我需要用于计数图像中细胞数量的代码，并且仅应计数粉红色的细胞。我使用了阈值化和分水岭方法。
import cv2
from skimage.feature import peak_local_max
from skimage.morphology import watershed
from scipy import ndimage
import numpy as np
import imutils
image = cv2.imread("cellorigin.jpg")
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
thresh = cv2.threshold(gray, 0, 255,
cv2.THRESH_BINARY_INV | cv2.THRESH_OTSU)[1]
cv2.imshow("Thresh", thresh)
D = ndimage.distance_transform_edt(thresh)
localMax = peak_local_max(D, indices=False, min_distance=20,
labels=thresh)
cv2.imshow("D image", D)
markers = ndimage.label(localMax, structure=np.ones((3, 3)))[0]
labels = watershed(-D, markers, mask=thresh)
print("[INFO] {} unique segments found".format(len(np.unique(labels)) - 1))
for label in np.unique(labels):
# if the label is zero, we are examining the 'background'
# so simply ignore it
if label == 0:
continue
# otherwise, allocate memory for the label region and draw
# it on the mask
mask = np.zeros(gray.shape, dtype="uint8")
mask[labels == label] = 255
# detect contours in the mask and grab the largest one
cnts = cv2.findContours(mask.copy(), cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)
cnts = imutils.grab_contours(cnts)
c = max(cnts, key=cv2.contourArea)
# draw a circle enclosing the object
((x, y), r) = cv2.minEnclosingCircle(c)
cv2.circle(image, (int(x), int(y)), int(r), (0, 255, 0), 2)
cv2.putText(image, "#{}".format(label), (int(x) - 10, int(y)),
cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 0, 255), 2)
cv2.imshow("input",image
cv2.waitKey(0)
我无法正确分割粉红色单元格。在某些地方，两个粉红色单元格连接在一起，也应分开。
输出:
最佳答案
由于细胞的可见性似乎不同于细胞核(深紫色)和背景(浅粉红色)，因此颜色阈值应在此处起作用。想法是将图像转换为HSV格式，然后使用较低和较高的颜色阈值隔离细胞。这将为我们提供一个二进制掩码，可用于计算单元数。
我们首先将图像转换为HSV格式，然后使用较低/较高的颜色阈值创建二进制掩码。从这里开始，我们执行形态学操作以使图像平滑并去除少量噪声。
有了 mask 后，我们就可以使用cv2.RETR_EXTERNAL参数找到轮廓，以确保仅采用外部轮廓。我们定义了几个面积阈值以滤除单元格
minimum_area = 200
average_cell_area = 650
connected_cell_area = 1000
minimum_area阈值可确保我们不计算单元格的细小部分。由于某些像元是连接的，因此某些轮廓可能会将多个相连的像元表示为单个轮廓，因此为了更好地估计像元，我们定义了average_cell_area参数，该参数估计单个像元的面积。 connected_cell_area参数检测连接的单元格，其中在连接的单元格轮廓上使用math.ceil()估计该轮廓中的单元格数量。要计算单元格的数量，我们遍历轮廓并根据其面积对轮廓求和。这是检测到的单元格以绿色突出显示Cells: 75
代码
import cv2
import numpy as np
import math
image = cv2.imread("1.jpg")
original = image.copy()
hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
hsv_lower = np.array([156,60,0])
hsv_upper = np.array([179,115,255])
mask = cv2.inRange(hsv, hsv_lower, hsv_upper)
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (3,3))
opening = cv2.morphologyEx(mask, cv2.MORPH_OPEN, kernel, iterations=1)
close = cv2.morphologyEx(opening, cv2.MORPH_CLOSE, kernel, iterations=2)
cnts = cv2.findContours(close, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if len(cnts) == 2 else cnts[1]
minimum_area = 200
average_cell_area = 650
connected_cell_area = 1000
cells = 0
for c in cnts:
area = cv2.contourArea(c)
if area > minimum_area:
cv2.drawContours(original, [c], -1, (36,255,12), 2)
if area > connected_cell_area:
cells += math.ceil(area / average_cell_area)
else:
cells += 1
print('Cells: {}'.format(cells))
cv2.imshow('close', close)
cv2.imshow('original', original)
cv2.waitKey()
关于python - 计算图像中的细胞数，我们在Stack Overflow上找到一个类似的问题：https://stackoverflow.com/questions/58751101/