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.

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)

Perform Object Detection on an image

Parameters

• image (numpy array of image in BGR format) – 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

publish_analytics(results, tag=None)

Publish Object Detection results to the alwaysAI Analytics Service

Parameters

• results (ObjectDetectionResults) – The results to publish.

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