MobileNetSSD通过Ncnn前向推理框架在Android端的使用--Cmake编译(目标检测 objection detection)补充篇章(多目标也可以显示) - Che_Hongshu - CSDN博客

一、前言

• 安装win10+linux-Ubuntu16.04的双系统（超细致）
• ubuntu16.04+caffe＋CUDA10.0+cudnn7.4+opencv2.4.9.1+python2.7　（超超细致）
• Caffe_ssd安装以及利用VOC2012,VOC2007数据集测试VGG_SSD网络
• Caffe实现MobileNetSSD以及各个文件的具体解释，利用自己的数据集dataset训练MobileNetSSD建立模型

推荐先把下面这两篇看完再来看这补充的一篇

• MobileNetSSD通过Ncnn前向推理框架在PC端的使用(目标检测 objection detection)
• MobileNetSSD通过Ncnn前向推理框架在Android端的使用–Cmake编译(目标检测 objection detection)

二、需要修改NDK部分代码以及java部分代码即可(下面有具体板块解释修改)

MobileNetssd.cpp文件修改在后面的Detect函数

// public native String Detect(Bitmap bitmap);
JNIEXPORT jfloatArray JNICALL Java_com_example_che_mobilenetssd_1demo_MobileNetssd_Detect(JNIEnv* env, jobject thiz, jobject bitmap)
{
    // ncnn from bitmap
    ncnn::Mat in;
    {
        AndroidBitmapInfo info;
        AndroidBitmap_getInfo(env, bitmap, &info);
//        int origin_w = info.width;
//        int origin_h = info.height;
//        int width = 300;
//        int height = 300;
        int width = info.width;
        int height = info.height;
        if (info.format != ANDROID_BITMAP_FORMAT_RGBA_8888)
            return NULL;

        void* indata;
        AndroidBitmap_lockPixels(env, bitmap, &indata);
        // 把像素转换成data，并指定通道顺序
        // 因为图像预处理每个网络层输入的数据格式不一样一般为300*300 128*128等等所以这类需要一个resize的操作可以在cpp中写，也可以是java读入图片时有个resize操作
//      in = ncnn::Mat::from_pixels_resize((const unsigned char*)indata, ncnn::Mat::PIXEL_RGBA2RGB, origin_w, origin_h, width, height);

        in = ncnn::Mat::from_pixels((const unsigned char*)indata, ncnn::Mat::PIXEL_RGBA2RGB, width, height);

        // 下面一行为debug代码
        //__android_log_print(ANDROID_LOG_DEBUG, "MobilenetssdJniIn", "Mobilenetssd_predict_has_input1, in.w: %d; in.h: %d", in.w, in.h);
        AndroidBitmap_unlockPixels(env, bitmap);
    }

    // ncnn_net
    std::vector cls_scores;
    {
        // 减去均值和乘上比例（这个数据和前面的归一化图片预处理形式一一对应）
        const float mean_vals[3] = {127.5f, 127.5f, 127.5f};
        const float scale[3] = {0.007843f, 0.007843f, 0.007843f};

        in.substract_mean_normalize(mean_vals, scale);// 归一化

        ncnn::Extractor ex = ncnn_net.create_extractor();//前向传播

        // 如果不加密是使用ex.input("data", in);
        // BLOB_data在id.h文件中可见，相当于datainput网络层的id
        ex.input(MobileNetSSD_deploy_param_id::BLOB_data, in);
        //ex.set_num_threads(4); 和上面一样一个对象

        ncnn::Mat out;
        // 如果时不加密是使用ex.extract("prob", out);
        //BLOB_detection_out.h文件中可见，相当于dataout网络层的id,输出检测的结果数据
        ex.extract(MobileNetSSD_deploy_param_id::BLOB_detection_out, out);

        int output_wsize = out.w;
        int output_hsize = out.h;

