
                            -F vehiclemake=Ford

Response

This action is asynchronous, so HPE Media Server always returns success accompanied by a token:

<autnresponse>
  <action>TRAINVEHICLEMODEL</action>
  <response>SUCCESS</response>
  <responsedata>
    <token>MTAuMi4xMDQuNTQ6MTgwMDA6VFJBSU5PQkpFQ1Q6MTQzMzQwODAwMTU0Mi0xNjAxNDc5NzMx</token>
  </responsedata>
</autnresponse>

You can use this token with the QueueInfo action to retrieve the status of the action:

<autnresponse>
  <action>QUEUEINFO</action>
  <response>SUCCESS</response>
  <responsedata>
    <actions>
      <action>
        <status>Finished</status>
        <queued_time>2016-Sep-16 15:14:17</queued_time>
        <time_in_queue>1</time_in_queue>
        <process_start_time>2016-Sep-16 15:14:18</process_start_time>
        <time_processing>1</time_processing>
        <process_end_time>2016-Sep-16 15:14:19</process_end_time>
        <build>
          <image>
            <index>1</index>
            <label>image1</label>
            <status>trained</status>
          </image>
          <image>
            <index>2</index>
            <label>image2</label>
            <status>trained</status>
          </image>
        </build>
        <database>Cars</database>
        <identifier>Mustang</identifier>
        <numimages>2</numimages>
        <numtrained>2</numtrained>
        <token>MTAuMi4xMDQuNTQ6MTgwMDA6VFJBSU5PQkpFQ1Q6MTQzMzQwODAwMTU0Mi0xNjAxNDc5NzMx</token>
      </action>
    </actions>
  </responsedata>
</autnresponse>

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