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detect1.py
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from threading import local
import cv2
import numpy as np
import sys
import os
# img = cv2.imread('/home/k8s/learn_opencv/images/01.jpg')
local_pic_path = ['../images/01.jpg','../images/02.jpg']
# img = cv2.imread('/home/k8s/learn_opencv/images/01.jpg')
#创建窗口
# size = img.shape
# w = size[1] #宽度
# h = size[0] #高度
# print(w,h)
cv2.namedWindow('AA',cv2.WINDOW_NORMAL)
cv2.namedWindow('Canny',cv2.WINDOW_NORMAL)
# cv2.namedWindow('Canny_2',cv2.WINDOW_NORMAL)
# cv2.namedWindow('Canny')
# img = cv2.resize(img, (0, 0), fx=0.5, fy=0.5, interpolation=cv2.INTER_CUBIC)
# img = cv2.resize(img, (600, 450))
# size = img.shape
# w = size[1] #宽度
# h = size[0] #高度
# print(w,h)
# cv2.resizeWindow("canny", 50, 50)
#定义回调函数
def nothing(x):
pass
#创建滑动条，分别对应Canny的两个阈值
cv2.createTrackbar('threshold1','Canny',100,500,nothing)
cv2.createTrackbar('threshold2','Canny',100,500,nothing)
cv2.createTrackbar('param1','Canny',50,60,nothing)
cv2.createTrackbar('param2','Canny',100,200,nothing)
cv2.createTrackbar('a','Canny',1,5,nothing)
cv2.createTrackbar('b','Canny',100,255,nothing)
cv2.createTrackbar('minRadius','Canny',0,255,nothing)
cv2.createTrackbar('maxRadius','Canny',0,255,nothing)
cv2.createTrackbar('ii','Canny',0,1,nothing)
def detect(readimg,Canny1,Canny2,param1,param2,a,b,minRadius,maxRadius):
# Canny 边缘检测
# Canny1 = 0
# Canny2 =[94,++] [129,140] +
# param1 =
# param2 = [0,21]
# b =
# minRadius = [11]
# maxRadius = [44]
canny = cv2.Canny(readimg, Canny1,Canny2)
#
# 圆形检测
if param2<50:
param2 = 50
circles = cv2.HoughCircles(
canny, cv2.HOUGH_GRADIENT, 1, 100, param1=20, param2=40, minRadius=20, maxRadius=100)
# canny, cv2.HOUGH_GRADIENT, 1, 100, param1=37, param2=1, minRadius=0, maxRadius=200)
# 测内侧圆心的累加器图像的分辨率于输入图像之比的倒数，如dp=1，累加器和输入图像具有相同的分辨率，
# 如果dp=2，累计器便有输入图像一半那么大的宽度和高度
# minDist表示两个圆之间圆心的最小距离
# param1有默认值100，它是method设置的检测方法的对应的参数，
# 对当前唯一的方法霍夫梯度法cv2.HOUGH_GRADIENT，它表示传递给canny边缘检测算子的高阈值，而低阈值为高阈值的一半
# param2有默认值100，它是method设置的检测方法的对应的参数，
# 对当前唯一的方法霍夫梯度法cv2.HOUGH_GRADIENT，它表示在检测阶段圆心的累加器阈值，
# 它越小，就越可以检测到更多根本不存在的圆，而它越大的话，能通过检测的圆就更加接近完美的圆形了
if circles is None:
return 0, 0, 0
else:
return circles[0, 0, 2], circles[0, 0, 0], circles[0, 0, 1]
while(1):
threshold1=cv2.getTrackbarPos('threshold1','Canny')
threshold2=cv2.getTrackbarPos('threshold2','Canny')
param1=cv2.getTrackbarPos('param1','Canny')
param2=cv2.getTrackbarPos('param2','Canny')
a=cv2.getTrackbarPos('a','Canny')
b=cv2.getTrackbarPos('b','Canny')
minRadius = cv2.getTrackbarPos('minRadius','Canny')
maxRadius = cv2.getTrackbarPos('maxRadius','Canny')
ii = cv2.getTrackbarPos('ii','Canny')
img = cv2.imread(local_pic_path[ii])
try:
output_img = img.copy()
# output_img_2 = img_2.copy()
r, x, y = (int(x) for x in detect(img,threshold1,threshold2,param1,param2,a,b,minRadius,maxRadius))
print(r,x,y)
cv2.circle(output_img, (x, y), 3, (225, 0, 0), 5)
cv2.circle(output_img, (x, y), r, (225, 0, 0), 3)
# cv2.circle(output_img_2, (x2, y2), 3, (225, 0, 0), 5)
# cv2.circle(output_img_2, (x2, y2), r2, (225, 0, 0), 3)
# cv2.resizeWindow("canny", 50, 50)
cv2.imshow('AA',output_img)
cv2.imshow('Canny_2',cv2.Canny(img, threshold1,threshold2))
except:
pass
#空格跳出
if cv2.waitKey(1)==ord(' '):
break
#摧毁所有窗口
cv2.destroyAllWindows()