C++OpenCV系统学习（17）——图像分割与抠图（5）证件照背景替换

2021-09-19 09:58:17 阅读：299 来源： 互联网

标签：src Mat 17 int mask C++ OpenCV col row

关键的知识点：

K-means
背景融合-高斯模糊
遮罩层生成

算法的流程：

实验步骤：

#include<opencv2\opencv.hpp>
#include<iostream>

using namespace cv;
using namespace std;

Mat mat_to_samples(Mat& image);
int main(int arc, char** argv) {
	Mat src = imread("F://testImage//input.png");
	namedWindow("input", WINDOW_AUTOSIZE);
	imshow("input", src);

	//组装数据
	Mat points = mat_to_samples(src);

	//运行KMeans
	int numCluster = 4;
	Mat labels;
	Mat centers;
	TermCriteria criteria = TermCriteria(TermCriteria::EPS + TermCriteria::COUNT, 10, 0.1);
	kmeans(points, numCluster, labels, criteria, 3, KMEANS_PP_CENTERS, centers);

	//去背景遮罩生成
	Mat mask = Mat::zeros(src.size(), CV_8UC1);
	int index = src.rows*2 + 2;
	int cindex = labels.at<int>(index, 0);
	int height = src.rows;
	int width = src.cols;
	Mat dst;
	src.copyTo(dst);

	for(int row=0;row<height;row++)
	{
		for (int col = 0; col < width; col++)
		{
			index = row * width + col;
			int label = labels.at<int>(index, 0);
			if (label == cindex)//背景
			{
				dst.at<Vec3b>(row, col)[0] = 0;
				dst.at<Vec3b>(row, col)[1] = 0;
				dst.at<Vec3b>(row, col)[2] = 0;
				mask.at<uchar>(row, col) = 0;
			}
			else
			{
				mask.at<uchar>(row, col) = 255;
			}
		}
	}
	imshow("mask", mask);
	imshow("KMeans-Result", dst);
	//腐蚀+高斯模糊
	
	waitKey(0);
	return 0;
}

Mat mat_to_samples(Mat& image)
{
	int w = image.cols;
	int h = image.rows;
	int samplecount = w * h;
	int dims = image.channels();
	Mat points(samplecount, dims, CV_32F, Scalar(10));
	int index = 0;
	for (int row = 0; row < h; row++)
	{
		for (int col = 0; col < w; col++)
		{
			index = row * w + col;
			Vec3b bgr = image.at<Vec3b>(row, col);
			points.at<float>(index, 0) = static_cast<int>(bgr[0]);
			points.at<float>(index, 1) = static_cast<int>(bgr[1]);
			points.at<float>(index, 2) = static_cast<int>(bgr[2]);
		}
	}
	return points;
}

去背景遮罩生成结果：

完整代码：

#include<opencv2\opencv.hpp>
#include<iostream>

using namespace cv;
using namespace std;

Mat mat_to_samples(Mat& image);
int main(int arc, char** argv) {
	Mat src = imread("F://testImage//input.png");
	namedWindow("input", WINDOW_AUTOSIZE);
	imshow("input", src);

	//组装数据
	Mat points = mat_to_samples(src);

	//运行KMeans
	int numCluster = 4;
	Mat labels;
	Mat centers;
	TermCriteria criteria = TermCriteria(TermCriteria::EPS + TermCriteria::COUNT, 10, 0.1);
	kmeans(points, numCluster, labels, criteria, 3, KMEANS_PP_CENTERS, centers);

	//去遮罩生成
	Mat mask = Mat::zeros(src.size(), CV_8UC1);
	int index = src.rows*2 + 2;
	int cindex = labels.at<int>(index, 0);
	int height = src.rows;
	int width = src.cols;
	Mat dst;
	src.copyTo(dst);

	for(int row=0;row<height;row++)
	{
		for (int col = 0; col < width; col++)
		{
			index = row * width + col;
			int label = labels.at<int>(index, 0);
			if (label == cindex)//背景
			{
				dst.at<Vec3b>(row, col)[0] = 0;
				dst.at<Vec3b>(row, col)[1] = 0;
				dst.at<Vec3b>(row, col)[2] = 0;
				mask.at<uchar>(row, col) = 0;
			}
			else
			{
				mask.at<uchar>(row, col) = 255;
			}
		}
	}
	imshow("mask", mask);
	imshow("KMeans-Result", dst);
	//腐蚀+高斯模糊
	Mat k = getStructuringElement(MORPH_RECT, Size(3, 3), Point(-1, -1));
	erode(mask, mask, k);
	imshow("erode-mask", mask);
	GaussianBlur(mask, mask, Size(3, 3), 0, 0);
	imshow("Blur Mask", mask);

	//通道混合
	Vec3b color;
	//RNG rng(12345);
	//背景替换为红色
	color[0] = 0;//rng.uniform(0, 255);
	color[1] = 0;//rng.uniform(0, 255);
	color[2] = 255;//rng.uniform(0, 255);
	Mat result(src.size(), src.type());

	double w = 0.0;
	int b = 0, g = 0, r = 0;
	int b1 = 0, g1 = 0, r1 = 0;
	int b2 = 0, g2 = 0, r2 = 0;

	for (int row = 0; row < height; row++)
	{
		for (int col = 0; col < width; col++)
		{
			int m = mask.at<uchar>(row, col);
			if (m == 255)
			{
				result.at<Vec3b>(row, col) = src.at<Vec3b>(row, col);//前景
			}
			else if(m==0)
			{
				result.at<Vec3b>(row, col) = color;//背景
			}
			else
			{
				w = m / 255.0;
				b1 = src.at<Vec3b>(row, col)[0];
				g1 = src.at<Vec3b>(row, col)[1];
				r1 = src.at<Vec3b>(row, col)[2];

				b2 = color[0];
				g2 = color[1];
				r2 = color[2];

				b = b1 * w + b2 * (1.0 - w);
				g = g1 * w + g2 * (1.0 - w);
				r = r1 * w + r2 * (1.0 - w);

				result.at<Vec3b>(row, col)[0] = b;
				result.at<Vec3b>(row, col)[1] = g;
				result.at<Vec3b>(row, col)[2] = r;
			}
		}
	}
	imshow("背景替换", result);
	waitKey(0);
	return 0;
}

Mat mat_to_samples(Mat& image)
{
	int w = image.cols;
	int h = image.rows;
	int samplecount = w * h;
	int dims = image.channels();
	Mat points(samplecount, dims, CV_32F, Scalar(10));
	int index = 0;
	for (int row = 0; row < h; row++)
	{
		for (int col = 0; col < w; col++)
		{
			index = row * w + col;
			Vec3b bgr = image.at<Vec3b>(row, col);
			points.at<float>(index, 0) = static_cast<int>(bgr[0]);
			points.at<float>(index, 1) = static_cast<int>(bgr[1]);
			points.at<float>(index, 2) = static_cast<int>(bgr[2]);
		}
	}
	return points;
}

结果如下所示：

标签：src,Mat,17,int,mask,C++,OpenCV,col,row
来源： https://blog.csdn.net/bigData1994pb/article/details/120255607

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C++OpenCV系统学习（17）——图像分割与抠图（5）证件照背景替换

关键的知识点：

算法的流程：

实验步骤：

完整代码：