这是一个人脸识别类，它从数据文件夹中读取人脸，但是当我运行此代码时，我出现了下面给出的错误。所有Related线程无法解决我的问题。任何帮助将不胜感激。
public class FaceRecognition {
/** the logger */
private static final Logger LOGGER = Logger.getLogger(FaceRecognition.class);
//JavaLoggingClassName.loginfo();
/** the number of training faces */
private int nTrainFaces = 0;
private int width = 320;
private int height = 240;
/** the training face image array */
IplImage[] trainingFaceImgArr = null;//IplImage.create(width, height, IPL_DEPTH_8U, 4);
/** the test face image array */
IplImage[] testFaceImgArr= null;
//IplImage image = IplImage.create(width, height, IPL_DEPTH_8U, 4);
// Bitmap mBitmap;
//IplImage image = IplImage.create(width, height, IPL_DEPTH_8U, 4);
//private Bitmap mBitmap;
/** the person number array **/
CvMat personNumTruthMat;
/** the number of persons **/
int nPersons;
/** the person names */
final List personNames = new ArrayList<>();
/** the number of eigenvalues */
int nEigens = 0;
/** eigenvectors */
IplImage[] eigenVectArr;
/** eigenvalues */
CvMat eigenValMat;
/** the average image */
IplImage pAvgTrainImg;
/** the projected training faces */
CvMat projectedTrainFaceMat;
/** Constructs a new FaceRecognition instance. */
public FaceRecognition() {
}
//JavaLoggingClassName.loginfo()
/** Trains from the data in the given training text index file, and store the trained data into the file 'data/facedata.xml'.
*
* @param trainingFileName the given training text index file
*/
public void learn(final String trainingFileName) {
int i;
// load training data
LOGGER.info("===========================================");
//IplImage
LOGGER.info("Loading the training images in " + trainingFileName);
//IplImage image = IplImage.create(width, height, IPL_DEPTH_8U, 4);
//mBitmap.copyPixelsFromBuffer(image.getByteBuffer());
try
{
trainingFaceImgArr /*mBitmap*/ = loadFaceImgArray(trainingFileName);
} catch (Exception e)
{
Log.i("ERROR", "ERROR in Code: " + e.toString());
e.printStackTrace();
}
nTrainFaces = trainingFaceImgArr.length;
LOGGER.info("Got " + nTrainFaces + " training images");
if (nTrainFaces < 3) {
LOGGER.error("Need 3 or more training faces\n"
+ "Input file contains only " + nTrainFaces);
return;
}
LOGGER.info("created projectedTrainFaceMat with " + nTrainFaces + " (nTrainFaces) rows and " + nEigens + " (nEigens) columns");
if (nTrainFaces < 5) {
LOGGER.info("projectedTrainFaceMat contents:\n" + oneChannelCvMatToString(projectedTrainFaceMat));
}
/* @param szFileTest the index file of test images
} catch (IOException ex) {
throw new RuntimeException(ex);
}
LOGGER.info("Data loaded from '" + filename + "': (" + nFaces + " images of " + nPersons + " people).");
final StringBuilder stringBuilder = new StringBuilder();
stringBuilder.append("People: ");
if (nPersons > 0) {
stringBuilder.append("<").append(personNames.get(0)).append(">");
}
for (i = 1; i < nPersons && i < personNames.size(); i++) {
stringBuilder.append(", <").append(personNames.get(i)).append(">");
}
LOGGER.info(stringBuilder.toString());
return faceImgArr;
}
/** Does the Principal Component Analysis, finding the average image and the eigenfaces that represent any image in the given dataset. */
