基于颜色进行轮廓分割，基于opencv和python

1. 阈值调整，交互式的阈值调整，确定上限和下限：

import cv2 as cv
import numpy as npdef Tcallback(event):H = cv.getTrackbarPos('H', 'show_pic')S = cv.getTrackbarPos('S', 'show_pic')V = cv.getTrackbarPos('V', 'show_pic')H0 = cv.getTrackbarPos('H0', 'show_pic')S0 = cv.getTrackbarPos('S0', 'show_pic')V0 = cv.getTrackbarPos('V0', 'show_pic')lower_red = np.array([H0, S0, V0])upper_red = np.array([H, S, V])mask = cv.inRange(hsv, lower_red, upper_red)res = cv.bitwise_and(img, img, mask=mask)cv.imshow('show_pic', res)img = cv.imread('sam2.png')#, 1)
hsv = cv.cvtColor(img, cv.COLOR_BGR2HSV_FULL)cv.namedWindow('show_pic')
cv.resizeWindow('show_pic', 640, 480)cv.createTrackbar('H', 'show_pic', 255, 255, Tcallback)
cv.createTrackbar('S', 'show_pic', 255, 255, Tcallback)
cv.createTrackbar('V', 'show_pic', 255, 255, Tcallback)  cv.createTrackbar('H0', 'show_pic', 0, 255, Tcallback)
cv.createTrackbar('S0', 'show_pic', 43, 255, Tcallback)
cv.createTrackbar('V0', 'show_pic', 46, 255, Tcallback)if cv.waitKey(0) == ord('q'):cv.destroyAllWindows()

2. 阈值分割，并找到最大轮廓

现根据mask找到所有轮廓

对所有轮廓排序，找到最大的

再对最大轮廓去凸多边形，用来对噪声做过滤。

img=cv2.resize(img,(640,480))
hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV_FULL)
mask_green=cv2.inRange(hsv, self.lower_green, self.upper_green)
img_c,contours, hierarchy = cv2.findContours(mask_green,cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)
list_c=sorted(contours,key=cv2.contourArea,reverse=True)
c_max=list_c[0]
approx = cv2.approxPolyDP(c_max, 0.01 * cv2.arcLength(c_max, True), True)
hull = cv2.convexHull(c_max)