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My color detection code detects some colors while also providing the percentage value of the detected color. However I would like to create a more visual based output like a histogram or graph so I would like any advice in how to create one. I've searched in some forums but most are for still images.

My code is below:
#importing modules

import cv2 
import numpy as np
import time
import matplotlib.pyplot as plt

#capturing video through webcam
cap=cv2.VideoCapture(0)

while(1):
 _, img = cap.read()

 #converting frame(img i.e BGR) to HSV (hue-saturation-value)

 hsv=cv2.cvtColor(img,cv2.COLOR_BGR2HSV)

 #definig the range of red color
 red_lower=np.array([166,84,141],np.uint8)
 red_upper=np.array([186,255,255],np.uint8)

 #definig the range of dark red color
 dred_lower=np.array([0,180,80],np.uint8)
 dred_upper=np.array([6,255,141],np.uint8)

 #defining the range of pink color
 pink_lower=np.array([137,17,130],np.uint8)
 pink_upper=np.array([150,165,255],np.uint8)

 #defining the Range of cream color
 cream_lower=np.array([0,5,168],np.uint8)
 cream_upper=np.array([33,113,255],np.uint8)

 #defining the Range of Blue color
 blue_lower=np.array([97,100,80],np.uint8)
 blue_upper=np.array([117,255,180],np.uint8)

 #defining the Range of light Blue color
 lblue_lower=np.array([100,56.1,142.8],np.uint8)
 lblue_upper=np.array([100,70,155.5],np.uint8)

 #defining the Range of cyan color
 cyan_lower=np.array([90,200,200],np.uint8)
 cyan_upper=np.array([90,255,255],np.uint8)

 #defining the Range of purple color
 purple_lower=np.array([128,40,125],np.uint8)
 purple_upper=np.array([138,255,255],np.uint8)

 #defining the Range of yellow color
 yellow_lower=np.array([24,45,110],np.uint8)
 yellow_upper=np.array([30,255,255],np.uint8)

 #defining the Range of green color
 green_lower=np.array([45,59,119],np.uint8)
 green_upper=np.array([68,255,255],np.uint8)

 #defining the Range of White color
 White_lower=np.array([0,0,200],np.uint8)
 White_upper=np.array([180,255,255],np.uint8)

 #defining the range of grey color
 grey_lower=np.array([106,5,168],np.uint8)
 grey_upper=np.array([120,75,255],np.uint8)

 #defining the Range of orange color
 orange_lower=np.array([15,30,60],np.uint8)
 orange_upper=np.array([15,255,255],np.uint8)

 #defining the range of offwhite color
 offwhite_lower=np.array([0,0,168],np.uint8)
 offwhite_upper=np.array([0,0,210],np.uint8)

 #defining the Range of black color
 Black_lower=np.array([0,0,0],np.uint8)
 Black_upper=np.array([180,255,40],np.uint8)

 #defining the Range of brown color
 Brown_lower=np.array([128,100,150],np.uint8)
 Brown_upper=np.array([160,150,255],np.uint8)

 #defining the range of beige color
 beige_lower=np.array([10,50,180],np.uint8)
 beige_upper=np.array([100,255,255],np.uint8)

 #finding the range of the colors in the image
 red=cv2.inRange(hsv, red_lower, red_upper)
 pink=cv2.inRange(hsv,pink_lower,pink_upper)
 cream=cv2.inRange(hsv,cream_lower,cream_upper)
 dred=cv2.inRange(hsv, dred_lower, dred_upper)
 blue=cv2.inRange(hsv,blue_lower,blue_upper)
 lblue=cv2.inRange(hsv,lblue_lower,lblue_upper)
 cyan=cv2.inRange(hsv,cyan_lower,cyan_upper)
 yellow=cv2.inRange(hsv,yellow_lower,yellow_upper)
 green=cv2.inRange(hsv,green_lower,green_upper)
 purple=cv2.inRange(hsv,purple_lower,purple_upper)
 orange=cv2.inRange(hsv,orange_lower,orange_upper)
 White=cv2.inRange(hsv,White_lower,White_upper)
 offwhite=cv2.inRange(hsv,offwhite_lower,offwhite_upper)
 Black=cv2.inRange(hsv,Black_lower,Black_upper)
 grey=cv2.inRange(hsv,grey_lower,grey_upper)
 brown=cv2.inRange(hsv,Brown_lower,Brown_upper)
 beige=cv2.inRange(hsv,beige_lower,beige_upper)