private void doPCA() {
int i;
CvTermCriteria calcLimit;
CvSize faceImgSize = new CvSize();
// set the number of eigenvalues to use
nEigens = nTrainFaces - 1;
LOGGER.info("allocating images for principal component analysis, using " + nEigens + (nEigens == 1 ? " eigenvalue" : " eigenvalues"));
// allocate the eigenvector images
faceImgSize.width(trainingFaceImgArr[0].width());
faceImgSize.height(trainingFaceImgArr[0].height());
eigenVectArr = new IplImage[nEigens];
for (i = 0; i < nEigens; i++) {
eigenVectArr[i] = cvCreateImage(
faceImgSize, // size
IPL_DEPTH_32F, // depth
1); // channels
}
// allocate the eigenvalue array
eigenValMat = cvCreateMat(
1, // rows
nEigens, // cols
CV_32FC1); // type, 32-bit float, 1 channel
// allocate the averaged image
pAvgTrainImg = cvCreateImage(
faceImgSize, // size
IPL_DEPTH_32F, // depth
1); // channels
// set the PCA termination criterion
calcLimit = cvTermCriteria(
CV_TERMCRIT_ITER, // type
nEigens, // max_iter
1); // epsilon
LOGGER.info("computing average image, eigenvalues and eigenvectors");
// compute average image, eigenvalues, and eigenvectors
cvCalcEigenObjects(
nTrainFaces, // nObjects
new PointerPointer(trainingFaceImgArr), // input
new PointerPointer(eigenVectArr), // output
CV_EIGOBJ_NO_CALLBACK, // ioFlags
0, // ioBufSize
null, // userData
calcLimit,
pAvgTrainImg, // avg
eigenValMat.data_fl()); // eigVals
LOGGER.info("normalizing the eigenvectors");
cvNormalize(
eigenValMat, // src (CvArr)
eigenValMat, // dst (CvArr)
1, // a
0, // b
CV_L1, // norm_type
null); // mask
}
/** Stores the training data to the file 'data/facedata.xml'. */
private void storeTrainingData() {
CvFileStorage fileStorage;
int i;
LOGGER.info("writing data/facedata.xml");
// create a file-storage interface
fileStorage = cvOpenFileStorage(
"data/facedata.xml", // filename
null, // memstorage
CV_STORAGE_WRITE, // flags
null); // encoding
// Store the person names. Added by Shervin.
cvWriteInt(
fileStorage, // fs
"nPersons", // name
nPersons); // value
for (i = 0; i < nPersons; i++) {
String varname = "personName_" + (i + 1);
String personame=(String)personNames.get(i);
cvWriteString(
fileStorage, // fs
varname, // name
personame, // string
0); // quote
}
// store all the data
cvWriteInt(
fileStorage, // fs
"nEigens", // name
nEigens); // value
cvWriteInt(
fileStorage, // fs
"nTrainFaces", // name
nTrainFaces); // value
cvWrite(
fileStorage, // fs
"trainPersonNumMat", // name
personNumTruthMat, // value
cvAttrList()); // attributes
cvWrite(
fileStorage, // fs
"eigenValMat", // name
eigenValMat, // value
cvAttrList()); // attributes
cvWrite(
fileStorage, // fs
"projectedTrainFaceMat", // name
projectedTrainFaceMat,
cvAttrList()); // value
cvWrite(fileStorage, // fs
"avgTrainImg", // name
pAvgTrainImg, // value
cvAttrList()); // attributes
for (i = 0; i < nEigens; i++) {
String varname = "eigenVect_" + i;
cvWrite(
fileStorage, // fs
varname, // name
eigenVectArr[i], // value
cvAttrList()); // attributes
}
// release the file-storage interface
cvReleaseFileStorage(fileStorage);
}
/** Opens the training data from the file 'data/facedata.xml'.
