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Co-authored-by: sfalexrog <sfalexrog@gmail.com> * Rework the structure. * Add Clever 4 assembling instruction. * Rework setup articles. * Many additional changes.
111 lines
3.3 KiB
Markdown
111 lines
3.3 KiB
Markdown
# Working with the camera
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To work with the main camera, make sure it is enabled in file `~/catkin_ws/src/clever/clever/launch/clever.launch`:
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```xml
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<arg name="main_camera" default="true"/>
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```
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Also make sure that [correct position and orientation are indicated] for the camera (camera_frame.md).
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The `clever` package must be restarted after the launch-file has been edited:
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```(bash)
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sudo systemctl restart clever
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```
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For monitoring images from the camera, you may use rqt or [web_video_server](web_video_server.md).
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## Computer vision
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For implementation of the computer vision algorithms, it is recommended to use the [OpenCV] library that is pre-installed in [the SD card image] (image.md) (https://opencv.org).
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### Python
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Main article: http://wiki.ros.org/cv_bridge/Tutorials/ConvertingBetweenROSImagesAndOpenCVImagesPython.
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An example of creating a subscriber for a topic with an image from the main camera for processing with OpenCV:
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```python
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import rospy
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import cv2
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from sensor_msgs.msg import Image
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from cv_bridge import CvBridge
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rospy.init_node('computer_vision_sample')
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bridge = CvBridge()
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def image_callback(data):
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cv_image = bridge.imgmsg_to_cv2(data, 'bgr8') # OpenCV image
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# Do any image processing with cv2...
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image_sub = rospy.Subscriber('main_camera/image_raw', Image, image_callback)
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rospy.spin()
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```
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To debug image processing, you can publish a separate topic with the processed image:
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```python
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image_pub = rospy.Publisher('~debug', Image)
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```
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Publishing the processed image (at the end of the image_callback function):
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```python
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image_pub.publish(bridge.cv2_to_imgmsg(cv_image, 'bgr8'))
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```
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The obtained images can be viewed using [web_video_server](web_video_server.md).
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### Examples
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#### Working with QR codes
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> **Hint** For high-speed recognition and positioning, it is better to use [ArUco markers](aruco.md).
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To program actions of the copter upon detection of [QR codes](https://en.wikipedia.org/wiki/QR_code) you can use the [ZBar] library (http://zbar.sourceforge.net). It should be installed using pip:
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```(bash)
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sudo pip install zbar
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```
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Recognizing QR codes in Python:
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```python
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import cv2
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import zbar
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from cv_bridge import CvBridge
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from sensor_msgs.msg import Image
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bridge = CvBridge()
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scanner = zbar.ImageScanner()
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scanner.parse_config('enable')
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# Image subscriber callback function
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def image_callback(data):
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cv_image = bridge.imgmsg_to_cv2(data, 'bgr8') # OpenCV image
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gray = cv2.cvtColor(cv_image, cv2.COLOR_BGR2GRAY, dstCn=0)
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pil = ImageZ.fromarray(gray)
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raw = pil.tobytes()
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image = zbar.Image(320, 240, 'Y800', raw) # Image params
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scanner.scan(image)
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for symbol in image:
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# print detected QR code
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print 'decoded', symbol.type, 'symbol', '"%s"' % symbol.data
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image_sub = rospy.Subscriber('main_camera/image_raw', Image, image_callback, queue_size=1)
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```
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The script will take up to 100% CPU capacity. To slow down the script artificially, you can run [throttling](http://wiki.ros.org/topic_tools/throttle) of frames from the camera, for example, at 5 Hz (`main_camera.launch`):
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```xml
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<node pkg="topic_tools" name="cam_throttle" type="throttle"
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args="messages main_camera/image_raw 5.0 main_camera/image_raw/throttled"/>
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```
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The topic for the subscriber in this case should be changed for `main_camera/image_raw/throttled`.
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