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