Pose Estimation and Human Segmentation

class dronevis.models.PoseSegEstimation(is_seg=False, is_seg_pose=False)

Pose estimation class for loading and predicting with mediapipe BlazePose model.

The model inherits from base CVModel and implements its abstract methods: load_model, transform_img, predict, detect_webcam.

__init__(is_seg=False, is_seg_pose=False)
load_model()

Load model from weights associated with mediapipe

transform_img(image)

Transform image from BGR to RGB

Parameters:

image (np.array) – input image

Returns:

transformed image

Return type:

np.array

predict(image, is_seg=False, is_seg_pose=False, all_formats=False)

Predict keypoints for pose and draw them on input image. Input image is assumed to be BGR.

Parameters:

  • image (np.array) – input image

  • is_seg (bool, optional) – flag whether a segmentation is desired. Defaults to False.

  • all_formats (bool, optional) – flag whether to return all image format (segmentation,

  • False. (pose estimation, and pose-segmentation). Defaults to) –

Returns:

output image with keypoints drawn, segmented image segmented image with pose points

Return type:

Tuple[np.array, …]

detect_webcam(video_index=0, window_name='Pose')

Start webcam pose estimation from video_index (to quit running this function press ‘q’)

The stream is retrieved and decoded using opencv library.

Parameters:

  • video_index (int | str, optional) – index of video stream device. Defaults to 0.

  • window_name (str, optional) – name of cv2 window. Defaults to “Pose”.

  • is_seg (bool, optional) – flag whether a segmentation is desired. Defaults to False.