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docs/en/camera.md

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+# Working with the camera
+
+To work with the main camera, make sure it is enabled in file `~/catkin_ws/src/clever/clever/launch/clever.launch`:
+
+```xml
+<arg name="main_camera" default="true"/>
+```
+
+Also make sure that [correct position and orientation are indicated] for the camera (camera_frame.md).
+
+The `clever` package must be restarted after the launch-file has been edited:
+
+```(bash)
+sudo systemctl restart clever
+```
+
+For monitoring images from the camera, you may use rqt or [web_video_server](web_video_server.md).
+
+## Computer vision
+
+For implementation of the computer vision algorithms, it is recommended to use the [OpenCV] library that is pre-installed in [the SD card image] (microsd_images.md) (https://opencv.org).
+
+### Python
+
+Main article: http://wiki.ros.org/cv_bridge/Tutorials/ConvertingBetweenROSImagesAndOpenCVImagesPython.
+
+An example of creating a subscriber for a topic with an image from the main camera for processing with OpenCV:
+
+```python
+import rospy
+import cv2
+from sensor_msgs.msg import Image
+from cv_bridge import CvBridge
+
+rospy.init_node('computer_vision_sample')
+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)
+
+rospy.spin()
+```
+
+To debug image processing, you can publish a separate topic with the processed image:
+
+```python
+image_pub = rospy.Publisher('~debug', Image)
+```
+
+Publishing the processed image (at the end of the image_callback function):
+
+```python
+image_pub.publish(bridge.cv2_to_imgmsg(cv_image, 'bgr8'))
+```
+
+The obtained images can be viewed using [web_video_server](web_video_server.md).
+
+### Examples
+
+#### Working with QR codes
+
+> **Hint** For high-speed recognition and positioning, it is better to use [ArUco markers] (aruco.md).
+
+To program actions of the copter upon detection of [QR codes] (https://ru.wikipedia.org/wiki/QR-код) you can use the [ZBar] library (http://zbar.sourceforge.net). It should be installed using pip:
+
+```(bash)
+sudo pip install zbar
+```
+
+Recognizing QR codes in Python:
+
+```python
+import cv2
+import zbar
+from cv_bridge import CvBridge
+from sensor_msgs.msg import Image
+
+bridge = CvBridge()
+scanner = zbar.ImageScanner()
+scanner.parse_config('enable')
+
+# 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
+    scanner.scan(image)
+
+    for symbol in image:
+        # print detected QR code
+        print 'decoded', symbol.type, 'symbol', '"%s"' % symbol.data
+
+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 run [throttling](http://wiki.ros.org/topic_tools/throttle) of frames from the camera, for example, at 5 Hz (`main_camera.launch`):
+
+```xml
+<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`.