measure_test.py

# coding=utf-8
import numpy as np
import scipy.ndimage as ndi
from skimage import measure, color
import matplotlib.pyplot as plt


# 编写一个函数来生成原始二值图像
def microstructure(l=256):
    n = 5
    x, y = np.ogrid[0:l, 0:l]  # 生成网络
    mask = np.zeros((l, l))
    generator = np.random.RandomState(1)  # 随机数种子
    points = l * generator.rand(2, n ** 2)
    mask[(points[0]).astype(int), (points[1]).astype(int)] = 1
    mask = ndi.gaussian_filter(mask, sigma=l / (4. * n))  # 高斯滤波
    return mask > mask.mean()


data = microstructure(l=128) * 1  # 生成测试图片

labels = measure.label(data, connectivity=2)  #

# 筛选连通区域大于５００的
properties = measure.regionprops(labels)
valid_label = set()
for prop in properties:
    if prop.area > 500:
        valid_label.add(prop.label)
current_bw = np.in1d(labels, list(valid_label)).reshape(labels.shape)

dst = color.label2rgb(current_bw)  # 根据不同的标记显示不同的颜色
print('regions number:', current_bw.max() + 1)  # 显示连通区域块数(从0开始标记)

fig, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(8, 4))
ax1.imshow(data, plt.cm.gray, interpolation='nearest')
ax1.axis('off')
ax2.imshow(current_bw, plt.cm.gray, interpolation='nearest')
ax2.axis('off')
ax3.imshow(dst, interpolation='nearest')
ax3.axis('off')

fig.tight_layout()
plt.show()