OpenCV_连通区域分析2 代码实现

参考博客：https://www.jianshu.com/p/a9cc11af270c

1.connectedComponents

用法：

ret, labels = cv2.connectedComponents(gray_img, connectivity=None)

# connectivity 4或8 临近像素： 周围4像素或8像素

 

案例import cvimport numpy as np

img = np.array([
 [0, 255, 0, 0],
 [0, 0, 0, 255],
 [0, 0, 0, 255],
 [255, 0, 0, 0]

], np.uint8)

_, labels = cv2.connectedComponents(img)
print(labels)
res = cv2.equalizeHist(cv2.convertScaleAbs(labels))
print(res)

结果：
"""
[[0 1 0 0]
 [0 0 0 2]
 [0 0 0 2]
 [3 0 0 0]]
[[ 0 64 0 0]
 [ 0 0 0 191]
 [ 0 0 0 191]
 [255 0 0 0]]
"""

2.connectedComponentsWithStats
用法：

_, labels, stats, centroids = cv2.connectedComponentsWithStats(img)

# stats N*5的矩阵，行对应每个label，五列分别为[x0, y0, width, height, area] # centroids 是每个域的质心坐标

样例：

import cv2
import numpy as np

img = np.array([
    [0, 255, 0, 0],
    [0, 0, 0, 255],
    [0, 0, 0, 255],
    [255, 0, 0, 0]

], np.uint8)
_, labels = cv2.connectedComponents(img)
# print(labels)

_, labels, stats, centroids = cv2.connectedComponentsWithStats(img)
print(labels)
print(stats)
print(centroids)

结果：
"""
[[0 1 0 0]
 [0 0 0 2]
 [0 0 0 2]
 [3 0 0 0]]
[[ 0  0  4  4 12]
 [ 1  0  1  1  1]
 [ 3  1  1  2  2]
 [ 0  3  1  1  1]]
[[1.41666667 1.5       ]
 [1.         0.        ]
 [3.         1.5       ]
 [0.         3.        ]]
"""