dragoneye-python 1.0.1

Official Python SDK for the Dragoneye computer vision API

dragoneye-python

The official Python SDK for Dragoneye — build and call custom computer vision models from Python.

Describe what you want to detect in plain English on the Dragoneye Playground, and the AI Model Builder assembles a vision model with your categories and attributes. This SDK lets you run that model on images and videos and get back structured predictions with bounding boxes, category scores, and attribute scores.

• 📘 Full documentation: https://docs.dragoneye.ai/integrating/python-sdk
• 🎮 Playground: https://playground.dragoneye.ai/
• 🐍 PyPI: https://pypi.org/project/dragoneye-python/

---

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

Tip — Prerequisites: Don't have an API key yet? See Creating an Access Token.

To call the classifier, follow these steps:

import asyncio
from dragoneye import Dragoneye, Image, Video

async def main():
    # The api_key can also be set via the DRAGONEYE_API_KEY environment variable.
    client = Dragoneye(api_key="<YOUR_ACCESS_TOKEN>")

    # Example: predict from an image
    image = Image.from_path("photo.jpg")
    image_result = await client.classification.predict_image(
        media=image,
        model_name="recognize_anything/your_model_name",  # change to your desired model
    )

    # Example: predict from a video
    # NOTE! When loading a file, you can optionally pass a file name or identifier
    # that you use to identify your own files.
    video = Video.from_path(
        path="example.mp4",
        name="any-file-name",
    )
    video_result = await client.classification.predict_video(
        media=video,
        model_name="recognize_anything/your_model_name",
    )

    # Accessing image results
    for obj in image_result.object_predictions:
        bbox = obj.normalizedBbox
        for pred in obj.predictions:
            print(f"Category: {pred.category.name} ({pred.category.score:.2f})")
            for attr in pred.attributes:
                top_option = max(attr.options, key=lambda o: o.score)
                print(f"  {attr.name}: {top_option.name} ({top_option.score:.2f})")

asyncio.run(main())

Note — Model names: Model names follow the format recognize_anything/model_name. Use the name you specified when creating the model.

Example Video Response

Below is an example of what a ClassificationPredictVideoResponse looks like for a Building Detection model. The response maps each sampled frame's timestamp (in microseconds) to the objects detected in that frame:

ClassificationPredictVideoResponse(
    prediction_task_uuid="a1b2c3d4-e5f6-7890-abcd-ef1234567890",
    original_file_name="any-file-name",
    frames_per_second=1,
    timestamp_us_to_predictions={
        0: [
            ClassificationVideoObjectPrediction(
                frame_id="frame_0",
                timestamp_microseconds=0,
                normalizedBbox=(0.12, 0.25, 0.55, 0.78),
                predictions=[
                    ClassificationCategoryPrediction(
                        category=ClassificationCategory(
                            id=2084323334,
                            name="House (detached)",
                            score=0.92,
                        ),
                        attributes=[
                            ClassificationAttributeResponse(
                                attribute_id=1371766615,
                                name="Building Exterior Color",
                                options=[
                                    ClassificationAttributeOption(option_id=3498033303, name="White / Off-white", score=0.85),
                                    ClassificationAttributeOption(option_id=496739380, name="Light gray", score=0.10),
                                    # ... remaining options omitted for brevity
                                ],
                            ),
                            ClassificationAttributeResponse(
                                attribute_id=448392115,
                                name="Building Exterior Material",
                                options=[
                                    ClassificationAttributeOption(option_id=3887467550, name="Wood (incl. timber siding)", score=0.78),
                                    ClassificationAttributeOption(option_id=562768697, name="Brick", score=0.12),
                                    # ...
                                ],
                            ),
                            ClassificationAttributeResponse(
                                attribute_id=4240554102,
                                name="Building Size (Stories)",
                                options=[
                                    ClassificationAttributeOption(option_id=3067238669, name="2 stories", score=0.91),
                                    ClassificationAttributeOption(option_id=2398426374, name="1 story", score=0.06),
                                    # ...
                                ],
                            ),
                        ],
                    ),
                ],
            ),
            ClassificationVideoObjectPrediction(
                frame_id="frame_0",
                timestamp_microseconds=0,
                normalizedBbox=(0.60, 0.30, 0.88, 0.75),
                predictions=[
                    ClassificationCategoryPrediction(
                        category=ClassificationCategory(
                            id=3212613421,
                            name="Garage (detached)",
                            score=0.87,
                        ),
                        attributes=[
                            # ... attributes omitted for brevity
                        ],
                    ),
                ],
            ),
        ],
        1000000: [
            # Objects detected at t=1s (1,000,000 microseconds)
            # ...
        ],
    },
)

Each timestamp key (e.g., 0, 1000000) corresponds to a sampled frame. Within each frame, every detected object has its own bounding box, category prediction with a confidence score, and attribute predictions with scored options.

