如何使用JNI将数据从本机c二进制传递到android模型？

Question

[我试图通过recognizer.h中的JNI实现将名为jni_face_rec.cpp的C ++头文件中的值传递给Android。

recognizer.h

#pragma once

#include <dlib/dnn.h>
#include <dlib/string.h>
#include <jni_common/jni_fileutils.h>
#include <jni_common/jni_utils.h>
#include <dlib/image_processing.h>
#include <dlib/image_processing/frontal_face_detector.h>
#include <dlib/opencv/cv_image.h>
#include <dlib/image_loader/load_image.h>
#include <glog/logging.h>
#include <jni.h>
#include <memory>
#include <opencv2/core/core.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/imgproc/imgproc.hpp>
#include <opencv2/opencv.hpp>
#include <stdio.h>
#include <string>
#include <vector>
#include <unordered_map>
#include <time.h>
#include <dirent.h>

using namespace dlib;
using namespace std;

// ResNet network copied from dnn_face_recognition_ex.cpp in dlib/examples
template <template <int,template<typename>class,int,typename> class block, int N, template<typename>class BN, typename SUBNET>
using residual = add_prev1<block<N,BN,1,tag1<SUBNET>>>;

template <template <int,template<typename>class,int,typename> class block, int N, template<typename>class BN, typename SUBNET>
using residual_down = add_prev2<avg_pool<2,2,2,2,skip1<tag2<block<N,BN,2,tag1<SUBNET>>>>>>;

template <int N, template <typename> class BN, int stride, typename SUBNET>
using block  = BN<con<N,3,3,1,1,relu<BN<con<N,3,3,stride,stride,SUBNET>>>>>;

template <int N, typename SUBNET> using ares      = relu<residual<block,N,affine,SUBNET>>;
template <int N, typename SUBNET> using ares_down = relu<residual_down<block,N,affine,SUBNET>>;

template <typename SUBNET> using alevel0 = ares_down<256,SUBNET>;
template <typename SUBNET> using alevel1 = ares<256,ares<256,ares_down<256,SUBNET>>>;
template <typename SUBNET> using alevel2 = ares<128,ares<128,ares_down<128,SUBNET>>>;
template <typename SUBNET> using alevel3 = ares<64,ares<64,ares<64,ares_down<64,SUBNET>>>>;
template <typename SUBNET> using alevel4 = ares<32,ares<32,ares<32,SUBNET>>>;

using anet_type = loss_metric<fc_no_bias<128,avg_pool_everything<
                    alevel0<
                    alevel1<
                    alevel2<
                    alevel3<
                    alevel4<
                    max_pool<3,3,2,2,relu<affine<con<32,7,7,2,2,
                    input_rgb_image_sized<150>
                    >>>>>>>>>>>>;

class DLibFaceRecognizer {
 private:
  std::string landmark_model;
  std::string model_dir_path;
  std::string image_dir_path;
  std::string dnn_model;
  anet_type net;
  dlib::shape_predictor sp;
  std::unordered_map<int, dlib::full_object_detection> mFaceShapeMap;
  dlib::frontal_face_detector face_detector;
  std::vector<dlib::rectangle> rects;
  std::vector<std::string> rec_names;
  std::vector<matrix<float,0,1>> rec_face_descriptors;
  std::vector<dlib::rectangle> rec_rects;
  std::vector<std::string> rec_labels;
  std::vector<matrix<float,0,1>> match_face_descriptors;
  bool is_training;

  inline void init() {
    LOG(INFO) << "init DLibFaceRecognizer";
    face_detector = dlib::get_frontal_face_detector();
    landmark_model = model_dir_path + "/shape_predictor_5_face_landmarks.dat";
    dnn_model = model_dir_path + "/dlib_face_recognition_resnet_model_v1.dat";
    image_dir_path = model_dir_path + "/images";
    is_training = false;
  }

public:
  inline void train() {
    LOG(INFO) << "train DLibFaceRecognizer";
    struct dirent *entry;
    DIR *dp;

    dp = opendir((image_dir_path).c_str());
    if (dp == NULL) {
        LOG(INFO) << ("Opendir: Path does not exist or could not be read.");
    }

    std::vector<matrix<rgb_pixel>> faces;
    std::vector<std::string> names;

