Classify an image and saves the result.
Format - uuid. The project id.
Specifies the name of the model to evaluate against.
Optional. Specifies the name of application using the endpoint.
[Binary image data]
------ExampleFormBoundary12345
Content-Disposition: form-data; name="imageData"; filename="filename.jpg"
Content-Type: image/jpeg
[Binary image data]
------ExampleFormBoundary12345--
OK
{
"id": "00000000-0000-0000-0000-000000000000",
"project": "00000000-0000-0000-0000-000000000000",
"iteration": "00000000-0000-0000-0000-000000000000",
"created": "string",
"predictions": [
{
"probability": 0.0,
"tagId": "00000000-0000-0000-0000-000000000000",
"tagName": "string",
"boundingBox": {
"left": 0.0,
"top": 0.0,
"width": 0.0,
"height": 0.0
},
"tagType": "Regular"
}
]
}
<ImagePrediction>
<id>00000000-0000-0000-0000-000000000000</id>
<project>00000000-0000-0000-0000-000000000000</project>
<iteration>00000000-0000-0000-0000-000000000000</iteration>
<created>string</created>
<predictions>
<probability>0</probability>
<tagId>00000000-0000-0000-0000-000000000000</tagId>
<tagName>string</tagName>
<boundingBox>
<left>0</left>
<top>0</top>
<width>0</width>
<height>0</height>
</boundingBox>
<tagType>Regular</tagType>
</predictions>
</ImagePrediction>
<ImagePrediction>
<id>00000000-0000-0000-0000-000000000000</id>
<project>00000000-0000-0000-0000-000000000000</project>
<iteration>00000000-0000-0000-0000-000000000000</iteration>
<created>string</created>
<predictions>
<probability>0</probability>
<tagId>00000000-0000-0000-0000-000000000000</tagId>
<tagName>string</tagName>
<boundingBox>
<left>0</left>
<top>0</top>
<width>0</width>
<height>0</height>
</boundingBox>
<tagType>Regular</tagType>
</predictions>
</ImagePrediction>
Error response
{
"code": "NoError",
"message": "string"
}
<CustomVisionError>
<code>NoError</code>
<message>string</message>
</CustomVisionError>
<CustomVisionError>
<code>NoError</code>
<message>string</message>
</CustomVisionError>
Classify an image url and saves the result.
Format - uuid. The project id.
Specifies the name of the model to evaluate against.
An ImageUrl that contains the url of the image to be evaluated.
Optional. Specifies the name of application using the endpoint.
{
"url": "string"
}
<ImageUrl>
<url>string</url>
</ImageUrl>
<ImageUrl>
<url>string</url>
</ImageUrl>
OK
{
"id": "00000000-0000-0000-0000-000000000000",
"project": "00000000-0000-0000-0000-000000000000",
"iteration": "00000000-0000-0000-0000-000000000000",
"created": "string",
"predictions": [
{
"probability": 0.0,
"tagId": "00000000-0000-0000-0000-000000000000",
"tagName": "string",
"boundingBox": {
"left": 0.0,
"top": 0.0,
"width": 0.0,
"height": 0.0
},
"tagType": "Regular"
}
]
}
<ImagePrediction>
<id>00000000-0000-0000-0000-000000000000</id>
<project>00000000-0000-0000-0000-000000000000</project>
<iteration>00000000-0000-0000-0000-000000000000</iteration>
<created>string</created>
<predictions>
<probability>0</probability>
<tagId>00000000-0000-0000-0000-000000000000</tagId>
<tagName>string</tagName>
<boundingBox>
<left>0</left>
<top>0</top>
<width>0</width>
<height>0</height>
</boundingBox>
<tagType>Regular</tagType>
</predictions>
</ImagePrediction>
<ImagePrediction>
<id>00000000-0000-0000-0000-000000000000</id>
<project>00000000-0000-0000-0000-000000000000</project>
<iteration>00000000-0000-0000-0000-000000000000</iteration>
<created>string</created>
<predictions>
<probability>0</probability>
<tagId>00000000-0000-0000-0000-000000000000</tagId>
<tagName>string</tagName>
<boundingBox>
<left>0</left>
<top>0</top>
<width>0</width>
<height>0</height>
</boundingBox>
<tagType>Regular</tagType>
</predictions>
</ImagePrediction>
Error response
{
"code": "NoError",
"message": "string"
}
<CustomVisionError>
<code>NoError</code>
<message>string</message>
</CustomVisionError>
<CustomVisionError>
<code>NoError</code>
<message>string</message>
</CustomVisionError>
Classify an image url without saving the result.
Format - uuid. The project id.
Specifies the name of the model to evaluate against.
An {Iris.Web.Api.Models.ImageUrl} that contains the url of the image to be evaluated.
Optional. Specifies the name of application using the endpoint.
{
"url": "string"
}
<ImageUrl>
<url>string</url>
</ImageUrl>
<ImageUrl>
<url>string</url>
</ImageUrl>
OK
{
"id": "00000000-0000-0000-0000-000000000000",
"project": "00000000-0000-0000-0000-000000000000",
"iteration": "00000000-0000-0000-0000-000000000000",
"created": "string",
"predictions": [
{
"probability": 0.0,
"tagId": "00000000-0000-0000-0000-000000000000",
"tagName": "string",
"boundingBox": {
"left": 0.0,
"top": 0.0,
"width": 0.0,
"height": 0.0
},
"tagType": "Regular"
}
]
}
<ImagePrediction>
<id>00000000-0000-0000-0000-000000000000</id>
<project>00000000-0000-0000-0000-000000000000</project>
<iteration>00000000-0000-0000-0000-000000000000</iteration>
<created>string</created>
<predictions>
<probability>0</probability>
<tagId>00000000-0000-0000-0000-000000000000</tagId>
<tagName>string</tagName>
<boundingBox>
<left>0</left>
<top>0</top>
<width>0</width>
<height>0</height>
</boundingBox>
<tagType>Regular</tagType>
</predictions>
</ImagePrediction>
<ImagePrediction>
<id>00000000-0000-0000-0000-000000000000</id>
<project>00000000-0000-0000-0000-000000000000</project>
<iteration>00000000-0000-0000-0000-000000000000</iteration>
<created>string</created>
<predictions>
<probability>0</probability>
<tagId>00000000-0000-0000-0000-000000000000</tagId>
<tagName>string</tagName>
<boundingBox>
<left>0</left>
<top>0</top>
<width>0</width>
<height>0</height>
</boundingBox>
<tagType>Regular</tagType>
</predictions>
</ImagePrediction>
Error response
{
"code": "NoError",
"message": "string"
}
<CustomVisionError>
<code>NoError</code>
<message>string</message>
</CustomVisionError>
<CustomVisionError>
<code>NoError</code>
<message>string</message>
</CustomVisionError>
Classify an image without saving the result.
Format - uuid. The project id.
Specifies the name of the model to evaluate against.
