java openCV

1、 openCV依赖
2、 人脸检测
3、 人脸比对

		    org.bytedeco
		    javacpp
		    1.4.3
		
		
		    org.bytedeco
		    javacv
		    1.4.3
		
		
		 
            org.bytedeco.javacpp-presets
            opencv
             3.4.3-1.4.3
        
 
		
            org.bytedeco.javacpp-presets
            opencv-platform
            3.4.3-1.4.3
         

1. 启动本地摄像头

0代表本地

OpenCVframeGrabber grabber = new OpenCVframeGrabber(0);
		grabber.start(); // 开始获取摄像头数据 
		ShowCamera.init(grabber); 

当前人脸中加入了检测到人脸后，获取人脸特征并做标记
2. 检测人脸

public static RectVector findFaces(Mat image) {  
		Mat imageGray = FaceAndEyeToos.doColorHist(image); 
		//进行人脸识别
		RectVector faceDetections = new RectVector(); 
		
		faceDetector.detectMultiScale(imageGray, faceDetections);
		if(faceDetections.empty()){
			return null;
		}   
		return faceDetections;
	}

效果同图一
3. 人脸比对

比对时查找本地人脸库中是否存在相似人脸照片

public static List findPeople(Mat oriframe,RectVector faces) {
	    
		Mat imageGray = FaceAndEyeToos.doColorHist(oriframe);
		//getTainFile(faceRecognizer);
		Mat saveFace = null; 
		List result = Lists.newArrayList();
		for (Rect rect : faces.get()) {  
			
			saveFace = new Mat(imageGray, rect); 
			opencv_imgproc.resize(saveFace, saveFace, new Size(Common.faceWidth, Common.faceHeight));
			//人脸对齐
			IntPointer label = new IntPointer(4);
			DoublePointer confidence = new DoublePointer(8);
			faceRecognizer.predict(saveFace, label, confidence);
			
			int predictedLabel = label.get(0);
			System.out.println(predictedLabel);
			if(predictedLabel != 0){
				//查找到当前人员信息直接返回
				result.add(FaceList.getFace(predictedLabel-1)); 
			}else{ 
				continue;
			}
			
		}
		return result;
	}

4. 保存人脸图像
   不存在则保存人脸图像功能

public static void saveFace(RectVector faces,Mat oriframe,boolean addToModel){

		if(faces == null || faces.empty()){
			return ;
		}
		Mat imageGray = doColorHist(oriframe);
		Mat saveFace = null; 
		for (Rect rect : faces.get()) {  
			saveFace = new Mat(oriframe, rect); 
			opencv_imgproc.resize(saveFace, saveFace, new Size(Common.faceWidth,Common.faceHeight));
			if(saveFace.sizeof() > 0){
				doSave(saveFace,Common.saveFacePath,addToModel);  
			} 
		}
		saveFace.close();
		imageGray.close();
	}

第一次会保存新的人脸，图片暂不展示
5. 训练模型 功能

public static void train(){  
		int faceSize = FaceList.getFaceFiles().size();
	    MatVector images = new MatVector(faceSize+1);
	    Mat labels = new Mat(faceSize+1, 1,opencv_core.CV_32SC1);
	    IntBuffer labelsBuf = labels.createBuffer();
	    labelsBuf.put(0,0); 
	    images.put(0,FaceList.getFace(PathUtil.getFilePath(Common.invalidFace)));
	    for (int i = 1; i < faceSize+1; i++) {
	        Face p = FaceList.getFaceFiles().get(i-1); 
	        images.put(i, p.getFaceMat());
	        labelsBuf.put(i, i); 
	    }  
	    
	    faceRecognizer.train(images, labels);  
	    saveTainFile(faceRecognizer);
	}

人脸模型则自动训练

目前人脸比功能和实际使用差距比较大，下一步将添加检测多个人脸，采用多线程去操作人脸比对，同时人脸比对为了提高识别结果，将结合Eigenface特征脸识别 （PCA）和Fisherface（LDA）线性判别分析，同一个结果检测两次，然后作为最终结果参考，同时在照片比对前，将获取到的人脸照片分多次转换以提高比对质量。