InferenceServer

class HttpInferenceServer(host, port, purpose)

Start an HTTP inference server

Typical usage:

server = HttpInferenceServer('0.0.0.0', '5002',
                             'ObjectDetection')
try:
    server.start()
finally:
    server.close()

Parameters

• host (str) – The IP address or hostname of the server.

• port (int) – The port of the server.

• purpose (str) – Purpose of the inference Server. Supported values: [‘ObjectDetection’]

start()

Start the inference server.

close()

Close the inference server.

class ObjectDetectionHttpClient(model_id, server_url)

Analyze and discover objects within an image by processing them on a hosted edgeiq.inference_server.HttpInferenceServer.

Typical usage:

obj_detect = edgeiq.ObjectDetectionHttpClient(
        'alwaysai/ssd_mobilenet_v1_coco_2018_01_28',
        'http://localhost:5002')
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 (str) – The ID of the model you want to use for object detection.

• server_url (str) – URL for connecting to the hosted HttpInferenceServer where the inferences performed.

load(engine=<Engine.DNN: 'DNN'>, accelerator=<Accelerator.DEFAULT: 'DEFAULT'>)

Load the model to an engine and accelerator.

Parameters

• engine (Engine) – The engine to load the model to

• accelerator (Accelerator) – The accelerator to load the model to

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

Perform Object Detection on an image by sending it to the HttpInferenceServer

Parameters

• image (ndarray) – The image to analyze.

• confidence_level (float) – The minimum confidence level required to successfully accept a detection.

• overlap_threshold (float) – 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.

Return type

ObjectDetectionResults

detect_objects_batch(images, confidence_level=0.3, overlap_threshold=0.3)

Perform Object Detection on a list of images by sending them to the HttpInferenceServer

Parameters

• images (List[ndarray]) – The list of images to analyze.

• confidence_level (float) – The minimum confidence level required to successfully accept a detection.

• overlap_threshold (float) – 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.

Return type

List[ObjectDetectionResults]