*
* @param pTrainPersonNumMat
* @return the person numbers during training, or null if not successful
*/
private CvMat loadTrainingData() {
LOGGER.info("loading training data");
CvMat pTrainPersonNumMat = null; // the person numbers during training
CvFileStorage fileStorage;
int i;
// create a file-storage interface
fileStorage = cvOpenFileStorage(
"data/facedata.xml", // filename
null, // memstorage
CV_STORAGE_READ, // flags
null); // encoding
if (fileStorage == null) {
LOGGER.error("Can't open training database file 'data/facedata.xml'.");
return null;
}
// Load the person names.
personNames.clear(); // Make sure it starts as empty.
nPersons = cvReadIntByName(
fileStorage, // fs
null, // map
"nPersons", // name
0); // default_value
if (nPersons == 0) {
LOGGER.error("No people found in the training database 'data/facedata.xml'.");
return null;
} else {
LOGGER.info(nPersons + " persons read from the training database");
}
// Load each person's name.
for (i = 0; i < nPersons; i++) {
String sPersonName;
String varname = "personName_" + (i + 1);
sPersonName = cvReadStringByName(
fileStorage, // fs
null, // map
varname,
"");
personNames.add(sPersonName);
}
LOGGER.info("person names: " + personNames);
// Load the data
nEigens = cvReadIntByName(
fileStorage, // fs
null, // map
"nEigens",
0); // default_value
nTrainFaces = cvReadIntByName(
fileStorage,
null, // map
"nTrainFaces",
0); // default_value
Pointer pointer = cvReadByName(
fileStorage, // fs
null, // map
"trainPersonNumMat", // name
cvAttrList()); // attributes
pTrainPersonNumMat = new CvMat(pointer);
pointer = cvReadByName(
fileStorage, // fs
null, // map
"eigenValMat", // nmae
cvAttrList()); // attributes
eigenValMat = new CvMat(pointer);
pointer = cvReadByName(
fileStorage, // fs
null, // map
"projectedTrainFaceMat", // name
cvAttrList()); // attributes
projectedTrainFaceMat = new CvMat(pointer);
pointer = cvReadByName(
fileStorage,
null, // map
"avgTrainImg",
cvAttrList()); // attributes
pAvgTrainImg = new IplImage(pointer);
eigenVectArr = new IplImage[nTrainFaces];
for (i = 0; i < nEigens; i++) {
String varname = "eigenVect_" + i;
pointer = cvReadByName(
fileStorage,
null, // map
varname,
cvAttrList()); // attributes
eigenVectArr[i] = new IplImage(pointer);
}
// release the file-storage interface
cvReleaseFileStorage(fileStorage);
LOGGER.info("Training data loaded (" + nTrainFaces + " training images of " + nPersons + " people)");
final StringBuilder stringBuilder = new StringBuilder();
stringBuilder.append("People: ");
if (nPersons > 0) {
stringBuilder.append("<").append(personNames.get(0)).append(">");
}
for (i = 1; i < nPersons; i++) {
stringBuilder.append(", <").append(personNames.get(i)).append(">");
}
LOGGER.info(stringBuilder.toString());
return pTrainPersonNumMat;
}
/** Saves all the eigenvectors as images, so that they can be checked. */
private void storeEigenfaceImages() {
// Store the average image to a file
LOGGER.info("Saving the image of the average face as 'data/out_averageImage.bmp'");
cvSaveImage("data/out_averageImage.bmp", pAvgTrainImg);
// Create a large image made of many eigenface images.
// Must also convert each eigenface image to a normal 8-bit UCHAR image instead of a 32-bit float image.
LOGGER.info("Saving the " + nEigens + " eigenvector images as 'data/out_eigenfaces.bmp'");
if (nEigens > 0) {
// Put all the eigenfaces next to each other.
int COLUMNS = 8; // Put upto 8 images on a row.
int nCols = Math.min(nEigens, COLUMNS);
int nRows = 1 + (nEigens / COLUMNS); // Put the rest on new rows.
int w = eigenVectArr[0].width();
int h = eigenVectArr[0].height();
CvSize size = cvSize(nCols * w, nRows * h);
final IplImage bigImg = cvCreateImage(
size,
IPL_DEPTH_8U, // depth, 8-bit Greyscale UCHAR image
1); // channels
for (int i = 0; i < nEigens; i++) {
// Get the eigenface image.