Optional. Specifies the name of application using the endpoint.
[Binary image data]
------ExampleFormBoundary12345
Content-Disposition: form-data; name="imageData"; filename="filename.jpg"
Content-Type: image/jpeg
[Binary image data]
------ExampleFormBoundary12345--
OK
{
"id": "00000000-0000-0000-0000-000000000000",
"project": "00000000-0000-0000-0000-000000000000",
"iteration": "00000000-0000-0000-0000-000000000000",
"created": "string",
"predictions": [
{
"probability": 0.0,
"tagId": "00000000-0000-0000-0000-000000000000",
"tagName": "string",
"boundingBox": {
"left": 0.0,
"top": 0.0,
"width": 0.0,
"height": 0.0
},
"tagType": "Regular"
}
]
}
<ImagePrediction>
<id>00000000-0000-0000-0000-000000000000</id>
<project>00000000-0000-0000-0000-000000000000</project>
<iteration>00000000-0000-0000-0000-000000000000</iteration>
<created>string</created>
<predictions>
<probability>0</probability>
<tagId>00000000-0000-0000-0000-000000000000</tagId>
<tagName>string</tagName>
<boundingBox>
<left>0</left>
<top>0</top>
<width>0</width>
<height>0</height>
</boundingBox>
<tagType>Regular</tagType>
</predictions>
</ImagePrediction>
<ImagePrediction>
<id>00000000-0000-0000-0000-000000000000</id>
<project>00000000-0000-0000-0000-000000000000</project>
<iteration>00000000-0000-0000-0000-000000000000</iteration>
<created>string</created>
<predictions>
<probability>0</probability>
<tagId>00000000-0000-0000-0000-000000000000</tagId>
<tagName>string</tagName>
<boundingBox>
<left>0</left>
<top>0</top>
<width>0</width>
<height>0</height>
</boundingBox>
<tagType>Regular</tagType>
</predictions>
</ImagePrediction>
Error response
{
"code": "NoError",
"message": "string"
}
<CustomVisionError>
<code>NoError</code>
<message>string</message>
</CustomVisionError>
<CustomVisionError>
<code>NoError</code>
<message>string</message>
</CustomVisionError>
Detect objects in an image and saves the result.
Format - uuid. The project id.
Specifies the name of the model to evaluate against.
Format - int32. Optional. Specifies the num of predicted tags per bounding box.
Optional. Specifies the name of application using the endpoint.
[Binary image data]
------ExampleFormBoundary12345
Content-Disposition: form-data; name="imageData"; filename="filename.jpg"
Content-Type: image/jpeg
[Binary image data]
------ExampleFormBoundary12345--
OK
{
"id": "00000000-0000-0000-0000-000000000000",
"project": "00000000-0000-0000-0000-000000000000",
"iteration": "00000000-0000-0000-0000-000000000000",
"created": "string",
"predictions": [
{
"probability": 0.0,
"tagId": "00000000-0000-0000-0000-000000000000",
"tagName": "string",
"boundingBox": {
"left": 0.0,
"top": 0.0,
"width": 0.0,
"height": 0.0
},
"tagType": "Regular"
}
]
}
<ImagePrediction>
<id>00000000-0000-0000-0000-000000000000</id>
<project>00000000-0000-0000-0000-000000000000</project>
<iteration>00000000-0000-0000-0000-000000000000</iteration>
<created>string</created>
<predictions>
<probability>0</probability>
<tagId>00000000-0000-0000-0000-000000000000</tagId>
<tagName>string</tagName>
<boundingBox>
<left>0</left>
<top>0</top>
<width>0</width>
<height>0</height>
</boundingBox>
<tagType>Regular</tagType>
</predictions>
</ImagePrediction>
<ImagePrediction>
<id>00000000-0000-0000-0000-000000000000</id>
<project>00000000-0000-0000-0000-000000000000</project>
<iteration>00000000-0000-0000-0000-000000000000</iteration>
<created>string</created>
<predictions>
<probability>0</probability>
<tagId>00000000-0000-0000-0000-000000000000</tagId>
<tagName>string</tagName>
<boundingBox>
<left>0</left>
<top>0</top>
<width>0</width>
<height>0</height>
</boundingBox>
<tagType>Regular</tagType>
</predictions>
</ImagePrediction>
Error response
{
"code": "NoError",
"message": "string"
}
<CustomVisionError>
<code>NoError</code>
<message>string</message>
</CustomVisionError>
<CustomVisionError>
<code>NoError</code>
<message>string</message>
</CustomVisionError>
Detect objects in an image url and saves the result.
Format - uuid. The project id.
Specifies the name of the model to evaluate against.
An ImageUrl that contains the url of the image to be evaluated.
Format - int32. Optional. Specifies the num of predicted tags per bounding box.
Optional. Specifies the name of application using the endpoint.
{
"url": "string"
}
<ImageUrl>
<url>string</url>
</ImageUrl>
<ImageUrl>
<url>string</url>
</ImageUrl>
OK
{
"id": "00000000-0000-0000-0000-000000000000",
"project": "00000000-0000-0000-0000-000000000000",
"iteration": "00000000-0000-0000-0000-000000000000",
"created": "string",
"predictions": [
{
"probability": 0.0,
"tagId": "00000000-0000-0000-0000-000000000000",
"tagName": "string",
"boundingBox": {
"left": 0.0,
"top": 0.0,
"width": 0.0,
"height": 0.0
},
"tagType": "Regular"
}
]
}
<ImagePrediction>
<id>00000000-0000-0000-0000-000000000000</id>
<project>00000000-0000-0000-0000-000000000000</project>
<iteration>00000000-0000-0000-0000-000000000000</iteration>
<created>string</created>
<predictions>
<probability>0</probability>
<tagId>00000000-0000-0000-0000-000000000000</tagId>
<tagName>string</tagName>
<boundingBox>
<left>0</left>
<top>0</top>
<width>0</width>
<height>0</height>
</boundingBox>
<tagType>Regular</tagType>
</predictions>
</ImagePrediction>
<ImagePrediction>
<id>00000000-0000-0000-0000-000000000000</id>
<project>00000000-0000-0000-0000-000000000000</project>
<iteration>00000000-0000-0000-0000-000000000000</iteration>
<created>string</created>
<predictions>
<probability>0</probability>
<tagId>00000000-0000-0000-0000-000000000000</tagId>
<tagName>string</tagName>
<boundingBox>
<left>0</left>
<top>0</top>
<width>0</width>
<height>0</height>
</boundingBox>
<tagType>Regular</tagType>
</predictions>
</ImagePrediction>
Error response
{
"code": "NoError",
"message": "string"
}
<CustomVisionError>
<code>NoError</code>
<message>string</message>
</CustomVisionError>
<CustomVisionError>
<code>NoError</code>
<message>string</message>
</CustomVisionError>
Detect objects in an image url without saving the result.
Format - uuid. The project id.
Specifies the name of the model to evaluate against.