        //输出整理
        jfloat *output[output_wsize * output_hsize];   // float类型
        for(int i = 0; i< out.h; i++) {
            for (int j = 0; j < out.w; j++) {
                output[i*output_wsize + j] = &out.row(i)[j];
            }
        }
        //建立float数组 长度为 output_wsize * output_hsize,如果只是ouput_size相当于只有一行的out的数据那就是一个object检测数据
        jfloatArray jOutputData = env->NewFloatArray(output_wsize * output_hsize);
        if (jOutputData == nullptr) return nullptr;
        env->SetFloatArrayRegion(jOutputData, 0,  output_wsize * output_hsize,
                                 reinterpret_cast(*output));
        return jOutputData;
    }
}
   

MainActivity.java修改后后为

package com.example.che.mobilenetssd_demo;

import android.Manifest;
import android.app.Activity;
import android.content.Intent;
import android.content.pm.PackageManager;
import android.content.res.AssetManager;
import android.graphics.Bitmap;
import android.graphics.Canvas;
import android.graphics.Color;
import android.graphics.Paint;
import android.net.Uri;
import android.support.annotation.NonNull;
import android.support.annotation.Nullable;
import android.support.v4.app.ActivityCompat;
import android.support.v4.content.ContextCompat;
import android.support.v7.app.AppCompatActivity;
import android.os.Bundle;
import android.text.method.ScrollingMovementMethod;
import android.util.Log;
import android.view.View;
import android.widget.Button;
import android.widget.ImageView;
import android.widget.TextView;
import android.widget.Toast;

import java.io.BufferedReader;
import java.io.IOException;
import java.io.InputStream;
import java.io.InputStreamReader;
import java.util.ArrayList;
import java.util.Arrays;
import java.util.List;


import com.bumptech.glide.Glide;
import com.bumptech.glide.load.engine.DiskCacheStrategy;
import com.bumptech.glide.request.RequestOptions;


public class MainActivity extends AppCompatActivity {

    private static final String TAG = MainActivity.class.getName();
    private static final int USE_PHOTO = 1001;
    private String camera_image_path;
    private ImageView show_image;
    private TextView result_text;
    private boolean load_result = false;
    private int[] ddims = {1, 3, 300, 300}; //这里的维度的值要和train model的input 一一对应
    private int model_index = 1;
    private List resultLabel = new ArrayList<>();
    private MobileNetssd mobileNetssd = new MobileNetssd(); //java接口实例化　下面直接利用java函数调用NDK c++函数

    @Override
    protected void onCreate(Bundle savedInstanceState) {
        super.onCreate(savedInstanceState);
        setContentView(R.layout.activity_main);
        try
        {
            initMobileNetSSD();
        } catch (IOException e) {
            Log.e("MainActivity", "initMobileNetSSD error");
        }
        init_view();
        readCacheLabelFromLocalFile();
}

    /**
     *
     * MobileNetssd初始化，也就是把model文件进行加载
     */
    private void initMobileNetSSD() throws IOException {
        byte[] param = null;
        byte[] bin = null;
        {
            //用io流读取二进制文件，最后存入到byte[]数组中
            InputStream assetsInputStream = getAssets().open("MobileNetSSD_deploy.param.bin");// param：  网络结构文件
            int available = assetsInputStream.available();
            param = new byte[available];
            int byteCode = assetsInputStream.read(param);
            assetsInputStream.close();
        }
        {
            //用io流读取二进制文件，最后存入到byte上，转换为int型
            InputStream assetsInputStream = getAssets().open("MobileNetSSD_deploy.bin");//bin：   model文件
            int available = assetsInputStream.available();
            bin = new byte[available];
            int byteCode = assetsInputStream.read(bin);
            assetsInputStream.close();
        }

        load_result = mobileNetssd.Init(param, bin);// 再将文件传入java的NDK接口(c++ 代码中的init接口 )
        Log.d("load model", "MobileNetSSD_load_model_result:" + load_result);
    }