    // load images from dlib image directory and extract faces
    while ((entry = readdir(dp))) {
      std::string filename = entry->d_name;
      if (filename=="." || filename=="..") continue;

      cv::Mat file_image = cv::imread(image_dir_path + "/" + filename, CV_LOAD_IMAGE_COLOR);
      LOG(INFO) << "Load image " << (entry->d_name);
      dlib::cv_image<dlib::bgr_pixel> img(file_image);

      std::vector<dlib::rectangle> frects = face_detector(img);
      if (frects.size()==1) {
        auto face = frects[0];
        auto shape = sp(img, face);
        matrix<rgb_pixel> face_chip;
        extract_image_chip(img, get_face_chip_details(shape,150,0.25), face_chip);
        faces.push_back(move(face_chip));
        names.push_back(filename);
        LOG(INFO) << "Added image " << filename;
      } else if (frects.size()==0) {
        LOG(INFO) << "No face found in image " << filename;
      } else {
        LOG(INFO) << "More than one face found in image " << filename;
      }
    }
    closedir(dp);

    is_training = true;
    // calculate face descriptors and set global vars
    LOG(INFO) << "Calculating face descriptors " << jniutils::currentDateTime();
    rec_face_descriptors = net(faces);
    LOG(INFO) << "Calculated face descriptors  " << jniutils::currentDateTime()<<" Size "<<rec_face_descriptors.size();
    rec_names = names;
    is_training = false;
  }


  DLibFaceRecognizer() { init(); }

  DLibFaceRecognizer(const std::string& dlib_rec_example_dir)
      : model_dir_path(dlib_rec_example_dir) {
    init();
    if (!landmark_model.empty() && jniutils::fileExists(landmark_model) && !dnn_model.empty() && jniutils::fileExists(dnn_model)) {
      // load the model weights
      dlib::deserialize(landmark_model) >> sp;
      dlib::deserialize(dnn_model) >> net;
      LOG(INFO) << "Models loaded";
    }
  }

  inline int rec(const cv::Mat& image) {
    if (is_training) return 0;
    if (image.empty())
      return 0;
    if (image.channels() == 1) {
      cv::cvtColor(image, image, CV_GRAY2BGR);
    }
    CHECK(image.channels() == 3);

    dlib::cv_image<dlib::bgr_pixel> img(image);

    std::vector<matrix<rgb_pixel>> faces;
    std::vector<dlib::rectangle> frects = face_detector(img);
    for (auto face : frects)
    {
      auto shape = sp(img, face);
      matrix<rgb_pixel> face_chip;
      extract_image_chip(img, get_face_chip_details(shape,150,0.25), face_chip);
      faces.push_back(move(face_chip));
    }

    if (faces.size() == 0)
    {
      LOG(INFO) << "No faces found in image!";
    }
    LOG(INFO) << "calculating face descriptor in image..." << jniutils::currentDateTime();
    std::vector<matrix<float,0,1>> face_descriptors = net(faces);
    LOG(INFO) << "face descriptors in camera image calculated   "<<jniutils::currentDateTime()<<" Size "<<face_descriptors.size();

    rec_rects.clear();
    rec_labels.clear();
    for (size_t i = 0; i < face_descriptors.size();  ++i) {
      for (size_t j = 0; j < rec_face_descriptors.size();  ++j) {
        if (length(face_descriptors[i]-rec_face_descriptors[j]) < 0.6) {
          LOG(INFO) << "HELLO "<< rec_names[j]<<" FOUND ";
          //LOG(INFO) << "128 input"<< face_descriptors[i] ;
          //LOG(INFO) << "128 enroll"<< rec_face_descriptors[j];
          LOG(INFO) << "Length" << length(face_descriptors[i]-rec_face_descriptors[j]);
          LOG(INFO) << "Row : " << face_descriptors[i].NR<< ",Column : "<<face_descriptors[i].NC ;
          dlib::rectangle r = frects[i];
          rec_rects.push_back(r);
          rec_labels.push_back(rec_names[j]);
          match_face_descriptors.push_back(face_descriptors[i]);
          LOG(INFO) << "128 enroll last"<< match_face_descriptors[i];
        }
      }
    }