An {Iris.Web.Api.Models.ImageUrl} that contains the url of the image to be evaluated.
Format - int32. Optional. Specifies the num of predicted tags per bounding box.
Optional. Specifies the name of application using the endpoint.
{
"url": "string"
}
<ImageUrl>
<url>string</url>
</ImageUrl>
<ImageUrl>
<url>string</url>
</ImageUrl>
OK
{
"id": "00000000-0000-0000-0000-000000000000",
"project": "00000000-0000-0000-0000-000000000000",
"iteration": "00000000-0000-0000-0000-000000000000",
"created": "string",
"predictions": [
{
"probability": 0.0,
"tagId": "00000000-0000-0000-0000-000000000000",
"tagName": "string",
"boundingBox": {
"left": 0.0,
"top": 0.0,
"width": 0.0,
"height": 0.0
},
"tagType": "Regular"
}
]
}
<ImagePrediction>
<id>00000000-0000-0000-0000-000000000000</id>
<project>00000000-0000-0000-0000-000000000000</project>
<iteration>00000000-0000-0000-0000-000000000000</iteration>
<created>string</created>
<predictions>
<probability>0</probability>
<tagId>00000000-0000-0000-0000-000000000000</tagId>
<tagName>string</tagName>
<boundingBox>
<left>0</left>
<top>0</top>
<width>0</width>
<height>0</height>
</boundingBox>
<tagType>Regular</tagType>
</predictions>
</ImagePrediction>
<ImagePrediction>
<id>00000000-0000-0000-0000-000000000000</id>
<project>00000000-0000-0000-0000-000000000000</project>
<iteration>00000000-0000-0000-0000-000000000000</iteration>
<created>string</created>
<predictions>
<probability>0</probability>
<tagId>00000000-0000-0000-0000-000000000000</tagId>
<tagName>string</tagName>
<boundingBox>
<left>0</left>
<top>0</top>
<width>0</width>
<height>0</height>
</boundingBox>
<tagType>Regular</tagType>
</predictions>
</ImagePrediction>
Error response
{
"code": "NoError",
"message": "string"
}
<CustomVisionError>
<code>NoError</code>
<message>string</message>
</CustomVisionError>
<CustomVisionError>
<code>NoError</code>
<message>string</message>
</CustomVisionError>
Detect objects in an image without saving the result.
Format - uuid. The project id.
Specifies the name of the model to evaluate against.
Format - int32. Optional. Specifies the num of predicted tags per bounding box.
Optional. Specifies the name of application using the endpoint.
[Binary image data]
------ExampleFormBoundary12345
Content-Disposition: form-data; name="imageData"; filename="filename.jpg"
Content-Type: image/jpeg
[Binary image data]
------ExampleFormBoundary12345--
OK
{
"id": "00000000-0000-0000-0000-000000000000",
"project": "00000000-0000-0000-0000-000000000000",
"iteration": "00000000-0000-0000-0000-000000000000",
"created": "string",
"predictions": [
{
"probability": 0.0,
"tagId": "00000000-0000-0000-0000-000000000000",
"tagName": "string",
"boundingBox": {
"left": 0.0,
"top": 0.0,
"width": 0.0,
"height": 0.0
},
"tagType": "Regular"
}
]
}
<ImagePrediction>
<id>00000000-0000-0000-0000-000000000000</id>
<project>00000000-0000-0000-0000-000000000000</project>
<iteration>00000000-0000-0000-0000-000000000000</iteration>
<created>string</created>
<predictions>
<probability>0</probability>
<tagId>00000000-0000-0000-0000-000000000000</tagId>
<tagName>string</tagName>
<boundingBox>
<left>0</left>
<top>0</top>
<width>0</width>
<height>0</height>
</boundingBox>
<tagType>Regular</tagType>
</predictions>
</ImagePrediction>
<ImagePrediction>
<id>00000000-0000-0000-0000-000000000000</id>
<project>00000000-0000-0000-0000-000000000000</project>
<iteration>00000000-0000-0000-0000-000000000000</iteration>
<created>string</created>
<predictions>
<probability>0</probability>
<tagId>00000000-0000-0000-0000-000000000000</tagId>
<tagName>string</tagName>
<boundingBox>
<left>0</left>
<top>0</top>
<width>0</width>
<height>0</height>
</boundingBox>
<tagType>Regular</tagType>
</predictions>
</ImagePrediction>
Error response
{
"code": "NoError",
"message": "string"
}
<CustomVisionError>
<code>NoError</code>
<message>string</message>
</CustomVisionError>
<CustomVisionError>
<code>NoError</code>
<message>string</message>
</CustomVisionError>
Classify an image and saves the result.
Format - uuid. The project id.
Specifies the name of the model to evaluate against.
Optional. Specifies the name of application using the endpoint.
[Binary image data]
------ExampleFormBoundary12345
Content-Disposition: form-data; name="imageData"; filename="filename.jpg"
Content-Type: image/jpeg
[Binary image data]
------ExampleFormBoundary12345--
OK
{
"id": "00000000-0000-0000-0000-000000000000",
"project": "00000000-0000-0000-0000-000000000000",
"iteration": "00000000-0000-0000-0000-000000000000",
"created": "string",
"predictions": [
{
"probability": 0.0,
"tagId": "00000000-0000-0000-0000-000000000000",
"tagName": "string",
"boundingBox": {
"left": 0.0,
"top": 0.0,
"width": 0.0,
"height": 0.0
},
"tagType": "Regular"
}
]
}
<ImagePrediction>
<id>00000000-0000-0000-0000-000000000000</id>
<project>00000000-0000-0000-0000-000000000000</project>
<iteration>00000000-0000-0000-0000-000000000000</iteration>
<created>string</created>
<predictions>
<probability>0</probability>
<tagId>00000000-0000-0000-0000-000000000000</tagId>
<tagName>string</tagName>
<boundingBox>
<left>0</left>
<top>0</top>
<width>0</width>
<height>0</height>
</boundingBox>
<tagType>Regular</tagType>
</predictions>
</ImagePrediction>
<ImagePrediction>
<id>00000000-0000-0000-0000-000000000000</id>
<project>00000000-0000-0000-0000-000000000000</project>
<iteration>00000000-0000-0000-0000-000000000000</iteration>
<created>string</created>
<predictions>
<probability>0</probability>
<tagId>00000000-0000-0000-0000-000000000000</tagId>
<tagName>string</tagName>
<boundingBox>
<left>0</left>
<top>0</top>
<width>0</width>
<height>0</height>
</boundingBox>
<tagType>Regular</tagType>
</predictions>
</ImagePrediction>
Error response
{
"code": "NoError",
"message": "string"
}
<CustomVisionError>
<code>NoError</code>
<message>string</message>
</CustomVisionError>
<CustomVisionError>
<code>NoError</code>
<message>string</message>
</CustomVisionError>
Classify an image url and saves the result.
