opencv联合python6——边缘检测，图像金字塔和轮廓检测，模板匹配_python利用图像金字塔加快模板匹配过程

边缘检测

#图像边缘检测
import numpy as np
import cv2

def cv_show(name,image):
	cv2.imshow(name,image)
	cv2.waitKey(0)
	cv2.destroyAllWindows()


img = cv2.imread("D:/6.jpg",cv2.IMREAD_GRAYSCALE)
plant = cv2.imread("D:/4.jpg")
res = cv2.Canny(img,50,120)

cv_show("res",res)
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图像金字塔和轮廓检测

高斯图像金字塔

#高斯金字塔

import numpy as np
import cv2

def cv_show(name,image):
	cv2.imshow(name,image)
	cv2.waitKey(0)
	cv2.destroyAllWindows()

#可连续上采样或者下采样
img = cv2.imread("D:/6.jpg")
plant = cv2.imread("D:/4.jpg")
print(img.shape)
up = cv2.pyrUp(img)
print(up.shape)
down = cv2.pyrDown(img)
print(down.shape)
cv_show("img",img) 
cv_show("up",up)
cv_show("down",down)

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拉普拉斯图像金字塔

原图不能直接减去先下采样再上采样的图，处理过的图比原图多一行，多一列，直接相减会报错

#拉普拉斯金字塔

import numpy as np
import cv2

def cv_show(name,image):
	cv2.imshow(name,image)
	cv2.waitKey(0)
	cv2.destroyAllWindows()

img = cv2.imread("D:/4.jpg")

down = cv2.pyrDown(img)
down_up = cv2.pyrUp(down)
print(img.shape)
print(down_up.shape)
print(down.shape)

lap = img - down_up  #会报错

print(lap.shape)

cv_show("img",img) 
cv_show("up",up)
cv_show("down",down)
cv_show("lap",lap)
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图像轮廓

寻找轮廓，画出轮廓，轮廓特征

#轮廓提取

import numpy as np
import cv2

def cv_show(name,image):
	cv2.imshow(name,image)
	cv2.waitKey(0)
	cv2.destroyAllWindows()

img = cv2.imread("D:/4.jpg")
#为了更高的准确率，使用二值图像
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
ret,thresh = cv2.threshold(gray,127,255,cv2.THRESH_BINARY)
cv_show("thresh",thresh)
#提取轮廓
#binary 返回的图像 contours轮廓点 hierarchy 层级
binary,contours,hierarchy = cv2.findContours(thresh,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
#contours轮廓为list类型
print(np.array(contours).shape)
#绘制轮廓
#传入绘制轮廓，轮廓，轮廓索引，颜色模式，线条宽度
#注意需要copy图像，否则原图会变
draw_img = img.copy()
res = cv2.drawContours(draw_img,contours,-1,(0,0,255),2)
#轮廓特征，要先将轮廓提取出来才可以用来计算，
#即先执行cnt = contours[i],再进行面积和周长计算
cnt = contours[10]
#面积
area = cv2.contourArea(cnt)
#周长，True表示闭合的
len1 = cv2.arcLength(cnt,True)
print(area)
print(len1)
cv_show("res",res)
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cnt = contours[0]
print(cv2.contourArea(cnt))
print(cv2.arcLength)
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轮廓近似

#轮廓近似

import numpy as np
import cv2

def cv_show(name,image):
	cv2.imshow(name,image)
	cv2.waitKey(0)
	cv2.destroyAllWindows()

img = cv2.imread("D:/contours2.png")
#为了更高的准确率，使用二值图像
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
ret,thresh = cv2.threshold(gray,127,255,cv2.THRESH_BINARY)
binary,contours,hierarchy = cv2.findContours(thresh,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
cnt = contours[0]
draw_img = img.copy()
res = cv2.drawContours(draw_img,[cnt],-1,(0,0,255),2)
#周长，True表示闭合的
len1 = cv2.arcLength(cnt,True)
epsilon = 0.1*len1  #比例系数越小越接近原始轮廓
#轮廓近似
approx = cv2.approxPolyDP(cnt,epsilon,True)
res = cv2.drawContours(draw_img,[approx],-1,(0,255,0),2)

