InstanceSegmentation¶

class InstanceSegmentationPrediction(box, mask, contours, hierarchy, confidence, label, index)¶

A single prediction from InstanceSegmentation.

Parameters

box (BoundingBox) – The bounding box around the detected object.
mask (ndarray) – The mask of the detected instance of the object.
contours (list) – The contours of the mask.
hierarchy (list) – Hierarchy of contours
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 mask¶

The mask of this detected instance of the object.

Return type: ndarray

property contours¶

The contours generated for the mask of the detected instance of the object

Return type: list

property hierarchy¶

The hierarchy of contours generated for the mask of the detected instance of the object

Return type: list

property box¶

The bounding box around the object.

Return type: BoundingBox

property confidence¶

The confidence of this prediction.

Return type: float

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

All the results of instance segmentation from :class: InstanceSegmentation.

Predictions are stored in sorted order, with descending order of confidence.

Parameters

predictions (List[InstanceSegmentationPrediction]) – The boxes and masks 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[InstanceSegmentationPrediction]

property image¶

The image the results were processed on.

Return type: ndarray

class InstanceSegmentation(model_id, model_config=None)¶

Detect, segment and classify individual objects in an image.

Typical usage:

instance_segmentation = edgeiq.InstanceSegmentation(
        'alwaysai/mask_rcnn')
instance_segmentation.load(engine=edgeiq.Engine.DNN)

<get image>
results = instance_segmentation.segment_image(image, confidence_level=.5)
image = instance_segmentation.markup_image(
        image, results.predictions)

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 instance segmentation.

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

publish_analytics(results, tag=None, **kwargs)¶

Publish results to the alwaysAI Analytics Service

Example usage:

try:
    inference.publish_analytics(results, tag='custom_tag')
except edgeiq.PublishError as e:
    # Retry publish
except edgeiq.ConnectionError as e:
    # Save state and exit app to reconnect

Parameters

results (~ResultsT) – 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.

segment_image(image, confidence_level=0.3)¶

Detect, segment and classify individual objects in an image.

Parameters

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

Return type

InstanceSegmentationResults

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

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

Parameters

image (ndarray) – The image to draw on.
predictions (List[InstanceSegmentationPrediction]) – 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 confidences of the prediction.
show_masks (bool) – Indicates whether to show the masks 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