Create and update an Image Warehouse

An Image Warehouse is where you can storage and manage your images, as well as annotations on images.

Create an Image Warehouse

First, you need to create a corpus.

REST & CMD LINE

Creates a corpus resource under the given project with the option to specify the Corpus display name and description.

Before using any of the request data, make the following replacements:

  • REGIONALIZED_ENDPOINT: Endpoint might include a prefix matching the LOCATION_ID such as europe-west4-. See more about regionalized endpoints.
  • PROJECT_NUMBER: Your Google Cloud project number.
  • LOCATION_ID: The region where you are using Vertex AI Vision. For example: us-central1, europe-west4. See available regions.
  • DISPLAY_NAME: Display name for the warehouse.
  • WAREHOUSE_DESCRIPTION: The description of the warehouse (corpus).

HTTP method and URL: 

POST https://warehouse-visionai.googleapis.com/v1/projects/PROJECT_NUMBER/locations/LOCATION_ID/corpora

Request JSON body: 

{
  "display_name": "DISPLAY_NAME",
  "description": "WAREHOUSE_DESCRIPTION",
  "type": "IMAGE",
  "search_capability_setting": {
    "search_capabilities": {
      "type": "EMBEDDING_SEARCH"
    }
  }
}

To send your request, choose one of these options:

curl

Save the request body in a file named request.json, and execute the following command: 

curl -X POST \
     -H "Authorization: Bearer $(gcloud auth print-access-token)" \
     -H "Content-Type: application/json; charset=utf-8" \
     -d @request.json \
     "https://warehouse-visionai.googleapis.com/v1/projects/PROJECT_NUMBER/locations/LOCATION_ID/corpora"

PowerShell

Save the request body in a file named request.json, and execute the following command: 

$cred = gcloud auth print-access-token
$headers = @{ "Authorization" = "Bearer $cred" }

Invoke-WebRequest `
    -Method POST `
    -Headers $headers `
    -ContentType: "application/json; charset=utf-8" `
    -InFile request.json `
    -Uri "https://warehouse-visionai.googleapis.com/v1/projects/PROJECT_NUMBER/locations/LOCATION_ID/corpora" | Select-Object -Expand Content

You should receive a JSON response similar to the following:

{
  "name": "projects/PROJECT_NUMBER/locations/LOCATION_ID/warehouseoperations/OPERATION_ID",
  "metadata": {
    "@type": "type.googleapis.com/google.cloud.visionai.v1.CreateCorpusMetadata"
  },
  "done": true,
  "response": {
    "@type": "type.googleapis.com/google.cloud.visionai.v1.Corpus",
    "name": "projects/PROJECT_NUMBER/locations/LOCATION_ID/corpora/CORPUS_ID",
    "displayName": "DISPLAY_NAME",
    "description": "WAREHOUSE_DESCRIPTION",
    "type": "IMAGE",
    "search_capability_setting": {
      "search_capabilities": {
        "type": "EMBEDDING_SEARCH"
      }
    }
  }
}

Create data schema

If you want to import annotations, you need to create a corresponding data schema before calling the Import API.

REST & CMD LINE

This sample shows you how to create a data schema in an existing corpus.

Before using any of the request data, make the following replacements:

  • REGIONALIZED_ENDPOINT: Endpoint might include a prefix matching the LOCATION_ID such as europe-west4-. See more about regionalized endpoints.
  • PROJECT_NUMBER: Your Google Cloud project number.
  • LOCATION_ID: The region where you are using Vertex AI Vision. For example: us-central1, europe-west4. See available regions.
  • CORPUS_ID: The ID of your target corpus.
  • DATASCHEMA_KEY: This key must match the key of a user-specified annotation and unique inside a corpus. For example, data-key.
  • ANNOTATION_DATA_TYPE: The data type of the annotation. Available values are:
    • DATA_TYPE_UNSPECIFIED
    • INTEGER
    • FLOAT
    • STRING
    • DATETIME
    • GEO_COORDINATE
    • PROTO_ANY
    • BOOLEAN

    For more information, see the API reference documentation.

  • ANNOTATION_GRANULARITY: The granularity of the annotations under this dataSchema. Available values are:
    • GRANULARITY_UNSPECIFIED - Unspecified granularity.
    • GRANULARITY_ASSET_LEVEL - Asset-level granularity (annotations must not contain temporal partition information for the media asset).
    • GRANULARITY_PARTITION_LEVEL - Partition-level granularity (annotations must contain temporal partition information for the media asset).
  • SEARCH_STRATEGY: One of the available enum values. The types of search strategies to be applied on the annotation key. Available values are:
    • NO_SEARCH
    • EXACT_SEARCH
    • SMART_SEARCH

HTTP method and URL: 

POST https://warehouse-visionai.googleapis.com/v1/projects/PROJECT_NUMBER/locations/LOCATION_ID/corpora/CORPUS_ID/dataSchemas

