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Yes, groupRectangles is good to solve your problem.

i can't provide python code but i want to mention some hints

it seems the documentation about the function is confusing.you can use only variant of the function below

void cv::groupRectangles    (   std::vector< Rect > &   rectList,
std::vector< int > &    weights,
int     groupThreshold,
double  eps = 0.2 
)       
Python:
rectList, weights   =   cv.groupRectangles( rectList, groupThreshold[, eps] )

also have a look at faster-non-maximum-suppression-python

Yes, groupRectangles is good to solve your problem.

i can't provide python code but i want to mention some hints

it seems the documentation documentation about the function is confusing.you can use only variant of the function below

void cv::groupRectangles    (   std::vector< Rect > &   rectList,
std::vector< int > &    weights,
int     groupThreshold,
double  eps = 0.2 
)       
Python:
rectList, weights   =   cv.groupRectangles( rectList, groupThreshold[, eps] )

also have a look at faster-non-maximum-suppression-python

Yes, groupRectangles is good to solve your problem.

i can't provide python code but i want to mention some hints

it seems the documentation about the function is confusing.you can use only variant of the function below

void cv::groupRectangles    (   std::vector< Rect > &   rectList,
std::vector< int > &    weights,
int     groupThreshold,
double  eps = 0.2 
)       
Python:
rectList, weights   =   cv.groupRectangles( rectList, groupThreshold[, eps] )

also have a look at faster-non-maximum-suppression-python for an alternative way

Yes, groupRectangles is good to solve your problem.

i can't provide python code but i want to mention some hints

it seems the documentation about the function is confusing.you can use only variant of the function below

void cv::groupRectangles    (   std::vector< Rect > &   rectList,
std::vector< int > &    weights,
int     groupThreshold,
double  eps = 0.2 
)       
Python:
rectList, weights   =   cv.groupRectangles( rectList, groupThreshold[, eps] )

also have a look at faster-non-maximum-suppression-python for an alternative way

import cv2
import numpy as np

def draw(rects,color):
 for r in rects:
  p1 = (r[0], r[1])
  p2 = (r[0]+r[2], r[1]+r[3])
  cv2.rectangle(img, p1,p2, color,4)

limit_area = 500   
x = 0   
y = 0   
w = 0   
h = 0   
nuclei = []   
count = 0   
number_name = 1   

img = cv2.imread('e:/t.png')
img = cv2.add(img, 0.70)
img_hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)   
mask1 = cv2.inRange(img_hsv, (90,90,0), (255,255,255))  
mask2 = cv2.inRange(img_hsv, (70,100,0), (255,255,255)) 
mask = mask1 + mask2  
kernel = np.ones((1,3),np.uint8)  
mask = cv2.dilate(mask,kernel,iterations = 2)   
mask = cv2.morphologyEx(mask, cv2.MORPH_OPEN, kernel)  
mask_blur = cv2.medianBlur(mask,5)  
canny = cv2.Canny(mask_blur, 100,300,3) 
contours, hierarchy = cv2.findContours(canny,cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)  

rects = []
for cnt in contours:   
    if cv2.contourArea(cnt) >= limit_area:   
        nuclei.append(cnt)
        print(cv2.contourArea(cnt))
        x, y, w, h = cv2.boundingRect(cnt)
        rects.append([x, y, w, h])
        rects.append([x, y, w, h])

rects,weights = cv2.groupRectangles(rects, 1, 1.5)
draw(rects, (0,0,255))
cv2.imshow('img', img)
cv2.waitKey(0)
cv2.destroyAllWindows()