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In case someone finds this in a search, I came up with a functional, albeit slow, method.

import cv2
import numpy as np
import serial


cap = cv2.VideoCapture(1)

loc = input('ACM: ')
ser = serial.Serial('/dev/ttyACM'+str(loc), 9600)

while(1):

    # Take each frame
    _, frame = cap.read()
    width = len(frame[0,:])
    # Convert BGR to HSV
    hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)

    # Define color range for masking
    lower = np.array([110,100,100])
    upper = np.array([130,255,255])

    # Threshold the HSV image to get only blue colors
    mask = cv2.inRange(hsv, lower, upper)

    # Bitwise-AND mask and original image
    result = cv2.bitwise_and(frame,frame, mask= mask)

    gray = cv2.cvtColor(result,cv2.COLOR_BGR2GRAY)

        #(image, max corners, quality level, minimum distance, mask)
    corners = cv2.goodFeaturesToTrack(gray,4,0.01,10)
    corners = np.int0(corners)

    for i in corners:

        x_array = []
        y_array = []


        x,y = i.ravel()
        cv2.circle(result,(x,y),3,255,-1)

        x_array.append(x)
        y_array.append(y)
        # Compare the average value of the X position to the midpoint of the frame
        # and print its relative location
        if (np.mean(x_array) <= int(width/2)):

            print 'left'

        else: 

            print 'right'


    #cv2.imshow('res',result)
    if cv2.waitKey(3) & 0xFF == ord('q'):
        break
        ser.write(str(chr(2)))
        break

cv2.destroyAllWindows()

In case someone finds this in a search, I came up with a functional, albeit slow, method.

import cv2
import numpy as np
import serial


cap = cv2.VideoCapture(1)

loc = input('ACM: ')
ser = serial.Serial('/dev/ttyACM'+str(loc), 9600)

while(1):

    # Take each frame
    _, frame = cap.read()
    width = len(frame[0,:])
    # Convert BGR to HSV
    hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)

    # Define color range for masking
    lower = np.array([110,100,100])
    upper = np.array([130,255,255])

    # Threshold the HSV image to get only blue colors
    mask = cv2.inRange(hsv, lower, upper)

    # Bitwise-AND mask and original image
    result = cv2.bitwise_and(frame,frame, mask= mask)

    gray = cv2.cvtColor(result,cv2.COLOR_BGR2GRAY)

        #(image, max corners, quality level, minimum distance, mask)
    corners = cv2.goodFeaturesToTrack(gray,4,0.01,10)
    corners = np.int0(corners)

    for i in corners:

        x_array = []
        y_array = []


        x,y = i.ravel()
        cv2.circle(result,(x,y),3,255,-1)

        x_array.append(x)
        y_array.append(y)
        # Compare the average value of the X position to the midpoint of the frame
        # and print its relative location
        if (np.mean(x_array) <= int(width/2)):

            print 'left'

        else: 

            print 'right'


    #cv2.imshow('res',result)
    if cv2.waitKey(3) & 0xFF == ord('q'):
        break
        ser.write(str(chr(2)))
        break


cv2.destroyAllWindows()