Scikit图像主动轮廓功能效果不佳

Question

• 我编写了以下代码来测试Scikit-Image库中的active-contour算法，但是结果看起来不如您在所附的图像中看到的那样：

• 如何获得更好的结果？我试图更改参数Alpha，Beta，Gamma，但结果也不是很好。

  1. 图片
  2. 标签
  3. 结果

import numpy as np
from skimage.io import imread, imsave
import skimage.filters as filters
from skimage.segmentation import active_contour
import matplotlib.pyplot as plt
from skimage.draw import polygon
#---------------------------------
img = imread('A.jpg')
mask = imread('label.png')

def activeContour(img, mask, alpha=0.015, beta=10, gamma=0.00000001, iterations=1000, max_step=0.7):
    lb_list = np.unique(mask)
    Result = np.zeros_like(img)
    for lb in lb_list:
        if(lb != 0):
            s = np.where(mask == lb)
            r = s[0].astype(np.uint8)
            c = s[1].astype(np.uint8)
            init = np.array([r, c]).T
            #-------PreProcessing----------------------------
            img2 = filters.gaussian(img, 3)
            #------- Active Contour  ------------------------
            snake = active_contour(img2, init, alpha=alpha, beta=beta, gamma=gamma, coordinates='rc',
             max_px_move=max_step, max_iterations=iterations)
            #------------------------------------------------
            Result[tuple(np.round(snake).astype(int).T)] += lb
    return Result.T

def sortVertices(array):
    s = np.where(array != 0)
    yr = s[0].astype(np.uint8)
    xc = s[1].astype(np.uint8)
    center_xc = np.sum(xc)/xc.shape 
    center_yr = np.sum(yr)/yr.shape 
    theta = np.arctan2(yr-center_yr, xc-center_xc) * 180 / np.pi
    indices = np.argsort(theta)
    x = xc[indices]
    y = yr[indices]
    return x,y

res2 = activeContour(img, mask, alpha=0.15, beta= 100, gamma= 0.000001)
labels = np.unique(res2)
ctr = 25
for label in labels:
    if label != 0:
        img2 = np.zeros_like(img)
        res = res2.copy()
        res[res != label]=0
        if np.count_nonzero(res)>1:
            x, y = sortVertices(res)
            rr, cc = polygon(x, y)
        else:
            rr, cc = np.where(res==label)

        img2[rr, cc] = 2*ctr
        img3 = (img2/img2.max())*img
        plt.imshow(img3, cmap='gray')
        plt.title('%d'%label)
        plt.axis('off')
        plt.show()
        ctr+=1