OpenCV(十一)-图像的分割与修复

一、图像分割的基本概念

• 图像分割
  • 将前景物体从背景中分离出来
• 分割方法
  • 传统的
    • 分水岭法
    • GrabCut法
    • MeanShift法
    • 背景扣除
  • 基于深度学习的

1.1、分水岭法

• 分水岭法的处理步骤
  1. 标记背景
  2. 标记前景
  3. 标记未知域
  4. 进行分割

# 分水岭API
watershed(img, masker)
masker,前景、背景设置不同的值用以区分它们

import cv2
import numpy as np

import matplotlib
matplotlib.use('Qt5Agg')  # or 'Qt5Agg' if you prefer

import matplotlib.pyplot as plt



# 获取背景
# 1、通过二值法得到黑白图片
# 2、通过形态学获取背景

img = cv2.imread('water_coins.jpeg')
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

# 二值化
# 自适应性寻找阈值
ret, thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)

# 开运算，膨胀，去除噪点
kernel = np.ones((3,3), np.int8)
open1 = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, kernel, iterations=2)

# 膨胀
bg = cv2.dilate(open1, kernel, iterations = 1)


# 获取前景物体
dist = cv2.distanceTransform(open1, cv2.DIST_L2, 5)

# 前景
ret, fg = cv2.threshold(dist, 0.7*dist.max(), 255, cv2.THRESH_BINARY)

# plt.imshow(dist, cmap='gray')
# plt.show()
# exit()

# 获取未知区域
fg = np.uint8(fg)
unknown = cv2.subtract(bg, fg)

# 创建连通域
ret, marker = cv2.connectedComponents(fg)
marker = marker + 1
marker[unknown==255] = 0

# 进行图像分割
result = cv2.watershed(img, marker)

img[result == -1] = [0, 0, 255]

cv2.imshow("img", img)
cv2.imshow("unknown", unknown)
cv2.imshow("fg", fg)
cv2.imshow("dist", dist)
cv2.imshow("bg", bg)
cv2.imshow("thresh", thresh)
cv2.waitKey(0)

1.2、GrabCut

• GrabCut
  • 通过交互的方式获得前景物体
• ==原理==
  • 用户指定前景的大体区域，剩下的为背景区域
  • 用户还可以明确指定某些地方为前景或背景
  • GrabCut采用分段迭代的方法分析前景物体形成模型树
  • 最后根据权重决定某个像素是前景还是背景

import cv2
import numpy as np

class App:

    flag_rect = False
    rect=(0, 0, 0, 0)
    startX = 0
    startY = 0

    def onmouse(self, event, x, y, flags, param):

        if event == cv2.EVENT_LBUTTONDOWN:
            self.flag_rect = True
            self.startX = x
            self.startY = y
            print("LBUTTIONDOWN")
        elif event == cv2.EVENT_LBUTTONUP:
            self.flag_rect = False
            cv2.rectangle(self.img, 
                            (self.startX, self.startY),
                            (x, y),
                            (0, 0, 255), 
                            3)
            self.rect = (min(self.startX, x), min(self.startY, y), 
                        abs(self.startX - x), 
                        abs(self.startY -y))

            print("LBUTTIONUP")
        elif event == cv2.EVENT_MOUSEMOVE:
            if self.flag_rect == True:
                self.img = self.img2.copy()
                cv2.rectangle(self.img, 
                                (self.startX, self.startY),
                                (x, y),
                                (255, 0, 0), 
                                3)
            print("MOUSEMOVE")  

        print("onmouse")

    def run(self):
        print("run...")

        cv2.namedWindow('input')
        cv2.setMouseCallback('input', self.onmouse)

        self.img = cv2.imread('./lena.png')
        self.img2 = self.img.copy()
        self.mask = np.zeros(self.img.shape[:2], dtype=np.uint8)
        self.output = np.zeros(self.img.shape, np.uint8)

        while(1):
            cv2.imshow('input', self.img)
            cv2.imshow('output', self.output)
            k = cv2.waitKey(100)
            if k == 27:
                break

            if k == ord('g'):
                bgdmodel = np.zeros((1, 65), np.float64)
                fgdmodel = np.zeros((1, 65), np.float64)
                cv2.grabCut(self.img2, self.mask, self.rect,
                            bgdmodel, fgdmodel,
                            1, 
                            cv2.GC_INIT_WITH_RECT)
            mask2 = np.where((self.mask==1)|(self.mask==3), 255, 0).astype('uint8')
            self.output = cv2.bitwise_and(self.img2, self.img2, mask=mask2)   
           
App().run()

1.3、meanshift图像分割

# 我一直显示报错，可能是opencv版本的原因吧
import cv2
import numpy as np
 
img = cv2.imread('key.png')
 
mean_img = cv2.pyrMeanShiftFiltering(img, 20, 30)
 
imgcanny = cv2.Canny(mean_img, 150, 300)
 
contours, _ = cv2.findContours(imgcanny, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
 
cv2.drawContours(img, contours, -1, (0, 0, 255), 2)
 
cv2.imshow('img', img)
cv2.imshow('mean_img', mean_img)
cv2.imshow('canny', imgcanny)
cv2.waitKey()

二、视频前后景分离

import cv2
import numpy as np

cap = cv2.VideoCapture('./vtest.avi')

mog = cv2.bgsegm.createBackgroundSubtractorMOG()

while(True):
    ret, frame = cap.read()
    fgmask = mog.apply(frame)

    cv2.imshow('img', fgmask)

    k = cv2.waitKey(10)
    if k == 27:
        break

cap.release()
cv2.destroyAllWindows()

三、其他对视频前后影分离的方法

import cv2
import numpy as np

cap = cv2.VideoCapture('./vtest.avi')

# mog = cv2.bgsegm.createBackgroundSubtractorMOG()
# 好处，可以计算出阴影部分
# 缺点，会产生很多噪点
mog = cv2.createBackgroundSubtractorMOG2()

while(True):
    ret, frame = cap.read()
    fgmask = mog.apply(frame)

    cv2.imshow('img', fgmask)

    k = cv2.waitKey(10)
    if k == 27:
        break

cap.release()
cv2.destroyAllWindows()

import cv2
import numpy as np

cap = cv2.VideoCapture('./vtest.avi')

# mog = cv2.bgsegm.createBackgroundSubtractorMOG()
# 好处，可以计算出阴影部分
# 缺点，会产生很多噪点
# mog = cv2.createBackgroundSubtractorMOG2()

# 好处，可以计算出阴影部分，同时减少了噪点
# 缺点，如果采用默认值，则在开始好长时间内没人作任何信息显示
# 解决办法，调整初始参考帧数量
mog = cv2.bgsegm.createBackgroundSubtractorGMG()

while(True):
    ret, frame = cap.read()
    fgmask = mog.apply(frame)

    cv2.imshow('img', fgmask)

    k = cv2.waitKey(10)
    if k == 27:
        break

cap.release()
cv2.destroyAllWindows()

四、图形修复

import cv2
import numpy as np

img = cv2.imread('inpaint.png')
mask = cv2.imread('inpaint_mask.png', 0)

dst = cv2.inpaint(img, mask, 5, cv2.INPAINT_TELEA)

cv2.imshow('dst', dst)
cv2.waitKey()