Function estimate_translation_np

detrsmpl/utils/geometry.py:278–326

Find camera translation that brings 3D joints S closest to 2D the corresponding joints_2d. Input: S: (25, 3) 3D joint locations joints: (25, 3) 2D joint locations and confidence Returns: (3,) camera translation vector

(S,
                            joints_2d,
                            joints_conf,
                            focal_length=5000,
                            img_size=224)

def estimate_translation_np(S,
                            joints_2d,
                            joints_conf,
                            focal_length=5000,
                            img_size=224):
    """Find camera translation that brings 3D joints S closest to 2D the
    corresponding joints_2d.

    Input:
        S: (25, 3) 3D joint locations
        joints: (25, 3) 2D joint locations and confidence
    Returns:
        (3,) camera translation vector
    """

    num_joints = S.shape[0]
    # focal length
    f = np.array([focal_length, focal_length])
    # optical center
    center = np.array([img_size / 2., img_size / 2.])

    # transformations
    Z = np.reshape(np.tile(S[:, 2], (2, 1)).T, -1)
    XY = np.reshape(S[:, 0:2], -1)
    OO = np.tile(center, num_joints)
    F = np.tile(f, num_joints)
    weight2 = np.reshape(np.tile(np.sqrt(joints_conf), (2, 1)).T, -1)

    # least squares
    Q = np.array([
        F * np.tile(np.array([1, 0]), num_joints),
        F * np.tile(np.array([0, 1]), num_joints),
        OO - np.reshape(joints_2d, -1)
    ]).T
    c = (np.reshape(joints_2d, -1) - OO) * Z - F * XY

    # weighted least squares
    W = np.diagflat(weight2)
    Q = np.dot(W, Q)
    c = np.dot(W, c)

    # square matrix
    A = np.dot(Q.T, Q)
    b = np.dot(Q.T, c)

    # solution
    trans = np.linalg.solve(A, b)

    return trans


def estimate_translation(S, joints_2d, focal_length=5000., img_size=224.):