import cv2
import numpy as np
# center定义
center = 320
# 打开摄像头,图像尺寸640*480(长*高),opencv存储值为480*640(行*列)
cap = cv2.VideoCapture(0)
while (1):
ret, frame = cap.read()
# 转化为灰度图
if ret == False: # 如果是最后一帧这个值为False
break
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
# 大津法二值化
retval, dst = cv2.threshold(gray, 0, 255, cv2.THRESH_OTSU)
# 膨胀,白区域变大
dst = cv2.dilate(dst, None, iterations=2)
# # 腐蚀,白区域变小
# dst = cv2.erode(dst, None, iterations=6)
cv2.imshow("dst",dst)
# 单看第400行的像素值
color = dst[400]
# 找到白色的像素点个数
white_count = np.sum(color == 0)
# 找到白色的像素点索引
white_count_judge = np.sum(color == 255)#利用这个变量来查找摄像头是否观察到黑色
if white_count_judge == 640:
print("黑色像素点为0")
pass
else:
white_index = np.where(color == 0)
# 防止white_count=0的报错
if white_count == 0:
white_count = 1
# 找到白色像素的中心点位置
center = (white_index[0][white_count - 1] + white_index[0][0]) / 2
direction = center - 320
print(direction)
# 计算出center与标准中心点的偏移量
if cv2.waitKey(1) & 0xFF == ord('q'):
break
# 释放清理
cap.release()
cv2.destroyAllWindows()
本代码适用于循下图所示的赛道,也就是单轨道黑线
声明:本代码为树莓派Opencv小车循迹专用,进阶版本代码可以看我这篇博客