[OPENCV编程案例详解 ,李立宗]对应章节java 实现（第二章）

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本文链接： https://blog.csdn.net/u013195275/article/details/79928600

package com.zxj.opencv.demo;

import org.opencv.core.Core;
import org.opencv.core.Core.MinMaxLocResult;
import org.opencv.core.CvType;
import org.opencv.core.Mat;
import org.opencv.core.Point;
import org.opencv.core.Scalar;
import org.opencv.core.Size;
import org.opencv.imgcodecs.Imgcodecs;
import org.opencv.imgproc.Imgproc;

/**
 * 2.医学图像处理
 * 
 * @author zxj
 *
 */
public class MedicineImageProcess {
	private static final String DIR = "d:/opencv/";
	private static final String TEST_IMG = "d:/11.jpg";

	public static void main(String[] args) {
		System.loadLibrary(Core.NATIVE_LIBRARY_NAME);// 加载opencv c++动态库
		equalizeHist();
		convertTo();
		filter2D();
		matchTemplate();
		warpAffine();
		// remap();
		threshold();
		adaptiveThreshold();
		compare();
	}

	/******** 2.2.1 增强 *******/

	public static void equalizeHist() {// 直方图均衡化
		Mat src = Imgcodecs.imread(TEST_IMG, Imgcodecs.IMREAD_GRAYSCALE);// 以单通道读取
		Mat dst = new Mat();
		Imgproc.equalizeHist(src, dst);
		Imgcodecs.imwrite(DIR + "equalizeHist.jpg", dst);// 写入文件
	}

	public static void convertTo() {// 亮度和对比度
		Mat src = Imgcodecs.imread(TEST_IMG, Imgcodecs.IMREAD_COLOR);
		Mat dst = new Mat();
		// alpha取图片默认,亮度设为50,可以改动这个值来调节效果
		src.convertTo(dst, 50);
		Imgcodecs.imwrite(DIR + "convertTo.jpg", dst);// 写入文件
	}

	/******** 2.2.2 卷积 *******/
	public static void filter2D() {// filter2D计算
		Mat src = Imgcodecs.imread(TEST_IMG, Imgcodecs.IMREAD_COLOR);
		Mat dst = new Mat();
		Imgproc.filter2D(src, dst, src.depth(), Mat.zeros(3, 3, CvType.CV_8UC1), new Point(-1, -1), 50,
				Core.BORDER_CONSTANT);
		Imgcodecs.imwrite(DIR + "filter2D.jpg", dst);// 写入文件
	}

	/******** 2.2.3 模版匹配 *******/
	public static void matchTemplate() {// 模版匹配
		Mat src = Imgcodecs.imread("d:/src1.jpg", Imgcodecs.IMREAD_COLOR);
		Mat templ = Imgcodecs.imread("d:/src2.jpg", Imgcodecs.IMREAD_COLOR);
		Mat dst = new Mat();
		dst.create(src.rows() - templ.rows() + 1, src.cols() - templ.cols() + 1, CvType.CV_32FC1);
		// matchMethod TM_SQDIFF、TM_CCOEFF_NORMED
		int matchMethod = Imgproc.TM_CCOEFF_NORMED;
		Imgproc.matchTemplate(src, templ, dst, matchMethod);
		Core.normalize(dst, dst, 0, 1, Core.NORM_MINMAX, -1, new Mat());
		MinMaxLocResult maxLocResult = Core.minMaxLoc(dst);
		Point matchLoc;
		if (matchMethod == Imgproc.TM_SQDIFF || matchMethod == Imgproc.TM_SQDIFF_NORMED) {
			matchLoc = maxLocResult.minLoc;
		} else {
			matchLoc = maxLocResult.maxLoc;
		}
		// 绘制矩形圈出匹配区域
		Imgproc.rectangle(src, matchLoc, new Point(matchLoc.x + templ.cols(), matchLoc.y + templ.rows()),
				new Scalar(0, 0, 255), 2, 8, 0);

