ObjectDetection¶

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

A single prediction from ObjectDetection.

Parameters

box (BoundingBox) – The bounding box around the detected object.
confidence (float) – The confidence of this prediction.
label (str) – The label describing this prediction result.
index (int) – The index of this result in the master label list.

property label¶

The label describing this prediction result.

Return type: str

property index¶

The index of this result in the master label list.

Return type: int

property box¶

The bounding box around the object.

Return type: BoundingBox

property confidence¶

The confidence of this prediction.

Return 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.

Parameters

predictions (List[ObjectDetectionPrediction]) – The boxes for each prediction.
duration (float) – The duration of the inference.
image (ndarray) – The image that the inference was performed on.

property duration¶

The duration of the inference in seconds.

Return type: float

property predictions¶

The list of predictions.

Return type: List[ObjectDetectionPrediction]

property image¶

The image the results were processed on.

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

Return type: ndarray

class ObjectDetection(model_id, model_config=None)¶

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 (str) – The ID of the model you want to use for object detection.
model_config (Optional[ModelConfig]) – The model configuration to load. model_id is ignored when model_config is set.

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

Perform Object Detection on an image

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

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]

publish_analytics(results, tag=None)¶

Publish Object Detection results to the alwaysAI Analytics Service

Parameters

results (ObjectDetectionResults) – The results to publish.
tag (Optional[Any]) – Additional information to assist in querying and visualizations.

Raises

ConnectionBlockedError when using connection to the alwaysAI Device Agent and resources are at capacity,

Raises

PacketRateError when publish rate exceeds current limit,

Raises

PacketSizeError when packet size exceeds current limit. Packet publish size and rate limits will be provided in the error message.

property accelerator¶

The accelerator being used.

Return type: Optional[Accelerator]

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.

Return type: Optional[ndarray]

property engine¶

The engine being used.

Return type: Optional[Engine]

property labels¶

The labels for the loaded model.

Note: Initialized to None when the model doesn’t have any labels.

Return type: Optional[List[str]]

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

property model_config¶

The configuration of the model that was loaded

Return type: ModelConfig

property model_id¶

The ID of the loaded model.

Return type: str

property model_purpose¶

The purpose of the model being used.

Return type: str

markup_image(image, predictions, show_labels=True, show_confidences=True, colors=None, line_thickness=2, font_size=0.5, font_thickness=2)¶

Draw boxes, labels, and confidences on the specified image.

Parameters

image (ndarray) – The image to draw on.
predictions (List[ObjectDetectionPrediction]) – The list of prediction results.
show_labels (bool) – Indicates whether to show the label of the prediction.
show_confidences (bool) – Indicates whether to show the confidence of the prediction.
colors (Optional[List[Tuple[int, int, int]]]) – A custom color list to use for the bounding boxes. The index of the color will be matched with a label index.
line_thickness (int) – The thickness of the lines that make up the bounding box.
font_size (float) – The scale factor for the text.
font_thickness (int) – The thickness of the lines used to draw the text.

Return type

ndarray

Returns

The marked-up image.

overlay_transparent_boxes(image, predictions, alpha=0.5, colors=None, show_labels=False, show_confidences=False)¶

Overlay area(s) of interest within an image. This utility is designed to work with object detection to display colored bounding boxes on the original image.

Parameters

image (ndarray) – The image to manipulate.
predictions (List[ObjectDetectionPrediction]) – The list of prediction results.
alpha (float) – Transparency of the overlay. The closer alpha is to 1.0, the more opaque the overlay will be. Similarly, the closer alpha is to 0.0, the more transparent the overlay will appear.
colors (Optional[List[Tuple[int, int, int]]]) – A custom color list to use for the bounding boxes or object classes pixel map
show_labels (bool) – Indicates whether to show the label of the prediction.
show_confidences (bool) – Indicates whether to show the confidence of the prediction.

Returns

The overlaid image.

blur_objects(image, predictions)¶

Blur objects detected in an image.

Parameters

image (ndarray) – The image to draw on.
predictions (List[ObjectDetectionPrediction]) – A list of predictions objects to blur.

Return type

ndarray

Returns

The image with objects blurred.

filter_predictions_by_label(predictions, label_list)¶

Filter a prediction list by label.

Typical usage:

people_and_apples = edgeiq.filter_predictions_by_label(predictions, ['person', 'apple'])

Parameters

predictions (List[ObjectDetectionPrediction]) – A list of predictions to filter.
label_list (List[str]) – The list of labels to keep in the filtered output.

Return type

List[ObjectDetectionPrediction]

Returns

The filtered predictions.

filter_predictions_by_area(predictions, min_area_thresh)¶

Filter a prediction list by bounding box area.

Typical usage:

larger_boxes = edgeiq.filter_predictions_by_area(predictions, 450)

Parameters

predictions (List[ObjectDetectionPrediction]) – A list of predictions to filter.
min_area_thresh (float) – The minimum bounding box area to keep in the filtered output.

Return type

List[ObjectDetectionPrediction]

Returns

The filtered predictions.