 #Morphological transformation, Dilation 
 kernal = np.ones((5 ,5), "uint8")

 red=cv2.dilate(red, kernal)
 res=cv2.bitwise_and(img, img, mask = red)

 pink=cv2.dilate(pink,kernal)
 res1=cv2.bitwise_and(img, img, mask = pink)

 cream=cv2.dilate(cream,kernal)
 res1=cv2.bitwise_and(img, img, mask = cream)

 dred=cv2.dilate(dred, kernal)
 res=cv2.bitwise_and(img, img, mask = dred)

 blue=cv2.dilate(blue,kernal)
 res1=cv2.bitwise_and(img, img, mask = blue)

 lblue=cv2.dilate(lblue,kernal)
 res1=cv2.bitwise_and(img, img, mask = lblue)

 cyan=cv2.dilate(cyan,kernal)
 res1=cv2.bitwise_and(img, img, mask = cyan)

 yellow=cv2.dilate(yellow,kernal)
 res2=cv2.bitwise_and(img, img, mask = yellow)

 purple=cv2.dilate(purple, kernal)
 res2=cv2.bitwise_and(img, img, mask = purple)

 green=cv2.dilate(green,kernal)
 res2=cv2.bitwise_and(img, img, mask = green)

 orange=cv2.dilate(orange,kernal)
 res2=cv2.bitwise_and(img, img, mask = orange)

 White=cv2.dilate(White,kernal)
 res2=cv2.bitwise_and(img, img, mask = White)

 offwhite=cv2.dilate(offwhite, kernal)
 res2=cv2.bitwise_and(img, img, mask = offwhite)

 Black=cv2.dilate(Black,kernal)
 res2=cv2.bitwise_and(img, img, mask = Black)

 grey=cv2.dilate(grey, kernal)
 res2=cv2.bitwise_and(img, img, mask = grey)

 brown=cv2.dilate(brown,kernal)
 res2=cv2.bitwise_and(img, img, mask = brown)

 beige=cv2.dilate(beige, kernal)
 res2=cv2.bitwise_and(img, img, mask = beige)



 #Tracking the Red Color
 (_,contours,hierarchy)=cv2.findContours(red,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)

 for pic, contour in enumerate(contours):
 area = cv2.contourArea(contour) 
 if(area>=300):

 x,y,w,h = cv2.boundingRect(contour) 
 img = cv2.rectangle(img,(x,y),(x+w,y+h),(0,0,255),2)
 cv2.putText(img,"RED color",(x,y),cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0,0,255))

 #Tracking the dark Red Color
 (_,contours,hierarchy)=cv2.findContours(dred,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)

 for pic, contour in enumerate(contours):
 area = cv2.contourArea(contour) 
 if(area>=300):

 x,y,w,h = cv2.boundingRect(contour) 
 img = cv2.rectangle(img,(x,y),(x+w,y+h),(0,0,139),2)
 cv2.putText(img,"Dark RED color",(x,y),cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0,0,139))

 #Tracking the pink Color
 (_,contours,hierarchy)=cv2.findContours(pink,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)

 for pic, contour in enumerate(contours):
 area = cv2.contourArea(contour) 
 if(area>=300):

 x,y,w,h = cv2.boundingRect(contour) 
 img = cv2.rectangle(img,(x,y),(x+w,y+h),(255,0,255),2)
 cv2.putText(img,"Pink color",(x,y),cv2.FONT_HERSHEY_SIMPLEX, 0.7, (255,0,255))

 #Tracking the cream Color
 (_,contours,hierarchy)=cv2.findContours(cream,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)

 for pic, contour in enumerate(contours):
 area = cv2.contourArea(contour) 
 if(area>=300):

 x,y,w,h = cv2.boundingRect(contour) 
 img = cv2.rectangle(img,(x,y),(x+w,y+h),(150,255,255),2)
 cv2.putText(img,"cream color",(x,y),cv2.FONT_HERSHEY_SIMPLEX, 0.7, (150,255,255))

