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我试图在 OpenCV 中平均生成两个 blob。为了实现这一点，我计划对通过以下方式预处理的图像使用分水岭算法：

cv::Mat common, diff, processed, result;
cv::bitwise_and(blob1, blob2, common); //calc common area of the two blobs
cv::absdiff(blob1, blob2, diff);       //calc area where they differ

cv::distanceTransform(diff, processed, CV_DIST_L2, 3); //idea here is that the highest intensity 
                                                       //will be in the middle of the differing area
cv::normalize(processed, processed, 0, 255, cv::NORM_MINMAX, CV_8U); //convert floats to bytes

cv::Mat watershedMarkers, watershedOutline;
common.convertTo(watershedMarkers, CV_32S, 1. / 255, 1); //change background to label 1, common area to label 2
watershedMarkers.setTo(0, processed); //set 0 (unknown) for area where blobs differ

cv::cvtColor(processed, processed, CV_GRAY2RGB); //watershed wants 3 channels
cv::watershed(processed, watershedMarkers);
cv::rectangle(watershedMarkers, cv::Rect(0, 0, watershedMarkers.cols, watershedMarkers.rows), 1); //remove the outline

//draw the boundary in red (for debugging)
watershedMarkers.convertTo(watershedOutline, CV_16S);
cv::threshold(watershedOutline, watershedOutline, 0, 255, CV_THRESH_BINARY_INV);
watershedOutline.convertTo(watershedOutline, CV_8U);
processed.setTo(cv::Scalar(CV_RGB(255, 0, 0)), watershedOutline);

//convert computed labels back to mask (blob), less relevant but shows my ultimate goal
watershedMarkers.convertTo(watershedMarkers, CV_8U);
cv::threshold(watershedMarkers, watershedMarkers, 1, 0, CV_THRESH_TOZERO_INV);
cv::bitwise_not(watershedMarkers * 255, result);

我对结果的问题是计算的边界（几乎）总是与两个斑点的共同区域相邻。以下是图片：

输入标记（黑色 = 0，灰色 = 1，白色 = 2） 输入标记

分水岭输入图像（距离变换结果），结果轮廓以红色绘制： 带绘制轮廓的分水岭输入图像

我希望边界沿着输入的最大强度区域（即沿着不同区域的中间）。相反（如您所见），它主要围绕标记为 2 的区域，稍微移动以触摸背景（标记为 1）。我在这里做错了什么，还是我误解了分水岭的工作原理？

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1 回答 1

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从这张图开始：

在此处输入图像描述

您只需将全零图像传递给分水岭算法即可获得正确的结果。然后从每个“边”开始， “盆地”同样充满“水” （然后只需记住删除分水岭算法默认设置的外边界）：-1

在此处输入图像描述

代码：

#include <opencv2\opencv.hpp>

using namespace cv;
using namespace std;

int main()
{
    Mat1b img = imread("path_to_image", IMREAD_GRAYSCALE);

    Mat1i markers(img.rows, img.cols, int(0));
    markers.setTo(1, img == 128);
    markers.setTo(2, img == 255);

    Mat3b image(markers.rows, markers.cols, Vec3b(0,0,0));
    markers.convertTo(markers, CV_32S);
    watershed(image, markers);

    Mat3b result;
    cvtColor(img, result, COLOR_GRAY2BGR);
    result.setTo(Scalar(0, 0, 255), markers == -1);

    imshow("Result", result);
    waitKey();

    return(0);
}
于 2017-02-09T13:52:16.353 回答