Request JSON body: 

{
  "key": "DATASCHEMA_KEY",
  "schema_details": {
    "type": "ANNOTATION_DATA_TYPE",
    "granularity": "ANNOTATION_GRANULARITY",
    "search_strategy": {
      "search_strategy_type": "SEARCH_STRATEGY"
    }
  }
}

To send your request, choose one of these options:

curl

Save the request body in a file named request.json, and execute the following command: 

curl -X POST \
     -H "Authorization: Bearer $(gcloud auth print-access-token)" \
     -H "Content-Type: application/json; charset=utf-8" \
     -d @request.json \
     "https://warehouse-visionai.googleapis.com/v1/projects/PROJECT_NUMBER/locations/LOCATION_ID/corpora/CORPUS_ID/dataSchemas"

PowerShell

Save the request body in a file named request.json, and execute the following command: 

$cred = gcloud auth print-access-token
$headers = @{ "Authorization" = "Bearer $cred" }

Invoke-WebRequest `
    -Method POST `
    -Headers $headers `
    -ContentType: "application/json; charset=utf-8" `
    -InFile request.json `
    -Uri "https://warehouse-visionai.googleapis.com/v1/projects/PROJECT_NUMBER/locations/LOCATION_ID/corpora/CORPUS_ID/dataSchemas" | Select-Object -Expand Content

You should receive a JSON response similar to the following:

{
  "name": "projects/PROJECT_NUMBER/locations/LOCATION_ID/corpora/CORPUS_ID/dataSchemas/DATASCHEMA_ID",
  "key": "data-key",
  "schemaDetails": {
    "type": "BOOLEAN",
    "granularity": "GRANULARITY_ASSET_LEVEL",
    "searchStrategy": {
      "search_strategy_type": "EXACT_SEARCH"
    }
  }
}

Import assets to an image corpus

Import assets (and optionally annotations) to an existing corpus under the given project.

The Cloud Storage file for the ImportAsset request needs to be in JSONL format. In the file, each line corresponds to one asset and will be converted into InputImageAsset proto. For example,

{"gcsUri":"gs://test/test1.png","assetId":"asset1","annotations":[{"key":"title","value":{"strValue":"cat"}}]}
{"gcsUri":"gs://test/test2.png","assetId":"asset2","annotations":[{"key":"title","value":{"strValue":"dog"}}]}
{"gcsUri":"gs://test/test3.png","assetId":"asset3","annotations":[{"key":"title","value":{"strValue":"rabbit"}}]}

REST & CMD LINE

This sample shows you how to import assets (and optionally annotations) to a corpus resource under the given project.

Before using any of the request data, make the following replacements:

  • REGIONALIZED_ENDPOINT: Endpoint might include a prefix matching the LOCATION_ID such as europe-west4-. See more about regionalized endpoints.
  • PROJECT_NUMBER: Your Google Cloud project number.
  • LOCATION_ID: The region where you are using Vertex AI Vision. For example: us-central1, europe-west4. See available regions.
  • CORPUS_ID: The ID of your target corpus.

HTTP method and URL: 

POST https://warehouse-visionai.googleapis.com/v1/projects/PROJECT_NUMBER/locations/LOCATION_ID/corpora/CORPUS_ID/assets:import

Request JSON body: 

{
  "parent": "projects/PROJECT_NUMBER/locations/LOCATION_ID/corpora/CORPUS_ID,
  "assets_gcs_uri": GCS_URI
}

To send your request, choose one of these options:

curl

Save the request body in a file named request.json, and execute the following command: 

curl -X POST \
     -H "Authorization: Bearer $(gcloud auth print-access-token)" \
     -H "Content-Type: application/json; charset=utf-8" \
     -d @request.json \
     "https://warehouse-visionai.googleapis.com/v1/projects/PROJECT_NUMBER/locations/LOCATION_ID/corpora/CORPUS_ID/assets:import"

PowerShell

Save the request body in a file named request.json, and execute the following command: 

$cred = gcloud auth print-access-token
$headers = @{ "Authorization" = "Bearer $cred" }

Invoke-WebRequest `
    -Method POST `
    -Headers $headers `
    -ContentType: "application/json; charset=utf-8" `
    -InFile request.json `
    -Uri "https://warehouse-visionai.googleapis.com/v1/projects/PROJECT_NUMBER/locations/LOCATION_ID/corpora/CORPUS_ID/assets:import" | Select-Object -Expand Content

You should receive a JSON response similar to the following:

{
  "name": "projects/PROJECT_NUMBER/locations/LOCATION_ID/corpora/CORPUS_ID/operations/OPERATION_ID",
}
w

Analyze assets in the corpus

To prepare for image search, AnalyzeCorpus needs to be run in order to generate the embedding signals from images.