    return rec_rects.size();
  }

  virtual inline int det(const cv::Mat& image) {
    if (is_training) return 0;
    if (image.empty())
      return 0;
    if (image.channels() == 1) {
      cv::cvtColor(image, image, CV_GRAY2BGR);
    }
    CHECK(image.channels() == 3);
    // TODO : Convert to gray image to speed up detection
    // It's unnecessary to use color image for face/landmark detection

    dlib::cv_image<dlib::bgr_pixel> img(image);

    std::vector<matrix<rgb_pixel>> faces;
    rects = face_detector(img);
    return rects.size();
  }

  inline std::vector<dlib::rectangle> getRecResultRects() { return rec_rects; }
  inline std::vector<std::string> getRecResultLabels() { return rec_labels; }
  inline std::vector<dlib::rectangle> getDetResultRects() { return rects; }
  //inline std::vector<matrix<float,0,1>> getExtractedResult() { return match_face_descriptors; }
};

并且JNI文件是jni_face_rec.cpp：

#include <android/bitmap.h>
#include <jni_common/jni_bitmap2mat.h>
#include <jni_common/jni_primitives.h>
#include <jni_common/jni_fileutils.h>
#include <jni_common/jni_utils.h>
#include <recognizer.h>
#include <jni.h>

using namespace cv;

extern JNI_VisionDetRet* g_pJNI_VisionDetRet;

namespace {

#define JAVA_NULL 0
using RecPtr = DLibFaceRecognizer*;

class JNI_FaceRec {
 public:
  JNI_FaceRec(JNIEnv* env) {
    jclass clazz = env->FindClass(CLASSNAME_FACE_REC);
    mNativeContext = env->GetFieldID(clazz, "mNativeFaceRecContext", "J");
    env->DeleteLocalRef(clazz);
  }

  RecPtr getRecognizerPtrFromJava(JNIEnv* env, jobject thiz) {
    RecPtr const p = (RecPtr)env->GetLongField(thiz, mNativeContext);
    return p;
  }

  void setRecognizerPtrToJava(JNIEnv* env, jobject thiz, jlong ptr) {
    env->SetLongField(thiz, mNativeContext, ptr);
  }

  jfieldID mNativeContext;
};

// Protect getting/setting and creating/deleting pointer between java/native
std::mutex gLock;

std::shared_ptr<JNI_FaceRec> getJNI_FaceRec(JNIEnv* env) {
  static std::once_flag sOnceInitflag;
  static std::shared_ptr<JNI_FaceRec> sJNI_FaceRec;
  std::call_once(sOnceInitflag, [env]() {
    sJNI_FaceRec = std::make_shared<JNI_FaceRec>(env);
  });
  return sJNI_FaceRec;
}

RecPtr const getRecPtr(JNIEnv* env, jobject thiz) {
  std::lock_guard<std::mutex> lock(gLock);
  return getJNI_FaceRec(env)->getRecognizerPtrFromJava(env, thiz);
}

// The function to set a pointer to java and delete it if newPtr is empty
void setRecPtr(JNIEnv* env, jobject thiz, RecPtr newPtr) {
  std::lock_guard<std::mutex> lock(gLock);
  RecPtr oldPtr = getJNI_FaceRec(env)->getRecognizerPtrFromJava(env, thiz);
  if (oldPtr != JAVA_NULL) {
    DLOG(INFO) << "setMapManager delete old ptr : " << oldPtr;
    delete oldPtr;
  }

  if (newPtr != JAVA_NULL) {
    DLOG(INFO) << "setMapManager set new ptr : " << newPtr;
  }

  getJNI_FaceRec(env)->setRecognizerPtrToJava(env, thiz, (jlong)newPtr);
}

}  // end unnamespace

#ifdef __cplusplus
extern "C" {
#endif


#define DLIB_FACE_JNI_METHOD(METHOD_NAME) \
  Java_com_tzutalin_dlib_FaceRec_##METHOD_NAME

