python实现对象测量

 1 import cv2 as cv
 2 import numpy as np
 3 
 4 def measure_object(image):
 5     gray = cv.cvtColor(image,cv.COLOR_BGR2GRAY) #转灰度图像
 6     ret, binary = cv.threshold(gray,0,255,cv.THRESH_OTSU|cv.THRESH_BINARY_INV)  #获取二值化图像
       #cv.THRESH_BINARY | cv.THRESH_OTSU 和cv.THRESH_BINARY_INV | cv.THRESH_OTSU（推荐）试一试

 7     print("thresold value:",ret)
 8     cv.imshow("binary image",binary)
 9     outImage,contours,hireachy = cv.findContours(binary,cv.RETR_EXTERNAL,cv.CHAIN_APPROX_SIMPLE)
10     for i,contour in enumerate(contours):
11         area = cv.contourArea(contour)      #获取每个轮廓面积
12         x,y,w,h = cv.boundingRect(contour)     #获取轮廓的外接矩形
13         rate = min(w,h)/max(w,h)    #获取外接矩形宽高比，可以起到一定的筛选作用
14         print("rectangle rate:%s"%rate)
15         mm = cv.moments(contour)            #求取轮廓的几何距
16         print(type(mm))
17         print(mm)
18         cx = mm[‘m10‘]/mm[‘m00‘]
19         cy = mm[‘m01‘]/mm[‘m00‘]    # 重心坐标
20         cv.circle(image,(np.int(cx),np.int(cy)),2,(0,255,255),-1) #根据几何距获取的中心点，画出中心圆
21         cv.rectangle(image,(x,y),(x+w,y+h),(0,0,255),2) #根据轮廓外接矩形返回数据，画出外接矩形
22         print("contour area:",area)
23     cv.imshow("measure_object",image)
24 
25 
26 src = cv.imread("./dg.png")  #读取图片
27 cv.namedWindow("input image",cv.WINDOW_AUTOSIZE)    #创建GUI窗口,形式为自适应
28 cv.imshow("input image",src)    #通过名字将图像和窗口联系
29 
30 measure_object(src)
31 
32 cv.waitKey(0)   #等待用户操作，里面等待参数是毫秒，我们填写0，代表是永远，等待用户操作
33 cv.destroyAllWindows()  #销毁所有窗口

mm-->{‘m02‘: 3301.25, ‘m12‘: 233530.03333333333, ‘m30‘: 6934377.600000001, ‘mu11‘: -10.721153846156085, ‘m21‘: 1265395.2, ‘nu02‘: 0.08336283484212434, ‘m01‘: 252.5, ‘mu30‘: -12.458842864260077, ‘nu21‘: 0.003451665836783194, ‘mu12‘: 11.46577909273492, ‘nu12‘: 0.006828369938345303, ‘mu02‘: 31.698717948717785, ‘mu21‘: 5.795825115486707, ‘m03‘: 43556.350000000006, ‘nu30‘: -0.007419782588937784, ‘m20‘: 97840.41666666666, ‘m00‘: 19.5, ‘m10‘: 1381.0, ‘nu20‘: 0.09806301522275791, ‘mu03‘: -11.52064431294275, ‘m11‘: 17871.458333333332, ‘nu03‘: -0.0068610445623109115, ‘mu20‘: 37.2884615384537, ‘nu11‘: -0.028195013402119877}

 1 import cv2 as cv
 2 import numpy as np
 3 
 4 
 5 def measure_object(image):
 6     gray = cv.cvtColor(image,cv.COLOR_BGR2GRAY) #转灰度图像
 7     ret, binary = cv.threshold(gray,0,255,cv.THRESH_OTSU|cv.THRESH_BINARY)  #获取二值化图像
 8     cv.imshow("binary image",binary)
 9     dst = cv.cvtColor(binary,cv.COLOR_GRAY2BGR)
10     outImage,contours,hireachy = cv.findContours(binary,cv.RETR_EXTERNAL,cv.CHAIN_APPROX_SIMPLE)
11     for i,contour in enumerate(contours):
12         mm = cv.moments(contour)            #求取轮廓的几何距
13         cx = mm[‘m10‘]/mm[‘m00‘]
14         cy = mm[‘m01‘]/mm[‘m00‘]
15         cv.circle(dst,(np.int(cx),np.int(cy)),2,(0,255,255),-1) #画出中心点
16         approxCurve = cv.approxPolyDP(contour,4,True)   #4是与阈值的间隔大小，越小越易找出，True是是否找闭合图像
17         print(approxCurve.shape)
18         if approxCurve.shape[0] >= 7:
19             cv.drawContours(dst,contours,i,(0,255,0),2)　　#画出轮廓
20         elif approxCurve.shape[0] == 4:
21             cv.drawContours(dst,contours,i,(0,0,255),2)
22         else:
23             cv.drawContours(dst,contours,i,(255,0,0),2)
24 
25     cv.imshow("measure_object",dst)
26 
27 
28 src = cv.imread("./lk.png")  #读取图片
29 cv.namedWindow("input image",cv.WINDOW_AUTOSIZE)    #创建GUI窗口,形式为自适应
30 cv.imshow("input image",src)    #通过名字将图像和窗口联系
31 
32 measure_object(src)
33 
34 cv.waitKey(0)   #等待用户操作，里面等待参数是毫秒，我们填写0，代表是永远，等待用户操作
35 cv.destroyAllWindows()  #销毁所有窗口