REST & CMD LINE

This sample shows you how to perform AnalyzeCorpus on a corpus resource.

Before using any of the request data, make the following replacements:

  • REGIONALIZED_ENDPOINT: Endpoint might include a prefix matching the LOCATION_ID such as europe-west4-. See more about regionalized endpoints.
  • PROJECT_NUMBER: Your Google Cloud project number.
  • LOCATION_ID: The region where you are using Vertex AI Vision. For example: us-central1, europe-west4. See available regions.
  • CORPUS_ID: The ID of your target corpus.

HTTP method and URL: 

POST https://warehouse-visionai.googleapis.com/v1/projects/PROJECT_NUMBER/locations/LOCATION_ID/corpora/CORPUS_ID:analyze

Request JSON body: 

{
  "name": "projects/PROJECT_NUMBER/locations/LOCATION_ID/corpora/CORPUS_ID
}

To send your request, choose one of these options:

curl

Save the request body in a file named request.json, and execute the following command: 

curl -X POST \
     -H "Authorization: Bearer $(gcloud auth print-access-token)" \
     -H "Content-Type: application/json; charset=utf-8" \
     -d @request.json \
     "https://warehouse-visionai.googleapis.com/v1/projects/PROJECT_NUMBER/locations/LOCATION_ID/corpora/CORPUS_ID:analyze"

PowerShell

Save the request body in a file named request.json, and execute the following command: 

$cred = gcloud auth print-access-token
$headers = @{ "Authorization" = "Bearer $cred" }

Invoke-WebRequest `
    -Method POST `
    -Headers $headers `
    -ContentType: "application/json; charset=utf-8" `
    -InFile request.json `
    -Uri "https://warehouse-visionai.googleapis.com/v1/projects/PROJECT_NUMBER/locations/LOCATION_ID/corpora/CORPUS_ID:analyze" | Select-Object -Expand Content

You should receive a JSON response similar to the following:

{
  "name": "projects/PROJECT_NUMBER/locations/LOCATION_ID/corpora/CORPUS_ID/operations/OPERATION_ID",
}

Create an index

REST & CMD LINE

This sample shows you how to create an index on a corpus resource.

Before using any of the request data, make the following replacements:

  • REGIONALIZED_ENDPOINT: Endpoint might include a prefix matching the LOCATION_ID such as europe-west4-. See more about regionalized endpoints.
  • PROJECT_NUMBER: Your Google Cloud project number.
  • LOCATION_ID: The region where you are using Vertex AI Vision. For example: us-central1, europe-west4. See available regions.
  • CORPUS_ID: The ID of your target corpus.
  • INDEX_ID: (Optional) A user-provided value for the index ID. In this request, the value is added to the request URL in the form:
    • https://REGIONALIZED_ENDPOINT/v1/[...]/corpora/CORPUS_ID/indexes?index_id=INDEX_ID

HTTP method and URL: 

POST https://warehouse-visionai.googleapis.com/v1/projects/PROJECT_NUMBER/locations/LOCATION_ID/corpora/CORPUS_ID/indexes

Request JSON body: 

{
  "display_name": "DISPLAY_NAME",
  "description": "INDEX_DESCRIPTION",
}

To send your request, choose one of these options:

curl

Save the request body in a file named request.json, and execute the following command: 

curl -X POST \
     -H "Authorization: Bearer $(gcloud auth print-access-token)" \
     -H "Content-Type: application/json; charset=utf-8" \
     -d @request.json \
     "https://warehouse-visionai.googleapis.com/v1/projects/PROJECT_NUMBER/locations/LOCATION_ID/corpora/CORPUS_ID/indexes"

PowerShell

Save the request body in a file named request.json, and execute the following command: 

$cred = gcloud auth print-access-token
$headers = @{ "Authorization" = "Bearer $cred" }

Invoke-WebRequest `
    -Method POST `
    -Headers $headers `
    -ContentType: "application/json; charset=utf-8" `
    -InFile request.json `
    -Uri "https://warehouse-visionai.googleapis.com/v1/projects/PROJECT_NUMBER/locations/LOCATION_ID/corpora/CORPUS_ID/indexes" | Select-Object -Expand Content

You should receive a JSON response similar to the following:

{
  "name": "projects/PROJECT_NUMBER/locations/LOCATION_ID/corpora/CORPUS_ID/indexes/INDEX_ID/operations/OPERATION_ID",
  "metadata": {
    "@type": "type.googleapis.com/google.cloud.visionai.v1.CreateIndexMetadata"
  }
}

Create an index endpoint

REST & CMD LINE

This sample shows you how to create an index endpoint.

