从背景中分割前景

Question

我目前正在做一个使用拉卡坦香蕉的项目，我想知道如何进一步分离前景和背景：

我已经获得了仅使用腐蚀、膨胀和阈值处理的分段图像。问题是它仍然没有正确分割。

这是我的代码：

    cv::Mat imggray, imgthresh, fg, bgt, bg;
    cv::cvtColor(src, imggray, CV_BGR2GRAY); //Grayscaling the image from RGB color space
    cv::threshold(imggray, imgthresh, 0, 255, CV_THRESH_BINARY_INV | CV_THRESH_OTSU); //Create an inverted binary image from the grayscaled image
    cv::erode(imgthresh, fg, cv::Mat(), cv::Point(-1, -1), 1); //erosion of the binary image and setting it as the foreground
    cv::dilate(imgthresh, bgt, cv::Mat(), cv::Point(-1, -1), 4); //dilation of the binary image to reduce the background region
    cv::threshold(bgt, bg, 1, 128, CV_THRESH_BINARY); //we get the background by setting the threshold to 1
    cv::Mat markers = cv::Mat::zeros(src.size(), CV_32SC1); //initializing the markers with a size same as the source image and setting its data type as 32-bit Single channel
    cv::add(fg, bg, markers); //setting the foreground and background as markers                                                                
    cv::Mat mask = cv::Mat::zeros(markers.size(), CV_8UC1);
    markers.convertTo(mask, CV_8UC1);    //converting the 32-bit single channel marker to a 8-bit single channel
    cv::Mat mthresh;
    cv::threshold(mask, mthresh, 0, 255, CV_THRESH_BINARY | CV_THRESH_OTSU); //threshold further the mask to reduce the noise
    // cv::erode(mthresh,mthresh,cv::Mat(), cv::Point(-1,-1),2);
    cv::Mat result;
    cv::bitwise_and(src, src, result, mthresh);  //use the mask to subtrack the banana from the background
    for (int x = 0; x < result.rows; x++) { //changing the black background to white
        for (int y = 0; y < result.cols; y++) {
            if (result.at<Vec3b>(x, y) == Vec3b(0, 0, 0)){
                result.at<Vec3b>(x, y)[0] = 255;
                result.at<Vec3b>(x, y)[1] = 255;
                result.at<Vec3b>(x, y)[2] = 255;
            }
        }
    }

这是我的结果：

Answer 1

由于背景接近灰色，请尝试使用色调通道和饱和度通道而不是灰度图像。
您可以轻松获得它们。

cv::Mat hsv;
cv::cvtColor(src, hsv, CV_BGR2HSV);
std::vector<cv::Mat> channels;
cv::split(src, channels);

cv::Mat hue = channels[0];
cv::Mat saturation = channels[1];

// If you want to combine those channels, use this code.
cv::Mat hs = cv::Mat::zeros(src.size(), CV_8U);
for(int r=0; r<src.rows; r++) {
    for(int c=0; c<src.cols; c++) {
        int hp = h.at<uchar>(r,c);
        int sp = s.at<uchar>(r,c);
        hs.at<uchar>(r, c) = static_cast<uchar>((h+s)>>1);
    }
}

Answer 2

adaptiveThreshold() 应该比仅仅 level-cut threshold() 工作得更好，因为它不考虑绝对颜色级别，而是考虑被检查点周围小区域的颜色变化。

尝试用自适应阈值替换。

Answer 3

使用礼帽而不是仅仅腐蚀/膨胀。它会同时处理背景变化。

那么在您的情况下，一个简单的阈值应该足以进行准确的分割。否则，您可以将其与分水岭结合起来。

（我会尽快分享一些图片）。

Answer 4

谢谢大家，我试着采纳你的建议并想出了这个

但是，正如您所看到的，仍然有一些背景，任何如何进一步“清理”这些的想法，我尝试进一步设置阈值，但它仍然会有这些位。我想出的代码如下，我深表歉意如果变量和编码风格有些混乱，没有时间对它们进行正确排序。

#include <stdio.h>
#include <iostream>
#include <opencv2\core.hpp>
#include <opencv2\opencv.hpp>
#include <opencv2\highgui.hpp>

using namespace cv;
using namespace std;

Mat COLOR_MAX(Scalar(65, 255, 255));
Mat COLOR_MIN(Scalar(15, 45, 45));


int main(int argc, char** argv){

Mat   src,hsv_img,mask,gray_img,initial_thresh;
Mat   second_thresh,add_res,and_thresh,xor_thresh;
Mat   result_thresh,rr_thresh,final_thresh;
// Load source Image
src = imread("sample11.jpg");
imshow("Original Image", src);
cvtColor(src,hsv_img,CV_BGR2HSV);
imshow("HSV Image",hsv_img);

//imwrite("HSV Image.jpg", hsv_img);

inRange(hsv_img,COLOR_MIN,COLOR_MAX, mask);
imshow("Mask Image",mask);

cvtColor(src,gray_img,CV_BGR2GRAY);
adaptiveThreshold(gray_img, initial_thresh, 255,ADAPTIVE_THRESH_GAUSSIAN_C,CV_THRESH_BINARY_INV,257,2);
imshow("AdaptiveThresh Image", initial_thresh);

add(mask,initial_thresh,add_res);
erode(add_res, add_res, Mat(), Point(-1, -1), 1);
dilate(add_res, add_res, Mat(), Point(-1, -1), 5);
imshow("Bitwise Res",add_res);

threshold(gray_img,second_thresh,170,255,CV_THRESH_BINARY_INV | CV_THRESH_OTSU);
imshow("TreshImge", second_thresh);

bitwise_and(add_res,second_thresh,and_thresh);
imshow("andthresh",and_thresh);

bitwise_xor(add_res, second_thresh, xor_thresh);
imshow("xorthresh",xor_thresh);

bitwise_or(and_thresh,xor_thresh,result_thresh);
imshow("Result image", result_thresh);

bitwise_and(add_res,result_thresh,final_thresh);
imshow("Final Thresh",final_thresh);
erode(final_thresh, final_thresh, Mat(), Point(-1,-1),5);

bitwise_and(src,src,rr_thresh,final_thresh);
imshow("Segmented Image", rr_thresh);
imwrite("Segmented Image.jpg", rr_thresh);

waitKey(0);
return 1;
}