Classification¶

class ClassificationPrediction(confidence, label)¶

A single prediction from Classification.

property label¶

The label describing this prediction result.

Type: string

property confidence¶

The confidence of this prediction.

Type: float

class ClassificationResults(confidences, labels, start, end)¶

All the results of classification from Classification.

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

property duration¶

The duration of the inference in seconds.

Type: float

property predictions¶

The list of predictions.

Type: list of ClassificationPrediction

class Classification(model_id)¶

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 (string) – The ID of the model you want to use for image classification.

property accelerator¶

The accelerator being used.

Type: string

classify_image(image, confidence_level=0.3)¶

Identify the most prominent object in the specified image.

Parameters

image (numpy array of image) – The image to analyze.
confidence_level (float in range [0.0, 1.0]) – The minimum confidence level required to successfully accept a classification.

Returns

ClassificationResults

property colors¶

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.

Type: list of (B, G, R) tuples.

property engine¶

The engine being used.

Type: string

property labels¶

The labels for the loaded model.

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

Type: list of strings.

load(engine=<Engine.DNN: 'DNN'>, accelerator=<Accelerator.DEFAULT: 'DEFAULT'>)¶

Initialize the inference engine and accelerator.

Parameters

engine (Engine) – The inference engine to use.
accelerator (Accelerator) – The hardware accelerator on which to run the inference engine.

property model_id¶

The ID of the loaded model.

Type: string