IplImage byteImg = convertFloatImageToUcharImage(eigenVectArr[i]);
// Paste it into the correct position.
int x = w * (i % COLUMNS);
int y = h * (i / COLUMNS);
CvRect ROI = cvRect(x, y, w, h);
cvSetImageROI(
bigImg, // image
ROI); // rect
cvCopy(
byteImg, // src
bigImg, // dst
null); // mask
cvResetImageROI(bigImg);
cvReleaseImage(byteImg);
}
cvSaveImage(
"data/out_eigenfaces.bmp", // filename
bigImg); // image
cvReleaseImage(bigImg);
}
}
/** Converts the given float image to an unsigned character image.
*
* @param srcImg the given float image
* @return the unsigned character image
*/
private IplImage convertFloatImageToUcharImage(IplImage srcImg) {
IplImage dstImg;
if ((srcImg != null) && (srcImg.width() > 0 && srcImg.height() > 0)) {
// Spread the 32bit floating point pixels to fit within 8bit pixel range.
CvPoint minloc = new CvPoint();
CvPoint maxloc = new CvPoint();
double[] minVal = new double[1];
double[] maxVal = new double[1];
cvMinMaxLoc(srcImg, minVal, maxVal, minloc, maxloc, null);
// Deal with NaN and extreme values, since the DFT seems to give some NaN results.
if (minVal[0] < -1e30) {
minVal[0] = -1e30;
}
if (maxVal[0] > 1e30) {
maxVal[0] = 1e30;
}
if (maxVal[0] - minVal[0] == 0.0f) {
maxVal[0] = minVal[0] + 0.001; // remove potential divide by zero errors.
} // Convert the format
dstImg = cvCreateImage(cvSize(srcImg.width(), srcImg.height()), 8, 1);
cvConvertScale(srcImg, dstImg, 255.0 / (maxVal[0] - minVal[0]), -minVal[0] * 255.0 / (maxVal[0] - minVal[0]));
return dstImg;
}
return null;
}
/** Find the most likely person based on a detection. Returns the index, and stores the confidence value into pConfidence.
*
* @param projectedTestFace the projected test face
* @param pConfidencePointer a pointer containing the confidence value
* @param iTestFace the test face index
* @return the index
*/
private int findNearestNeighbor(float projectedTestFace[], FloatPointer pConfidencePointer) {
double leastDistSq = Double.MAX_VALUE;
int i = 0;
int iTrain = 0;
int iNearest = 0;
LOGGER.info("................");
LOGGER.info("find nearest neighbor from " + nTrainFaces + " training faces");
for (iTrain = 0; iTrain < nTrainFaces; iTrain++) {
//LOGGER.info("considering training face " + (iTrain + 1));
double distSq = 0;
for (i = 0; i < nEigens; i++) {
//LOGGER.debug(" projected test face distance from eigenface " + (i + 1) + " is " + projectedTestFace[i]);
float projectedTrainFaceDistance = (float) projectedTrainFaceMat.get(iTrain, i);
float d_i = projectedTestFace[i] - projectedTrainFaceDistance;
distSq += d_i * d_i; // / eigenValMat.data_fl().get(i); // Mahalanobis distance (might give better results than Eucalidean distance)
// if (iTrain < 5) {
// LOGGER.info(" ** projected training face " + (iTrain + 1) + " distance from eigenface " + (i + 1) + " is " + projectedTrainFaceDistance);
// LOGGER.info(" distance between them " + d_i);
// LOGGER.info(" distance squared " + distSq);
// }
}
if (distSq < leastDistSq) {
leastDistSq = distSq;
iNearest = iTrain;
LOGGER.info(" training face " + (iTrain + 1) + " is the new best match, least squared distance: " + leastDistSq);
}
}
// Return the confidence level based on the Euclidean distance,
// so that similar images should give a confidence between 0.5 to 1.0,
// and very different images should give a confidence between 0.0 to 0.5.
float pConfidence = (float) (1.0f - Math.sqrt(leastDistSq / (float) (nTrainFaces * nEigens)) / 255.0f);
pConfidencePointer.put(pConfidence);
LOGGER.info("training face " + (iNearest + 1) + " is the final best match, confidence " + pConfidence);
return iNearest;
}
/** Returns a string representation of the given float array.
