Python Color Reco to Unity

import numpy as np
import cv2
import math
import socket
import time

UDP_IP = "127.0.0.1"
UDP_PORT = 5065

sock = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)

last = (0,0)

# Open Camera
try:
    default = 0 # Try Changing it to 1 if webcam not found
    capture = cv2.VideoCapture(default)
except:
    print("No Camera Source Found!")

while capture.isOpened():

    # Capture frames from the camera
    ret, frame = capture.read()

    # Get hand data from the rectangle sub window
    #cv2.rectangle(frame,(100,100),(300,300),(0,255,0),0)
    crop_image = frame[100:500, 100:500]

    # Apply Gaussian blur
    blur = cv2.GaussianBlur(crop_image, (3,3), 0)

    # Change color-space from BGR -> HSV
    hsv = cv2.cvtColor(blur, cv2.COLOR_BGR2HSV)

    # lower mask (0-10)
    lower_red = np.array([0,50,50])
    upper_red = np.array([10,255,255])
    mask0 = cv2.inRange(hsv, lower_red, upper_red)

    # upper mask (170-180)
    lower_red = np.array([100,50,50])
    upper_red = np.array([140,255,255])
    mask1 = cv2.inRange(hsv, lower_red, upper_red)

    # join my masks
    mask2 = mask0+mask1

    # Create a binary image with where white will be skin colors and rest is black
    #mask2 = cv2.inRange(hsv, np.array([2,0,0]), np.array([20,255,255]))

    # Kernel for morphological transformation
    kernel = np.ones((5,5))

    # Apply morphological transformations to filter out the background noise
    dilation = cv2.dilate(mask1, kernel, iterations = 1)
    erosion = cv2.erode(dilation, kernel, iterations = 1)

    # Apply Gaussian Blur and Threshold
    filtered = cv2.GaussianBlur(erosion, (3,3), 0)
    ret,thresh = cv2.threshold(filtered, 127, 255, 0)

    # Show threshold image
    # cv2.imshow("Thresholded", thresh)

    # Find contours
    contours, hierarchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE )

    try:
        # Find contour with maximum area
        contour = max(contours, key = lambda x: cv2.contourArea(x))

        # Create bounding rectangle around the contour
        x,y,w,h = cv2.boundingRect(contour)
        cv2.rectangle(crop_image,(x,y),(x+w,y+h),(0,0,255),0)

        # Find convex hull
        hull = cv2.convexHull(contour)

        # Draw contour
        drawing = np.zeros(crop_image.shape, np.uint8)
        cv2.drawContours(drawing,[contour],-1,(0,255,0),0)
        cv2.drawContours(drawing,[hull],-1,(0,0,255),0)

        # Show required images
        cv2.imshow("Full Frame", frame)
        all_image = np.hstack((drawing, crop_image))
        cv2.imshow('Recognition', all_image)
        cv2.imshow("Threshold Binary", thresh)
        # cv2.imshow("Filtered IMG 0", mask0)
        cv2.imshow("Filtered IMG 1", mask1)

        pos = ((x+w)/2,(y+h)/2)
        if pos != last:
            info = str(pos[0]) + "," +str(pos[1])
            sock.sendto( (info).encode(), (UDP_IP, UDP_PORT) )
            print("_"*10, "Curr Pos: "+ info, "_"*10)
            last = ((x+w)/2,(y+h)/2);
            time.sleep(0.04);


    except:
        pass

    # Close the camera if 'q' is pressed
    if cv2.waitKey(1) == ord('q'):
        break

capture.release()
cv2.destroyAllWindows()