PoseEstimation¶

class PoseEstimation(model_id)¶

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 (string) – The ID of the model you want to use for pose estimation.

estimate(image)¶

Estimate poses within the specified image.

Parameters: image (numpy array of image) – The image to analyze.
Returns: HumanPoseResult

property accelerator¶

The accelerator being used.

Type: string

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.

Type: list of (B, G, R) tuples.

property engine¶

The engine being used.

Type: string

property labels¶

The labels for the loaded model.

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

Type: list of strings.

load(engine=<Engine.DNN: 'DNN'>, accelerator=<Accelerator.DEFAULT: 'DEFAULT'>)¶

Initialize the inference engine and accelerator.

Parameters

engine (Engine) – The inference engine to use.
accelerator (Accelerator) – The hardware accelerator on which to run the inference engine.

property model_id¶

The ID of the loaded model.

Type: string

HumanPose¶

class Pose(key_points, score)¶

property key_points¶

Key Points corresponding to body parts.

Body Part Names
Nose
Neck
Right Shoulder
Right Elbow
Right Wrist
Left Shoulder
Left Elbow
Left Wrist
Right Hip
Right Knee
Right Ankle
Left Hip
Left Knee
Left Ankle
Right Eye
Left Eye
Right Ear
Left Ear

Returns: dict of coordinate tuples (x, y) mapped to their corresponding body part names

property score¶

Confidence level associated with pose.

Type: float in range [0.0, 1.0]

class HumanPoseResult(result_data, start, end, image, network_height, **kwargs)¶

The results of pose estimation from PoseEstimation.

property image¶

The image the results were processed on.

Type: numpy array – The image in BGR format

property poses¶

Poses found in image.

Type: list of Pose

property duration¶

The duration of the inference in seconds.

Type: float

draw_poses_background(color)¶

Draw poses found on image on a background color.

Parameters: color (tuple which contains values for B, G, R color channels.) – The color of the background in which the poses will be drawn on.
Returns: image: numpy array of image in BGR format

draw_poses(image=None)¶

Draws poses found on image.

Parameters: image (numpy.array) – An image to draw the poses found
Returns: image: numpy array of image in BGR format

property raw_results¶

The raw results returned from model.

Type: list of 2 numpy arrays shaped (1, 38, 32, 57) (1, 19, 32, 57) respectively, data type float32

property feature_maps¶

The feature map returned by the model.

Type: numpy array shape (19, 32, 57), data type float

property partial_affinity_fields¶

The partial affinity fields returned by the model.

Type: numpy array shape (38, 32, 57), data type float

draw_aliens()¶

Returns: image: numpy array of image in BGR format