opencv-使用GrabCut提取背景图像

参考：
1、https://stackoverflow.com/questions/17698431/extracting-background-image-using-grabcut
2、https://stackoverflow.com/questions/15536222/opencv-grabcut-algorithm-example-not-working

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1、提取前景图像

c++

#include "opencv2/opencv.hpp"#include <iostream>using namespace cv;using namespace std;int main( ){ // Open another image    Mat image;    image= cv::imread("images/mango11a.jpg");    // define bounding rectangle     cv::Rect rectangle(50,70,image.cols-150,image.rows-180);    cv::Mat result; // segmentation result (4 possible values)    cv::Mat bgModel,fgModel; // the models (internally used)    // GrabCut segmentation    cv::grabCut(image,    // input image                    result,   // segmentation result                            rectangle,// rectangle containing foreground                             bgModel,fgModel, // models                            1,        // number of iterations                            cv::GC_INIT_WITH_RECT); // use rectangle    cout << "oks pa dito" <<endl;    // Get the pixels marked as likely foreground    cv::compare(result,cv::GC_PR_FGD,result,cv::CMP_EQ);    // Generate output image    cv::Mat foreground(image.size(),CV_8UC3,cv::Scalar(255,255,255));    image.copyTo(foreground,result); // bg pixels not copied    // draw rectangle on original image    cv::rectangle(image, rectangle, cv::Scalar(255,255,255),1);    cv::namedWindow("Image");    cv::imshow("Image",image);    // display result    cv::namedWindow("Segmented Image");    cv::imshow("Segmented Image",foreground);    waitKey();    return 0;}

python

# -*- coding: UTF-8 -*-import cv2import numpy as npimage=cv2.imread("messi5.jpg")# define bounding rectanglerectangle = (50,70,image.shape[1]-100,image.shape[0]-200)result = np.zeros(image.shape[:2],np.uint8)# bgdModel = np.zeros((1,65),np.float64)# fgdModel = np.zeros((1,65),np.float64)bgdModel = NonefgdModel = None# GrabCut segmentationcv2.grabCut(image,result,rectangle,bgdModel,fgdModel,5,cv2.GC_INIT_WITH_RECT)# """# Get the pixels marked as likely foregroundresult=cv2.compare(result,cv2.GC_PR_FGD,cv2.CMP_EQ) # 获取前景像素值  GC_PR_BGD 背景像素值# Generate output imagemask=np.zeros(image.shape,np.uint8)mask[:,:,0]=resultmask[:,:,1]=resultmask[:,:,2]=resultforeground=cv2.bitwise_and(image,mask)# """# 或mask= np.where((result==2)|(result==0),0,1).astype('uint8')foreground = image*mask[:,:,np.newaxis]# draw rectangle on original imagecv2.rectangle(image,rectangle[:2],rectangle[2:],(255,255,255),1)cv2.namedWindow("Image",0)cv2.imshow("Image",image)# display resultcv2.namedWindow("Segmented Image",0)cv2.imshow("Segmented Image",foreground)cv2.waitKey(0)cv2.destroyAllWindows()

2、提取背景颜色

python

# -*- coding: UTF-8 -*-import cv2import numpy as npimage=cv2.imread("messi5.jpg")# define bounding rectanglerectangle = (50,70,image.shape[1]-100,image.shape[0]-200)result = np.zeros(image.shape[:2],np.uint8)# bgdModel = np.zeros((1,65),np.float64)# fgdModel = np.zeros((1,65),np.float64)bgdModel = NonefgdModel = None# GrabCut segmentationcv2.grabCut(image,result,rectangle,bgdModel,fgdModel,5,cv2.GC_INIT_WITH_RECT)# Get the pixels marked as likely foregroundresult=cv2.compare(result,cv2.GC_PR_BGD,cv2.CMP_EQ) # GC_PR_BGD 背景像素值# Generate output image_,result=cv2.threshold(result,128,255,cv2.THRESH_BINARY)mask=np.zeros(image.shape,np.uint8)mask[:,:,0]=resultmask[:,:,1]=resultmask[:,:,2]=resultbackground=cv2.bitwise_and(image,mask)# draw rectangle on original imagecv2.rectangle(image,rectangle[:2],rectangle[2:],(255,255,255),1)cv2.namedWindow("Image",0)cv2.imshow("Image",image)# display resultcv2.namedWindow("Segmented Image",0)cv2.imshow("Segmented Image",background)cv2.waitKey(0)cv2.destroyAllWindows()

C++

#include <opencv2/opencv.hpp>#include <iostream>using namespace cv;using namespace std;int main( ){// Open another imageMat image;image= cv::imread("images/abc.jpg");Mat image2 = image.clone();// define bounding rectanglecv::Rect rectangle(40,90,image.cols-80,image.rows-170);cv::Mat result; // segmentation result (4 possible values)cv::Mat bgModel,fgModel; // the models (internally used)// GrabCut segmentationcv::grabCut(image,    // input image            result,   // segmentation result            rectangle,// rectangle containing foreground            bgModel,fgModel, // models            1,        // number of iterations            cv::GC_INIT_WITH_RECT); // use rectanglecout << "oks pa dito" <<endl;// Get the pixels marked as likely foregroundcv::compare(result,cv::GC_PR_FGD,result,cv::CMP_EQ);// Generate output imagecv::Mat foreground(image.size(),CV_8UC3,cv::Scalar(255,255,255));//cv::Mat background(image.size(),CV_8UC3,cv::Scalar(255,255,255));image.copyTo(foreground,result); // bg pixels not copied// draw rectangle on original imagecv::rectangle(image, rectangle, cv::Scalar(255,255,255),1);imwrite("img_1.jpg",image);imwrite("Foreground.jpg",foreground);Mat background = image2 - foreground;imwrite("Background.jpg",background);return 0;}