    // initialize view
    private void init_view() {
        request_permissions();
        show_image = (ImageView) findViewById(R.id.show_image);
        result_text = (TextView) findViewById(R.id.result_text);
        result_text.setMovementMethod(ScrollingMovementMethod.getInstance());
        Button use_photo = (Button) findViewById(R.id.use_photo);
        // use photo click
        use_photo.setOnClickListener(new View.OnClickListener() {
            @Override
            public void onClick(View view) {
                if (!load_result) {
                    Toast.makeText(MainActivity.this, "never load model", Toast.LENGTH_SHORT).show();
                    return;
                }
                PhotoUtil.use_photo(MainActivity.this, USE_PHOTO);
            }
        });
    }

    // load label's name
    private void readCacheLabelFromLocalFile() {
        try {
            AssetManager assetManager = getApplicationContext().getAssets();
            BufferedReader reader = new BufferedReader(new InputStreamReader(assetManager.open("words.txt")));//这里是label的文件
            String readLine = null;
            while ((readLine = reader.readLine()) != null) {
                resultLabel.add(readLine);
            }
            reader.close();
        } catch (Exception e) {
            Log.e("labelCache", "error " + e);
        }
    }


    protected void onActivityResult(int requestCode, int resultCode, @Nullable Intent data) {
        String image_path;
        RequestOptions options = new RequestOptions().skipMemoryCache(true).diskCacheStrategy(DiskCacheStrategy.NONE);
        if (resultCode == Activity.RESULT_OK) {
            switch (requestCode) {
                case USE_PHOTO:
                    if (data == null) {
                        Log.w(TAG, "user photo data is null");
                        return;
                    }
                    Uri image_uri = data.getData();

                    //Glide.with(MainActivity.this).load(image_uri).apply(options).into(show_image);

                    // get image path from uri
                    image_path = PhotoUtil.get_path_from_URI(MainActivity.this, image_uri);
                    // predict image
                    predict_image(image_path);
                    break;
            }
        }
    }

    //  predict image
    private void predict_image(String image_path) {
        // picture to float array
        Bitmap bmp = PhotoUtil.getScaleBitmap(image_path);
        Bitmap rgba = bmp.copy(Bitmap.Config.ARGB_8888, true);
        // resize
        Bitmap input_bmp = Bitmap.createScaledBitmap(rgba, ddims[2], ddims[3], false);
        try {
            // Data format conversion takes too long
            // Log.d("inputData", Arrays.toString(inputData));
            long start = System.currentTimeMillis();
            // get predict result
            float[] result = mobileNetssd.Detect(input_bmp);
            // time end
            long end = System.currentTimeMillis();
            Log.d(TAG, "origin predict result:" + Arrays.toString(result));
            long time = end - start;
            Log.d("result length", "length of result: " + String.valueOf(result.length));
            // show predict result and time
            // float[] r = get_max_result(result);

            String show_text = "result：" + Arrays.toString(result) + "\nname：" + resultLabel.get((int) result[0]) + "\nprobability：" + result[1] + "\ntime：" + time + "ms" ;
            result_text.setText(show_text);

            // 画布配置
            Canvas canvas = new Canvas(rgba);
            //图像上画矩形
            Paint paint = new Paint();
            paint.setColor(Color.RED);
            paint.setStyle(Paint.Style.STROKE);//不填充
            paint.setStrokeWidth(5); //线的宽度


            float get_finalresult[][] = TwoArry(result);
            Log.d("zhuanhuan",get_finalresult+"");
            int object_num = 0;
            int num = result.length/6;// number of object
            //continue to draw rect
            for(object_num = 0; object_num < num; object_num++){
                Log.d(TAG, "haha :" + Arrays.toString(get_finalresult));
                // 画框
                paint.setColor(Color.RED);
                paint.setStyle(Paint.Style.STROKE);//不填充
                paint.setStrokeWidth(5); //线的宽度
                canvas.drawRect(get_finalresult[object_num][2] * rgba.getWidth(), get_finalresult[object_num][3] * rgba.getHeight(),
                        get_finalresult[object_num][4] * rgba.getWidth(), get_finalresult[object_num][5] * rgba.getHeight(), paint);

                paint.setColor(Color.YELLOW);
                paint.setStyle(Paint.Style.FILL);//不填充
                paint.setStrokeWidth(1); //线的宽度
                canvas.drawText(resultLabel.get((int) get_finalresult[object_num][0]) + "\n" + get_finalresult[object_num][1],
                        get_finalresult[object_num][2]*rgba.getWidth(),get_finalresult[object_num][3]*rgba.getHeight(),paint);
            }

            show_image.setImageBitmap(rgba);


