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POST
/
api
/
{projectId}
/
classify
/
image
Classify an image
curl --request POST \
  --url https://studio.edgeimpulse.com/v1/api/{projectId}/classify/image \
  --header 'Content-Type: multipart/form-data' \
  --header 'x-api-key: <api-key>' \
  --form image='@example-file'
{
  "success": true,
  "error": "<string>",
  "result": {},
  "boundingBoxes": [
    {
      "label": "<string>",
      "x": 123,
      "y": 123,
      "width": 123,
      "height": 123,
      "score": 123
    }
  ],
  "freeformResult": {
    "outputTensors": [
      {
        "shape": [
          123
        ],
        "data": [
          123
        ]
      }
    ]
  },
  "anomalyResult": [
    {
      "boxes": [
        {
          "label": "<string>",
          "x": 123,
          "y": 123,
          "width": 123,
          "height": 123,
          "score": 123
        }
      ],
      "scores": [
        [
          123
        ]
      ],
      "meanScore": 123,
      "maxScore": 123
    }
  ]
}

Authorizations

​
x-api-key
string
header
required

Path Parameters

​
projectId
integer
required

Project ID

Query Parameters

​
impulseId
integer

Impulse ID. If this is unset then the default impulse is used.

Body

multipart/form-data
​
image
file
required

Response

200 - application/json

OK

​
success
boolean
required

Whether the operation succeeded

​
error
string

Optional error description (set if 'success' was false)

​
result
object

Classification value per label. For a neural network this will be the confidence, for anomalies the anomaly score.

​
boundingBoxes
object[]
​
freeformResult
object
​
anomalyResult
object[]

Anomaly scores and computed metrics for visual anomaly detection, one item per window.