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.

Return type: float

property class_map: ndarray¶

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

Return type: ndarray

property image: ndarray¶

The image the results were processed on.

Return type: ndarray

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: Optional[Accelerator]¶

The accelerator being used.

Return type: Optional[Accelerator]

property colors: Optional[ndarray]¶

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: Optional[Engine]¶

The engine being used.

Return type: Optional[Engine]

property labels: Optional[List[str]]¶

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, 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

Return type: ModelConfig

property model_id: str¶

The ID of the loaded model.

Return type: str

property model_purpose: SupportedPurposes¶

The purpose of the model being used.

Return type: SupportedPurposes

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.