OpenCV笔记（三十二）基于距离变换与分水岭的图像分割
文章目录
- 一、理论
-
- 1.图像分割
- 2.距离变换
- 3.分水岭变换
- 二、相关API
-
- 1.distanceTransform
- 2.watershed
- 三、处理步骤
- 四、综合例程
一、理论
1.图像分割
Image Segmentation
- 图像分割是图像处理最重要的处理手段之一
- 图像分割的目标是将图像中像素根据一定的规则分为若干(N)个cluster集合，每个集合包含一类像素。
- 根据算法分为监督学习方法和无监督学习方法，图像分割的算法多数都是无监督学习方法 - KMeans
2.距离变换
测试点多边形得到结果跟距离变换相似
距离变换常见算法有两种：
1. 不断膨胀/ 腐蚀得到
2. 基于倒角距离
3.分水岭变换
分水岭变换常见的算法: 基于浸泡理论实现
二、相关API
1.distanceTransform
距离变换
cv::distanceTransform(
InputArray src,
OutputArray dst,
OutputArray labels,
int distanceType,
int maskSize,
int labelType=DIST_LABEL_CCOMP
)
distanceType = DIST_L1/DIST_L2,
maskSize = 3x3 最新的支持5x5，推荐3x3、
labels 离散维诺图输出
dst 输出8位或者32位的浮点数，单一通道，大小与输入图像一致
2.watershed
分水岭
cv::watershed(
InputArray image,
InputOutputArray markers
)
三、处理步骤
- 加载图像
- 将白色背景变成黑色-目的是为后面的变换做准备
- 使用filter2D与拉普拉斯算子实现图像对比度提高，sharp
-
转为二值图像通过threshold
-
距离变换
二值距离变换
-
对距离变换结果进行归一化到[0~1]之间
-
使用阈值，再次二值化，得到标记
- 腐蚀得到每个Peak - erode
-
发现轮廓 – findContours
-
绘制轮廓- drawContours
-
分水岭变换 watershed
- 对每个分割区域着色输出结果
四、综合例程
#include
#include
#include
using namespace std;
using namespace cv;
int main(int argc, char** argv) {
char input_win[] = "input image";
char watershed_win[] = "watershed segmentation demo";
Mat src = imread("D:/vcprojects/images/cards.png");
// Mat src = imread("D:/kuaidi.jpg");
if (src.empty()) {
printf("could not load image...\n");
return -1;
}
namedWindow(input_win, CV_WINDOW_AUTOSIZE);
imshow(input_win, src);
// 1. change background
for (int row = 0; row < src.rows; row++) {
for (int col = 0; col < src.cols; col++) {
if (src.at(row, col) == Vec3b(255, 255, 255)) {
src.at(row, col)[0] = 0;
src.at(row, col)[1] = 0;
src.at(row, col)[2] = 0;
}
}
}
namedWindow("black background", CV_WINDOW_AUTOSIZE);
imshow("black background", src);
// sharpen
Mat kernel = (Mat_(3, 3) << 1, 1, 1, 1, -8, 1, 1, 1, 1);
Mat imgLaplance;
Mat sharpenImg = src;
filter2D(src, imgLaplance, CV_32F, kernel, Point(-1, -1), 0, BORDER_DEFAULT);
src.convertTo(sharpenImg, CV_32F);
Mat resultImg = sharpenImg - imgLaplance;
resultImg.convertTo(resultImg, CV_8UC3);
imgLaplance.convertTo(imgLaplance, CV_8UC3);
imshow("sharpen image", resultImg);
// src = resultImg; // copy back
// convert to binary
Mat binaryImg;
cvtColor(src, resultImg, CV_BGR2GRAY);
threshold(resultImg, binaryImg, 40, 255, THRESH_BINARY | THRESH_OTSU);
imshow("binary image", binaryImg);
Mat distImg;
distanceTransform(binaryImg, distImg, DIST_L1, 3, 5);
normalize(distImg, distImg, 0, 1, NORM_MINMAX);
imshow("distance result", distImg);
// binary again
threshold(distImg, distImg, .4, 1, THRESH_BINARY);
Mat k1 = Mat::ones(13, 13, CV_8UC1);
erode(distImg, distImg, k1, Point(-1, -1));
imshow("distance binary image", distImg);
// markers
Mat dist_8u;
distImg.convertTo(dist_8u, CV_8U);
vector> contours;
findContours(dist_8u, contours, RETR_EXTERNAL, CHAIN_APPROX_SIMPLE, Point(0, 0));
// create makers
Mat markers = Mat::zeros(src.size(), CV_32SC1);
for (size_t i = 0; i < contours.size(); i++) {
drawContours(markers, contours, static_cast(i), Scalar::all(static_cast(i) + 1), -1);
}
circle(markers, Point(5, 5), 3, Scalar(255, 255, 255), -1);
imshow("my markers", markers*1000);
// perform watershed
watershed(src, markers);
Mat mark = Mat::zeros(markers.size(), CV_8UC1);
markers.convertTo(mark, CV_8UC1);
bitwise_not(mark, mark, Mat());
imshow("watershed image", mark);
// generate random color
vector colors;
for (size_t i = 0; i < contours.size(); i++) {
int r = theRNG().uniform(0, 255);
int g = theRNG().uniform(0, 255);
int b = theRNG().uniform(0, 255);
colors.push_back(Vec3b((uchar)b, (uchar)g, (uchar)r));
}
// fill with color and display final result
Mat dst = Mat::zeros(markers.size(), CV_8UC3);
for (int row = 0; row < markers.rows; row++) {
for (int col = 0; col < markers.cols; col++) {
int index = markers.at(row, col);
if (index > 0 && index <= static_cast(contours.size())) {
dst.at(row, col) = colors[index - 1];
}
else {
dst.at(row, col) = Vec3b(0, 0, 0);
}
}
}
imshow("Final Result", dst);
waitKey(0);
return 0;
}