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(predictions, duration, image)

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

property image

The image the results were processed on.

Image is not available when results are obtained from EyeCloud Cameras.

Type

numpy array – The image in BGR format

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.

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

publish_analytics(results, tag=None)

Publish Classification results to the alwaysAI Analytics Service

Parameters
  • results (ClassificationResults) – The results to publish.

  • tag (JSON-serializable object) – Additional information to assist in querying and visualizations.

property accelerator

The accelerator being used.

Type

string

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'>)
property model_id

The ID of the loaded model.

Type

string

property model_purpose

The purpose of the model being used.

Type

string