How to change the person my code was following/tracking by clicking or selecting another person by mouse? Rest details of project below

Question

I'm creating a code for tshirt detection of a person and follow him in a group of peoples. So, that in a vedio of group of peoples my code will follow/track a single person, by identifying the color of his tshirt and then follow him.
Now I want to add new feature that i should be able to click and select a person manually which i want my code to follow. So, if i want to change the person to follow manually by clicking another person, with another color of tshirt, my code should follow that person which I selected.

This is my code:

import cv2
import numpy as np
import time
import matplotlib.pyplot as plt
from gtts import gTTS
import string
import speech_recognition as sr

r = sr.Recognizer()
m = sr.Microphone()
test=1

def checkspeech(r):
    with sr.Microphone() as source:

        audio = r.listen(source)
        try:
            print("You said: " + r.recognize_google(audio))
            return (r.recognize_google(audio))
        except sr.UnknownValueError:
            print("Could not understand audio")
            return ("WW")
        except sr.RequestError as e:
            print("Could not request results; {0}".format(e))
            return ("WW")


while (test==0):
    
    r.pause_threshold = 0.7
    r.energy_threshold =1700
    with m as source:
        #r.adjust_for_ambient_noise(source)
        print("Set minimum energy threshold to {}".format(r.energy_threshold))
    print("MICROPHONE")
    speech=str(checkspeech(r))
    print(speech)
    if (speech=="Follow me" or speech=="follow me" ):
        test=1
        print("Following")
    else:
        test=0

    
    #speech3=speech.split(' ',1)[0]


drawing = False 
ix,iy = -1,-1
R1=0
G1=0
B1=0
b=0
g=0
r=0
set1=0
cx1=0
cy1=0
time1 = time.time()
time2=0
a=0



cap=cv2.VideoCapture(0)
if (test==1):
    while(cap.isOpened()):  
        ret,frame = cap.read()
        frame = cv2.cvtColor(frame,cv2.COLOR_BGR2HLS)
        #cv2.imshow("frames",frame)
        if(set1==0):           
            left = 280
            right = 360
            bottom = 280
            top = 200
            kernel = frame[top:bottom,left:right]
            b,g,r,_=np.uint8(cv2.mean(kernel))
            lb=b-30
            ub=b+30
            lg=g-30
            ug=g+30
            lr=r-30
            ur=r+30
            lower_blue = np.array([lb,lg,lr])
            upper_blue = np.array([ub,ug,ur])
            mask = cv2.inRange(frame, lower_blue, upper_blue)       
            dilation = cv2.dilate(mask,(5,5),iterations = 1)
            erosion = cv2.dilate(dilation,(7,7),iterations = 1)
            ret,cnts,heirarchy=cv2.findContours(erosion.astype(np.uint8),cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)
            set1=1
        #cv2.imshow('kernel',kernel)
        mask1 = np.zeros_like(frame)
            
        if(a==1):
            #print(frame.shape[0])
            #print(mask1.shape[0])
            #print(frame.shape[1])
            #print(mask1.shape[1])
            lroi = cx - 150
            rroi = cx + 150
            broi = cy + 150
            troi = cy - 150
            if(lroi<0):
                lroi=0
            if(rroi>640):
                rroi=640
            if(broi>480):
                broi=480
            if(troi<0):
                troi=0
            mask1[troi:broi,lroi:rroi]=(255,255,255)
            masked = cv2.bitwise_and(frame,mask1)
            lower_blue = np.array([lb,lg,lr])
            upper_blue = np.array([ub,ug,ur])
            mask = cv2.inRange(masked, lower_blue, upper_blue)       
            dilation = cv2.dilate(mask,(5,5),iterations = 1)
            erosion = cv2.dilate(dilation,(7,7),iterations = 1)
            ret,cnts,heirarchy=cv2.findContours(erosion.astype(np.uint8),cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)

            #cv2.imshow("A",masked)
            #frame[0:480,0:lroi]=[0,0,0]
            #frame[0:troi,0:640] = [0,0,0]
            #frame[0:480,rroi:640] = [0,0,0]
            #frame[0:640,broi:480] = [0,0,0]
##        lower_blue = np.array([lb,lg,lr])
##        upper_blue = np.array([ub,ug,ur])
##        mask = cv2.inRange(frame, lower_blue, upper_blue)       
##        dilation = cv2.dilate(mask,(5,5),iterations = 1)
##        erosion = cv2.dilate(dilation,(7,7),iterations = 1)
##        ret,cnts,heirarchy=cv2.findContours(erosion.astype(np.uint8),cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)

        if len(cnts) != 0:
            maxcnt = max(cnts,key=cv2.contourArea)
            M = cv2.moments(maxcnt)
            if(M['m00']==0):
                continue
            else:     
                cx = int(M['m10']/M['m00'])
                cy = int(M['m01']/M['m00'])
                cv2.circle(frame, (cx, cy), 10, (255, 255, 255), -1)
                    
        if (set1==1):
            time2=time.time()
            if (time2-time1>=0.5):               
                left = cx - 30               
                right = cx + 30              
                bottom = cy + 30                
                top = cy - 30
                a=1
                kernel = frame[top:bottom,left:right]
                b,g,r,_=np.uint8(cv2.mean(kernel))
                lb = b-40
                ub=b+40
                lg=g-40
                ug=g+40
                lr=r-40
                ur=r+40
                time1=time2
                print(lb)

        #cv2.drawContours(frame, approx, -1, (0,255,0), 3)
        res = cv2.bitwise_and(frame,frame, mask = mask)
        cv2.imshow('tester',frame)
        cv2.imshow('tester1',dilation)
        
        k = cv2.waitKey(1)
        if (k==ord('q')):
            break

cap.release()
cv2.destroyAllWindows()

If you want you can remove the follow me feature, but please help me how to add this new click and follow fearure!!! Thank you!

I followed these steps:

Define a mouse callback function that will capture the mouse click event and record the coordinates of the click.

Utilize the coordinates obtained from the mouse click to define the initial region of interest (ROI) where the person to be tracked is located.

Modify the code to use this manually selected ROI for color calibration and t-shirt detection.

But It didn't work, maybe my code was wrong or my approach was wrong.