ObjectDetection¶

class ObjectDetectionPrediction(box, confidence, label, index)¶

A single prediction from ObjectDetection.

property label¶

The label describing this prediction result.

Type: string

property index¶

The index of this result in the master label list.

Type: integer

property box¶

The bounding box around the object.

Type: BoundingBox

property confidence¶

The confidence of this prediction.

Type: float

class ObjectDetectionResults(boxes, confidences, labels, indexes, start, end, image)¶

All the results of object detection from ObjectDetection.

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 ObjectDetectionPrediction

class ObjectDetection(model_id)¶

Analyze and discover objects within an image.

Typical usage:

obj_detect = edgeiq.ObjectDetection(
        'alwaysai/ssd_mobilenet_v1_coco_2018_01_28')
obj_detect.load(engine=edgeiq.Engine.DNN)

<get image>
results = obj_detect.detect_objects(image, confidence_level=.5)
image = edgeiq.markup_image(
        image, results.predictions, colors=obj_detect.colors)

for prediction in results.predictions:
        text.append("{}: {:2.2f}%".format(
            prediction.label, prediction.confidence * 100))

Parameters: model_id (string) – The ID of the model you want to use for object detection.

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'>)¶

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

detect_objects(image, confidence_level=0.3, overlap_threshold=0.3)¶

Detect objects within the specified image.

Parameters

image (numpy array of image) – The image to analyze.
confidence_level (float) – The minimum confidence level required to successfully accept an object identification.
overlap_threshold (float) – The maximum overlap for accepting a bounding box. This threshold is only applied for Darknet models.

Returns

ObjectDetectionResults