基于opencv+java实现简单图形识别程序

目录

• 前言
• 方法如下
• 总结

前言

OpenCV的 全称是：Open Source Computer Vision Library。OpenCV是一个基于BSD许可（开源）发行的跨平台计算机视觉库，可以运行在Linux、Windows、Android和Mac OS操作系统上。它轻量级而且高效——由一系列 C 函数和少量 C++ 类 构成，同时提供了Python、Ruby、MATLAB等语言的接口，实现了 图像处理和计算机视觉方面的很多通用算法。

OpenCV用C++语言编写，它的主要接口也是C++语言，但是依然保留了大量的C语言接口。该库也有大量的Python, Java and MATLAB/OCTAVE (版本2.5)的接口。这些语言的API接口函数可以通过在线文档获得。如今也提供对于C#,Ch, Ruby的支持。

本文着重讲述opencv+java的实现程序，关于opencv的如何引入dll库等操作以及c的实现就不在这里概述了

方法如下

直接开始，首先下载opencv，引入opencv-246.jar包以及对应dll库

1.背景去除 简单案列，只适合背景单一的图像

import java.util.ArrayList;
import java.util.List;

import org.opencv.core.Core;
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.highgui.Highgui;
import org.opencv.imgproc.Imgproc;

/**
 * @Description 背景去除 简单案列，只适合背景单一的图像
 * @author XPY
 * @date 2016年8月30日下午4:14:32
 */
public class demo1 {
	public static void main(String[] args) {
		System.loadLibrary("opencv_java246");
		Mat img = Highgui.imread("E:\\opencv_img\\source\\1.jpg");//读图像
		Mat new_img = doBackgroundRemoval(img);
		Highgui.imwrite("E:\\opencv_img\\target\\1.jpg",new_img);//写图像
	}

	private static Mat doBackgroundRemoval(Mat frame) {
		// init
		Mat hsvImg = new Mat();
		List<Mat> hsvPlanes = new ArrayList<>();
		Mat thresholdImg = new Mat();

		int thresh_type = Imgproc.THRESH_BINARY_INV;

		// threshold the image with the average hue value
		hsvImg.create(frame.size(), CvType.CV_8U);
		Imgproc.cvtColor(frame, hsvImg, Imgproc.COLOR_BGR2HSV);
		Core.split(hsvImg, hsvPlanes);

		// get the average hue value of the image

		Scalar average = Core.mean(hsvPlanes.get(0));
		double threshValue = average.val[0];
		Imgproc.threshold(hsvPlanes.get(0), thresholdImg, threshValue, 179.0,
				thresh_type);

		Imgproc.blur(thresholdImg, thresholdImg, new Size(5, 5));

		// dilate to fill gaps, erode to smooth edges
		Imgproc.dilate(thresholdImg, thresholdImg, new Mat(),
				new Point(-1, -1), 1);
		Imgproc.erode(thresholdImg, thresholdImg, new Mat(), new Point(-1, -1),
				3);

		Imgproc.threshold(thresholdImg, thresholdImg, threshValue, 179.0,
				Imgproc.THRESH_BINARY);

		// create the new image
		Mat foreground = new Mat(frame.size(), CvType.CV_8UC3, new Scalar(255,
				255, 255));
		thresholdImg.convertTo(thresholdImg, CvType.CV_8U);
		frame.copyTo(foreground, thresholdImg);// 掩膜图像复制
		return foreground;
	}
}

2.边缘检测

import org.opencv.core.Core;
import org.opencv.core.Mat;
import org.opencv.core.Size;
import org.opencv.highgui.Highgui;
import org.opencv.imgproc.Imgproc;

/**
 * @Description 边缘检测
 * @author XPY
 * @date 2016年8月30日下午5:01:01
 */
public class demo2 {
	public static void main(String[] args) {
		System.loadLibrary(Core.NATIVE_LIBRARY_NAME);
		Mat img = Highgui.imread("E:\\face7.jpg");//读图像
		Mat new_img = doCanny(img);
		Highgui.imwrite("E:\\opencv_img\\target\\2.jpg",new_img);//写图像
	}

	private static Mat doCanny(Mat frame)
	{
	    // init
	    Mat grayImage = new Mat();
	    Mat detectedEdges = new Mat();
	    double threshold = 10;
	    // convert to grayscale
	    Imgproc.cvtColor(frame, grayImage, Imgproc.COLOR_BGR2GRAY);
	   // reduce noise with a 3x3 kernel
	    Imgproc.blur(grayImage, detectedEdges, new Size(3, 3));
	    // canny detector, with ratio of lower:upper threshold of 3:1
	    Imgproc.Canny(detectedEdges, detectedEdges, threshold, threshold * 3);
	    // using Canny's output as a mask, display the result
	    Mat dest = new Mat();
	    frame.copyTo(dest, detectedEdges);
	    return dest;
	}
}

3.人脸检测技术 （靠边缘的和侧脸检测不准确）

import org.opencv.core.Core;
import org.opencv.core.Mat;
import org.opencv.core.MatOfRect;
import org.opencv.core.Point;
import org.opencv.core.Rect;
import org.opencv.core.Scalar;
import org.opencv.highgui.Highgui;
import org.opencv.objdetect.CascadeClassifier;  

/**
 *
 * @Description 人脸检测技术 （靠边缘的和侧脸检测不准确）
 * @author XPY
 * @date 2016年9月1日下午4:47:33
 */
public class demo3 {  

	 public static void main(String[] args) {
		    System.out.println("Hello, OpenCV");
		    // Load the native library.
		    System.loadLibrary("opencv_java246");
		    new demo3().run();
		  }  

  public void run() {
    System.out.println("\nRunning DetectFaceDemo");
    System.out.println(getClass().getResource("/haarcascade_frontalface_alt2.xml").getPath());
    // Create a face detector from the cascade file in the resources
    // directory.
    //CascadeClassifier faceDetector = new CascadeClassifier(getClass().getResource("haarcascade_frontalface_alt2.xml").getPath());
    //Mat image = Highgui.imread(getClass().getResource("lena.png").getPath());
    //注意：源程序的路径会多打印一个‘/'，因此总是出现如下错误
        /*
         * Detected 0 faces Writing faceDetection.png libpng warning: Image
         * width is zero in IHDR libpng warning: Image height is zero in IHDR
         * libpng error: Invalid IHDR data
         */
    //因此，我们将第一个字符去掉
    String xmlfilePath=getClass().getResource("/haarcascade_frontalface_alt2.xml").getPath().substring(1);
    CascadeClassifier faceDetector = new CascadeClassifier(xmlfilePath);
    Mat image = Highgui.imread("E:\\face2.jpg");
    // Detect faces in the image.
    // MatOfRect is a special container class for Rect.
    MatOfRect faceDetections = new MatOfRect();
    faceDetector.detectMultiScale(image, faceDetections);  

    System.out.println(String.format("Detected %s faces", faceDetections.toArray().length));  

    // Draw a bounding box around each face.
    for (Rect rect : faceDetections.toArray()) {
        Core.rectangle(image, new Point(rect.x, rect.y), new Point(rect.x + rect.width, rect.y + rect.height), new Scalar(0, 255, 0));
    }  

    // Save the visualized detection.
    String filename = "E:\\faceDetection.png";
    System.out.println(String.format("Writing %s", filename));
    System.out.println(filename);
    Highgui.imwrite(filename, image);
  }  

}

人脸检测需要自行下载haarcascade_frontalface_alt2.xml文件

附上demo下载地址：点击这里，运行需自行引入opencv的dll文件

总结

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