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(predictions, duration, image, **kwargs)¶

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

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 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.

publish_analytics(results, tag=None)¶

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.

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

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 detection.
overlap_threshold (float in range [0.0, 1.0]) – The minimum IOU threshold used to reject detections with Non-maximal Suppression during object detection using YOLO models. A higher value will result in a greater number of overlapping bounding boxes returned.

Returns

ObjectDetectionResults

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