Photo Effects

Apply effects to your photo. Select person on a photo, blur or grayscale background

Request parameters

Description
Parameter’s name Parameter’s type Description
raw_input boolean False - Use Tensorflow protobuf record format fot feeding data to model, True - Use numpy arrays.
out_mime_type string Optional. Used on a client side to identify content nature.
out_filters string Optional. Comma separated list of model outputs to return.
inputs bytes Input image
max_objects int Max number of objects to detect
object_class string Object to find
effect string Effect to apply
blur_radius int Radius for Blur
form-data
curl -X POST \
    -H 'Authorization: Bearer YOUR_USER_TOKEN' \
    -H 'Content-Type: multipart/form-data' \
    -F "out_filters=output" \
    -F "raw_input=true" \
    -F "out_mime_type=image/png" \
    -F "bytes_inputs=@PATH_TO_INPUTS" \
    -F "int_max_objects=max_objects_value" \
    -F "string_object_class=object_class_value" \
    -F "string_effect=effect_value" \
    -F "int_blur_radius=blur_radius_value" \
    https://cloud.kibernetika.io/api/v0.2/workspace/kuberlab-demo/serving/photo-robot/tfproxy/9000/any
json
curl -X POST \
    -H 'Authorization: Bearer YOUR_USER_TOKEN' \
    -H 'Content-Type: application/json' \
    -d '
      {
        "inputs": {
          "blur_radius": {
            "data": 86,
            "dtype": 9
          },
          "effect": {
            "data": "base64_encoded_effect_value",
            "dtype": 7
          },
          "inputs": {
            "data": "base64_encoded_inputs_contents",
            "dtype": 7
          },
          "max_objects": {
            "data": 39,
            "dtype": 9
          },
          "object_class": {
            "data": "base64_encoded_object_class_value",
            "dtype": 7
          }
        },
        "out_filters": "output",
        "out_mime_type": "image/png",
        "raw_input": true
      }
    '
    https://cloud.kibernetika.io/api/v0.2/workspace/kuberlab-demo/serving/photo-robot/tfproxy/9000/any

Response format

{
  "output": [
    [
      "bytes"
    ]
  ]
}