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