在我的应用程序中,我尝试使用 Open CV 对特定图像进行人脸识别,首先我训练一张图像,然后在训练该图像后,如果我对该图像运行人脸识别,它会成功识别该训练过的人脸。但是,当我转向同一个人的另一张照片时,识别不起作用。它只适用于经过训练的图像,所以我的问题是如何纠正它?
更新:
我想要做的是,用户应该从存储中选择一个人的图像,然后在训练所选图像后,我想从存储中获取与我训练过的图像的面部相匹配的所有图像
这是我的 Activity 课:
public class MainActivity extends AppCompatActivity {
private Mat rgba,gray;
private CascadeClassifier classifier;
private MatOfRect faces;
private ArrayList<Mat> images;
private ArrayList<String> imagesLabels;
private Storage local;
ImageView mimage;
Button prev,next;
ArrayList<Integer> imgs;
private int label[] = new int[1];
private double predict[] = new double[1];
Integer pos = 0;
private String[] uniqueLabels;
FaceRecognizer recognize;
private boolean trainfaces() {
if(images.isEmpty())
return false;
List<Mat> imagesMatrix = new ArrayList<>();
for (int i = 0; i < images.size(); i++)
imagesMatrix.add(images.get(i));
Set<String> uniqueLabelsSet = new HashSet<>(imagesLabels); // Get all unique labels
uniqueLabels = uniqueLabelsSet.toArray(new String[uniqueLabelsSet.size()]); // Convert to String array, so we can read the values from the indices
int[] classesNumbers = new int[uniqueLabels.length];
for (int i = 0; i < classesNumbers.length; i++)
classesNumbers[i] = i + 1; // Create incrementing list for each unique label starting at 1
int[] classes = new int[imagesLabels.size()];
for (int i = 0; i < imagesLabels.size(); i++) {
String label = imagesLabels.get(i);
for (int j = 0; j < uniqueLabels.length; j++) {
if (label.equals(uniqueLabels[j])) {
classes[i] = classesNumbers[j]; // Insert corresponding number
break;
}
}
}
Mat vectorClasses = new Mat(classes.length, 1, CvType.CV_32SC1); // CV_32S == int
vectorClasses.put(0, 0, classes); // Copy int array into a vector
recognize = LBPHFaceRecognizer.create(3,8,8,8,200);
recognize.train(imagesMatrix, vectorClasses);
if(SaveImage())
return true;
return false;
}
public void cropedImages(Mat mat) {
Rect rect_Crop=null;
for(Rect face: faces.toArray()) {
rect_Crop = new Rect(face.x, face.y, face.width, face.height);
}
Mat croped = new Mat(mat, rect_Crop);
images.add(croped);
}
public boolean SaveImage() {
File path = new File(Environment.getExternalStorageDirectory(), "TrainedData");
path.mkdirs();
String filename = "lbph_trained_data.xml";
File file = new File(path, filename);
recognize.save(file.toString());
if(file.exists())
return true;
return false;
}
private BaseLoaderCallback callbackLoader = new BaseLoaderCallback(this) {
@Override
public void onManagerConnected(int status) {
switch(status) {
case BaseLoaderCallback.SUCCESS:
faces = new MatOfRect();
//reset
images = new ArrayList<Mat>();
imagesLabels = new ArrayList<String>();
local.putListMat("images", images);
local.putListString("imagesLabels", imagesLabels);
images = local.getListMat("images");
imagesLabels = local.getListString("imagesLabels");
break;
default:
super.onManagerConnected(status);
break;
}
}
};
@Override
protected void onResume() {
super.onResume();
if(OpenCVLoader.initDebug()) {
Log.i("hmm", "System Library Loaded Successfully");
callbackLoader.onManagerConnected(BaseLoaderCallback.SUCCESS);
} else {
Log.i("hmm", "Unable To Load System Library");
OpenCVLoader.initAsync(OpenCVLoader.OPENCV_VERSION, this, callbackLoader);
}
}
@Override
protected void onCreate(Bundle savedInstanceState) {
super.onCreate(savedInstanceState);
setContentView(R.layout.activity_main);
prev = findViewById(R.id.btprev);
