Classification

class ClassificationPrediction(confidence, label)

A single prediction from Classification.

Parameters:

confidence (float) – The confidence of this prediction.
label (str) – The label describing this prediction result.

property label: str: The label describing this prediction result.

property confidence: float: The confidence of this prediction.

class ClassificationResults(predictions, duration, image)

All the results of classification from Classification.

Predictions are stored in sorted order, with descending order of confidence.

Parameters:

predictions (List[ClassificationPrediction]) – The list of predictions ordered by confidence descending.
duration (float) – The duration of the inference.
image (ndarray) – The image that the inference was performed on.

property duration: float: The duration of the inference in seconds.

property predictions: List[ClassificationPrediction]: The list of predictions.

property image: ndarray: The image the results were processed on.

class Classification(model_id, model_config=None)

Identify the most prominent object in an image.

Typical usage:

classifier = edgeiq.Classification('alwaysai/googlenet')
classifier.load(engine=edgeiq.Engine.DNN)

<get image>
results = classifier.classify_image(image)
for prediction in results.predictions:
    print('Label: {}, confidence: {}'.format(
        prediction.label, prediction.confidence))

Parameters:: model_id (str) – The ID of the model you want to use for image classification.

classify_image(image, confidence_level=0.3)

Identify the most prominent object in the specified image.

Parameters:

image (ndarray) – The image to analyze.
confidence_level (float) – The minimum confidence level required to successfully accept a classification. Expected range: [0.0, 1.0].

Return type:

ClassificationResults

property accelerator: Accelerator | None: The accelerator being used.

property colors: ndarray | None

The auto-generated colors for the loaded model.

Note: Initialized to None when the model doesn’t have any labels. Note: To update, the new colors list must be same length as the label list.

property engine: Engine | None: The engine being used.

property labels: List[str] | None

The labels for the loaded model.

Note: Initialized to None when the model doesn’t have any labels.

load(engine=Engine.DNN, accelerator=Accelerator.DEFAULT)

Load the model to an engine and accelerator.

Parameters:

engine (Engine) – The engine to load the model to
accelerator (Accelerator) – The accelerator to load the model to

property model_config: ModelConfig: The configuration of the model that was loaded

property model_id: str: The ID of the loaded model.

property model_purpose: SupportedPurposes: The purpose of the model being used.

publish_analytics(results, tag=None, **kwargs)

Publish results to the alwaysAI Analytics Service

Example usage:

try:
    inference.publish_analytics(results, tag='custom_tag')
except edgeiq.PublishError as e:
    # Retry publish
except edgeiq.ConnectionError as e:
    # Save state and exit app to reconnect

Parameters:

results (TypeVar(ResultsT)) – The results to publish.
tag (Optional[Any]) – Additional information to assist in querying and visualizations.

Raises:

ConnectionBlockedError when using connection to the alwaysAI Device Agent and resources are at capacity,

Raises:

PacketRateError when publish rate exceeds current limit,

Raises:

PacketSizeError when packet size exceeds current limit. Packet publish size and rate limits will be provided in the error message.