OpenCV轮廓按面积大小排序(附Python/C++代码)

import cv2
import numpy as np

# putText函数使用的字体定义
font = cv2.FONT_HERSHEY_SIMPLEX
PI = 3.1415926
 
# 读取图片、灰度转换、OTSU阈值
img = cv2.imread("test.png")
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
ret,thresh = cv2.threshold(gray,0,255,cv2.THRESH_BINARY_INV+cv2.THRESH_OTSU)
# 查看二值化结果
cv2.imshow("thres", thresh)
cv2.imwrite("thres.jpg", thresh)

# 轮廓查找
_, contours,hierarchy = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)

def cnt_area(cnt):
  area = cv2.contourArea(cnt)
  return area

contours.sort(key = cnt_area, reverse=False)
for i in range(0, len(contours)):
  (x, y, w, h) = cv2.boundingRect(contours[i])
  cv2.rectangle(img,(x,y),(x+w, y+h),(255,0,0),2, cv2.LINE_AA)
  cv2.putText(img,"No.%d"%(i+1),(x,y-5),font,0.8,(255,0,0),2)

cv2.imshow("contours", img)
cv2.imwrite("result1.jpg",img)
cv2.waitKey(0)
cv2.destroyAllWindows()

def cnt_area(cnt):
  area = cv2.contourArea(cnt)
  return area
contours.sort(key = cnt_area, reverse=False)

//比较轮廓面积(USB_Port_Lean用来进行轮廓排序)
bool Contour_Area(vector<Point> contour1, vector<Point> contour2)
{
  return contourArea(contour1) > contourArea(contour2);
}

vector<vector<Point>> contours;
vector<Vec4i> hierarcy;
bool ret = false;
findContours(thres, contours, hierarcy, RETR_EXTERNAL, CHAIN_APPROX_NONE); //查找轮廓
sort(contours.begin(), contours.end(), Contour_Area);
  for (int i = 0; i < contours.size(); i++)
{
  double area = contourArea(contours[i]);
  cout << area << endl;
  }