def ncut_graph_matrix(im,sigma_d=1e2,sigma_g=1e-2):
    
    m,n = im.shape[:2] 
    N = m*n
    
    # normalize and create feature vector of RGB or grayscale
    if len(im.shape)==3:
        for i in range(3):
            im[:,:,i] = im[:,:,i] / im[:,:,i].max()
        vim = im.reshape((-1,3))
    else:
        im = im / im.max()
        vim = im.flatten()
    
    # x,y coordinates for distance computation
    xx,yy = meshgrid(range(n),range(m))
    x,y = xx.flatten(),yy.flatten()
    
    # create matrix with edge weights
    W = zeros((N,N),'f')
    for i in range(N):
        for j in range(i,N):
            d = (x[i]-x[j])**2 + (y[i]-y[j])**2 
            W[i,j] = W[j,i] = exp(-1.0*sum((vim[i]-vim[j])**2)/sigma_g) * exp(-d/sigma_d)
    
    return W
Create matrix for normalized cut. The parameters are 
dataset/ETHPy150Open jesolem/PCV/PCV/tools/ncut.py/ncut_graph_matrix