initialization.py 文件源码

def imblur(Y, sig=5, siz=11, nDimBlur=None, kernel=None):
    """Spatial filtering with a Gaussian or user defined kernel
    The parameters are specified in GreedyROI
    """
    from scipy.ndimage.filters import correlate

    X = np.zeros(np.shape(Y))

    if kernel is None:
        if nDimBlur is None:
            nDimBlur = Y.ndim - 1
        else:
            nDimBlur = np.min((Y.ndim, nDimBlur))

        if np.isscalar(sig):
            sig = sig * np.ones(nDimBlur)

        if np.isscalar(siz):
            siz = siz * np.ones(nDimBlur)

        # xx = np.arange(-np.floor(siz[0] / 2), np.floor(siz[0] / 2) + 1)
        # yy = np.arange(-np.floor(siz[1] / 2), np.floor(siz[1] / 2) + 1)

        # hx = np.exp(-xx**2 / (2 * sig[0]**2))
        # hx /= np.sqrt(np.sum(hx**2))

        # hy = np.exp(-yy**2 / (2 * sig[1]**2))
        # hy /= np.sqrt(np.sum(hy**2))

        # temp = correlate(Y, hx[:, np.newaxis, np.newaxis], mode='constant')
        # X = correlate(temp, hy[np.newaxis, :, np.newaxis], mode='constant')

        # the for loop helps with memory
        # for t in range(np.shape(Y)[-1]):
        # temp = correlate(Y[:,:,t],hx[:,np.newaxis])#,mode='constant', cval=0.0)
        # X[:,:,t] = correlate(temp,hy[np.newaxis,:])#,mode='constant', cval=0.0)

        X = Y.copy()
        for i in range(nDimBlur):
            h = np.exp(-np.arange(-np.floor(siz[i] / 2), np.floor(siz[i] / 2) + 1)**2
                       / (2 * sig[i]**2))
            h /= np.sqrt(h.dot(h))
            shape = [1] * len(Y.shape)
            shape[i] = -1
            X = correlate(X, h.reshape(shape), mode='constant')

    else:
        X = correlate(Y, kernel[..., np.newaxis], mode='constant')
        # for t in range(np.shape(Y)[-1]):
        #    X[:,:,t] = correlate(Y[:,:,t],kernel,mode='constant', cval=0.0)

    return X

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