Function p_mpjpe

common/eval_cal.py:163–195

(predicted, target)

def p_mpjpe(predicted, target):
    assert predicted.shape == target.shape

    muX = np.mean(target, axis=1, keepdims=True)
    muY = np.mean(predicted, axis=1, keepdims=True)

    X0 = target - muX
    Y0 = predicted - muY

    normX = np.sqrt(np.sum(X0 ** 2, axis=(1, 2), keepdims=True))
    normY = np.sqrt(np.sum(Y0 ** 2, axis=(1, 2), keepdims=True))

    X0 /= normX
    Y0 /= normY

    H = np.matmul(X0.transpose(0, 2, 1), Y0)
    U, s, Vt = np.linalg.svd(H)
    V = Vt.transpose(0, 2, 1)
    R = np.matmul(V, U.transpose(0, 2, 1))

    sign_detR = np.sign(np.expand_dims(np.linalg.det(R), axis=1))
    V[:, :, -1] *= sign_detR
    s[:, -1] *= sign_detR.flatten()
    R = np.matmul(V, U.transpose(0, 2, 1))

    tr = np.expand_dims(np.sum(s, axis=1, keepdims=True), axis=2)

    a = tr * normX / normY 
    t = muX - a * np.matmul(muY, R)

    predicted_aligned = a * np.matmul(predicted, R) + t

    return np.mean(np.linalg.norm(predicted_aligned - target, axis=len(target.shape) - 1), axis=len(target.shape) - 2)