C++ OpenCV学习之图像金字塔与图像融合详解

int main()
{
    Mat img_1 = imread("1.jpg", 1);
    Mat img_2 = imread("2.jpg", 1);
    Mat dstImg;
    imgFusion(img_1, img_2, dstImg, 0.2);
    waitKey(0);
    return 0;
}

void imgFusion(Mat leftImg, Mat rightImg, Mat& dstImg, float threshold)
{
	vector<Mat> gaussPyrLeft, gaussPyrRight, laplacePyrLeft, laplacePyrRight;		// 声明高斯金字塔数据结构
	vector<Mat> maskGaussPyr;														// 声明掩码的高斯金字塔
	vector<Mat> blendLapPyr;														// 声明融合拉普拉斯金字塔
	Mat imgHighest;																	// 声明图像融合的起点图像
	Mat mask = Mat::zeros(PYRHEIGHT, PYRWIDTH, CV_32FC1);							// 构造掩码，大小与金字塔原图像相同
	mask(Range::all(), Range(mask.cols * threshold, mask.cols)) = 1.0;
	cvtColor(mask, mask, COLOR_GRAY2BGR);											// 将掩码颜色通道拓展，以适配原图像
	buildGaussPyr(mask, maskGaussPyr, 3);											// 建立掩码的高斯金字塔

	resize(leftImg, leftImg, Size(PYRWIDTH, PYRHEIGHT));
	resize(rightImg, rightImg, Size(PYRWIDTH, PYRHEIGHT));

	leftImg.convertTo(leftImg, CV_32F);		//转换成CV_32F, 用于和mask类型匹配,且CV_32F 类型精度高, 有利于计算
	rightImg.convertTo(rightImg, CV_32F);

	// 建立高斯金字塔与拉普拉斯金字塔
	buildGaussPyr(leftImg, gaussPyrLeft, 3);
	buildGaussPyr(rightImg, gaussPyrRight, 3);
	buildLaplacePyr(gaussPyrLeft, laplacePyrLeft, 3);
	buildLaplacePyr(gaussPyrRight, laplacePyrRight, 3);

	// 确定起点图像
	imgHighest = gaussPyrLeft.back().mul(maskGaussPyr.back()) +
		((gaussPyrRight.back()).mul(Scalar(1.0, 1.0, 1.0) - maskGaussPyr.back()));

	// 融合拉普拉斯金字塔
	blendLaplacePyr(laplacePyrLeft, laplacePyrRight, maskGaussPyr, blendLapPyr);

	// 融合图像重建
	dstImg = imgLaplaceBlend(imgHighest, blendLapPyr);
	dstImg.convertTo(dstImg, CV_8UC3);
	imshow("imgProcess::seamOpt_laplace", dstImg);
}