next = findViewById(R.id.btnext);
mimage = findViewById(R.id.mimage);
local = new Storage(this);
imgs = new ArrayList();
imgs.add(R.drawable.jonc);
imgs.add(R.drawable.jonc2);
imgs.add(R.drawable.randy1);
imgs.add(R.drawable.randy2);
imgs.add(R.drawable.imgone);
imgs.add(R.drawable.imagetwo);
mimage.setBackgroundResource(imgs.get(pos));
prev.setOnClickListener(new View.OnClickListener() {
@Override
public void onClick(View view) {
if(pos!=0){
pos--;
mimage.setBackgroundResource(imgs.get(pos));
}
}
});
next.setOnClickListener(new View.OnClickListener() {
@Override
public void onClick(View view) {
if(pos<5){
pos++;
mimage.setBackgroundResource(imgs.get(pos));
}
}
});
Button train = (Button)findViewById(R.id.btn_train);
train.setOnClickListener(new View.OnClickListener() {
@RequiresApi(api = Build.VERSION_CODES.KITKAT)
@Override
public void onClick(View view) {
rgba = new Mat();
gray = new Mat();
Mat mGrayTmp = new Mat();
Mat mRgbaTmp = new Mat();
classifier = FileUtils.loadXMLS(MainActivity.this);
Bitmap icon = BitmapFactory.decodeResource(getResources(),
imgs.get(pos));
Bitmap bmp32 = icon.copy(Bitmap.Config.ARGB_8888, true);
Utils.bitmapToMat(bmp32, mGrayTmp);
Utils.bitmapToMat(bmp32, mRgbaTmp);
Imgproc.cvtColor(mGrayTmp, mGrayTmp, Imgproc.COLOR_BGR2GRAY);
Imgproc.cvtColor(mRgbaTmp, mRgbaTmp, Imgproc.COLOR_BGRA2RGBA);
/*Core.transpose(mGrayTmp, mGrayTmp); // Rotate image
Core.flip(mGrayTmp, mGrayTmp, -1); // Flip along both*/
gray = mGrayTmp;
rgba = mRgbaTmp;
Imgproc.resize(gray, gray, new Size(200,200.0f/ ((float)gray.width()/ (float)gray.height())));
if(gray.total() == 0)
Toast.makeText(getApplicationContext(), "Can't Detect Faces", Toast.LENGTH_SHORT).show();
classifier.detectMultiScale(gray,faces,1.1,3,0|CASCADE_SCALE_IMAGE, new Size(30,30));
if(!faces.empty()) {
if(faces.toArray().length > 1)
Toast.makeText(getApplicationContext(), "Mutliple Faces Are not allowed", Toast.LENGTH_SHORT).show();
else {
if(gray.total() == 0) {
Log.i("hmm", "Empty gray image");
return;
}
cropedImages(gray);
imagesLabels.add("Baby");
Toast.makeText(getApplicationContext(), "Picture Set As Baby", Toast.LENGTH_LONG).show();
if (images != null && imagesLabels != null) {
local.putListMat("images", images);
local.putListString("imagesLabels", imagesLabels);
Log.i("hmm", "Images have been saved");
if(trainfaces()) {
images.clear();
imagesLabels.clear();
}
}
}
}else {
/* Bitmap bmp = null;
Mat tmp = new Mat(250, 250, CvType.CV_8U, new Scalar(4));
try {
//Imgproc.cvtColor(seedsImage, tmp, Imgproc.COLOR_RGB2BGRA);
Imgproc.cvtColor(gray, tmp, Imgproc.COLOR_GRAY2RGBA, 4);
bmp = Bitmap.createBitmap(tmp.cols(), tmp.rows(), Bitmap.Config.ARGB_8888);
Utils.matToBitmap(tmp, bmp);
} catch (CvException e) {
Log.d("Exception", e.getMessage());
}*/
/* mimage.setImageBitmap(bmp);*/
Toast.makeText(getApplicationContext(), "Unknown Face", Toast.LENGTH_SHORT).show();
}
}
});
Button recognize = (Button)findViewById(R.id.btn_recognize);
recognize.setOnClickListener(new View.OnClickListener() {
@Override
public void onClick(View view) {
if(loadData())
Log.i("hmm", "Trained data loaded successfully");
rgba = new Mat();
gray = new Mat();
faces = new MatOfRect();
Mat mGrayTmp = new Mat();
Mat mRgbaTmp = new Mat();
classifier = FileUtils.loadXMLS(MainActivity.this);
Bitmap icon = BitmapFactory.decodeResource(getResources(),
imgs.get(pos));
