# Basic Vision Example

This is an example of a basic vision setup that posts the target's location in the aiming coordinate system described :ref:`here <docs/software/vision-processing/introduction/identifying-and-processing-the-targets:Measurements>` to NetworkTables, and uses CameraServer to display a bounding rectangle of the contour detected. This example will display the framerate of the processing code on the images sent to CameraServer.

.. tab-set-code::

   ```py
   from cscore import CameraServer
   import ntcore
   import cv2
   import json
   import numpy as np
   import time
   def main():
      with open('/boot/frc.json') as f:
         config = json.load(f)
      camera = config['cameras'][0]
         width = camera['width']
      height = camera['height']
      nt = ntcore.NetworkTableInstance.getDefault()
      CameraServer.startAutomaticCapture()
      input_stream = CameraServer.getVideo()
      output_stream = CameraServer.putVideo('Processed', width, height)
      # Table for vision output information
      vision_nt = nt.getTable('Vision')
      # Allocating new images is very expensive, always try to preallocate
      img = np.zeros(shape=(240, 320, 3), dtype=np.uint8)
      # Wait for NetworkTables to start
      time.sleep(0.5)
      while True:
         start_time = time.time()
         frame_time, input_img = input_stream.grabFrame(img)
         output_img = np.copy(input_img)
         # Notify output of error and skip iteration
         if frame_time == 0:
            output_stream.notifyError(input_stream.getError())
            continue
         # Convert to HSV and threshold image
         hsv_img = cv2.cvtColor(input_img, cv2.COLOR_BGR2HSV)
         binary_img = cv2.inRange(hsv_img, (0, 0, 100), (85, 255, 255))
         _, contour_list = cv2.findContours(binary_img, mode=cv2.RETR_EXTERNAL, method=cv2.CHAIN_APPROX_SIMPLE)
         x_list = []
         y_list = []
            for contour in contour_list:
               # Ignore small contours that could be because of noise/bad thresholding
               if cv2.contourArea(contour) < 15:
                  continue
               cv2.drawContours(output_img, contour, -1, color=(255, 255, 255), thickness=-1)
               rect = cv2.minAreaRect(contour)
               center, size, angle = rect
               center = tuple([int(dim) for dim in center])  # Convert to int so we can draw
               # Draw rectangle and circle
               cv2.drawContours(output_img, [cv2.boxPoints(rect).astype(int)], -1, color=(0, 0, 255), thickness=2)
               cv2.circle(output_img, center=center, radius=3, color=(0, 0, 255), thickness=-1)
               x_list.append((center[0] - width / 2) / (width / 2))
               x_list.append((center[1] - width / 2) / (width / 2))
         vision_nt.putNumberArray('target_x', x_list)
         vision_nt.putNumberArray('target_y', y_list)
         processing_time = time.time() - start_time
         fps = 1 / processing_time
         cv2.putText(output_img, str(round(fps, 1)), (0, 40), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255))
         output_stream.putFrame(output_img)
   main()
   ```