Format - uuid. The project id.
Specifies the name of the model to evaluate against.
An ImageUrl that contains the url of the image to be evaluated.
Optional. Specifies the name of application using the endpoint.
{
"url": "string"
}
<ImageUrl>
<url>string</url>
</ImageUrl>
<ImageUrl>
<url>string</url>
</ImageUrl>
OK
{
"id": "00000000-0000-0000-0000-000000000000",
"project": "00000000-0000-0000-0000-000000000000",
"iteration": "00000000-0000-0000-0000-000000000000",
"created": "string",
"predictions": [
{
"probability": 0.0,
"tagId": "00000000-0000-0000-0000-000000000000",
"tagName": "string",
"boundingBox": {
"left": 0.0,
"top": 0.0,
"width": 0.0,
"height": 0.0
},
"tagType": "Regular"
}
]
}
<ImagePrediction>
<id>00000000-0000-0000-0000-000000000000</id>
<project>00000000-0000-0000-0000-000000000000</project>
<iteration>00000000-0000-0000-0000-000000000000</iteration>
<created>string</created>
<predictions>
<probability>0</probability>
<tagId>00000000-0000-0000-0000-000000000000</tagId>
<tagName>string</tagName>
<boundingBox>
<left>0</left>
<top>0</top>
<width>0</width>
<height>0</height>
</boundingBox>
<tagType>Regular</tagType>
</predictions>
</ImagePrediction>
<ImagePrediction>
<id>00000000-0000-0000-0000-000000000000</id>
<project>00000000-0000-0000-0000-000000000000</project>
<iteration>00000000-0000-0000-0000-000000000000</iteration>
<created>string</created>
<predictions>
<probability>0</probability>
<tagId>00000000-0000-0000-0000-000000000000</tagId>
<tagName>string</tagName>
<boundingBox>
<left>0</left>
<top>0</top>
<width>0</width>
<height>0</height>
</boundingBox>
<tagType>Regular</tagType>
</predictions>
</ImagePrediction>
Error response
{
"code": "NoError",
"message": "string"
}
<CustomVisionError>
<code>NoError</code>
<message>string</message>
</CustomVisionError>
<CustomVisionError>
<code>NoError</code>
<message>string</message>
</CustomVisionError>
Classify an image url without saving the result.
Format - uuid. The project id.
Specifies the name of the model to evaluate against.
An {Iris.Web.Api.Models.ImageUrl} that contains the url of the image to be evaluated.
Optional. Specifies the name of application using the endpoint.
{
"url": "string"
}
<ImageUrl>
<url>string</url>
</ImageUrl>
<ImageUrl>
<url>string</url>
</ImageUrl>
OK
{
"id": "00000000-0000-0000-0000-000000000000",
"project": "00000000-0000-0000-0000-000000000000",
"iteration": "00000000-0000-0000-0000-000000000000",
"created": "string",
"predictions": [
{
"probability": 0.0,
"tagId": "00000000-0000-0000-0000-000000000000",
"tagName": "string",
"boundingBox": {
"left": 0.0,
"top": 0.0,
"width": 0.0,
"height": 0.0
},
"tagType": "Regular"
}
]
}
<ImagePrediction>
<id>00000000-0000-0000-0000-000000000000</id>
<project>00000000-0000-0000-0000-000000000000</project>
<iteration>00000000-0000-0000-0000-000000000000</iteration>
<created>string</created>
<predictions>
<probability>0</probability>
<tagId>00000000-0000-0000-0000-000000000000</tagId>
<tagName>string</tagName>
<boundingBox>
<left>0</left>
<top>0</top>
<width>0</width>
<height>0</height>
</boundingBox>
<tagType>Regular</tagType>
</predictions>
</ImagePrediction>
<ImagePrediction>
<id>00000000-0000-0000-0000-000000000000</id>
<project>00000000-0000-0000-0000-000000000000</project>
<iteration>00000000-0000-0000-0000-000000000000</iteration>
<created>string</created>
<predictions>
<probability>0</probability>
<tagId>00000000-0000-0000-0000-000000000000</tagId>
<tagName>string</tagName>
<boundingBox>
<left>0</left>
<top>0</top>
<width>0</width>
<height>0</height>
</boundingBox>
<tagType>Regular</tagType>
</predictions>
</ImagePrediction>
Error response
{
"code": "NoError",
"message": "string"
}
<CustomVisionError>
<code>NoError</code>
<message>string</message>
</CustomVisionError>
<CustomVisionError>
<code>NoError</code>
<message>string</message>
</CustomVisionError>
Classify an image without saving the result.
Format - uuid. The project id.
Specifies the name of the model to evaluate against.
Optional. Specifies the name of application using the endpoint.
[Binary image data]
------ExampleFormBoundary12345
Content-Disposition: form-data; name="imageData"; filename="filename.jpg"
Content-Type: image/jpeg
[Binary image data]
------ExampleFormBoundary12345--
OK
{
"id": "00000000-0000-0000-0000-000000000000",
"project": "00000000-0000-0000-0000-000000000000",
"iteration": "00000000-0000-0000-0000-000000000000",
"created": "string",
"predictions": [
{
"probability": 0.0,
"tagId": "00000000-0000-0000-0000-000000000000",
"tagName": "string",
"boundingBox": {
"left": 0.0,
"top": 0.0,
"width": 0.0,
"height": 0.0
},
"tagType": "Regular"
}
]
}
<ImagePrediction>
<id>00000000-0000-0000-0000-000000000000</id>
<project>00000000-0000-0000-0000-000000000000</project>
<iteration>00000000-0000-0000-0000-000000000000</iteration>
<created>string</created>
<predictions>
<probability>0</probability>
<tagId>00000000-0000-0000-0000-000000000000</tagId>
<tagName>string</tagName>
<boundingBox>
<left>0</left>
<top>0</top>
<width>0</width>
<height>0</height>
</boundingBox>
<tagType>Regular</tagType>
</predictions>
</ImagePrediction>
<ImagePrediction>
<id>00000000-0000-0000-0000-000000000000</id>
<project>00000000-0000-0000-0000-000000000000</project>
<iteration>00000000-0000-0000-0000-000000000000</iteration>
<created>string</created>
<predictions>
<probability>0</probability>
<tagId>00000000-0000-0000-0000-000000000000</tagId>
<tagName>string</tagName>
<boundingBox>
<left>0</left>
<top>0</top>
<width>0</width>
<height>0</height>
</boundingBox>
<tagType>Regular</tagType>
</predictions>
</ImagePrediction>
Error response
{
"code": "NoError",
"message": "string"
}
<CustomVisionError>
<code>NoError</code>
<message>string</message>
</CustomVisionError>
<CustomVisionError>
<code>NoError</code>
<message>string</message>
</CustomVisionError>
Detect objects in an image and saves the result.
Format - uuid. The project id.
Specifies the name of the model to evaluate against.
Format - int32. Optional. Specifies the num of predicted tags per bounding box.
