如何将 ros 订阅者图像转换为打开的 cv 图像？

Question

我有一个程序可以检测我知道从视频 0 读取图像时有效的激光点，但我不知道如何使程序从 ros 订阅者图像中工作。我需要知道如何将 ros 图像订阅者转换为名为“image”的可用 opencv 图像我已经研究过如何做到这一点，我遇到了几个都使用函数 bridge.imgmsg_to_cv2 但我无法让它工作的解决方案我确信这是一个简单的修复，我只是不知道我在做什么。不过，这应该相对简单。这是我的代码：

# 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 numPixels > 300:
            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()

目前我得到的错误是：

Traceback (most recent call last):
  File "lazerSub2.py", line 18, in <module>
    roslib.load_manifest('my_package')
  File "/opt/ros/indigo/lib/python2.7/dist-packages/roslib/launcher.py", line 62, in load_manifest
    sys.path = _generate_python_path(package_name, _rospack) + sys.path
  File "/opt/ros/indigo/lib/python2.7/dist-packages/roslib/launcher.py", line 93, in _generate_python_path
    m = rospack.get_manifest(pkg)
  File "/usr/lib/python2.7/dist-packages/rospkg/rospack.py", line 167, in get_manifest
    return self._load_manifest(name)
  File "/usr/lib/python2.7/dist-packages/rospkg/rospack.py", line 211, in _load_manifest
    retval = self._manifests[name] = parse_manifest_file(self.get_path(name), self._manifest_name, rospack=self)
  File "/usr/lib/python2.7/dist-packages/rospkg/rospack.py", line 203, in get_path
    raise ResourceNotFound(name, ros_paths=self._ros_paths)
rospkg.common.ResourceNotFound: my_package
ROS path [0]=/opt/ros/indigo/share/ros
ROS path [1]=/home/robot/catkin_ws/src
ROS path [2]=/opt/ros/indigo/share
ROS path [3]=/opt/ros/indigo/stacks

Answer 1

我认为这就是您想要做的，将 ros 和 cv 组件分开。您的第一个错误是当您将 ros 类型与 cv 处理严重混合时。如果你把 ros 的东西扔进一个 main/main 类，把所有的 ros 类型都放在那里，把你的函数处理放在别处。

''' image_converter.py '''
from __future__ import print_function
import cv2
import numpy as np
import imutils
from imutils import contours
from skimage import measure

'''
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, 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 numPixels > 300:
            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 = CvBridge()
    def show_img(cv_image):
        (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)
    image_pub = rospy.Publisher("image_topic_2", Image)
    def callback(data):
        try:
            cv_image = self.bridge.imgmsg_to_cv2(data, "bgr8")
            show_img(cv_image)
            cv_image2 = convert_image(cv_image)
            image_pub.publish(bridge.cv2_to_imgmsg(cv_image2, "bgr8"))
        except CvBridgeError as e:
            print(e)
    image_sub = rospy.Subscriber("CM_040GE/image_raw", Image, callback)
    rospy.init_node('image_converter', anonymous=True)
    rospy.spin()
    print("image_converter: Shutting down")
    cv2.destroyAllWindows()