---

Client

Dragoneye

The main client used to interact with the API.

client = Dragoneye(
    api_key="<YOUR_ACCESS_TOKEN>",
    max_retries=10,
    max_backoff_time=120,
)

Arguments:

• api_key (Optional[str]): Your API key. If omitted, the SDK reads from the DRAGONEYE_API_KEY environment variable.
• max_retries (int): Maximum retry attempts on rate-limit (429) responses. Default: 10.
• max_backoff_time (int): Maximum backoff time in seconds for exponential backoff. Default: 120.

---

Media Classes

Image and Video are used to wrap media before passing it to a prediction endpoint. Each class restricts the MIME type to its respective media type (image/* or video/*).

Constructors

from_path

media = Image.from_path(
    path="photo.jpg",
    name="my-photo",          # optional identifier
    mime_type=None,            # auto-detected from extension by default
    guess_from_extension=True, # set False to require explicit mime_type
    read_into_memory=False,    # set True to load bytes into memory immediately
)

from_bytes

media = Image.from_bytes(
    data=raw_bytes,
    mime_type="image/jpeg",
    name="my-photo",  # optional
)

from_stream

media = Video.from_stream(
    stream=open("clip.mp4", "rb"),
    mime_type="video/mp4",
    name="my-clip",  # optional
)

---

Types and Endpoints

Types

The response types form a nested hierarchy. Here's how they fit together for image predictions:

ClassificationPredictImageResponse
└── object_predictions: [ClassificationObjectPrediction]
    ├── normalizedBbox: (x1, y1, x2, y2)
    └── predictions: [ClassificationCategoryPrediction]
        ├── category: ClassificationCategory (id, name, score)
        └── attributes: [ClassificationAttributeResponse]
            └── options: [ClassificationAttributeOption] (option_id, name, score)

For video predictions, ClassificationPredictVideoResponse maps timestamps to lists of ClassificationVideoObjectPrediction (which extends ClassificationObjectPrediction with frame_id and timestamp_microseconds).

---

ClassificationCategory Represents a predicted category.

Attributes:

• id (int): Unique identifier for the category.
• name (str): The name of the category.
• score (float): Confidence score for the prediction.

ClassificationAttributeOption Represents a single option within an attribute prediction.

Attributes:

• option_id (int): Unique identifier for the option.
• name (str): The name of the option.
• score (float): Confidence score for this option.

ClassificationAttributeResponse Represents a predicted attribute with its possible options.

Attributes:

• attribute_id (int): Unique identifier for the attribute.
• name (str): The name of the attribute.
• options (List[ClassificationAttributeOption]): The predicted options for this attribute.

ClassificationCategoryPrediction Represents a category prediction along with its associated attribute predictions.

Attributes:

• category (ClassificationCategory): The predicted category.
• attributes (List[ClassificationAttributeResponse]): Attribute predictions for this category.

ClassificationObjectPrediction Represents the prediction of a detected object in an image.

Attributes:

• normalizedBbox (NormalizedBbox): A bounding box for the detected object (coordinates are normalized).
• predictions (List[ClassificationCategoryPrediction]): Category and attribute predictions for this object.

ClassificationVideoObjectPrediction Extends ClassificationObjectPrediction with video-specific fields.

Attributes:

• normalizedBbox (NormalizedBbox): A bounding box for the detected object.
• predictions (List[ClassificationCategoryPrediction]): Category and attribute predictions.
• frame_id (str): Identifier for the frame.
• timestamp_microseconds (int): Timestamp of the frame in microseconds.

ClassificationPredictImageResponse The response object returned after predicting an image.

Attributes:

• object_predictions (List[ClassificationObjectPrediction]): Detected objects and their predictions.
• prediction_task_uuid (str): The unique identifier for the prediction task.
• original_file_name (Optional[str]): The file name of the original media, if provided.

ClassificationPredictVideoResponse The response object returned after predicting a video.