 #Tracking the light Blue Color
 (_,contours,hierarchy)=cv2.findContours(lblue,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)

 for pic, contour in enumerate(contours):
 area = cv2.contourArea(contour) 
 if(area>=300):

 x,y,w,h = cv2.boundingRect(contour) 
 img = cv2.rectangle(img,(x,y),(x+w,y+h),(180,0,0),2)
 cv2.putText(img,"light blue color",(x,y),cv2.FONT_HERSHEY_SIMPLEX, 0.7, (180,0,0))

 #Tracking the Blue Color
 (_,contours,hierarchy)=cv2.findContours(blue,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
 for pic, contour in enumerate(contours):
 area = cv2.contourArea(contour)
 if(area>=300):
 x,y,w,h = cv2.boundingRect(contour) 
 img = cv2.rectangle(img,(x,y),(x+w,y+h),(255,0,0),2)
 cv2.putText(img,"Blue color",(x,y),cv2.FONT_HERSHEY_SIMPLEX, 0.7, (255,0,0))

 #Tracking the cyan Color
 (_,contours,hierarchy)=cv2.findContours(cyan,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)

 for pic, contour in enumerate(contours):
 area = cv2.contourArea(contour) 
 if(area>=300):

 x,y,w,h = cv2.boundingRect(contour) 
 img = cv2.rectangle(img,(x,y),(x+w,y+h),(255,255,0),2)
 cv2.putText(img,"cyan color",(x,y),cv2.FONT_HERSHEY_SIMPLEX, 0.7, (255,255,0))

 #Tracking the yellow Color
 (_,contours,hierarchy)=cv2.findContours(yellow,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
 for pic, contour in enumerate(contours):
 area = cv2.contourArea(contour)
 if(area>=300):
 x,y,w,h = cv2.boundingRect(contour) 
 img = cv2.rectangle(img,(x,y),(x+w,y+h),(0,255,217),2)
 cv2.putText(img,"yellow color",(x,y),cv2.FONT_HERSHEY_SIMPLEX, 1.0, (0,255,217))

 #Tracking the purple Color
 (_,contours,hierarchy)=cv2.findContours(purple,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)

 for pic, contour in enumerate(contours):
 area = cv2.contourArea(contour) 
 if(area>=300):

 x,y,w,h = cv2.boundingRect(contour) 
 img = cv2.rectangle(img,(x,y),(x+w,y+h),(128,0,128),2)
 cv2.putText(img,"Purple color",(x,y),cv2.FONT_HERSHEY_SIMPLEX, 0.7, (128,0,128))

 #Tracking the green Color
 (_,contours,hierarchy)=cv2.findContours(green,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
 for pic, contour in enumerate(contours):
 area = cv2.contourArea(contour)
 if(area>=300):
 x,y,w,h = cv2.boundingRect(contour) 
 img = cv2.rectangle(img,(x,y),(x+w,y+h),(0,255,0),2)
 cv2.putText(img,"green color",(x,y),cv2.FONT_HERSHEY_SIMPLEX, 1.0, (0,255,0))

 #Tracking the orange Color
 (_,contours,hierarchy)=cv2.findContours(orange,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
 for pic, contour in enumerate(contours):
 area = cv2.contourArea(contour)
 if(area>=300):
 x,y,w,h = cv2.boundingRect(contour) 
 img = cv2.rectangle(img,(x,y),(x+w,y+h),(0,140,255),2)
 cv2.putText(img,"orange color",(x,y),cv2.FONT_HERSHEY_SIMPLEX, 1.0, (0,140,255))

 #Tracking the White Color
 (_,contours,hierarchy)=cv2.findContours(White,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
 for pic, contour in enumerate(contours):
 area = cv2.contourArea(contour)
 if(area>=300):
 x,y,w,h = cv2.boundingRect(contour) 
 img = cv2.rectangle(img,(x,y),(x+w,y+h),(255,255,255),2)
 cv2.putText(img,"White color",(x,y),cv2.FONT_HERSHEY_SIMPLEX, 1.0, (255,255,255))

