opencv使用——玉米粒识别和优劣判断

Posted 2020-12-14 pine-apple

运行环境：python3.6.9 opencv3.4.10

 

import cv2
import numpy as np
font=cv2.FONT_HERSHEY_COMPLEX
kernel = np.ones((7,7),np.uint8)
img=cv2.imread(‘corn.jpg‘,1) # 1代表深度8，3通道
img_orgin=img
cv2.imshow(‘orgin‘,img)
cv2.waitKey(0)


gray_img=cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)#灰度处理
#cv2.imshow(‘gray_img‘,gray_img)
#cv2.waitKey(0)


ret,th1=cv2.threshold(gray_img,120,255,cv2.THRESH_BINARY)
#cv2.imshow(‘th1‘,th1)
#cv2.waitKey(0)


erosion = cv2.erode(th1,kernel,iterations = 1)#腐蚀
#cv2.imshow(‘erosion‘,erosion)
#cv2.waitKey(0)
dist_img = cv2.distanceTransform(erosion, cv2.DIST_L1, cv2.DIST_MASK_3)#距离变换
#cv2.imshow(‘距离变换‘,dist_img)
#cv2.waitKey(0)
dist_output = cv2.normalize(dist_img, 0, 1.0, cv2.NORM_MINMAX)#归一化
#cv2.imshow(‘dist_output‘,dist_output*80)
#cv2.waitKey(0)
 
ret,th2=cv2.threshold(dist_output*80,0.3,255,cv2.THRESH_BINARY)
#cv2.imshow(‘th2‘,th2)
#cv2.waitKey(0)
kernel = np.ones((5,5),np.uint8)
opening = cv2.morphologyEx(th2, cv2.MORPH_OPEN, kernel)
#cv2.imshow(‘opening‘,opening)
#cv2.waitKey(0)
opening = np.array(opening,np.uint8)
img,contours, hierarchy = cv2.findContours(opening,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)#轮廓提取
count_sum=count_1=count_2=count_3=0
for cnt in contours:
    (x,y),radius = cv2.minEnclosingCircle(cnt)
    center = (int(x),int(y))
    radius = int(radius)
    circle_img = cv2.circle(opening,center,radius,(255,255,255),1)
    area = cv2.contourArea(cnt)
    area_circle=3.14*radius*radius
    #print(area/area_circle)
    if area/area_circle <=0.5:
        #img = cv2.drawContours(img_orgin, cnt, -1, (0,0,255), 5)#差（红色）
        img=cv2.putText(img_orgin,‘bad‘,center,font,0.5,(0,0,255))
        count_1+=1
    
    elif area/area_circle >=0.6:
        #img = cv2.drawContours(img_orgin, cnt, -1, (0,255,0), 5)#优（绿色）
        img=cv2.putText(img_orgin,‘good‘,center,font,0.5,(0,255,0))
        count_2+=1
    else:
        #img = cv2.drawContours(img_orgin, cnt, -1, (255,0,0), 5)#良（蓝色）
        img=cv2.putText(img_orgin,‘normal‘,center,font,0.5,(255,0,0))
        count_3+=1
count_sum=count_1+count_2+count_3
img=cv2.putText(img,(‘sum=‘+str(count_sum)),(30,50),font,1,(0,0,0))
img=cv2.putText(img,(‘good=‘+str(count_2)),(30,80),font,1,(0,255,0))
img=cv2.putText(img,(‘normal=‘+str(count_3)),(30,110),font,1,(255,0,0))
img=cv2.putText(img,(‘bad=‘+str(count_1)),(30,140),font,1,(0,0,255))
cv2.imshow(‘circle_img‘,img)
cv2.waitKey(0)
cv2.destroyAllWindows()
print(‘玉米粒共有：‘,count_sum)

 

运行结果：