SemanticSegmentation

class SemanticSegmentationResults(class_map, duration, image)

The results of semantic segmentation from SemanticSegmentation.

Parameters:

class_map (ndarray) – The class label with the highest probability for each and every (x, y)-coordinate in the image
duration (float) – The duration of the inference.
image (ndarray) – The image that the inference was performed on.

property duration: float: The duration of the inference in seconds.

property class_map: ndarray: The class label with the highest probability for each and every (x, y)-coordinate in the image.

property image: ndarray: The image the results were processed on.

class SemanticSegmentation(model_id, model_config=None)

Classify every pixel in an image.

The build_legend() is useful when used with the Streamer.

Typical usage:

semantic_segmentation = edgeiq.SemanticSegmentation('alwaysai/enet')
semantic_segmentation.load(engine=edgeiq.Engine.DNN)

with edgeiq.Streamer() as streamer:
    <get image>
    results = semantic_segmentation.segment_image(image)

    text = 'Inference time: {:1.3f} s'.format(results.duration)
    text.append('Legend:')
    text.append(semantic_segmentation.build_legend())

    mask = semantic_segmentation.build_image_mask(results.class_map)
    blended = edgeiq.blend_images(image, mask, alpha=0.5)

    streamer.send_data(blended, text)

Parameters:: model_id (str) – The ID of the model you want to use for semantic segmentation.

segment_image(image)

Classify every pixel within the specified image.

Parameters:: image (ndarray) – The image to analyze.
Return type:: SemanticSegmentationResults

build_image_mask(class_map)

Create an image mask by mapping colors to the class map. Colors can be set by the colors attribute.

Parameters:: class_map (ndarray) – The class label with the highest probability for each and every (x, y)-coordinate in the image
Return type:: ndarray
Returns:: Class color visualization for each pixel

build_legend()

Create a class legend that associates color with a class object

Return type:: str
Returns:: An HTML table with class labels and colors that can be used with the streamer.

build_object_map(class_map, class_list)

Create a object map by isolating classes within the class map.

Parameters:

class_map (ndarray) – The class with the highest probability for each and every (x, y)-coordinate in the image
class_list (List[str]) – The list of labels to include in the object map.

Return type:

ndarray

Returns:

The specific classes from the class list for each and every (x, y)-coordinate in the original image. Other classes not in the specified class list are rendered as non-labled or background.

property accelerator: Accelerator | None: The accelerator being used.

property colors: ndarray | None

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.

property engine: Engine | None: The engine being used.

property labels: List[str] | None

The labels for the loaded model.

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

load(engine=Engine.DNN, accelerator=Accelerator.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: ModelConfig: The configuration of the model that was loaded

property model_id: str: The ID of the loaded model.

property model_purpose: SupportedPurposes: The purpose of the model being used.

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 (TypeVar(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.