*
* @param floatArray the given float array
* @return a string representation of the given float array
*/
private String floatArrayToString(final float[] floatArray) {
final StringBuilder stringBuilder = new StringBuilder();
boolean isFirst = true;
stringBuilder.append('[');
for (int i = 0; i < floatArray.length; i++) {
if (isFirst) {
isFirst = false;
} else {
stringBuilder.append(", ");
}
stringBuilder.append(floatArray[i]);
}
stringBuilder.append(']');
return stringBuilder.toString();
}
/** Returns a string representation of the given float pointer.
*
* @param floatPointer the given float pointer
* @return a string representation of the given float pointer
*/
private String floatPointerToString(final FloatPointer floatPointer) {
final StringBuilder stringBuilder = new StringBuilder();
boolean isFirst = true;
stringBuilder.append('[');
for (int i = 0; i < floatPointer.capacity(); i++) {
if (isFirst) {
isFirst = false;
} else {
stringBuilder.append(", ");
}
stringBuilder.append(floatPointer.get(i));
}
stringBuilder.append(']');
return stringBuilder.toString();
}
/** Returns a string representation of the given one-channel CvMat object.
*
* @param cvMat the given CvMat object
* @return a string representation of the given CvMat object
*/
public String oneChannelCvMatToString(final CvMat cvMat) {
//Preconditions
if (cvMat.channels() != 1) {
throw new RuntimeException("illegal argument - CvMat must have one channel");
}
final int type = cvMat.type();
StringBuilder s = new StringBuilder("[ ");
for (int i = 0; i < cvMat.rows(); i++) {
for (int j = 0; j < cvMat.cols(); j++) {
if (type == CV_32FC1 || type == CV_32SC1) {
s.append(cvMat.get(i, j));
} else {
throw new RuntimeException("illegal argument - CvMat must have one channel and type of float or signed integer");
}
if (j < cvMat.cols() - 1) {
s.append(", ");
}
}
if (i < cvMat.rows() - 1) {
s.append("\n ");
}
}
s.append(" ]");
return s.toString();
}
/** Executes this application.
*
* @param args the command line arguments
*/
public static void main(final String[] args) {
BasicConfigurator.configure();
// PropertyConfigurator.configure(args[0]);
// if(args[0]!=null)
/*{
System.out.println("null index");
}
else continue;
*/
final FaceRecognition faceRecognition = new FaceRecognition();
// main myMain = new main();
// myMain.FaceRecognition();
//faceRecognition.learn("data/some-training-faces.txt");
// faceRecognition.learn("G:\\android_support\\javacv-examples\\OpenCV2_Cookbook\\data\\all10.txt");
faceRecognition.learn("data/all100.txt");
//faceRecognition.recognizeFileList("data/some-test-faces.txt");
// faceRecognition.recognizeFileList("G:\\android_support\\javacv-examples\\OpenCV2_Cookbook\\data\\lower3.txt");
faceRecognition.recognizeFileList("data/lower3.txt");
}
}
错误
Exception in thread "main" java.lang.RuntimeException: Stub!
at android.util.Log.i(Log.java:9)
at FaceRecognition.learn(FaceRecognition.java:126)
at FaceRecognition.main(FaceRecognition.java:846)
最佳答案
您似乎尝试在主机上测试 android 类。也许使用 JUnit。 这不起作用。有两个选项可供选择:
- 如果可能，将功能移至库中，这样就不会
android使用包。例如使用java.util.logging而不是android.util.Log. - 使用 Android 仪器测试(谷歌搜索)。
如果您使用 Maven:请记住 android 库是 <scope>provided</scope> .
关于java - 线程 "main"中的异常 java.lang.RuntimeException : Stub , 查看了所有线程但没有用，我们在Stack Overflow上找到一个类似的问题： https://stackoverflow.com/questions/16032172/