Optional. Specifies the name of application using the endpoint.
[Binary image data]
------ExampleFormBoundary12345
Content-Disposition: form-data; name="imageData"; filename="filename.jpg"
Content-Type: image/jpeg
[Binary image data]
------ExampleFormBoundary12345--
OK
{
"id": "00000000-0000-0000-0000-000000000000",
"project": "00000000-0000-0000-0000-000000000000",
"iteration": "00000000-0000-0000-0000-000000000000",
"created": "string",
"predictions": [
{
"probability": 0.0,
"tagId": "00000000-0000-0000-0000-000000000000",
"tagName": "string",
"boundingBox": {
"left": 0.0,
"top": 0.0,
"width": 0.0,
"height": 0.0
},
"tagType": "Regular"
}
]
}
<ImagePrediction>
<id>00000000-0000-0000-0000-000000000000</id>
<project>00000000-0000-0000-0000-000000000000</project>
<iteration>00000000-0000-0000-0000-000000000000</iteration>
<created>string</created>
<predictions>
<probability>0</probability>
<tagId>00000000-0000-0000-0000-000000000000</tagId>
<tagName>string</tagName>
<boundingBox>
<left>0</left>
<top>0</top>
<width>0</width>
<height>0</height>
</boundingBox>
<tagType>Regular</tagType>
</predictions>
</ImagePrediction>
<ImagePrediction>
<id>00000000-0000-0000-0000-000000000000</id>
<project>00000000-0000-0000-0000-000000000000</project>
<iteration>00000000-0000-0000-0000-000000000000</iteration>
<created>string</created>
<predictions>
<probability>0</probability>
<tagId>00000000-0000-0000-0000-000000000000</tagId>
<tagName>string</tagName>
<boundingBox>
<left>0</left>
<top>0</top>
<width>0</width>
<height>0</height>
</boundingBox>
<tagType>Regular</tagType>
</predictions>
</ImagePrediction>
Error response
{
"code": "NoError",
"message": "string"
}
<CustomVisionError>
<code>NoError</code>
<message>string</message>
</CustomVisionError>
<CustomVisionError>
<code>NoError</code>
<message>string</message>
</CustomVisionError>
Detect objects in an image url and saves the result.
Format - uuid. The project id.
Specifies the name of the model to evaluate against.
An ImageUrl that contains the url of the image to be evaluated.
Format - int32. Optional. Specifies the num of predicted tags per bounding box.
Optional. Specifies the name of application using the endpoint.
{
"url": "string"
}
<ImageUrl>
<url>string</url>
</ImageUrl>
<ImageUrl>
<url>string</url>
</ImageUrl>
OK
{
"id": "00000000-0000-0000-0000-000000000000",
"project": "00000000-0000-0000-0000-000000000000",
"iteration": "00000000-0000-0000-0000-000000000000",
"created": "string",
"predictions": [
{
"probability": 0.0,
"tagId": "00000000-0000-0000-0000-000000000000",
"tagName": "string",
"boundingBox": {
"left": 0.0,
"top": 0.0,
"width": 0.0,
"height": 0.0
},
"tagType": "Regular"
}
]
}
<ImagePrediction>
<id>00000000-0000-0000-0000-000000000000</id>
<project>00000000-0000-0000-0000-000000000000</project>
<iteration>00000000-0000-0000-0000-000000000000</iteration>
<created>string</created>
<predictions>
<probability>0</probability>
<tagId>00000000-0000-0000-0000-000000000000</tagId>
<tagName>string</tagName>
<boundingBox>
<left>0</left>
<top>0</top>
<width>0</width>
<height>0</height>
</boundingBox>
<tagType>Regular</tagType>
</predictions>
</ImagePrediction>
<ImagePrediction>
<id>00000000-0000-0000-0000-000000000000</id>
<project>00000000-0000-0000-0000-000000000000</project>
<iteration>00000000-0000-0000-0000-000000000000</iteration>
<created>string</created>
<predictions>
<probability>0</probability>
<tagId>00000000-0000-0000-0000-000000000000</tagId>
<tagName>string</tagName>
<boundingBox>
<left>0</left>
<top>0</top>
<width>0</width>
<height>0</height>
</boundingBox>
<tagType>Regular</tagType>
</predictions>
</ImagePrediction>
Error response
{
"code": "NoError",
"message": "string"
}
<CustomVisionError>
<code>NoError</code>
<message>string</message>
</CustomVisionError>
<CustomVisionError>
<code>NoError</code>
<message>string</message>
</CustomVisionError>
Detect objects in an image url without saving the result.
Format - uuid. The project id.
Specifies the name of the model to evaluate against.
An {Iris.Web.Api.Models.ImageUrl} that contains the url of the image to be evaluated.
Format - int32. Optional. Specifies the num of predicted tags per bounding box.
Optional. Specifies the name of application using the endpoint.
{
"url": "string"
}
<ImageUrl>
<url>string</url>
</ImageUrl>
<ImageUrl>
<url>string</url>
</ImageUrl>
OK
{
"id": "00000000-0000-0000-0000-000000000000",
"project": "00000000-0000-0000-0000-000000000000",
"iteration": "00000000-0000-0000-0000-000000000000",
"created": "string",
"predictions": [
{
"probability": 0.0,
"tagId": "00000000-0000-0000-0000-000000000000",
"tagName": "string",
"boundingBox": {
"left": 0.0,
"top": 0.0,
"width": 0.0,
"height": 0.0
},
"tagType": "Regular"
}
]
}
<ImagePrediction>
<id>00000000-0000-0000-0000-000000000000</id>
<project>00000000-0000-0000-0000-000000000000</project>
<iteration>00000000-0000-0000-0000-000000000000</iteration>
<created>string</created>
<predictions>
<probability>0</probability>
<tagId>00000000-0000-0000-0000-000000000000</tagId>
<tagName>string</tagName>
<boundingBox>
<left>0</left>
<top>0</top>
<width>0</width>
<height>0</height>
</boundingBox>
<tagType>Regular</tagType>
</predictions>
</ImagePrediction>
<ImagePrediction>
<id>00000000-0000-0000-0000-000000000000</id>
<project>00000000-0000-0000-0000-000000000000</project>
<iteration>00000000-0000-0000-0000-000000000000</iteration>
<created>string</created>
<predictions>
<probability>0</probability>
<tagId>00000000-0000-0000-0000-000000000000</tagId>
<tagName>string</tagName>
<boundingBox>
<left>0</left>
<top>0</top>
<width>0</width>
<height>0</height>
</boundingBox>
<tagType>Regular</tagType>
</predictions>
</ImagePrediction>
Error response
{
"code": "NoError",
"message": "string"
}
<CustomVisionError>
<code>NoError</code>
<message>string</message>
</CustomVisionError>
<CustomVisionError>
<code>NoError</code>
<message>string</message>
</CustomVisionError>
Detect objects in an image without saving the result.
