Working with the camera

Make sure the camera is enabled in the ~/catkin_ws/src/clever/clever/launch/clever.launch file:

<arg name="main_camera" default="true"/>

Also make sure that position and orientation of the camera is correct.

The clever package must be restarted after the launch-file has been edited:

sudo systemctl restart clever

You may use rqt or web_video_server to view the camera stream.


If the camera stream is missing, try using the raspistill utility to check whether the camera works.

First, stop the Clever service:

sudo systemctl stop clever

Then use raspistill to capture an image from the camera:

raspistill -o test-image.jpeg

If it doesn't work, check the camera cable connections and the cable itself. Replace the cable if it is damaged. Also, make sure the camera screws don't touch any components on the camera board.

Camera parameters

Some camera parameters, such as image size, FPS cap, and exposure, may be configured in the main_camera.launch file. The list of supported parameters can be found in the cv_camera repository.

Additionally you can specify an arbitrary capture parameter using its OpenCV code. For example, add the following parameters to the camera node to set exposition manually:

<param name="property_0_code" value="21"/> <!-- property code 21 is CAP_PROP_AUTO_EXPOSURE -->
<param name="property_0_value" value="0.25"/> <!-- property values are normalized as per OpenCV specs, even for "menu" controls; 0.25 means "use manual exposure" -->
<param name="cv_cap_prop_exposure" value="0.3"/> <!-- set exposure to 30% of maximum value -->

Computer vision

The SD card image comes with a preinstalled OpenCV library, which is commonly used for various comupter vision-related tasks. Additional libraries for converting from ROS messages to OpenCV images and back are preinstalled as well.


Main article:

An example of creating a subscriber for a topic with an image from the main camera for processing with OpenCV:

import rospy
import cv2
from sensor_msgs.msg import Image
from cv_bridge import CvBridge

bridge = CvBridge()

def image_callback(data):
    cv_image = bridge.imgmsg_to_cv2(data, 'bgr8')  # OpenCV image
    # Do any image processing with cv2...

image_sub = rospy.Subscriber('main_camera/image_raw', Image, image_callback)


To debug image processing, you can publish a separate topic with the processed image:

image_pub = rospy.Publisher('~debug', Image)

Publishing the processed image (at the end of the image_callback function):

image_pub.publish(bridge.cv2_to_imgmsg(cv_image, 'bgr8'))

The obtained images can be viewed using web_video_server.


Working with QR codes

For high-speed recognition and positioning, it is better to use ArUco markers.

To program actions of the copter upon detection of QR codes you can use the [ZBar] library ( It should be installed using pip:

sudo pip install zbar

QR codes recognition in Python:

import cv2
import zbar
from cv_bridge import CvBridge
from sensor_msgs.msg import Image

bridge = CvBridge()
scanner = zbar.ImageScanner()

# Image subscriber callback function
def image_callback(data):
    cv_image = bridge.imgmsg_to_cv2(data, 'bgr8')  # OpenCV image
    gray = cv2.cvtColor(cv_image, cv2.COLOR_BGR2GRAY, dstCn=0)

    pil = ImageZ.fromarray(gray)
    raw = pil.tobytes()

    image = zbar.Image(320, 240, 'Y800', raw)  # Image params

    for symbol in image:
        # print detected QR code
        print 'decoded', symbol.type, 'symbol', '"%s"' %

image_sub = rospy.Subscriber('main_camera/image_raw', Image, image_callback, queue_size=1)

The script will take up to 100% CPU capacity. To slow down the script artificially, you can use throttling of frames from the camera, for example, at 5 Hz (main_camera.launch):

<node pkg="topic_tools" name="cam_throttle" type="throttle"
    args="messages main_camera/image_raw 5.0 main_camera/image_raw/throttled"/>

The topic for the subscriber in this case should be changed for main_camera/image_raw/throttled.

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