Gets prediction for image object detection using the predict method.
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For detailed documentation that includes this code sample, see the following:
Code sample
Java
Before trying this sample, follow the Java setup instructions in the Vertex AI quickstart using client libraries . For more information, see the Vertex AI Java API reference documentation .
To authenticate to Vertex AI, set up Application Default Credentials. For more information, see Set up authentication for a local development environment .
import
com.google.cloud.aiplatform.util. ValueConverter
;
import
com.google.cloud.aiplatform.v1. EndpointName
;
import
com.google.cloud.aiplatform.v1. PredictResponse
;
import
com.google.cloud.aiplatform.v1. PredictionServiceClient
;
import
com.google.cloud.aiplatform.v1. PredictionServiceSettings
;
import
com.google.cloud.aiplatform.v1.schema.predict. instance
. ImageObjectDetectionPredictionInstance
;
import
com.google.cloud.aiplatform.v1.schema.predict.params. ImageObjectDetectionPredictionParams
;
import
com.google.cloud.aiplatform.v1.schema.predict.prediction. ImageObjectDetectionPredictionResult
;
import
com.google.protobuf. Value
;
import
java.io.IOException
;
import
java.nio.charset.StandardCharsets
;
import
java.nio.file.Files
;
import
java.nio.file.Paths
;
import
java.util.ArrayList
;
import
java.util.Base64
;
import
java.util.List
;
public
class
PredictImageObjectDetectionSample
{
public
static
void
main
(
String
[]
args
)
throws
IOException
{
// TODO(developer): Replace these variables before running the sample.
String
project
=
"YOUR_PROJECT_ID"
;
String
fileName
=
"YOUR_IMAGE_FILE_PATH"
;
String
endpointId
=
"YOUR_ENDPOINT_ID"
;
predictImageObjectDetection
(
project
,
fileName
,
endpointId
);
}
static
void
predictImageObjectDetection
(
String
project
,
String
fileName
,
String
endpointId
)
throws
IOException
{
PredictionServiceSettings
settings
=
PredictionServiceSettings
.
newBuilder
()
.
setEndpoint
(
"us-central1-aiplatform.googleapis.com:443"
)
.
build
();
// Initialize client that will be used to send requests. This client only needs to be created
// once, and can be reused for multiple requests. After completing all of your requests, call
// the "close" method on the client to safely clean up any remaining background resources.
try
(
PredictionServiceClient
predictionServiceClient
=
PredictionServiceClient
.
create
(
settings
))
{
String
location
=
"us-central1"
;
EndpointName
endpointName
=
EndpointName
.
of
(
project
,
location
,
endpointId
);
byte
[]
contents
=
Base64
.
getEncoder
().
encode
(
Files
.
readAllBytes
(
Paths
.
get
(
fileName
)));
String
content
=
new
String
(
contents
,
StandardCharsets
.
UTF_8
);
ImageObjectDetectionPredictionParams
params
=
ImageObjectDetectionPredictionParams
.
newBuilder
()
.
setConfidenceThreshold
((
float
)
(
0.5
))
.
setMaxPredictions
(
5
)
.
build
();
ImageObjectDetectionPredictionInstance
instance
=
ImageObjectDetectionPredictionInstance
.
newBuilder
().
setContent
(
content
).
build
();
List<Value>
instances
=
new
ArrayList
<> ();
instances
.
add
(
ValueConverter
.
toValue
(
instance
));
PredictResponse
predictResponse
=
predictionServiceClient
.
predict
(
endpointName
,
instances
,
ValueConverter
.
toValue
(
params
));
System
.
out
.
println
(
"Predict Image Object Detection Response"
);
System
.
out
.
format
(
"\tDeployed Model Id: %s\n"
,
predictResponse
.
getDeployedModelId
());
System
.
out
.
println
(
"Predictions"
);
for
(
Value
prediction
:
predictResponse
.
getPredictionsList
())
{
ImageObjectDetectionPredictionResult
.
Builder
resultBuilder
=
ImageObjectDetectionPredictionResult
.
newBuilder
();
ImageObjectDetectionPredictionResult
result
=
(
ImageObjectDetectionPredictionResult
)
ValueConverter
.
fromValue
(
resultBuilder
,
prediction
);
for
(
int
i
=
0
;
i
<
result
.
getIdsCount
();
i
++
)
{
System
.
out
.
printf
(
"\tDisplay name: %s\n"
,
result
.
getDisplayNames
(
i
));
System
.
out
.
printf
(
"\tConfidences: %f\n"
,
result
.
getConfidences
(
i
));
System
.
out
.
printf
(
"\tIDs: %d\n"
,
result
.
getIds
(
i
));
System
.
out
.
printf
(
"\tBounding boxes: %s\n"
,
result
.
getBboxes
(
i
));
}
}
}
}
}
Node.js
Before trying this sample, follow the Node.js setup instructions in the Vertex AI quickstart using client libraries . For more information, see the Vertex AI Node.js API reference documentation .
To authenticate to Vertex AI, set up Application Default Credentials. For more information, see Set up authentication for a local development environment .