Bitmap bmp32 = icon.copy(Bitmap.Config.ARGB_8888, true);
Utils.bitmapToMat(bmp32, mGrayTmp);
Utils.bitmapToMat(bmp32, mRgbaTmp);
Imgproc.cvtColor(mGrayTmp, mGrayTmp, Imgproc.COLOR_BGR2GRAY);
Imgproc.cvtColor(mRgbaTmp, mRgbaTmp, Imgproc.COLOR_BGRA2RGBA);
/*Core.transpose(mGrayTmp, mGrayTmp); // Rotate image
Core.flip(mGrayTmp, mGrayTmp, -1); // Flip along both*/
gray = mGrayTmp;
rgba = mRgbaTmp;
Imgproc.resize(gray, gray, new Size(200,200.0f/ ((float)gray.width()/ (float)gray.height())));
if(gray.total() == 0)
Toast.makeText(getApplicationContext(), "Can't Detect Faces", Toast.LENGTH_SHORT).show();
classifier.detectMultiScale(gray,faces,1.1,3,0|CASCADE_SCALE_IMAGE, new Size(30,30));
if(!faces.empty()) {
if(faces.toArray().length > 1)
Toast.makeText(getApplicationContext(), "Mutliple Faces Are not allowed", Toast.LENGTH_SHORT).show();
else {
if(gray.total() == 0) {
Log.i("hmm", "Empty gray image");
return;
}
recognizeImage(gray);
}
}else {
Toast.makeText(getApplicationContext(), "Unknown Face", Toast.LENGTH_SHORT).show();
}
}
});
}
private void recognizeImage(Mat mat) {
Rect rect_Crop=null;
for(Rect face: faces.toArray()) {
rect_Crop = new Rect(face.x, face.y, face.width, face.height);
}
Mat croped = new Mat(mat, rect_Crop);
recognize.predict(croped, label, predict);
int indice = (int)predict[0];
Log.i("hmmcheck:",String.valueOf(label[0])+" : "+String.valueOf(indice));
if(label[0] != -1 && indice < 125)
Toast.makeText(getApplicationContext(), "Welcome "+uniqueLabels[label[0]-1]+"", Toast.LENGTH_SHORT).show();
else
Toast.makeText(getApplicationContext(), "You're not the right person", Toast.LENGTH_SHORT).show();
}
private boolean loadData() {
String filename = FileUtils.loadTrained();
if(filename.isEmpty())
return false;
else
{
recognize.read(filename);
return true;
}
}
}
我的文件实用程序类:
public class FileUtils {
private static String TAG = FileUtils.class.getSimpleName();
private static boolean loadFile(Context context, String cascadeName) {
InputStream inp = null;
OutputStream out = null;
boolean completed = false;
try {
inp = context.getResources().getAssets().open(cascadeName);
File outFile = new File(context.getCacheDir(), cascadeName);
out = new FileOutputStream(outFile);
byte[] buffer = new byte[4096];
int bytesread;
while((bytesread = inp.read(buffer)) != -1) {
out.write(buffer, 0, bytesread);
}
completed = true;
inp.close();
out.flush();
out.close();
} catch (IOException e) {
Log.i(TAG, "Unable to load cascade file" + e);
}
return completed;
}
public static CascadeClassifier loadXMLS(Activity activity) {
InputStream is = activity.getResources().openRawResource(R.raw.lbpcascade_frontalface);
File cascadeDir = activity.getDir("cascade", Context.MODE_PRIVATE);
File mCascadeFile = new File(cascadeDir, "lbpcascade_frontalface_improved.xml");
FileOutputStream os = null;
try {
os = new FileOutputStream(mCascadeFile);
byte[] buffer = new byte[4096];
int bytesRead;
while ((bytesRead = is.read(buffer)) != -1) {
os.write(buffer, 0, bytesRead);
}
is.close();
os.close();
} catch (FileNotFoundException e) {
e.printStackTrace();
} catch (IOException e) {
e.printStackTrace();
}
return new CascadeClassifier(mCascadeFile.getAbsolutePath());
}
public static String loadTrained() {
File file = new File(Environment.getExternalStorageDirectory(), "TrainedData/lbph_trained_data.xml");
return file.toString();
}
}
这些是我试图在这里比较的图像,人脸是相同的,但仍然承认它不匹配!
