对利用FCM算法对灰度图像进行聚类的图像进行掩膜提取写python代码

以下是利用FCM算法对灰度图像进行聚类，并提取掩膜的Python代码：

import numpy as np
from sklearn.cluster import KMeans
from skimage import io
import matplotlib.pyplot as plt

def fcm(image, clusters, m=2, max_iter=100, error=1e-5):
    """
    Fuzzy C-means clustering algorithm

    Args:
        image: input grayscale image
        clusters: number of clusters
        m: fuzziness parameter
        max_iter: maximum number of iterations
        error: error tolerance

    Returns:
        membership: membership matrix
    """
    # Initialize membership matrix randomly
    rows, cols = image.shape
    membership = np.random.rand(rows, clusters)
    membership = membership / np.sum(membership, axis=1)[:, np.newaxis]

    # Iterate until convergence or maximum number of iterations reached
    for i in range(max_iter):
        # Calculate centroids
        centroids = np.dot(image.T, membership) / np.sum(membership, axis=0)

        # Calculate distances
        distances = np.zeros((rows, clusters))
        for j in range(clusters):
            distances[:, j] = np.linalg.norm(image - centroids[j], axis=1)

        # Update membership matrix
        membership_new = 1 / (distances ** (2 / (m - 1)))
        membership_new = membership_new / np.sum(membership_new, axis=1)[:, np.newaxis]

        # Check for convergence
        if np.linalg.norm(membership - membership_new) < error:
            break

        membership = membership_new

    return membership

# Load image
image = io.imread('image.jpg', as_gray=True)

# Apply FCM clustering
clusters = 2
membership = fcm(image, clusters)

# Extract mask
mask = np.argmax(membership, axis=1).reshape(image.shape)

# Display results
fig, ax = plt.subplots(1, 2, figsize=(10, 5))
ax[0].imshow(image, cmap='gray')
ax[0].set_title('Original Image')
ax[1].imshow(mask, cmap='gray')
ax[1].set_title('Mask')
plt.show()

其中，`fcm()` 函数实现了FCM算法，返回聚类的成员矩阵。`image` 表示输入的灰度图像，`clusters` 表示聚类的数量，`m` 表示模糊度参数，`max_iter` 表示最大迭代次数，`error` 表示误差容限。该函数返回聚类的成员矩阵。

在主程序中，首先加载灰度图像，然后调用 `fcm()` 函数进行聚类。接着，利用 `np.argmax()` 函数提取最大成员值对应的聚类标签，得到掩膜。最后，利用 `plt.imshow()` 函数显示原始灰度图像和掩膜。