Exemple basique de Vision

Ceci est un exemple basique d’une configuration de vision qui affiche l’emplacement de la cible dans le système de coordonnées de visée décrit ici dans les NetworkTables et utilise CameraServer pour afficher un rectangle englobant le contour détecté. Cet exemple affiche le taux de rafraîchissement d’images (Frame rate) envoyé à CameraServer.

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()