cv_show("res",res)
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边界矩形

#边界矩形

import numpy as np
import cv2

def cv_show(name,image):
	cv2.imshow(name,image)
	cv2.waitKey(0)
	cv2.destroyAllWindows()

img = cv2.imread("D:/contours2.png")
#为了更高的准确率，使用二值图像
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
ret,thresh = cv2.threshold(gray,127,255,cv2.THRESH_BINARY)
binary,contours,hierarchy = cv2.findContours(thresh,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
cnt = contours[0]
#得到外接矩形
x,y,w,h = cv2.boundingRect(cnt)
#画出外接矩形
img = cv2.rectangle(img,(x,y),(x+w,y+h),(0,255,0),2)
cv_show("img",img)
#轮廓面积和边界矩形比
area = cv2.contourArea(cnt)
rect_area = w*h
i = float(area)/rect_area
print("比值",i)
#外接圆
(x,y),radius = cv2.minEnclosingCircle(cnt)
center = (int(x),int(y))
radius = int(radius)
img = cv2.circle(img,center,radius,(255,0,0),2)
cv_show("img",img)

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圆检测，遍历轮廓操作

import cv2
import numpy as np
#圆检测  可通过更改150，120这两个参数检测出所有圆
image = cv2.imread("E:/3.jpg")
gray = cv2.cvtColor(image,cv2.COLOR_BGR2GRAY)
img = cv2.medianBlur(gray,5)

circles = cv2.HoughCircles(img,cv2.HOUGH_GRADIENT,1,150,param1 = 120,param2 = 30,minRadius = 0,maxRadius = 0)
circles = np.uint16(np.around(circles))
for i in circles[0,:]:
	cv2.circle(image,(i[0],i[1]),i[2],(0,0,255),2)
	cv2.circle(image,(i[0],i[1]),2,(0,255,0),2)

cv2.imshow("img",image)

cv2.waitKey(0)
cv2.destroyAllWindows()

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模板匹配

模板匹配

#模板匹配

import numpy as np
import cv2

def cv_show(name,image):
	cv2.imshow(name,image)
	cv2.waitKey(0)
	cv2.destroyAllWindows()

img = cv2.imread("D:/原图.png")
template = cv2.imread("模板.png")
gray1 = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
gray2 = cv2.cvtColor(template,cv2.COLOR_BGR2GRAY)

h,w = template.shape[:2]
#匹配
#TM_SQDIFF  匹配方法，归一化的方法更好用
res = cv2.matchTemplate(gray1,gray2,cv2.TM_SQDIFF)
#得到极值坐标
min_val,max_val,min_loc,max_loc = cv2.minMaxLoc(res)
top_left = min_loc
bottom_right = (top_left[0]+w,top_left[1]+h)
#画出人脸
cv2.rectangle(img,top_left,bottom_right,255,2)
cv_show("img",img)
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匹配多个对象

#模板匹配,匹配多个对象

import numpy as np
import cv2

def cv_show(name,image):
	cv2.imshow(name,image)
	cv2.waitKey(0)
	cv2.destroyAllWindows()

img = cv2.imread("D:/mario.jpg")
img_gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
template = cv2.imread("D:/mario_coin.jpg",0)
#gray1 = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
#gray2 = cv2.cvtColor(template,cv2.COLOR_BGR2GRAY)
h,w = template.shape[:2]
#匹配
#TM_SQDIFF  匹配方法，归一化的方法更好用
res = cv2.matchTemplate(img_gray,template,cv2.TM_CCOEFF_NORMED)
#设置匹配阈值
threshold = 0.8
#np.where() 它有三个参数：第一个参数为数组，该数组的元素值为真或假。函数会返回同样维度的数组；输入数组的元素值为真时，输出数组的相应元素为函数的第二个参数，否则，输入数组的元素为假时输出数组相应的元素为函数的第三个参数
loc = np.where(res>=threshold)
for pt in zip(*loc[::-1]): # *号表示可选参数
	#得到极值坐标
	bottom_right = (pt[0]+w,pt[1]+h)
	#画出人脸
	cv2.rectangle(img,pt,bottom_right,(0,255,255),2)
cv_show("img",img)
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