本文介绍了JNI检测到应用程序中的错误:使用已删除的弱全局引用的处理方法,对大家解决问题具有一定的参考价值,需要的朋友们下面随着小编来一起学习吧!
问题描述
JNI文件看起来像
#include <jni.h>
#include <opencv2/core.hpp>
#include <opencv2/imgproc.hpp>
#include <opencv2/features2d.hpp>
#include <opencv2/face.hpp>
#include <opencv2/core/utility.hpp>
#include <vector>
#include <opencv2/highgui.hpp>
#include "opencv2/imgcodecs.hpp"
#include "opencv2/highgui/highgui.hpp"
#include "opencv2/imgproc/imgproc.hpp"
#include <iostream>
#include <fstream>
#include <sstream>
#include <map>
#include <android/log.h>
#define LOG_TAG "Opencv3_test"
#define LOGD(...) __android_log_print(ANDROID_LOG_DEBUG, LOG_TAG, __VA_ARGS__)
using namespace std;
using namespace cv;
using namespace cv::face;
extern "C" {
JNIEXPORT void JNICALL Java_com_opencv_test_FaceRecognizer_train
(JNIEnv *, jobject, jlongArray, jintArray);
JNIEXPORT void JNICALL Java_com_opencv_test_FaceRecognizer_predict
(JNIEnv *, jobject, long, jintArray, jdoubleArray);
Ptr<LBPHFaceRecognizer> model;
JNIEXPORT void JNICALL Java_com_opencv_test_FaceRecognizer_train
(JNIEnv *env, jobject thisObj, jlongArray images1, jintArray labels1) {
model = LBPHFaceRecognizer::create();
jsize length = (*env).GetArrayLength(images1);
jlong *mats = (*env).GetLongArrayElements(images1, 0);
jint *matLabels = (*env).GetIntArrayElements(labels1, 0);
if (length > 0) {
vector<Mat> images;
vector<int> labels;
for (int i = 0; i < length; i++) {
Mat &mGr = *(Mat *) mats[i];
images.push_back(mGr);
labels.push_back(matLabels[i]);
mGr.release();
}
model->train(images, labels);
images.clear();
labels.clear();
}
(*env).ReleaseLongArrayElements(images1, mats, 0);
(*env).ReleaseIntArrayElements(labels1, matLabels, 0);
}
JNIEXPORT void JNICALL Java_com_opencv_test_FaceRecognizer_predict
(JNIEnv *env, jobject thisObj, long images1, jintArray label, jdoubleArray confidence) {
Mat &mGr = *(Mat *) images1;
jint *labelBody = (*env).GetIntArrayElements(label, 0);
jdouble *confidenceBody = (*env).GetDoubleArrayElements(confidence, 0);
int &myLabel = labelBody[0];
double &myConfidence = confidenceBody[0];
LOGD("Prediction started in JNI");
model->predict(mGr, myLabel, myConfidence);
LOGD("Prediction ended in JNI");
(*env).ReleaseIntArrayElements(label, labelBody, 0);
(*env).ReleaseDoubleArrayElements(confidence, confidenceBody, 0);
}
}
FaceRecognizer.java
看起来像
public class FaceRecognizer {
private static final String TAG = FaceRecognizer.class.getSimpleName();
public FaceRecognizer() {
}
public void predict(Mat mGrey, int[] label, double[] confidence) {
predict(mGrey.getNativeObjAddr(), label, confidence);
}
public boolean train() {
File root = new File(activity.getFilesDir().getAbsolutePath() + "/" + TRAIN.name() + "/");
FilenameFilter pngFilter = new FilenameFilter() {
public boolean accept(File dir, String name) {
return name.toLowerCase().endsWith(".jpg");
}
};
File[] imageFiles = root.listFiles(pngFilter);
long[] mats = new long[imageFiles.length];
int[] IDs = new int[imageFiles.length];
int counter = 0;
for (File file : imageFiles) {
Log.v(TAG, file.getAbsolutePath());
Bitmap bitmap = BitmapFactory.decodeFile(file.getAbsolutePath());
if (bitmap != null) {
Bitmap bmp32 = bitmap.copy(Bitmap.Config.ARGB_8888, true);
Mat b = new Mat(bmp32.getWidth(), bmp32.getHeight(), CvType.CV_8UC1);
Utils.bitmapToMat(bmp32, b);
Imgproc.cvtColor(b, b, Imgproc.COLOR_RGB2GRAY);
int id = Integer.parseInt(file.getAbsolutePath().split("_")[1]);
Log.v(TAG, "id : " + id);
mats[counter] = b.getNativeObjAddr();
IDs[counter] = id;
counter++;
}
}
train(mats, IDs);
return true;
}
public native void train(long[] mats, int[] label);
public native void predict(long mats, int[] integer, double[] d);
}
但是当我尝试预测时,应用程序崩溃并显示以下logcat错误.
But when I am trying predict, app crashes with following logcat error.
推荐答案
之所以会发生这种情况,是因为本机方法路径为
Java_com_opencv_test_FaceRecognizer_predict
-> com.opencv.test.FaceRecognizer#predict
,但此类路径为com.idesign.opencvmaketest.FaceRecognizer
This happens because native method path is
Java_com_opencv_test_FaceRecognizer_predict
->com.opencv.test.FaceRecognizer#predict
but this class path is com.idesign.opencvmaketest.FaceRecognizer
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