Before using any of the request data, make the following replacements:

  • REGIONALIZED_ENDPOINT: Endpoint might include a prefix matching the LOCATION_ID such as europe-west4-. See more about regionalized endpoints.
  • PROJECT_NUMBER: Your Google Cloud project number.
  • LOCATION_ID: The region where you are using Vertex AI Vision. For example: us-central1, europe-west4. See available regions.
  • INDEX_ENDPOINT_ID: (Optional) A user-provided value for the index endpoint ID. In this request, the value is added to the request URL in the form:
    • https://REGIONALIZED_ENDPOINT/v1/projects/PROJECT_NUMBER/locations/LOCATION_ID/indexEndpoints?index_endpoint_id=INDEX_ENDPOINT_ID

HTTP method and URL: 

POST https://warehouse-visionai.googleapis.com/v1/projects/PROJECT_NUMBER/locations/LOCATION_ID/indexEndpoints

Request JSON body: 

{
  "display_name": "DISPLAY_NAME",
  "description": "DESCRIPTION",
}

To send your request, choose one of these options:

curl

Save the request body in a file named request.json, and execute the following command: 

curl -X POST \
     -H "Authorization: Bearer $(gcloud auth print-access-token)" \
     -H "Content-Type: application/json; charset=utf-8" \
     -d @request.json \
     "https://warehouse-visionai.googleapis.com/v1/projects/PROJECT_NUMBER/locations/LOCATION_ID/indexEndpoints"

PowerShell

Save the request body in a file named request.json, and execute the following command: 

$cred = gcloud auth print-access-token
$headers = @{ "Authorization" = "Bearer $cred" }

Invoke-WebRequest `
    -Method POST `
    -Headers $headers `
    -ContentType: "application/json; charset=utf-8" `
    -InFile request.json `
    -Uri "https://warehouse-visionai.googleapis.com/v1/projects/PROJECT_NUMBER/locations/LOCATION_ID/indexEndpoints" | Select-Object -Expand Content

You should receive a JSON response similar to the following:

{
  "name": "projects/PROJECT_NUMBER/locations/LOCATION_ID/indexEndpoints/INDEX_ENDPOINT_ID/operations/OPERATION_ID",
  "metadata": {
    "@type": "type.googleapis.com/google.cloud.visionai.v1.CreateIndexEndpointMetadata"
  }
}

Deploy index to index endpoint

REST & CMD LINE

This sample shows you how to deploy an index to an index endpoint resource.

Before using any of the request data, make the following replacements:

  • REGIONALIZED_ENDPOINT: Endpoint might include a prefix matching the LOCATION_ID such as europe-west4-. See more about regionalized endpoints.
  • PROJECT_NUMBER: Your Google Cloud project number.
  • LOCATION_ID: The region where you are using Vertex AI Vision. For example: us-central1, europe-west4. See available regions.
  • INDEX_ENDPOINT_ID: The ID of your target index endpoint.
  • CORPUS_ID: The ID of your target corpus.
  • INDEX_ID: The ID of your target index.

HTTP method and URL: 

POST https://warehouse-visionai.googleapis.com/v1/projects/PROJECT_NUMBER/locations/LOCATION_ID/indexEndpoints/INDEX_ENDPOINT_ID:deployIndex

Request JSON body: 

{
  "deployedIndex": {
    "index": "projects/PROJECT_NUMBER/locations/LOCATION_ID/corpora/CORPUS_ID/indexes/INDEX_ID"
  }
}

To send your request, choose one of these options:

curl

Save the request body in a file named request.json, and execute the following command: 

curl -X POST \
     -H "Authorization: Bearer $(gcloud auth print-access-token)" \
     -H "Content-Type: application/json; charset=utf-8" \
     -d @request.json \
     "https://warehouse-visionai.googleapis.com/v1/projects/PROJECT_NUMBER/locations/LOCATION_ID/indexEndpoints/INDEX_ENDPOINT_ID:deployIndex"

PowerShell

Save the request body in a file named request.json, and execute the following command: 

$cred = gcloud auth print-access-token
$headers = @{ "Authorization" = "Bearer $cred" }

Invoke-WebRequest `
    -Method POST `
    -Headers $headers `
    -ContentType: "application/json; charset=utf-8" `
    -InFile request.json `
    -Uri "https://warehouse-visionai.googleapis.com/v1/projects/PROJECT_NUMBER/locations/LOCATION_ID/indexEndpoints/INDEX_ENDPOINT_ID:deployIndex" | Select-Object -Expand Content

You should receive a JSON response similar to the following:

{
  "name": "projects/PROJECT_NUMBER/locations/LOCATION_ID/indexEndpoints/INDEX_ENDPOINT_ID/operations/OPERATION_ID",
  "metadata": {
    "@type": "type.googleapis.com/google.cloud.visionai.v1.DeployIndexMetadata",
    "deployedIndex": "projects/PROJECT_NUMBER/locations/LOCATION_ID/corpora/CORPUS_ID/indexes/INDEX_ID"
  }
}