OpenCV 图像分割之分水岭算法

import cv2
import numpy as np
from matplotlib import pyplot as plt

img = cv2.imread("2.jpg")
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
#使用Otsu的二值化方法把图片变成二值图像
ret, thresh = cv2.threshold(gray, 0 ,255, cv2.THRESH_BINARY_INV+ cv2.THRESH_OTSU)
#降噪 ，膨胀
kernel = np.ones((3,3), np.uint8)
opening = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, kernel)
#找到确定的背景区域
sure_bg = cv2.dilate(opening, kernel, iterations = 3)
#背景之外是可能的前景，距离变换（“膨胀”）确定的背景的边界线得到确定的前景
dist_transform = cv2.distanceTransform(opening, cv2.DIST_L2, 5)
alpha =0.3 #距离变换系数，越小则会找到更多的前景区域
ret, sure_fg = cv2.threshold(dist_transform, alpha*dist_transform.max(), 255, 0) #用距离变换找到确定的前景区域
sure_fg = np.uint8(sure_fg)

#确定分割边界
unknown = cv2.subtract(sure_bg, sure_fg)
ret, markers = cv2. connectedComponents(sure_fg)
markers += 1
markers[unknown==255] =0
markers = cv2.watershed(img, markers)
img1 = cv2.cvtColor(img.copy(), cv2.COLOR_BGR2RGB)
img1[markers ==-1] = [0, 0, 255] #分水岭 标蓝色

#使用matplotlib 绘图
plt.subplot(221)
plt.imshow(img1)
plt.title("watershed")
plt.xticks([]); plt.yticks([]) #不显示坐标轴刻度
plt.subplot(222)
plt.imshow(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))
plt.title("original")
plt.xticks([]); plt.yticks([])
plt.subplot(223)
sure_fg_ = np.where(sure_fg>128, 1, 0) #转2值图
sure_fg_ = sure_fg_.repeat(3,axis=1).reshape(img.shape) * cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
#plt.imshow(sure_fg, cmap ="gray")
plt.imshow(sure_fg_)
plt.title("sure foreground")
plt.xticks([]); plt.yticks([])
plt.subplot(224)
sure_bg_ =255- sure_bg #黑白颠倒
sure_bg_ = np.where(sure_bg_>128, 1, 0) #转2值图
sure_bg_ = sure_bg_.repeat(3,axis=1).reshape(img.shape) * cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
#plt.imshow(sure_bg, cmap ="gray_r")
plt.imshow(sure_bg_)
plt.title("sure background")
plt.xticks([]); plt.yticks([])
plt.show()