#灰度值转换
imgGray = cv2.cvtColor(copy_img,cv2.COLOR_BGR2GRAY)
#高斯滤波去噪
imgBlur = cv2.GaussianBlur(imgGray,(5,5),0)
#阈值处理
ret,thresh = cv2.threshold(imgBlur,127,255,cv2.THRESH_BINARY)
#腐蚀
imgEro = cv2.erode(thresh,kernel1,iterations=2)
#膨胀
imgDia = cv2.dilate(imgEro,kernel2,iterations=4)

#轮廓检测
_,contouts,hie = cv2.findContours(imgDia,cv2.RETR_TREE,cv2.CHAIN_APPROX_NONE)
cnt = contouts
cv2.drawContours(copy_img, cnt, -1, (0, 255, 0), 2)

for i in cnt:
  #坐标赋值
  x,y,w,h = cv2.boundingRect(i)
  #roi位置判断
  if y>350 and y<450 and x<1200 and w>50 and h>10:
    # 画出轮廓
    cv2.drawContours(copy_img, i, -1, (0, 255, 0), 2)

import cv2
import numpy as np
#定义两个核	（kernel_Ero用于腐蚀，kernel_Dia用于膨胀）
kernel_Ero = np.ones((3,1),np.uint8)
kernel_Dia = np.ones((3,5),np.uint8)

img = cv2.imread("../images/bmx.png")
copy_img = img.copy()
#原图copy修改尺寸
copy_img = cv2.resize(copy_img,(1600,800))
#灰度值转换
imgGray = cv2.cvtColor(copy_img,cv2.COLOR_BGR2GRAY)
#高斯滤波去噪
imgBlur = cv2.GaussianBlur(imgGray,(5,5),0)
#阈值处理
ret,thresh = cv2.threshold(imgBlur,127,255,cv2.THRESH_BINARY)
#腐蚀
imgEro = cv2.erode(thresh,kernel_Ero,iterations=2)
#膨胀
imgDia = cv2.dilate(imgEro,kernel_Dia,iterations=4)

#轮廓检测
_,contouts,hie = cv2.findContours(imgDia,cv2.RETR_TREE,cv2.CHAIN_APPROX_NONE)
cnt = contouts

for i in cnt:
  #坐标赋值
  x,y,w,h = cv2.boundingRect(i)
  #roi位置判断
  if y>350 and y<450 and x<1200 and w>50 and h>10:
    # 画出轮廓
    cv2.drawContours(copy_img, i, -1, (0, 255, 0), 2)

cv2.imshow("img",copy_img)
cv2.waitKey(0)
cv2.destroyAllWindows()