I need code for counting the number of cells in the image and only the cells that are in pink color should be counted .I have used thresholding and watershed method.
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)
I am not able to segment the pink cells properly.At some places two pink cells are attached together those also should be separated.
output:
3) Select Plugins → 1 analysis → Cell Counter (or Plugins → Cell Counter). Two new windows will open, a counter window with your image on top of a row of buttons, and a results window where cells will tally. 4) To begin counting, click one of the buttons at the bottom of the counter window.
Using a microscope, focus on the grid lines of the counting area with a 5-10x objective. Count the cells in one set of 16 squares (1×1 mm square area; the blue area). You should set a counting rule. To calculate the number of cells per mL: Take the average cell count from each of the sets of 16 corner squares.
Since the cells seem to be visibility different from the nucleus (dark purple) and the background (light pink), color thresholding should work here. The idea is to convert the image to HSV format then use a lower and upper color threshold to isolate the cells. This will give us a binary mask which we can use to count the number of cells.
We begin by converting the image to HSV format then use a lower/upper color threshold to create a binary mask. From here we perform morphological operations to smooth the image and remove small bits of noise.
Now that we have the mask, we find contours with the cv2.RETR_EXTERNAL
parameter to ensure that we only take the outer contours. We define several area thresholds to filter out the cells
minimum_area = 200
average_cell_area = 650
connected_cell_area = 1000
The minimum_area
threshold ensures that we do not count tiny sections of a cell. Since some of the cells are connected, some contours may have multiple connected cells represented as a single contour so to estimate the cells better, we define an average_cell_area
parameter which estimates the area of a single cell. The connected_cell_area
parameter detects connected cells where use math.ceil()
on a connected cell contour to estimate the number of cells in that contour. To count the number of cells, we iterate through the contours and sum up the contours based on their area. Here's the detected cells highlighted in green
Cells: 75
Code
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()
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