Attributes:

• timestamp_us_to_predictions (Dict[int, List[ClassificationVideoObjectPrediction]]): A mapping from timestamp (in microseconds) to object predictions for that frame.
• frames_per_second (int): The number of frames per second that were sampled.
• prediction_task_uuid (str): The unique identifier for the prediction task.
• original_file_name (Optional[str]): The file name of the original media, if provided.

PredictionTaskStatusResponse Represents the status of a prediction task.

Attributes:

• prediction_task_uuid (str): The unique identifier for the task.
• prediction_type (str): Either "image" or "video".
• status (str): The current task status (predicted, failed, etc.).

NormalizedBbox Type alias for normalized bounding boxes, represented as a tuple of four float values.

---

Endpoints

client.classification.predict_image

await client.classification.predict_image(
    media: Image,
    model_name: str,
    timeout_seconds: Optional[int] = None,
) -> ClassificationPredictImageResponse

Performs a classification prediction on a single image.

Parameter	Type	Default	Description
media	Image	required	An Image object (from from_path, from_bytes, or from_stream).
model_name	str	required	The name of the model to use for prediction.
timeout_seconds	Optional[int]	None	Maximum wait time in seconds. Raises PredictionTimeoutException on timeout. None polls indefinitely.

Returns: ClassificationPredictImageResponse — detected objects and their predictions.

---

client.classification.predict_video

await client.classification.predict_video(
    media: Video,
    model_name: str,
    frames_per_second: int = 1,
    timeout_seconds: Optional[int] = None,
) -> ClassificationPredictVideoResponse

Performs a classification prediction on a video.

Parameter	Type	Default	Description
media	Video	required	A Video object (from from_path, from_bytes, or from_stream).
model_name	str	required	The name of the model to use for prediction.
frames_per_second	int	1	How many frames per second to sample from the video.
timeout_seconds	Optional[int]	None	Maximum wait time in seconds. Raises PredictionTimeoutException on timeout. None polls indefinitely.

Returns: ClassificationPredictVideoResponse — frame-level prediction results.

---

client.classification.status

await client.classification.status(
    prediction_task_uuid: str,
) -> PredictionTaskStatusResponse

Checks the status of a prediction task.

Parameter	Type	Default	Description
prediction_task_uuid	str	required	The UUID of the prediction task.

Returns: PredictionTaskStatusResponse — the task's current status.

---

client.classification.get_image_results

await client.classification.get_image_results(
    prediction_task_uuid: str,
) -> ClassificationPredictImageResponse

Retrieves the results of a completed image prediction task.

Parameter	Type	Default	Description
prediction_task_uuid	str	required	The UUID of the prediction task.

Returns: ClassificationPredictImageResponse

---

client.classification.get_video_results

await client.classification.get_video_results(
    prediction_task_uuid: str,
) -> ClassificationPredictVideoResponse

Retrieves the results of a completed video prediction task.

Parameter	Type	Default	Description
prediction_task_uuid	str	required	The UUID of the prediction task.

Returns: ClassificationPredictVideoResponse

---

Error Handling

The SDK defines the following exception types:

Exception	When it's raised
PredictionTimeoutException	The prediction did not complete within the specified timeout_seconds.
PredictionTaskError	The prediction task failed on the server.
PredictionUploadError	The media file could not be uploaded.
PredictionTaskBeginError	The prediction task could not be started.
PredictionTaskResultsUnavailableError	Results were requested for a task that has not completed.

from dragoneye import Dragoneye, Image
from dragoneye.types.exception import (
    PredictionTimeoutException,
    PredictionTaskError,
    PredictionUploadError,
)

try:
    result = await client.classification.predict_image(
        media=image,
        model_name="recognize_anything/your_model_name",
        timeout_seconds=60,
    )
except PredictionTimeoutException:
    print("Prediction timed out — try increasing timeout_seconds")
except PredictionUploadError:
    print("Failed to upload media — check file path and format")
except PredictionTaskError:
    print("Prediction task failed on the server")

---

Notes

• All public methods are asynchronous. Use asyncio.run or an async loop to call them.
• For images, use predict_image with an Image object. For videos, use predict_video with a Video object. Passing the wrong media type will raise a ValueError.
• Predictions are executed as tasks: the SDK automatically handles task creation, media upload, polling, and result retrieval.
• The SDK automatically retries on rate-limit (429) responses using exponential backoff. You can configure this behavior via the max_retries and max_backoff_time parameters on the Dragoneye client.