Python图像超分割SLIC算法 计算邻接矩阵及邻接列表

Obtain the pixel indices of each superpixel

sp_label_matrix=fs.slic(img_rgb, n_segments=400, compactness=10, sigma=1, enforce_connectivity=False)

sp_label_vector=sp_label_matrix.reshape(1,-1)
sp_num=len(np.unique(sp_label_matrix))
sp_ind=np.empty((sp_num,2),dtype=object)
for i in range(sp_num):
    sp_ind[i,:]=np.where(sp_label_vector==i)

compute the feature representation for each superpixel and form a feature matrix（行数为特征维度，列数为超像素个数）

def compute_sp_feature_matrix(np_img,sp_ind):
    gray_feature=np_img.reshape(1,-1)
    median_feature=cv2.medianBlur(np.float32(np_img),3).reshape(1,-1)
    average_feature=cv2.blur(np_img,(3,3)).reshape(1,-1)
    feature_matrix=np.concatenate((gray_feature,median_feature,average_feature),axis=0)

    rows,cols=feature_matrix.shape

    sp_num=sp_ind.shape[0]

    sp_feature_matrix=np.empty((rows,sp_num))

    for i in range(sp_num):
        sub_matrix=feature_matrix[:,sp_ind[i,1]]
        sp_feature_matrix[:,i]=np.mean(sub_matrix,axis=1)
    return sp_feature_matrix

compute the adjacent matrix and adjacent list

def compute_sp_adjacency(sp_label_matrix):

    labels=np.unique(sp_label_matrix)
    sp_num=len(labels)
    # adjacent matrix
    am=np.zeros((sp_num,sp_num))
    # adjacent list
    al=np.empty((sp_num,1),dtype=object)

    for i in range(sp_num):
        r=np.zeros(shape=sp_label_matrix.shape,dtype=np.uint8)
        r[sp_label_matrix==i]=1
        r_dilate_one_pixel=cv2.dilate(r,np.ones((3,3),dtype=np.uint8))
        r_boundary=r_dilate_one_pixel-r
        cur_adj=np.unique(np.setdiff1d(r_boundary*sp_label_matrix,[0]))
        al[i,0]=cur_adj
        am[i,cur_adj]=1
    return am,al

 

本文地址：https://blog.csdn.net/jzwong/article/details/107651872