void JNIEXPORT
    DLIB_FACE_JNI_METHOD(jniNativeClassInit)(JNIEnv* env, jclass _this) {}

jobjectArray getRecResult(JNIEnv* env, RecPtr faceRecognizer,
                             const int& size) {
  LOG(INFO) << "getRecResult";
  jobjectArray jDetRetArray = JNI_VisionDetRet::createJObjectArray(env, size);
  for (int i = 0; i < size; i++) {
    jobject jDetRet = JNI_VisionDetRet::createJObject(env);
    env->SetObjectArrayElement(jDetRetArray, i, jDetRet);
    dlib::rectangle rect = faceRecognizer->getRecResultRects()[i];
    std::string label = faceRecognizer->getRecResultLabels()[i];
    //std::matrix<float,0,1> value = faceRecognizer->getExtractedResult()[i];
    g_pJNI_VisionDetRet->setRect(env, jDetRet, rect.left(), rect.top(),
                                 rect.right(), rect.bottom());
    g_pJNI_VisionDetRet->setLabel(env, jDetRet, label);
    //g_pJNI_VisionDetRet->setValue(env, jDetRet, value);
  }
  return jDetRetArray;
}

jobjectArray getDetResult(JNIEnv* env, RecPtr faceRecognizer,
                             const int& size) {
  LOG(INFO) << "getDetResult";
  jobjectArray jDetRetArray = JNI_VisionDetRet::createJObjectArray(env, size);
  for (int i = 0; i < size; i++) {
    jobject jDetRet = JNI_VisionDetRet::createJObject(env);
    env->SetObjectArrayElement(jDetRetArray, i, jDetRet);
    dlib::rectangle rect = faceRecognizer->getDetResultRects()[i];
    std::string label = "face";
    g_pJNI_VisionDetRet->setRect(env, jDetRet, rect.left(), rect.top(),
                                 rect.right(), rect.bottom());
    g_pJNI_VisionDetRet->setLabel(env, jDetRet, label);
  }
  return jDetRetArray;
}

JNIEXPORT jobjectArray JNICALL
    DLIB_FACE_JNI_METHOD(jniBitmapDetect)(JNIEnv* env, jobject thiz,
                                          jobject bitmap) {
  LOG(INFO) << "jniBitmapFaceDet";
  cv::Mat rgbaMat;
  cv::Mat bgrMat;
  jniutils::ConvertBitmapToRGBAMat(env, bitmap, rgbaMat, true);
  cv::cvtColor(rgbaMat, bgrMat, cv::COLOR_RGBA2BGR);
  RecPtr mRecPtr = getRecPtr(env, thiz);
  jint size = mRecPtr->det(bgrMat);
  LOG(INFO) << "det face size: " << size;
  return getDetResult(env, mRecPtr, size);
}

JNIEXPORT jobjectArray JNICALL
    DLIB_FACE_JNI_METHOD(jniBitmapRec)(JNIEnv* env, jobject thiz,
                                          jobject bitmap) {
  LOG(INFO) << "jniBitmapFaceDet";
  cv::Mat rgbaMat;
  cv::Mat bgrMat;
  jniutils::ConvertBitmapToRGBAMat(env, bitmap, rgbaMat, true);
  cv::cvtColor(rgbaMat, bgrMat, cv::COLOR_RGBA2BGR);
  RecPtr mRecPtr = getRecPtr(env, thiz);
  jint size = mRecPtr->rec(bgrMat);
  LOG(INFO) << "rec face size: " << size;
  return getRecResult(env, mRecPtr, size);
}

jint JNIEXPORT JNICALL DLIB_FACE_JNI_METHOD(jniInit)(JNIEnv* env, jobject thiz,
                                                     jstring jDirPath) {
  LOG(INFO) << "jniInit";
  std::string dirPath = jniutils::convertJStrToString(env, jDirPath);
  RecPtr mRecPtr = new DLibFaceRecognizer(dirPath);
  setRecPtr(env, thiz, mRecPtr);
  return JNI_OK;
}

