msaf/algorithms/foote/segmenter.py
def compute_gaussian_krnl(M):
    """Creates a gaussian kernel following Foote's paper."""
    g = signal.gaussian(M, M // 3., sym=True)
    G = np.dot(g.reshape(-1, 1), g.reshape(1, -1))
    G[M // 2:, :M // 2] = -G[M // 2:, :M // 2]
    G[:M // 2, M // 2:] = -G[:M // 2, M // 2:]
    return G
def compute_gaussian_krnl(M):
    """Creates a gaussian kernel following Foote's paper."""
    g = signal.gaussian(M, M // 3., sym=True)
    G = np.dot(g.reshape(-1, 1), g.reshape(1, -1))
    G[M // 2:, :M // 2] = -G[M // 2:, :M // 2]
    G[:M // 2, M // 2:] = -G[:M // 2, M // 2:]
    return G
def compute_gaussian_krnl ( M ) : g = signal . gaussian ( M , M // 3. , sym = True ) G = np . dot ( g . reshape ( - 1 , 1 ) , g . reshape ( 1 , - 1 ) ) G [ M // 2 : , : M // 2 ] = - G [ M // 2 : , : M // 2 ] G [ : M // 2 , M // 2 : ] = - G [ : M // 2 , M // 2 : ] return G
Creates a gaussian kernel following Foote's paper.
Creates a gaussian kernel following Foote s paper .