msaf/algorithms/cnmf/segmenter.py
def filter_activation_matrix(G, R):
    """Filters the activation matrix G, and returns a flattened copy."""

    #import pylab as plt
    #plt.imshow(G, interpolation="nearest", aspect="auto")
    #plt.show()

    idx = np.argmax(G, axis=1)
    max_idx = np.arange(G.shape[0])
    max_idx = (max_idx, idx.flatten())
    G[:, :] = 0
    G[max_idx] = idx + 1

    # TODO: Order matters?
    G = np.sum(G, axis=1)
    G = median_filter(G[:, np.newaxis], R)

    return G.flatten()
def filter_activation_matrix(G, R):
    #import pylab as plt
    #plt.imshow(G, interpolation="nearest", aspect="auto")
    #plt.show()

    idx = np.argmax(G, axis=1)
    max_idx = np.arange(G.shape[0])
    max_idx = (max_idx, idx.flatten())
    G[:, :] = 0
    G[max_idx] = idx + 1

    # TODO: Order matters?
    G = np.sum(G, axis=1)
    G = median_filter(G[:, np.newaxis], R)

    return G.flatten()
def filter_activation_matrix ( G , R ) : #import pylab as plt #plt.imshow(G, interpolation="nearest", aspect="auto") #plt.show() idx = np . argmax ( G , axis = 1 ) max_idx = np . arange ( G . shape [ 0 ] ) max_idx = ( max_idx , idx . flatten ( ) ) G [ : , : ] = 0 G [ max_idx ] = idx + 1 # TODO: Order matters? G = np . sum ( G , axis = 1 ) G = median_filter ( G [ : , np . newaxis ] , R ) return G . flatten ( )
Filters the activation matrix G, and returns a flattened copy.
Filters the activation matrix G and returns a flattened copy .