如何找到图像中的簇数?
How to find number of clusters in a image?
找出这张图片中的簇数:
我正在尝试查找此图像中的簇数。我尝试了 openCV morphologyEx 和侵蚀,但似乎无法为每个簇获得单个像素。请建议最好在 Python 中使用 openCV 计算图像中簇数的最佳方法。
--编辑
我尝试了细化、侵蚀和 morphologyEx(closing),但无法将簇聚成单个像素。以下是我尝试过的一些方法。
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)
这个怎么样?
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
out.jpg :
解决方法就这么简单。您应该找到图像的轮廓数并计算它们。为此,您可以使用带有以下参数的 cv2.findContours
方法。有关 cv2.findContours
的更多详细信息,请查看 documentation。
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:
973
找出这张图片中的簇数:
--编辑
我尝试了细化、侵蚀和 morphologyEx(closing),但无法将簇聚成单个像素。以下是我尝试过的一些方法。
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)
这个怎么样?
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
out.jpg :
解决方法就这么简单。您应该找到图像的轮廓数并计算它们。为此,您可以使用带有以下参数的 cv2.findContours
方法。有关 cv2.findContours
的更多详细信息,请查看 documentation。
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:
973