 #Tracking the offwhite Color
 (_,contours,hierarchy)=cv2.findContours(offwhite,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)

 for pic, contour in enumerate(contours):
 area = cv2.contourArea(contour) 
 if(area>=300):

 x,y,w,h = cv2.boundingRect(contour) 
 img = cv2.rectangle(img,(x,y),(x+w,y+h),(128,128,128),2)
 cv2.putText(img,"Offwhite color",(x,y),cv2.FONT_HERSHEY_SIMPLEX, 0.7, (128,128,128))


 #Tracking the black Color
 (_,contours,hierarchy)=cv2.findContours(Black,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
 for pic, contour in enumerate(contours):
 area = cv2.contourArea(contour)
 if(area>=300):
 x,y,w,h = cv2.boundingRect(contour) 
 img = cv2.rectangle(img,(x,y),(x+w,y+h),(0,0,0),2)
 cv2.putText(img,"black color",(x,y),cv2.FONT_HERSHEY_SIMPLEX, 1.0, (0,0,0))

 #Tracking the grey Color
 (_,contours,hierarchy)=cv2.findContours(grey,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)

 for pic, contour in enumerate(contours):
 area = cv2.contourArea(contour) 
 if(area>=300):

 x,y,w,h = cv2.boundingRect(contour) 
 img = cv2.rectangle(img,(x,y),(x+w,y+h),(220,220,220),2)
 cv2.putText(img,"Grey color",(x,y),cv2.FONT_HERSHEY_SIMPLEX, 0.7, (220,220,220))

 #Tracking the brown Color
 (_,contours,hierarchy)=cv2.findContours(brown,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
 for pic, contour in enumerate(contours):
 area = cv2.contourArea(contour)
 if(area>=300):
 x,y,w,h = cv2.boundingRect(contour) 
 img = cv2.rectangle(img,(x,y),(x+w,y+h),(33,67,101),2)
 cv2.putText(img,"brown color",(x,y),cv2.FONT_HERSHEY_SIMPLEX, 1.0, (33,67,101))

 #Tracking the beige Color
 (_,contours,hierarchy)=cv2.findContours(beige,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)

 for pic, contour in enumerate(contours):
 area = cv2.contourArea(contour) 
 if(area>=300):

 x,y,w,h = cv2.boundingRect(contour) 
 img = cv2.rectangle(img,(x,y),(x+w,y+h),(126,169,230),2)
 cv2.putText(img,"Beige color",(x,y),cv2.FONT_HERSHEY_SIMPLEX, 0.7, (126,169,236))

 #Show red percentage
 red_per = red
 Total_image = (blue+yellow+green+dred+White+Black+offwhite+cream+pink+purple+lblue+beige+brown+grey+orange+cyan+red)
 Per_Red = np.mean((red_per/Total_image)*100)
 print('Percentage of red color',Per_Red)

 #Show blue percentage
 blue_per = blue
 Total_image2 = (blue+red+yellow+green+dred+White+Black+offwhite+cream+pink+purple+lblue+beige+brown+grey+orange+cyan)
 Per_Blue = np.mean((blue_per/Total_image2)*100)
 print('Percentage blue',Per_Blue)

 #Show green percentage
 green_per = green
 Total_image3 = (green+blue+yellow+dred+White+Black+offwhite+cream+pink+purple+lblue+beige+brown+grey+orange+cyan+red)
 Per_Green = np.mean((green_per/Total_image3)*100)
 print('Percentage green',Per_Green)

 #Show yellow percentage
 yellow_per = yellow
 Total_image4 = (yellow+blue+red+green+dred+White+Black+offwhite+cream+pink+purple+lblue+beige+brown+grey+orange+cyan)
 Per_Yellow = np.mean((yellow_per/Total_image4)*100)
 print('Percentage yellow',Per_Yellow)

 #Show dark red percentage
 dred_per = dred
 Total_image = (dred+blue+yellow+green+red+White+Black+offwhite+cream+pink+purple+lblue+beige+brown+grey+orange+cyan)
 Per_dR = np.mean((dred_per/Total_image)*100)
 print('Percentage of dark red color',Per_dR)

 #Show Cream percentage
 Cream_per = cream
 Total_image2 = (cream+blue+yellow+green+dred+White+Black+offwhite+red+pink+purple+lblue+beige+brown+grey+orange+cyan)
 Per_Cream = np.mean((Cream_per/Total_image2)*100)
 print('Percentage of cream color',Per_Cream)

