Ora-lytics

Using Python for OCI Vision – Part 1

Posted on December 4, 2023

I’ve written a few blog posts over the past few weeks/months on how to use OCI Vision, which is part of the Oracle OCI AI Services. My blog posts have shown how to get started with OCI Vision, right up to creating your own customer models using this service.

In this post, the first in a series of blog posts, I’ll give you some examples of how to use these custom AI Vision models using Python. Being able to do this, opens the models you create to a larger audience of developers, who can now easily integrate these custom models into their applications.

In a previous post, I covered how to setup and configure your OCI connection. Have a look at that post as you will need to have completed the steps in it before you can follow the steps below.

To inspect the config file we can spool the contents of that file

!cat ~/.oci/config

I’ve hidden some of the details as these are related to my Oracle Cloud accountThis allows us to quickly inspect that we have everything setup correctly.

The next step is to load this config file, and its settings, into our Python environment.

config = oci.config.from_file()

config

We can now list all the projects I’ve created in my compartment for OCI Vision services.

#My Compartment ID
COMPARTMENT_ID = "<compartment-id>"

#List all the AI Vision Projects available in My compartment
response = ai_service_vision_client.list_projects(compartment_id=COMPARTMENT_ID)

#response.data
for item in response.data.items:
    print('- ', item.display_name)
    print('    ProjectId= ', item.id)
    print('')

Which lists the following OCI Vision projects.

We can also list out all the Models in my various projects. When listing these I print out the OCID of each, as this is needed when we want to use one of these models to process an image. I’ve redacted these as there is a minor cost associated with each time these are called.

#List all the AI Vision Models available in My compartment
list_models = ai_service_vision_client.list_models(
    # this is the compartment containing the model
    compartment_id=COMPARTMENT_ID
)

print("Compartment Id=", COMPARTMENT_ID)
print("")
for item in list_models.data.items:
    print('  ', item.display_name, '--', item.model_type)
    print('     OCID= ',item.id)
    print('     ProjectId= ', item.project_id)
    print('')

I’ll have other posts in this series on using the pre-built and custom model to label different image files on my desktop.