Format - uuid. The project id.
Specifies the name of the model to evaluate against.
Format - int32. Optional. Specifies the num of predicted tags per bounding box.
Optional. Specifies the name of application using the endpoint.
[Binary image data]
------ExampleFormBoundary12345
Content-Disposition: form-data; name="imageData"; filename="filename.jpg"
Content-Type: image/jpeg
[Binary image data]
------ExampleFormBoundary12345--
OK
{
"id": "00000000-0000-0000-0000-000000000000",
"project": "00000000-0000-0000-0000-000000000000",
"iteration": "00000000-0000-0000-0000-000000000000",
"created": "string",
"predictions": [
{
"probability": 0.0,
"tagId": "00000000-0000-0000-0000-000000000000",
"tagName": "string",
"boundingBox": {
"left": 0.0,
"top": 0.0,
"width": 0.0,
"height": 0.0
},
"tagType": "Regular"
}
]
}
<ImagePrediction>
<id>00000000-0000-0000-0000-000000000000</id>
<project>00000000-0000-0000-0000-000000000000</project>
<iteration>00000000-0000-0000-0000-000000000000</iteration>
<created>string</created>
<predictions>
<probability>0</probability>
<tagId>00000000-0000-0000-0000-000000000000</tagId>
<tagName>string</tagName>
<boundingBox>
<left>0</left>
<top>0</top>
<width>0</width>
<height>0</height>
</boundingBox>
<tagType>Regular</tagType>
</predictions>
</ImagePrediction>
<ImagePrediction>
<id>00000000-0000-0000-0000-000000000000</id>
<project>00000000-0000-0000-0000-000000000000</project>
<iteration>00000000-0000-0000-0000-000000000000</iteration>
<created>string</created>
<predictions>
<probability>0</probability>
<tagId>00000000-0000-0000-0000-000000000000</tagId>
<tagName>string</tagName>
<boundingBox>
<left>0</left>
<top>0</top>
<width>0</width>
<height>0</height>
</boundingBox>
<tagType>Regular</tagType>
</predictions>
</ImagePrediction>
Error response
{
"code": "NoError",
"message": "string"
}
<CustomVisionError>
<code>NoError</code>
<message>string</message>
</CustomVisionError>
<CustomVisionError>
<code>NoError</code>
<message>string</message>
</CustomVisionError>
Classify an image and saves the result.
Format - uuid. The project id.
Specifies the name of the model to evaluate against.
Optional. Specifies the name of application using the endpoint.
[Binary image data]
------ExampleFormBoundary12345
Content-Disposition: form-data; name="imageData"; filename="filename.jpg"
Content-Type: image/jpeg
[Binary image data]
------ExampleFormBoundary12345--
OK
{
"id": "00000000-0000-0000-0000-000000000000",
"project": "00000000-0000-0000-0000-000000000000",
"iteration": "00000000-0000-0000-0000-000000000000",
"created": "string",
"predictions": [
{
"probability": 0.0,
"tagId": "00000000-0000-0000-0000-000000000000",
"tagName": "string",
"boundingBox": {
"left": 0.0,
"top": 0.0,
"width": 0.0,
"height": 0.0
},
"tagType": "Regular"
}
]
}
<ImagePrediction>
<id>00000000-0000-0000-0000-000000000000</id>
<project>00000000-0000-0000-0000-000000000000</project>
<iteration>00000000-0000-0000-0000-000000000000</iteration>
<created>string</created>
<predictions>
<probability>0</probability>
<tagId>00000000-0000-0000-0000-000000000000</tagId>
<tagName>string</tagName>
<boundingBox>
<left>0</left>
<top>0</top>
<width>0</width>
<height>0</height>
</boundingBox>
<tagType>Regular</tagType>
</predictions>
</ImagePrediction>
<ImagePrediction>
<id>00000000-0000-0000-0000-000000000000</id>
<project>00000000-0000-0000-0000-000000000000</project>
<iteration>00000000-0000-0000-0000-000000000000</iteration>
<created>string</created>
<predictions>
<probability>0</probability>
<tagId>00000000-0000-0000-0000-000000000000</tagId>
<tagName>string</tagName>
<boundingBox>
<left>0</left>
<top>0</top>
<width>0</width>
<height>0</height>
</boundingBox>
<tagType>Regular</tagType>
</predictions>
</ImagePrediction>
Error response
{
"code": "NoError",
"message": "string"
}
<CustomVisionError>
<code>NoError</code>
<message>string</message>
</CustomVisionError>
<CustomVisionError>
<code>NoError</code>
<message>string</message>
</CustomVisionError>
Classify an image url and saves the result.
Format - uuid. The project id.
Specifies the name of the model to evaluate against.
An ImageUrl that contains the url of the image to be evaluated.
Optional. Specifies the name of application using the endpoint.
{
"url": "string"
}
<ImageUrl>
<url>string</url>
</ImageUrl>
<ImageUrl>
<url>string</url>
</ImageUrl>
OK
{
"id": "00000000-0000-0000-0000-000000000000",
"project": "00000000-0000-0000-0000-000000000000",
"iteration": "00000000-0000-0000-0000-000000000000",
"created": "string",
"predictions": [
{
"probability": 0.0,
"tagId": "00000000-0000-0000-0000-000000000000",
"tagName": "string",
"boundingBox": {
"left": 0.0,
"top": 0.0,
"width": 0.0,
"height": 0.0
},
"tagType": "Regular"
}
]
}
<ImagePrediction>
<id>00000000-0000-0000-0000-000000000000</id>
<project>00000000-0000-0000-0000-000000000000</project>
<iteration>00000000-0000-0000-0000-000000000000</iteration>
<created>string</created>
<predictions>
<probability>0</probability>
<tagId>00000000-0000-0000-0000-000000000000</tagId>
<tagName>string</tagName>
<boundingBox>
<left>0</left>
<top>0</top>
<width>0</width>
<height>0</height>
</boundingBox>
<tagType>Regular</tagType>
</predictions>
</ImagePrediction>
<ImagePrediction>
<id>00000000-0000-0000-0000-000000000000</id>
<project>00000000-0000-0000-0000-000000000000</project>
<iteration>00000000-0000-0000-0000-000000000000</iteration>
<created>string</created>
<predictions>
<probability>0</probability>
<tagId>00000000-0000-0000-0000-000000000000</tagId>
<tagName>string</tagName>
<boundingBox>
<left>0</left>
<top>0</top>
<width>0</width>
<height>0</height>
</boundingBox>
<tagType>Regular</tagType>
</predictions>
</ImagePrediction>
Error response
{
"code": "NoError",
"message": "string"
}
<CustomVisionError>
<code>NoError</code>
<message>string</message>
</CustomVisionError>
<CustomVisionError>
<code>NoError</code>
<message>string</message>
</CustomVisionError>
Classify an image url without saving the result.
Format - uuid. The project id.
Specifies the name of the model to evaluate against.