最佳答案
更新
根据问题中的新编辑,您需要一种方法来即时识别新人,他们的照片在模型的训练阶段可能不可用。这些任务称为少镜头学习。这类似于情报/警察机构使用闭路电视摄像机镜头寻找目标的要求。由于通常没有足够的特定目标图像,因此在训练期间,他们使用 FaceNet 等模型。我真的建议阅读这篇论文,但是,我在这里解释了它的一些亮点:
元素几乎等于 0,一个接近 1。接近 1 的元素决定了分类器对输入标签的预测。
在庞大的人脸数据集上使用具有特定损失函数的分类器网络,您可以使用半最终层输出作为任何人脸的表示,无论它是否在训练集中,作者称之为 vector 人脸嵌入。
FaceNet 的一种实现可以在 here 中找到。我建议您尝试在您的计算机上运行它以了解您实际处理的内容。之后,最好执行以下操作:
tflite 版本(this 博文可能有帮助)
原答案
您遇到了机器学习最普遍的挑战之一:过度拟合。人脸检测和识别本身就是一个巨大的研究领域,几乎所有合理准确的模型都使用某种深度学习。请注意,即使准确检测人脸也没有看起来那么容易,但是,当您在 android 上进行此操作时,您可以使用 Face API 来完成此任务。 (其他更先进的技术,如 MTCNN 太慢/难以在手机上部署)。已经证明,仅仅用带有大量背景噪音或多人的面部照片喂养模型是行不通的。所以,你真的不能跳过这一步。
从背景中获得候选目标的漂亮修剪后,您需要克服识别检测到的人脸的挑战。同样,据我所知,所有有能力的模型都在使用某种深度学习/卷积神经网络。在手机上使用它们是一个挑战,但多亏了 Tensorflow Lite,您可以缩小它们并在您的应用程序中运行它们。我做过的一个关于安卓手机人脸识别的项目是here,你可以查一下。
请记住,任何好的模型都应该在大量标记数据实例上进行训练,但是已经有大量模型在人脸或其他图像识别任务的大型数据集上进行了训练,为了调整它们并使用他们现有的知识,我们可以使用 转移学习 ,用于快速开始对象检测和转移学习,这与您的案例检查 this 博客文章密切相关。
总的来说,你必须得到大量你想要检测的人脸的实例加上大量你不关心的人的人脸照片,然后你需要根据上述资源训练一个模型,然后你需要使用 TensorFlow lite 减小其大小并将其嵌入到您的应用程序中。然后,对于每一帧,您调用 android Face API 并将(可能检测到的人脸)输入模型并识别人物。
根据您对延迟的容忍程度以及训练集大小和目标数量,您可以获得各种结果,但是,如果您只有几个目标人,则可以轻松实现 %90+ 的准确度。
关于java - 打开简历人脸识别不准确,我们在Stack Overflow上找到一个类似的问题: https://stackoverflow.com/questions/58852046/