jint JNIEXPORT JNICALL DLIB_FACE_JNI_METHOD(jniTrain)(JNIEnv* env, jobject thiz) {
  LOG(INFO) << "jniTrain";
  RecPtr mRecPtr = getRecPtr(env, thiz);
  mRecPtr->train();
  return JNI_OK;
}

jint JNIEXPORT JNICALL
    DLIB_FACE_JNI_METHOD(jniDeInit)(JNIEnv* env, jobject thiz) {
  LOG(INFO) << "jniDeInit";
  setRecPtr(env, thiz, JAVA_NULL);
  return JNI_OK;
}

#ifdef __cplusplus
}
#endif

并且Android中定义的Model类为VisonDecRec.java

import android.graphics.Point;

import java.util.ArrayList;

/**
 * A VisionDetRet contains all the information identifying the location and confidence value of the detected object in a bitmap.
 */
public final class VisionDetRet {
    private String mLabel;
    private float mConfidence;
    private int mLeft;
    private int mTop;
    private int mRight;
    private int mBottom;
    private ArrayList<Point> mLandmarkPoints = new ArrayList<>();
    private float[] extractedValue;

    VisionDetRet() {
    }

    /**
     * @param label      Label name
     * @param confidence A confidence factor between 0 and 1. This indicates how certain what has been found is actually the label.
     * @param l          The X coordinate of the left side of the result
     * @param t          The Y coordinate of the top of the result
     * @param r          The X coordinate of the right side of the result
     * @param b          The Y coordinate of the bottom of the result
     * @param extractedValue The Extracted 128 as float value
     */
    public VisionDetRet(String label, float confidence, int l, int t, int r, int b, float[] extractedValue) {
        mLabel = label;
        mLeft = l;
        mTop = t;
        mRight = r;
        mBottom = b;
        mConfidence = confidence;
        this.extractedValue = extractedValue;
    }

    /**
     * @return The X coordinate of the left side of the result
     */
    public int getLeft() {
        return mLeft;
    }

    /**
     * @return The Y coordinate of the top of the result
     */
    public int getTop() {
        return mTop;
    }

    /**
     * @return The X coordinate of the right side of the result
     */
    public int getRight() {
        return mRight;
    }

    /**
     * @return The Y coordinate of the bottom of the result
     */
    public int getBottom() {
        return mBottom;
    }

    /**
     * @return A confidence factor between 0 and 1. This indicates how certain what has been found is actually the label.
     */
    public float getConfidence() {
        return mConfidence;
    }

    /**
     * @return The label of the result
     */
    public String getLabel() {
        return mLabel;
    }

    /**
     * Add landmark to the list. Usually, call by jni
     * @param x Point x
     * @param y Point y
     * @return true if adding landmark successfully
     */
    public boolean addLandmark(int x, int y) {
        return mLandmarkPoints.add(new Point(x, y));
    }

    /**
     * Return the list of landmark points
     * @return ArrayList of android.graphics.Point
     */
    public ArrayList<Point> getFaceLandmarks() {
        return mLandmarkPoints;
    }

    @Override
    public String toString() {
        StringBuilder sb = new StringBuilder();
        sb.append("Left:");
        sb.append(mLabel);
        sb.append(", Top:");
        sb.append(mTop);
        sb.append(", Right:");
        sb.append(mRight);
        sb.append(", Bottom:");
        sb.append(mBottom);
        sb.append(", Label:");
        sb.append(mLabel);
        return sb.toString();
    }

    public float[] getExtractedValue() {
        return extractedValue;
    }

    public void setExtractedValue(float[] extractedValue) {
        this.extractedValue = extractedValue;
    }
}

我想在Java中将match_face_descriptors向量的值（定义如下）作为float[]。

std::vector<matrix<float,0,1>> match_face_descriptors;

我可以将其导出为以逗号分隔的String，但希望使用float[]。

Answer 1

在没有更多信息的情况下，我假设您想将向量矩阵结构保留为float[][]，其中每个float[]都对应一个矩阵。