This is an example of a basic vision setup that posts the target's location in the aiming coordinate system described 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.
py
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()