PoseEstimation¶

class HumanPoseResult(poses, duration, input_dimension, image, **kwargs)¶

The results of pose estimation from PoseEstimation.

Parameters

poses (List[Pose]) – The poses from the inference.
duration (float) – Total time taken by the inference.
input_dimension (Tuple[int, int]) – The dimensions of the input image after padding.
image (ndarray) – The image that the inference was performed on.

property duration¶

The duration of the inference in seconds.

Return type: float

property poses¶

Poses found in image.

Return type: List[Pose]

property image¶

The image the results were processed on.

Return type: ndarray

draw_poses_background(color)¶

Draw poses found on image on a background color.

Parameters: color (Tuple[int, int, int]) – The color of the background in which the poses will be drawn on, in the format (B, G, R).
Return type: ndarray
Returns: image: numpy array of image in BGR format

draw_poses(image=None)¶

Draws poses found on image.

Parameters: image (Optional[ndarray]) – An image to draw the poses found
Return type: ndarray
Returns: image: numpy array of image in BGR format

draw_aliens()¶

Return type: ndarray
Returns: image: numpy array of image in BGR format

class PoseEstimation(model_id, model_config=None)¶

Find poses within an image.

Typical usage:

pose_estimator = edgeiq.PoseEstimation("alwaysai/human-pose")
pose_estimator.load(engine=edgeiq.Engine.DNN)

<get image>
results = pose_estimator.estimate(image)

for ind, pose in enumerate(results.poses):
            print('Person {}'.format(ind))
            print('-'*10)
            print('Key Points:')
            for key_point in pose.key_points:
                print(str(key_point))
image = results.draw_poses(image)

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

estimate(image)¶

Estimate poses within the specified image.

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

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.