Python get_a_normalized_standing_skeleton Examples

Example #1

TOTAL_JOINTS = 13
NECK = 0
L_ARMS = [1, 2, 3]
R_ARMS = [4, 5, 6]
L_KNEE = 8
L_ANKLE = 9
R_KNEE = 11
R_ANKLE = 12
L_LEGS = [8, 9]
R_LEGS = [11, 12]
ARMS_LEGS = L_ARMS + R_ARMS + L_LEGS + R_LEGS
L_THIGH = 7
R_THIGH = 10

STAND_SKEL_NORMED = retrain_only_body_joints(
    get_a_normalized_standing_skeleton())

# -- Functions


def train_test_split(X, Y, ratio_of_test_size):
    ''' Split training data by ratio '''
    IS_SPLIT_BY_SKLEARN_FUNC = True

    # Use sklearn.train_test_split
    if IS_SPLIT_BY_SKLEARN_FUNC:
        RAND_SEED = 1
        tr_X, te_X, tr_Y, te_Y = sklearn.model_selection.train_test_split(
            X, Y, test_size=ratio_of_test_size, random_state=RAND_SEED)

    # Make train/test the same.

Example #2

File: lib_feature_proc.py Project: rashidch/Falling-Person-Detection

TOTAL_JOINTS = 13
NECK = 0
L_ARMS = [1, 2, 3]
R_ARMS = [4, 5, 6]
L_KNEE = 8
L_ANKLE = 9
R_KNEE = 11
R_ANKLE = 12
L_LEGS = [8, 9]
R_LEGS = [11, 12]
ARMS_LEGS = L_ARMS + R_ARMS + L_LEGS + R_LEGS
L_THIGH = 7
R_THIGH = 10

STAND_SKEL_NORMED = retrain_only_body_joints(get_a_normalized_standing_skeleton())

# -- Functions


def extract_multi_frame_features(
    X, Y, video_indices, window_size, is_adding_noise=False, is_print=False
):
    """From image index and raw skeleton positions,
    Extract features of body velocity, joint velocity, and normalized joint positions.
    """
    X_new = []
    Y_new = []
    N = len(video_indices)

    # Loop through all data