基本视觉示例

在本例子中, 通过设置基础视觉来展示在位置坐标系统中将目标的位置传输到网络表中：ref:here <docs/software/vision-processing/introduction/identifying-and-processing-the-targets:Measurements> , 并使用CameraServer生成一个矩形来展示检测到的目标的边界。此示例将会显示发送到CameraServer上的图像处理代码的帧率。

Python

from cscore import CameraServer
from networktables import NetworkTables

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']

   CameraServer.startAutomaticCapture()

   input_stream = CameraServer.getVideo()
   output_stream = CameraServer.putVideo('Processed', width, height)

   # Table for vision output information
   vision_nt = NetworkTables.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, (65, 65, 200), (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()