        } catch (Exception e) {
            e.printStackTrace();
        }
    }

    //一维数组转化为二维数组(自己新写的)
    public static float[][] TwoArry(float[] inputfloat){
        int n = inputfloat.length;
        int num = inputfloat.length/6;
        float[][] outputfloat = new float[num][6];
        int k = 0;
        for(int i = 0; i < num ; i++)
        {
            int j = 0;

            while(j<6)
            {
                outputfloat[i][j] =  inputfloat[k];
                k++;
                j++;
            }

        }

        return outputfloat;
    }

    /*
    // get max probability label
    private float[] get_max_result(float[] result) {
        int num_rs = result.length / 6;
        float maxProp = result[1];
        int maxI = 0;
        for(int i = 1; i permissionList = new ArrayList<>();
        if (ContextCompat.checkSelfPermission(this, Manifest.permission.CAMERA) != PackageManager.PERMISSION_GRANTED) {
            permissionList.add(Manifest.permission.CAMERA);
        }
        if (ContextCompat.checkSelfPermission(this, Manifest.permission.WRITE_EXTERNAL_STORAGE) != PackageManager.PERMISSION_GRANTED) {
            permissionList.add(Manifest.permission.WRITE_EXTERNAL_STORAGE);
        }
        if (ContextCompat.checkSelfPermission(this, Manifest.permission.READ_EXTERNAL_STORAGE) != PackageManager.PERMISSION_GRANTED) {
            permissionList.add(Manifest.permission.READ_EXTERNAL_STORAGE);
        }
        // if list is not empty will request permissions
        if (!permissionList.isEmpty()) {
            ActivityCompat.requestPermissions(this, permissionList.toArray(new String[permissionList.size()]), 1);
        }
    }

    @Override
    public void onRequestPermissionsResult(int requestCode, @NonNull String[] permissions, @NonNull int[] grantResults) {
        super.onRequestPermissionsResult(requestCode, permissions, grantResults);
        switch (requestCode) {
            case 1:
                if (grantResults.length > 0) {
                    for (int i = 0; i < grantResults.length; i++) {
                        int grantResult = grantResults[i];
                        if (grantResult == PackageManager.PERMISSION_DENIED) {
                            String s = permissions[i];
                            Toast.makeText(this, s + "permission was denied", Toast.LENGTH_SHORT).show();
                        }
                    }
                }
                break;
        }
    }



}
   

三、运行结果

具体解释

.cpp端不需要过多解释给出的代码有相应的注释
.java中需要解释一些
首先通过前向推理最后输出的数据格式为
6位，　label+概率(置信度)+左＋上＋右＋下
当然最后你可以在上图中也发现为小数，其实他是为对应的长或宽的比例
比如上图result 中的现实的数据前六位
13.0 0.99939597 0.40525293 0.18877083 0.839892715 0.943314

• 13.0就是对应所有label之前在label文件中定义过的,0为backgroud以此列推
• 0.999就为概率
• 0.4则为对应　宽长×0.4的位置
• 0.188 则对应于　高长×0.188的位置
• 以此列推，这个时候再对应于前面的代码做一下消化就没问题了
• 所以处理这种数据首先需要每6个做一下区分，所以也就有了下面我自己写的 public static float[][] TwoArry(float[] inputfloat)函数，回过头看一下一切舒服了
  这边我又加入了一些颜色和框的粗细的改变以及框的旁边的txt文本label的添加

代码地址

已在我的github上，希望大家给个star follow
https://github.com/chehongshu/ncnnforandroid_objectiondetection_Mobilenetssd/tree/master/MobileNetSSD_demo