why does this laser detector not work

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I have this program that worked before when I was using a video 0 camera but now does not work now that I have changed it to a ros camera. I am not sure why. the program is supposed to detect laser dots. could anyone offer any sudgestions as to why this program is now not working? the program apears to be working fine as in there are no errors but the program is not picking up on the laser dots like it did before

# import the necessary packages
from __future__ import print_function
from imutils import contours
from skimage import measure,morphology
import numpy as np
import argparse
import imutils
import cv2
import message_filters
from sensor_msgs.msg import Image, CameraInfo
from std_msgs.msg import Int32, Float32MultiArray
import rospy
from cv_bridge import CvBridge, CvBridgeError
import roslib
#roslib.load_manifest('my_package')
import sys
from sensor_msgs.msg import Image
from cv_bridge import CvBridge, CvBridgeError

'''
def getPoint(cameraTip,dotXY,normalPoint):
    slope= (cameraTip[2]-dotXY[2])/(cameraTip[1]-dotXY[1])
    b=cameraTip[2]-(slope*cameraTip[1])
    z=slope*normalPoint[1]+b
    return [normalPoint[0],normalPoint[1],z]
'''
# Image Processing functions
def convert_image(image): # Image of kind bgr8
    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
    blurred = cv2.GaussianBlur(gray, (11, 11), 0)
        #threshold the image to reveal light regions in the
        # blurred image
    thresh = cv2.threshold(blurred, 200, 255, cv2.THRESH_BINARY)[1]
        # perform a series of erosions and dilations to remove
        # any small blobs of noise from the thresholded image
    thresh = cv2.erode(thresh, None, iterations=2)
    thresh = cv2.dilate(thresh, None, iterations=4)
        # perform a connected component analysis on the thresholded
        # image, then initialize a mask to store only the "large"
        # components
    labels = morphology.label(thresh, neighbors=8, background=0)
    mask = np.zeros(thresh.shape, dtype="uint8")

        # loop over the unique components
    for label in np.unique(labels):
            # if this is the background label, ignore it
        if label == 0:
            continue

            # otherwise, construct the label mask and count the
                # number of pixels
        labelMask = np.zeros(thresh.shape, dtype="uint8")
        labelMask[labels == label] = 255
        numPixels = cv2.countNonZero(labelMask)

                # if the number of pixels in the component is sufficiently
                # large, then add it to our mask of "large blobs"
        if numPixels > 20:
            mask = cv2.add(mask, labelMask)
    # find the contours in the mask, then sort them from left to
    # right
    cnts = cv2.findContours(mask.copy(), cv2.RETR_EXTERNAL,
        cv2.CHAIN_APPROX_SIMPLE)
    cnts = imutils.grab_contours(cnts)
    cnts = contours.sort_contours(cnts)[0]

        # loop over the contours
    for (i, c) in enumerate(cnts):
            # draw the bright spot on the image
        (x, y, w, h) = cv2.boundingRect(c)
        ((cX, cY), radius) = cv2.minEnclosingCircle(c)
        #x and y center are cX and cY
        cv2.circle(image, (int(cX), int(cY)), int(radius),
            (0, 0, 255), 3)
        cv2.putText(image, "#{}".format(i + 1), (x, y - 15),
            cv2.FONT_HERSHEY_SIMPLEX, 0.45, (0, 0, 255), 2)

        # show the output image
    cv2.imshow("Image", image)
    cv2.waitKey(1)
        #camera.release()
    return image

# ROS Interface
if __name__ == "__main__":
    import rospy
    from cv_bridge import CvBridge, CvBridgeError
    from sensor_msgs.msg import Image
    bridge ...