主动轮廓模型：Snake模型的python实现

最新推荐文章于 2024-04-02 20:32:56 发布

原创

最新推荐文章于 2024-04-02 20:32:56 发布 · 1.3w 阅读

108 ·

CC 4.0 BY-SA版权

文章标签：

#主动轮廓模型 #Snake模型

本文介绍使用Skimage和Numpy库实现Snake模型进行图像分割的方法。Snake模型是一种活动轮廓模型，适用于2D单通道或多通道图像的分割。通过调整长度、平滑度、亮度吸引和边缘吸引等参数，Snake模型可以捕捉图像特征并拟合轮廓。文中提供了详细的函数参数说明、代码示例及结果展示。

质量声明：原创文章，内容质量问题请评论吐槽。如对您产生干扰，可私信删除。
主要参考：Active Contour Model — skimage v0.16.dev0 docs - scikit-image

文章目录

skimage实现

函数声明

Active contours by fitting snakes to features of images. Supports single and multichannel 2D images. Snakes can be periodic (for segmentation) or have fixed and/or free ends. The output snake has the same length as the input boundary. As the number of points is constant, make sure that the initial snake has enough points to capture the details of the final contour.

active_contour(image, snake, alpha=0.01, beta=0.1, w_line=0, w_edge=1, gamma=0.01,
        bc='periodic', max_px_move=1.0, max_iterations=2500, convergence=0.1)

    Parameters
    ----------
    image : (N, M) or (N, M, 3) ndarray
        Input image.
    snake : (N, 2) ndarray
        Initial snake coordinates. For periodic boundary conditions, endpoints
        must not be duplicated.
    alpha : float, optional
        Snake length shape parameter. Higher values makes snake contract
        faster.
    beta : float, optional
        Snake smoothness shape parameter. Higher values makes snake smoother.
    w_line : float, optional
        Controls attraction to brightness. Use negative values to attract toward
        dark regions.
    w_edge : float, optional
        Controls attraction to edges. Use negative values to repel snake from
        edges.
    gamma : float, optional
        Explicit time stepping parameter.
    bc : {'periodic', 'free', 'fixed'}, optional
        Boundary conditions for worm. 'periodic' attaches the two ends of the
        snake, 'fixed' holds the end-points in place, and 'free' allows free
        movement of the ends. 'fixed' and 'free' can be combined by parsing
        'fixed-free', 'free-fixed'. Parsing 'fixed-fixed' or 'free-free'
        yields same behaviour as 'fixed' and 'free', respectively.
    max_px_move : float, optional
        Maximum pixel distance to move per iteration.
    max_iterations : int, optional
        Maximum iterations to optimize snake shape.
    convergence: float, optional
        Convergence criteria.

    Returns
    -------
    snake : (N, 2) ndarray
        Optimised snake, same shape as input parameter.

    References
    ----------
    .. [1]  Kass, M.; Witkin, A.; Terzopoulos, D. "Snakes: Active contour
            models". International Journal of Computer Vision 1 (4): 321
            (1988). DOI:`10.1007/BF00133570`

代码示例

import numpy as np
from matplotlib import pyplot as plt
from skimage.color import rgb2gray
from skimage import data
from skimage.filters import gaussian
from skimage.segmentation import active_contour

img = data.astronaut() # 读入图像
img = rgb2gray(img) # 灰度化

# 圆的参数方程：(220, 100) r=100
t = np.linspace(0, 2*np.pi, 400) # 参数t, [0,2π]
x = 220 + 100*np.cos(t)
y = 100 + 100*np.sin(t)

# 构造初始Snake
init = np.array([x, y]).T # shape=(400, 2)

# Snake模型迭代输出
snake = active_contour(gaussian(img,3), snake=init, alpha=0.1, beta=1, gamma=0.01, w_line=0, w_edge=10)

# 绘图显示
plt.figure(figsize=(5, 5))
plt.imshow(img, cmap="gray")
plt.plot(init[:, 0], init[:, 1], '--r', lw=3)
plt.plot(snake[:,

最低0.47元/天解锁文章