 #Show lblue percentage
 lblue_per = lblue
 Total_image3 = (lblue+blue+yellow+green+dred+White+Black+offwhite+cream+pink+purple+red+beige+brown+grey+orange+cyan)
 Per_lBlue = np.mean((lblue_per/Total_image3)*100)
 print('Percentage of light blue color',Per_lBlue)

 #Show White percentage
 White_per = White
 Total_image4 = (White+blue+yellow+green+dred+red+Black+offwhite+cream+pink+purple+lblue+beige+brown+grey+orange+cyan)
 Per_White = np.mean((White_per/Total_image4)*100)
 print('Percentage of white color',Per_White)

 #Show orange percentage
 orange_per = orange
 Total_image = (orange+blue+yellow+green+dred+White+Black+offwhite+cream+pink+purple+lblue+beige+brown+grey+red+cyan)
 Per_Orange = np.mean((orange_per/Total_image)*100)
 print('Percentage of orange color',Per_Orange)

 #Show purple percentage
 purple_per = purple
 Total_image2 = (purple+blue+yellow+green+dred+White+Black+offwhite+cream+pink+red+lblue+beige+brown+grey+orange+cyan)
 Per_Purple = np.mean((purple_per/Total_image2)*100)
 print('Percentage of purple color',Per_Purple)

 #Show pink percentage
 pink_per = pink
 Total_image3 = (pink+blue+yellow+green+dred+White+Black+offwhite+cream+red+purple+lblue+beige+brown+grey+orange+cyan)
 Per_Pink = np.mean((pink_per/Total_image3)*100)
 print('Percentage of pink color',Per_Pink)

 #Show cyan percentage
 cyan_per = cyan
 Total_image4 = (cyan+blue+yellow+green+dred+White+Black+offwhite+cream+pink+purple+lblue+beige+brown+grey+orange+red)
 Per_Cyan = np.mean((cyan_per/Total_image4)*100)
 print('Percentage of cyan color',Per_Cyan)

 #Show grey percentage
 grey_per = grey
 Total_image = (grey+blue+yellow+green+dred+White+Black+offwhite+cream+pink+purple+lblue+beige+brown+red+orange+cyan)
 Per_Grey = np.mean((grey_per/Total_image)*100)
 print('Percentage of grey color',Per_Grey)

 #Show black percentage
 black_per = Black
 Total_image1 = (Black+blue+yellow+green+dred+White+red+offwhite+cream+pink+purple+lblue+beige+brown+grey+orange+cyan)
 Per_Black = np.mean((black_per/Total_image1)*100)
 print('Percentage of black color',Per_Black)

 #Show beige percentage
 beige_per = beige
 Total_image2 = (beige+blue+yellow+green+dred+White+Black+offwhite+cream+pink+purple+lblue+red+brown+grey+orange+cyan)
 Per_Beige = np.mean((beige_per/Total_image2)*100)
 print('Percentage of beige',Per_Beige)

 #Show brown percentage
 brown_per = brown
 Total_image3 = (brown+blue+yellow+green+dred+White+Black+offwhite+cream+pink+purple+lblue+beige+red+grey+orange+cyan)
 Per_Brown = np.mean((brown_per/Total_image3)*100)
 print('Percentage of brown color',Per_Brown)

 #Show offwhite percentage
 offwhite_per = offwhite
 Total_image4 = (offwhite+blue+yellow+green+dred+White+Black+red+cream+pink+purple+lblue+beige+brown+grey+orange+cyan)
 Per_Offwhite = np.mean((offwhite_per/Total_image4)*100)
 print('Percentage of offwhite',Per_Offwhite)




 cv2.imshow("Color Tracking",img) 
 if cv2.waitKey(10) & 0xFF == ord('q'):
 cap.release()
 cv2.destroyAllWindows()
 break 

What I'm trying to achieve to the code to present a histogram where it shows the y-axis (0-100)% and x-axis(the colors that will be detected) and shows how the data is changing when more colors are being detected and how the values fluctuates as more and more colors are being detected.

Thank you in advance!