1. Visualizing Data in Oracle Analytics Server
  2. Visualize Data
  3. Use Oracle Analytics Predictive Models and Oracle Machine Learning Models
  4. Use Oracle Cloud Infrastructure Vision Models in Oracle Analytics

Use Oracle Cloud Infrastructure Vision Models in Oracle Analytics

Use pretrained Oracle Cloud Infrastructure (OCI) Vision models to build image recognition and text recognition into your applications without machine learning (ML) or artificial intelligence (AI) expertise.

For example, when you use OCI pretrained Vision models, you can detect objects such as cars and faces in photographs and then blur the images to protect identities of the individuals.

Oracle Analytics supports these models:
  • Image Classification.
  • Image Face Detection.
  • Image Text Detection.
  • Object Detection.
Prerequisites:

Note:

OCI face detection with Oracle Analytics can identify a maximum of 250 faces per image.
  1. On the Oracle Analytics Home page, click Create, and then click Data Flow.
  2. Select the prepared dataset and click Add.
  3. From the Data Flow Steps pane, double-click Apply AI Model, and then select the model to use.
  4. In Select AI Model, click a model, and then click OK
  5. In Apply Model, expand Parameters. Click the Input Column link and select a column. From the Input Type list, select the type of URL in the Input Column.
    • If you're referencing your source images by bucket, in Input Column select URL, and in Input Type select Buckets.
      Description of vision_parameters.png follows
      Description of the illustration vision_parameters.png

    • If you're referencing your source images individually, in Input Column select File Location, and in Input Type select Images.
  6. Use the Outputs and Parameters options to configure the model (the options available depend on the model type; use the on-screen guidance).
    Description of oci-vision-face-recognition.png follows
    Description of the illustration oci-vision-face-recognition.png

  7. From the Data Flow Steps pane, double-click Save Data.
  8. Enter a name for the dataset and select a location for saving the dataset.
    For example, you might call the dataset 'Car Parking Analysis Results'.
  9. Click Save, enter a name for the data flow, and click OK to save the data flow.
  10. Click Run Data Flow to analyze the images and output the results in a new dataset.
  11. On the Oracle Analytics Home page, click Data, and open the dataset that you specified in Step 8.
If you have fewer than 20,000 images, you can process them in a single data flow. If you have more than 20,000 images, create a separate data flow to process each bucket (that is, using a separate dataset for each bucket), and use a Sequence to sequentially process multiple data flows. After you've created multiple data flows, on the Oracle Analytics Home page, click Create, and then click Sequence.

To locate the generated dataset, from the Oracle Analytics home page, navigate to Data, then Datasets.


Description of vision_output_ds.png follows
Description of the illustration vision_output_ds.png

For more detail about the generated results, see Output Data Generated for Face Detection, Object Detection, Image Classification, and Text Detection Analysis Models.