how to convert a subscriber image into an open cv image?
I have a program that detects laser points that I know works when I read an image from video 0 but I do not know how to make the program work from a ros subscriber image. I need to know how to convert a ros image subscriber into a usable opencv image named "image" I have researched how to do this and I have come across several solutions that all use the function bridge.imgmsg_to_cv2 but I can not get this to work I am sure it is a simple fix I just don't know what I am doing. This should be relatively simple though. here is my code:
# import the necessary packages
from __future__ import print_function
from imutils import contours
from skimage import measure
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
import rospy
import cv2
from std_msgs.msg import String
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]
'''
class image_converter:
def __init__(self):
self.image_pub = rospy.Publisher("image_topic_2",Image)
self.bridge = CvBridge()
self.image_sub = rospy.Subscriber("CM_040GE/image_raw",Image,self.callback)
def callback(self,data):
try:
cv_image = self.bridge.imgmsg_to_cv2(data, "bgr8")
except CvBridgeError as e:
print(e)
(rows,cols,channels) = cv_image.shape
if cols > 60 and rows > 60 :
cv2.circle(cv_image, (50,50), 10, 255)
cv2.imshow("Image window", cv_image)
cv2.waitKey(3)
try:
self.image_pub.publish(self.bridge.cv2_to_imgmsg(cv_image, "bgr8"))
except CvBridgeError as e:
print(e)
def main(args):
ic = image_converter()
rospy.init_node('image_converter', anonymous=True)
try:
rospy.spin()
except KeyboardInterrupt:
print("Shutting down")
cv2.destroyAllWindows()
if __name__ == '__main__':
main(sys.argv)
image = bridge.imgmsg_to_cv2(image_message, desired_encoding="passthrough")
while(1):
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, 30, 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 = measure.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 ...