An {Iris.Web.Api.Models.ImageUrl} that contains the url of the image to be evaluated.
Optional. Specifies the name of application using the endpoint.
{
"url": "string"
}
<ImageUrl>
<url>string</url>
</ImageUrl>
<ImageUrl>
<url>string</url>
</ImageUrl>
OK
{
"id": "00000000-0000-0000-0000-000000000000",
"project": "00000000-0000-0000-0000-000000000000",
"iteration": "00000000-0000-0000-0000-000000000000",
"created": "string",
"predictions": [
{
"probability": 0.0,
"tagId": "00000000-0000-0000-0000-000000000000",
"tagName": "string",
"boundingBox": {
"left": 0.0,
"top": 0.0,
"width": 0.0,
"height": 0.0
},
"tagType": "Regular"
}
]
}
<ImagePrediction>
<id>00000000-0000-0000-0000-000000000000</id>
<project>00000000-0000-0000-0000-000000000000</project>
<iteration>00000000-0000-0000-0000-000000000000</iteration>
<created>string</created>
<predictions>
<probability>0</probability>
<tagId>00000000-0000-0000-0000-000000000000</tagId>
<tagName>string</tagName>
<boundingBox>
<left>0</left>
<top>0</top>
<width>0</width>
<height>0</height>
</boundingBox>
<tagType>Regular</tagType>
</predictions>
</ImagePrediction>
<ImagePrediction>
<id>00000000-0000-0000-0000-000000000000</id>
<project>00000000-0000-0000-0000-000000000000</project>
<iteration>00000000-0000-0000-0000-000000000000</iteration>
<created>string</created>
<predictions>
<probability>0</probability>
<tagId>00000000-0000-0000-0000-000000000000</tagId>
<tagName>string</tagName>
<boundingBox>
<left>0</left>
<top>0</top>
<width>0</width>
<height>0</height>
</boundingBox>
<tagType>Regular</tagType>
</predictions>
</ImagePrediction>
Error response
{
"code": "NoError",
"message": "string"
}
<CustomVisionError>
<code>NoError</code>
<message>string</message>
</CustomVisionError>
<CustomVisionError>
<code>NoError</code>
<message>string</message>
</CustomVisionError>
Classify an image without saving the result.
Format - uuid. The project id.
Specifies the name of the model to evaluate against.
Optional. Specifies the name of application using the endpoint.
[Binary image data]
------ExampleFormBoundary12345
Content-Disposition: form-data; name="imageData"; filename="filename.jpg"
Content-Type: image/jpeg
[Binary image data]
------ExampleFormBoundary12345--
OK
{
"id": "00000000-0000-0000-0000-000000000000",
"project": "00000000-0000-0000-0000-000000000000",
"iteration": "00000000-0000-0000-0000-000000000000",
"created": "string",
"predictions": [
{
"probability": 0.0,
"tagId": "00000000-0000-0000-0000-000000000000",
"tagName": "string",
"boundingBox": {
"left": 0.0,
"top": 0.0,
"width": 0.0,
"height": 0.0
},
"tagType": "Regular"
}
]
}
<ImagePrediction>
<id>00000000-0000-0000-0000-000000000000</id>
<project>00000000-0000-0000-0000-000000000000</project>
<iteration>00000000-0000-0000-0000-000000000000</iteration>
<created>string</created>
<predictions>
<probability>0</probability>
<tagId>00000000-0000-0000-0000-000000000000</tagId>
<tagName>string</tagName>
<boundingBox>
<left>0</left>
<top>0</top>
<width>0</width>
<height>0</height>
</boundingBox>
<tagType>Regular</tagType>
</predictions>
</ImagePrediction>
<ImagePrediction>
<id>00000000-0000-0000-0000-000000000000</id>
<project>00000000-0000-0000-0000-000000000000</project>
<iteration>00000000-0000-0000-0000-000000000000</iteration>
<created>string</created>
<predictions>
<probability>0</probability>
<tagId>00000000-0000-0000-0000-000000000000</tagId>
<tagName>string</tagName>
<boundingBox>
<left>0</left>
<top>0</top>
<width>0</width>
<height>0</height>
</boundingBox>
<tagType>Regular</tagType>
</predictions>
</ImagePrediction>
Error response
{
"code": "NoError",
"message": "string"
}
<CustomVisionError>
<code>NoError</code>
<message>string</message>
</CustomVisionError>
<CustomVisionError>
<code>NoError</code>
<message>string</message>
</CustomVisionError>
Detect objects in an image and saves the result.
Format - uuid. The project id.
Specifies the name of the model to evaluate against.
Format - int32. Optional. Specifies the num of predicted tags per bounding box.
Optional. Specifies the name of application using the endpoint.
[Binary image data]
------ExampleFormBoundary12345
Content-Disposition: form-data; name="imageData"; filename="filename.jpg"
Content-Type: image/jpeg
[Binary image data]
------ExampleFormBoundary12345--
OK
{
"id": "00000000-0000-0000-0000-000000000000",
"project": "00000000-0000-0000-0000-000000000000",
"iteration": "00000000-0000-0000-0000-000000000000",
"created": "string",
"predictions": [
{
"probability": 0.0,
"tagId": "00000000-0000-0000-0000-000000000000",
"tagName": "string",
"boundingBox": {
"left": 0.0,
"top": 0.0,
"width": 0.0,
"height": 0.0
},
"tagType": "Regular"
}
]
}
<ImagePrediction>
<id>00000000-0000-0000-0000-000000000000</id>
<project>00000000-0000-0000-0000-000000000000</project>
<iteration>00000000-0000-0000-0000-000000000000</iteration>
<created>string</created>
<predictions>
<probability>0</probability>
<tagId>00000000-0000-0000-0000-000000000000</tagId>
<tagName>string</tagName>
<boundingBox>
<left>0</left>
<top>0</top>
<width>0</width>
<height>0</height>
</boundingBox>
<tagType>Regular</tagType>
</predictions>
</ImagePrediction>
<ImagePrediction>
<id>00000000-0000-0000-0000-000000000000</id>
<project>00000000-0000-0000-0000-000000000000</project>
<iteration>00000000-0000-0000-0000-000000000000</iteration>
<created>string</created>
<predictions>
<probability>0</probability>
<tagId>00000000-0000-0000-0000-000000000000</tagId>
<tagName>string</tagName>
<boundingBox>
<left>0</left>
<top>0</top>
<width>0</width>
<height>0</height>
</boundingBox>
<tagType>Regular</tagType>
</predictions>
</ImagePrediction>
Error response
{
"code": "NoError",
"message": "string"
}
<CustomVisionError>
<code>NoError</code>
<message>string</message>
</CustomVisionError>
<CustomVisionError>
<code>NoError</code>
<message>string</message>
</CustomVisionError>
Detect objects in an image url and saves the result.
Format - uuid. The project id.
Specifies the name of the model to evaluate against.
