Track colour change of individual LEDs on RGB LED strip using OpenCV

I am trying to make a program in OpenCV that will allow me to track if the colour of individual LEDs on an RGB LED strip, specifically log exactly when they change in colour. I have managed to segment the individual LEDs, but I am stuck at this point.

When I extract the RGB value from the center of each LED, it is always white (255, 255, 255), but extracting from one of the corners of the bounding rectangle causes the pixel values to vary too much. I have attached a picture of my camera output as reference. LED strip after masking and applying segmentation

Is there any way by which I can reliably extract the LED colours and more importantly, determine if they have changed colour?

Edit: Here is the raw video feed from my camera that can be used to reproduce the issue:
Raw Feed
Raw Feed 2

Edit 2: Screenshots of various possible frames that need to be processed:
Image 1
Image 2
Image 3
Image 4
Image 5

For reference, this is the source code:

import cv2
import numpy as np
import csv
import datetime
import os
              
lower = np.array([0, 0, 230])
upper = np.array([179, 120, 255])
pixels = [[],[],[],[],[],[],[],[],[],[],[]]

def sliderCallback(x):
    global lower
    global upper

    lower[0] = cv2.getTrackbarPos('Hue Min', 'Trackbars')
    upper[0] = cv2.getTrackbarPos('Hue Max', 'Trackbars')
    lower[1] = cv2.getTrackbarPos('Saturation Min', 'Trackbars')
    upper[1] = cv2.getTrackbarPos('Saturation Max', 'Trackbars')
    lower[2] = cv2.getTrackbarPos('Value Min', 'Trackbars')
    upper[2] = cv2.getTrackbarPos('Value Max', 'Trackbars')

def getPos(event,x,y,flags,param):
    if(event == cv2.EVENT_LBUTTONDOWN):
        print(event, x, y)

def getPixelNumber(x):
    n = 1
    temp = 640 / 11
    while(1):
        if((temp * n) > x):
            return n;
        else:
            n += 1

def storeToCSV(pixel_list):
    try:
        timestamp = datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S")
        filename = f"pixels.csv"

        write_header = not os.path.exists(filename)

        with open(filename, mode='a', newline='') as file:
            writer = csv.writer(file)

            if write_header:
                header = ['Timestamp']
                for i, pixel in enumerate(pixel_list, 1):
                    header.extend([f'Pixel_{i}_B', f'Pixel_{i}_G', f'Pixel_{i}_R'])
                writer.writerow(header)

            row = [timestamp]
            for pixel in pixel_list:
                b, g, r = pixel
                row.extend([b, g, r])
            writer.writerow(row)
    except:
        pass



cv2.namedWindow('Trackbars')
cv2.createTrackbar('Hue Min', 'Trackbars', 0, 179, sliderCallback)
cv2.createTrackbar('Hue Max', 'Trackbars', 0, 179, sliderCallback)
cv2.createTrackbar('Saturation Min', 'Trackbars', 0, 255, sliderCallback)
cv2.createTrackbar('Saturation Max', 'Trackbars', 0, 255, sliderCallback)
cv2.createTrackbar('Value Min', 'Trackbars', 0, 255, sliderCallback)
cv2.createTrackbar('Value Max', 'Trackbars', 0, 255, sliderCallback)

cv2.namedWindow('image')
cv2.setMouseCallback('image',getPos)

cap = cv2.VideoCapture(0)


while True:
    ret, frame = cap.read()
    if ret:
        frame = frame[180:260, :]
        hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
        mask = cv2.inRange(hsv, lower, upper)
        #mask = cv2.bitwise_not(mask)
        adjusted = cv2.bitwise_and(frame, frame, mask=mask);
        (h, s, v) = cv2.split(adjusted)
        contours, hierarchy = cv2.findContours(v, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)

        contourCount = 0
        for c in contours:
            area = cv2.contourArea(c)
            if(area > 100):
                contourCount += 1
                x, y, h, w = cv2.boundingRect(c)
                pixel = frame[y - int(h / 10), x - int(w / 10)]
                pixels[getPixelNumber(x) - 1] = pixel
                cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)
                cv2.drawContours(frame, c, contourIdx=-1, color=(255, 0, 0), thickness=3)
        cv2.imshow('image', frame)
        cv2.imshow('mask', mask)
        cv2.imshow('adjusted', adjusted)
        storeToCSV(pixels)
        
        

    key = cv2.waitKey(10) & 0xff
    
    if key == 27:  # ESC
        break
    
cv2.destroyAllWindows()
cap.release()

Solution

As Mark Setchell explained in the comments, the images in my camera feed were overexposed which were causing the issues. The solution was to find a way to somehow reduce the exposure.
In order to do so, I replaced my original web cam (which had no controls other than focus) with my smartphone camera and used an application called Camo Studio to adjust the images before they were loaded into my program.
the resulting image looks like this: Image processed in Camo Studio
I reduced the shutter speed and ISO to the minimum allowed values, made sure that the white balance was kept constant, and set the saturation to 0.5.
Due to the nature of the LEDs, there are still some excessively bright spots that change location depending on the colour, so my solution is to get the average colour inside each contour.

for c in contours:
            area = cv2.contourArea(c)
            if(area > 50):
                x, y, h, w = cv2.boundingRect(c)
                cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)
                contourMask = np.zeros(frame.shape[:2], np.uint8)
                cv2.fillPoly(contourMask, pts=[c], color=(255, 255, 255))
                pixels[getPixelNumber(x) - 1] = cv2.mean(frame, mask=contourMask)[:3]