		Imgcodecs.imwrite(DIR + "matchTemplate.jpg", src);// 写入文件
	}

	/******** 2.2.4 仿射变换&同1.2.5旋转 *******/
	public static void warpAffine() {// 仿射变换
		Mat src = Imgcodecs.imread(TEST_IMG, Imgcodecs.IMREAD_COLOR);
		Mat dst = new Mat();
		Point center = new Point(src.cols() / 2, src.rows() / 2);// 原图中心点
		double angle = 50;// 旋转角度
		double scale = 1;// 缩放比例
		Mat rot_mat = Imgproc.getRotationMatrix2D(center, angle, scale);
		Imgproc.warpAffine(src, dst, rot_mat, src.size(), Imgproc.INTER_LINEAR, Core.BORDER_DEFAULT, Scalar.all(0));//
		Imgcodecs.imwrite(DIR + "warpAffine.jpg", dst);// 写入文件
	}

	/******** 2.2.5 重映射 *******/
	public static void remap() {// 重映射
		Mat src = Imgcodecs.imread("d:/26e813228fca8bca2434f1877067dc245965d7e6f1b3e.png", Imgcodecs.IMREAD_COLOR);
		Mat map_x = new Mat(src.size(), CvType.CV_32FC1);
		Mat map_y = new Mat(src.size(), CvType.CV_32FC1);
		Mat dst = new Mat();
		// 复制
		for (int i = 0; i < src.rows(); i++) {
			for (int j = 0; j < src.cols(); i++) {
				map_x.put(i, j, j);
				map_y.put(i, j, i);
			}

		}
		Imgproc.remap(src, dst, map_x, map_y, Imgproc.INTER_LINEAR, Core.BORDER_DEFAULT, new Scalar(0, 0, 0));
		// Mat kernel=Imgproc.getGaussianKernel(5, 2,CvType.CV_32F);
		Imgcodecs.imwrite(DIR + "copy.jpg", dst);// 写入文件

	}

	/******** 2.2.6 分割 *******/
	public static void threshold() {// 基本阈值分割
		Mat src = Imgcodecs.imread(TEST_IMG, Imgcodecs.IMREAD_COLOR);
		Mat dst = new Mat();
		Imgproc.threshold(src, dst, 128, 255, Imgproc.THRESH_BINARY);// type=THRESH_BINARY和THRESH_BINARY_INV maxval需要设定
		Imgcodecs.imwrite(DIR + "threshold.jpg", dst);// 写入文件
	}

	public static void adaptiveThreshold() {// 自适应阈值分割
		Mat src = Imgcodecs.imread(TEST_IMG, Imgcodecs.IMREAD_GRAYSCALE);// 8位单通道
		Mat dst = new Mat();
		Imgproc.adaptiveThreshold(src, dst, 128, Imgproc.ADAPTIVE_THRESH_GAUSSIAN_C, Imgproc.THRESH_BINARY, 3, 1);// type只能为THRESH_BINARY或THRESH_BINARY_INV
		Imgcodecs.imwrite(DIR + "adaptiveThreshold.jpg", dst);// 写入文件
	}

	public static void compare() {// compare函数
		Mat src = Imgcodecs.imread(TEST_IMG, Imgcodecs.IMREAD_COLOR);
		Mat src2 = Imgcodecs.imread("d:/31.jpg", Imgcodecs.IMREAD_COLOR);
		Mat dst = new Mat();
		int cmpop = Core.CMP_GE;
		if (src.size().area() > src.size().area()) {
			cmpop = Core.CMP_GT;
		} else if (src.size().area() == src.size().area()) {
			cmpop = Core.CMP_EQ;
		} else {
			cmpop = Core.CMP_LT;
		}
		Core.compare(src, src2, dst, cmpop);
		Imgcodecs.imwrite(DIR + "compare.jpg", dst);// 写入文件
	}
 
}