/**
* TODO(developer): Uncomment these variables before running the sample.\
* (Not necessary if passing values as arguments)
*/
// const filename = "YOUR_PREDICTION_FILE_NAME";
// const endpointId = "YOUR_ENDPOINT_ID";
// const project = 'YOUR_PROJECT_ID';
// const location = 'YOUR_PROJECT_LOCATION';
const
aiplatform
=
require
(
' @google-cloud/aiplatform
'
);
const
{
instance
,
params
,
prediction
}
=
aiplatform
.
protos
.
google
.
cloud
.
aiplatform
.
v1
.
schema
.
predict
;
// Imports the Google Cloud Prediction Service Client library
const
{
PredictionServiceClient
}
=
aiplatform
.
v1
;
// Specifies the location of the api endpoint
const
clientOptions
=
{
apiEndpoint
:
'us-central1-aiplatform.googleapis.com'
,
};
// Instantiates a client
const
predictionServiceClient
=
new
PredictionServiceClient
(
clientOptions
);
async
function
predictImageObjectDetection
()
{
// Configure the endpoint resource
const
endpoint
=
`projects/
${
project
}
/locations/
${
location
}
/endpoints/
${
endpointId
}
`
;
const
parametersObj
=
new
params
.
ImageObjectDetectionPredictionParams
({
confidenceThreshold
:
0.5
,
maxPredictions
:
5
,
});
const
parameters
=
parametersObj
.
toValue
();
const
fs
=
require
(
'fs'
);
const
image
=
fs
.
readFileSync
(
filename
,
'base64'
);
const
instanceObj
=
new
instance
.
ImageObjectDetectionPredictionInstance
({
content
:
image
,
});
const
instanceVal
=
instanceObj
.
toValue
();
const
instances
=
[
instanceVal
];
const
request
=
{
endpoint
,
instances
,
parameters
,
};
// Predict request
const
[
response
]
=
await
predictionServiceClient
.
predict
(
request
);
console
.
log
(
'Predict image object detection response'
);
console
.
log
(
`\tDeployed model id :
${
response
.
deployedModelId
}
`
);
const
predictions
=
response
.
predictions
;
console
.
log
(
'Predictions :'
);
for
(
const
predictionResultVal
of
predictions
)
{
const
predictionResultObj
=
prediction
.
ImageObjectDetectionPredictionResult
.
fromValue
(
predictionResultVal
);
for
(
const
[
i
,
label
]
of
predictionResultObj
.
displayNames
.
entries
())
{
console
.
log
(
`\tDisplay name:
${
label
}
`
);
console
.
log
(
`\tConfidences:
${
predictionResultObj
.
confidences
[
i
]
}
`
);
console
.
log
(
`\tIDs:
${
predictionResultObj
.
ids
[
i
]
}
`
);
console
.
log
(
`\tBounding boxes:
${
predictionResultObj
.
bboxes
[
i
]
}
\n\n`
);
}
}
}
predictImageObjectDetection
();
Python
Before trying this sample, follow the Python setup instructions in the Vertex AI quickstart using client libraries . For more information, see the Vertex AI Python API reference documentation .
To authenticate to Vertex AI, set up Application Default Credentials. For more information, see Set up authentication for a local development environment .
import
base64
from
google.cloud
import
aiplatform
from
google.cloud.aiplatform.gapic.schema
import
predict
def
predict_image_object_detection_sample
(
project
:
str
,
endpoint_id
:
str
,
filename
:
str
,
location
:
str
=
"us-central1"
,
api_endpoint
:
str
=
"us-central1-aiplatform.googleapis.com"
,
):
# The AI Platform services require regional API endpoints.
client_options
=
{
"api_endpoint"
:
api_endpoint
}
# Initialize client that will be used to create and send requests.
# This client only needs to be created once, and can be reused for multiple requests.
client
=
aiplatform
.
gapic
.
PredictionServiceClient
(
client_options
=
client_options
)
with
open
(
filename
,
"rb"
)
as
f
:
file_content
=
f
.
read
()
# The format of each instance should conform to the deployed model's prediction input schema.
encoded_content
=
base64
.
b64encode
(
file_content
)
.
decode
(
"utf-8"
)
instance
=
predict
.
instance
.
ImageObjectDetectionPredictionInstance
(
content
=
encoded_content
,
)
.
to_value
()
instances
=
[
instance
]
# See gs://google-cloud-aiplatform/schema/predict/params/image_object_detection_1.0.0.yaml for the format of the parameters.
parameters
=
predict
.
params
.
ImageObjectDetectionPredictionParams
(
confidence_threshold
=
0.5
,
max_predictions
=
5
,
)
.
to_value
()
endpoint
=
client
.
endpoint_path
(
project
=
project
,
location
=
location
,
endpoint
=
endpoint_id
)
response
=
client
.
predict
(
endpoint
=
endpoint
,
instances
=
instances
,
parameters
=
parameters
)
print
(
"response"
)
print
(
" deployed_model_id:"
,
response
.
deployed_model_id
)
# See gs://google-cloud-aiplatform/schema/predict/prediction/image_object_detection_1.0.0.yaml for the format of the predictions.
predictions
=
response
.
predictions
for
prediction
in
predictions
:
print
(
" prediction:"
,
dict
(
prediction
))
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