opencv 分割二维码 opencv的分割算法设计

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mob64ca140a1f7c 2024-03-28 17:09:29

文章标签 opencv 分割二维码 #include 分水岭算法像素点 文章分类 计算机视觉人工智能

分水岭算法实现（C++、opencv）

1.作用：

通常用于分割图像，主要实现以临近像素间的相似性作为重要的参考依据，从而将在空间位置上相近并且灰度值相近的像素点互相连接起来构成一个封闭的轮廓，封闭性是分水岭算法的一个重要特征。相对于基于阈值的图像分割，边缘检测等都不会考虑像素在空间关系上的相似性和封闭性这一概念，彼此像素间互相独立，没有统一性。分水岭算法较其他分割方法更具有思想性，更符合人眼对图像的印象。

2.实现：

#include <cmath>
#include <iostream>
#include <memory>
#include <opencv2/core/core.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/imgproc/imgproc.hpp>
#include <opencv2/video/background_segm.hpp>
#include "opencv2/calib3d/calib3d.hpp"
#include <opencv2/opencv.hpp>
#include <time.h>
#include <thread>
#include <future>
#include <chrono>
#include <vector>
#include "time.h"
#include <string>
#include <fstream>

using namespace std;
using namespace cv;

Vec3b RandomColor(int value)    //生成随机颜色函数
{
	value = value % 255;  //生成0~255的随机数
	RNG rng;
	int aa = rng.uniform(0, value);
	int bb = rng.uniform(0, value);
	int cc = rng.uniform(0, value);
	return Vec3b(aa, bb, cc);
}

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

	cv::Mat rgb_image = cv::imread("../example/timg.jpg", CV_LOAD_IMAGE_UNCHANGED);
	cv::Mat rgb_image_blur;
	GaussianBlur(rgb_image, rgb_image_blur, Size(5, 5), 0, 0);
	cv::Mat rgb_image_canny;
	Canny(rgb_image_blur, rgb_image_canny, 10, 120, 3, false);

	cv::imshow("rgb_roi_binary", rgb_image_canny);

	vector<vector<Point>>contours;
	vector<Vec4i>hierarchy;
	findContours(rgb_image_canny, contours, hierarchy, RETR_LIST, CHAIN_APPROX_SIMPLE, Point());
	Mat imageContours = Mat::zeros(rgb_image.size(), CV_8UC1);  //轮廓	
	Mat marks(rgb_image.size(), CV_32S);   //Opencv分水岭第二个矩阵参数
	marks = Scalar::all(0);
	int index = 0;
	int compCount = 0;
	for (; index >= 0; index = hierarchy[index][0], compCount++)
	{
		//对marks进行标记，对不同区域的轮廓进行编号，相当于设置注水点，有多少轮廓，就有多少注水点
		drawContours(marks, contours, index, Scalar::all(compCount + 1), 1, 8, hierarchy);
		drawContours(imageContours, contours, index, Scalar(255), 1, 8, hierarchy);
	}

	//我们来看一下传入的矩阵marks里是什么东西
	Mat marksShows;
	convertScaleAbs(marks, marksShows);
	imshow("marksShow", marksShows);
	imshow("轮廓", imageContours);
	cv::watershed(rgb_image, marks);

	Mat afterWatershed;
	convertScaleAbs(marks, afterWatershed);
	imshow("After Watershed", afterWatershed);

	//对每一个区域进行颜色填充
	Mat PerspectiveImage = Mat::zeros(rgb_image.size(), CV_8UC3);
	for (int i = 0; i < marks.rows; i++)
	{
		for (int j = 0; j < marks.cols; j++)
		{
			int index = marks.at<int>(i, j);
			if (marks.at<int>(i, j) == -1)
			{
				PerspectiveImage.at<Vec3b>(i, j) = Vec3b(255, 255, 255);
			}
			else
			{
				PerspectiveImage.at<Vec3b>(i, j) = RandomColor(index);
			}
		}
	}
	imshow("After ColorFill", PerspectiveImage);

	//分割并填充颜色的结果跟原始图像融合
	Mat wshed;
	addWeighted(rgb_image, 0.4, PerspectiveImage, 0.6, 0, wshed);
	imshow("AddWeighted Image", wshed);

	cv::waitKey(0);
	return 0;
}