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

publish_analytics(results, tag=None)

Publish Pose Estimation results to the alwaysAI Analytics Service

Parameters
  • results (HumanPoseResult) – 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