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Using the Python SDK

If you're integrating with our APIs using Python, the Dragoneye SDK streamlines the process with minimal setup. Here's how you can get started and explore the types and endpoints in detail.

Installation​

Install the package using pip.

pip install dragoneye-python

Quick Start​

To call the classifier, follow these steps:

from dragoneye import Dragoneye, Image

dragoneye_client = Dragoneye(api_key=<YOUR_ACCESS_TOKEN>)

prediction_result = dragoneye_client.classification.predict(
    image=Image(file_or_bytes=image_file.file.read()),
    model_name="dragoneye/fashion", # Change to your desired model name
)

Types and Endpoints​

Types​

TaxonType (Enum)

Represents the type of taxon in the prediction.

  • CATEGORY: Represents a category taxon.
  • TRAIT: Represents a trait taxon.

TaxonPrediction

Represents a predicted taxon (category or trait) returned by the API.

Attributes:

  • id: Unique identifier for the taxon (TaxonID).
  • type: The type of the taxon (TaxonType).
  • name: The internal name of the taxon.
  • displayName: The user-friendly name of the taxon.
  • score: Optional confidence score for the prediction.
  • children: A sequence of nested child TaxonPrediction objects.

ClassificationObjectPrediction

Represents the prediction of an object in an image.

Attributes:

  • normalizedBbox: A bounding box for the detected object (coordinates are normalized).
  • category: The predicted category for the object (TaxonPrediction).
  • traits: A sequence of trait predictions (ClassificationTraitRootPrediction).

ClassificationPredictImageResponse

The response object returned after predicting an image.

Attributes:

  • predictions: A sequence of ClassificationObjectPrediction results.

TaxonID

A type alias for taxon IDs, represented as an int.

NormalizedBbox

A type alias for normalized bounding boxes, represented as a tuple of four float values.

Endpoints​

predict (Image Classification)​

Performs a classification prediction on a single image. You can pass either the image file or the URL of the image, and the SDK will return predictions based on the model specified.

Arguments:​
  • image: The image to be classified (can be bytes or a BinaryIO object).
  • model_name: The name of the model to be used for the prediction.
Response:​

Returns a ClassificationPredictImageResponse object containing prediction results.

predict_product (Product Classification)​

This method predicts categories and traits for a sequence of images of the same product.

Arguments:​
  • images: A sequence of images (either as bytes or BinaryIO).
  • model_name: The name of the model to be used for prediction.
Response:​

Returns a ClassificationPredictProductResponse object containing product prediction results.