An ImageUrl that contains the url of the image to be evaluated.
Format - int32. Optional. Specifies the num of predicted tags per bounding box.
Optional. Specifies the name of application using the endpoint.
{
"url": "string"
}
<ImageUrl>
<url>string</url>
</ImageUrl>
<ImageUrl>
<url>string</url>
</ImageUrl>
OK
{
"id": "00000000-0000-0000-0000-000000000000",
"project": "00000000-0000-0000-0000-000000000000",
"iteration": "00000000-0000-0000-0000-000000000000",
"created": "string",
"predictions": [
{
"probability": 0.0,
"tagId": "00000000-0000-0000-0000-000000000000",
"tagName": "string",
"boundingBox": {
"left": 0.0,
"top": 0.0,
"width": 0.0,
"height": 0.0
},
"tagType": "Regular"
}
]
}
<ImagePrediction>
<id>00000000-0000-0000-0000-000000000000</id>
<project>00000000-0000-0000-0000-000000000000</project>
<iteration>00000000-0000-0000-0000-000000000000</iteration>
<created>string</created>
<predictions>
<probability>0</probability>
<tagId>00000000-0000-0000-0000-000000000000</tagId>
<tagName>string</tagName>
<boundingBox>
<left>0</left>
<top>0</top>
<width>0</width>
<height>0</height>
</boundingBox>
<tagType>Regular</tagType>
</predictions>
</ImagePrediction>
<ImagePrediction>
<id>00000000-0000-0000-0000-000000000000</id>
<project>00000000-0000-0000-0000-000000000000</project>
<iteration>00000000-0000-0000-0000-000000000000</iteration>
<created>string</created>
<predictions>
<probability>0</probability>
<tagId>00000000-0000-0000-0000-000000000000</tagId>
<tagName>string</tagName>
<boundingBox>
<left>0</left>
<top>0</top>
<width>0</width>
<height>0</height>
</boundingBox>
<tagType>Regular</tagType>
</predictions>
</ImagePrediction>
Error response
{
"code": "NoError",
"message": "string"
}
<CustomVisionError>
<code>NoError</code>
<message>string</message>
</CustomVisionError>
<CustomVisionError>
<code>NoError</code>
<message>string</message>
</CustomVisionError>
Detect objects in an image url without saving the result.
Format - uuid. The project id.
Specifies the name of the model to evaluate against.
An {Iris.Web.Api.Models.ImageUrl} that contains the url of the image to be evaluated.
Format - int32. Optional. Specifies the num of predicted tags per bounding box.
Optional. Specifies the name of application using the endpoint.
{
"url": "string"
}
<ImageUrl>
<url>string</url>
</ImageUrl>
<ImageUrl>
<url>string</url>
</ImageUrl>
OK
{
"id": "00000000-0000-0000-0000-000000000000",
"project": "00000000-0000-0000-0000-000000000000",
"iteration": "00000000-0000-0000-0000-000000000000",
"created": "string",
"predictions": [
{
"probability": 0.0,
"tagId": "00000000-0000-0000-0000-000000000000",
"tagName": "string",
"boundingBox": {
"left": 0.0,
"top": 0.0,
"width": 0.0,
"height": 0.0
},
"tagType": "Regular"
}
]
}
<ImagePrediction>
<id>00000000-0000-0000-0000-000000000000</id>
<project>00000000-0000-0000-0000-000000000000</project>
<iteration>00000000-0000-0000-0000-000000000000</iteration>
<created>string</created>
<predictions>
<probability>0</probability>
<tagId>00000000-0000-0000-0000-000000000000</tagId>
<tagName>string</tagName>
<boundingBox>
<left>0</left>
<top>0</top>
<width>0</width>
<height>0</height>
</boundingBox>
<tagType>Regular</tagType>
</predictions>
</ImagePrediction>
<ImagePrediction>
<id>00000000-0000-0000-0000-000000000000</id>
<project>00000000-0000-0000-0000-000000000000</project>
<iteration>00000000-0000-0000-0000-000000000000</iteration>
<created>string</created>
<predictions>
<probability>0</probability>
<tagId>00000000-0000-0000-0000-000000000000</tagId>
<tagName>string</tagName>
<boundingBox>
<left>0</left>
<top>0</top>
<width>0</width>
<height>0</height>
</boundingBox>
<tagType>Regular</tagType>
</predictions>
</ImagePrediction>
Error response
{
"code": "NoError",
"message": "string"
}
<CustomVisionError>
<code>NoError</code>
<message>string</message>
</CustomVisionError>
<CustomVisionError>
<code>NoError</code>
<message>string</message>
</CustomVisionError>
Detect objects in an image without saving the result.
Format - uuid. The project id.
Specifies the name of the model to evaluate against.
Format - int32. Optional. Specifies the num of predicted tags per bounding box.
Optional. Specifies the name of application using the endpoint.
[Binary image data]
------ExampleFormBoundary12345
Content-Disposition: form-data; name="imageData"; filename="filename.jpg"
Content-Type: image/jpeg
[Binary image data]
------ExampleFormBoundary12345--
OK
{
"id": "00000000-0000-0000-0000-000000000000",
"project": "00000000-0000-0000-0000-000000000000",
"iteration": "00000000-0000-0000-0000-000000000000",
"created": "string",
"predictions": [
{
"probability": 0.0,
"tagId": "00000000-0000-0000-0000-000000000000",
"tagName": "string",
"boundingBox": {
"left": 0.0,
"top": 0.0,
"width": 0.0,
"height": 0.0
},
"tagType": "Regular"
}
]
}
<ImagePrediction>
<id>00000000-0000-0000-0000-000000000000</id>
<project>00000000-0000-0000-0000-000000000000</project>
<iteration>00000000-0000-0000-0000-000000000000</iteration>
<created>string</created>
<predictions>
<probability>0</probability>
<tagId>00000000-0000-0000-0000-000000000000</tagId>
<tagName>string</tagName>
<boundingBox>
<left>0</left>
<top>0</top>
<width>0</width>
<height>0</height>
</boundingBox>
<tagType>Regular</tagType>
</predictions>
</ImagePrediction>
<ImagePrediction>
<id>00000000-0000-0000-0000-000000000000</id>
<project>00000000-0000-0000-0000-000000000000</project>
<iteration>00000000-0000-0000-0000-000000000000</iteration>
<created>string</created>
<predictions>
<probability>0</probability>
<tagId>00000000-0000-0000-0000-000000000000</tagId>
<tagName>string</tagName>
<boundingBox>
<left>0</left>
<top>0</top>
<width>0</width>
<height>0</height>
</boundingBox>
<tagType>Regular</tagType>
</predictions>
</ImagePrediction>
Error response
{
"code": "NoError",
"message": "string"
}
<CustomVisionError>
<code>NoError</code>
<message>string</message>
</CustomVisionError>
<CustomVisionError>
<code>NoError</code>
<message>string</message>
</CustomVisionError>