java 图形识别_图形识别-基于opencv+java简单程序

前言：如需转载请注明出处：

http://blog.csdn.net/xiaopy_0508/article/details/55044341

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

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 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：http://download.csdn.net/download/xiaopy_0508/9848511，运行需自行引入opencv的dll文件