如何计算图像中的簇数？

Question

查找此图像中的群集数：

我在试着找出这个图像中的簇数。我尝试了openCV morphologyEx和腐蚀，但似乎无法为每个簇获得一个像素。请建议使用openCV（最好是Python）来计算图像中的簇数的最佳方法。在

——编辑

我尝试了细化、腐蚀和形态分析（关闭），但无法将这些簇聚合成一个像素。下面是我尝试过的一些方法。在

kernel = np.ones((2, 2), np.uint8) #[[1,1,1],[1,1,1],[1,1,1]
erosion = cv2.erode(img, kernel, iterations=1)
closing = cv2.morphologyEx(img, cv2.MORPH_CLOSE, kernel)
cv2.imwrite('test1.jpg', erosion)
cv2.imwrite('test2.jpg', closing)

img = cv2.imread(file, 0)
size = np.size(img)
skel = np.zeros(img.shape, np.uint8)

#ret, img = cv2.threshold(img, 127, 255, 0)
element = cv2.getStructuringElement(cv2.MORPH_CROSS, (3, 3))
done = False

while (not done):
    eroded = cv2.erode(img, element)
    temp = cv2.dilate(eroded, element)
    temp = cv2.subtract(img, temp)
    skel = cv2.bitwise_or(skel, temp)
    img = eroded.copy()

    zeros = size - cv2.countNonZero(img)
    if zeros == size:
        done = True

cv2.imwrite('thinning.jpg', skel)

Answer 1

这是怎么回事？在

import numpy as np
import cv2

img = cv2.imread('points.jpg')
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
ret, thresh = cv2.threshold(gray,0,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU)

n_labels, labels, stats, centroids = cv2.connectedComponentsWithStats(thresh)

print(n_labels)

size_thresh = 1
for i in range(1, n_labels):
    if stats[i, cv2.CC_STAT_AREA] >= size_thresh:
        #print(stats[i, cv2.CC_STAT_AREA])
        x = stats[i, cv2.CC_STAT_LEFT]
        y = stats[i, cv2.CC_STAT_TOP]
        w = stats[i, cv2.CC_STAT_WIDTH]
        h = stats[i, cv2.CC_STAT_HEIGHT]
        cv2.rectangle(img, (x, y), (x+w, y+h), (0, 255, 0), thickness=1)

cv2.imwrite("out.jpg", img)

簇数：974
输出.jpg：

Answer 2

解决办法就这么简单。你应该找出图像的轮廓数，并对其进行计数。为此，您可以使用带有以下参数的cv2.findContours方法。有关cv2.findContours的详细信息，请查看documentation。在

import cv2
img = cv2.imread('test.jpg', 0)
cv2.threshold(img,0,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU,img)

image, contours, hier = cv2.findContours(img, cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_NONE)
count = len(contours)
print(count)

输出：

^{pr2}$