MainActivity.java

package com.example.opencvv2;

import android.os.Bundle;
import android.os.Environment;
import android.util.Log;
import android.view.SurfaceView;
import android.view.View;

import androidx.appcompat.app.AppCompatActivity;

import org.opencv.android.BaseLoaderCallback;
import org.opencv.android.CameraBridgeViewBase;
import org.opencv.android.JavaCameraView;
import org.opencv.android.OpenCVLoader;
import org.opencv.core.Core;
import org.opencv.core.CvType;
import org.opencv.core.Mat;
import org.opencv.core.MatOfFloat;
import org.opencv.core.MatOfInt;
import org.opencv.core.MatOfRect;
import org.opencv.core.Point;
import org.opencv.core.Rect;
import org.opencv.core.Scalar;
import org.opencv.core.Size;
import org.opencv.dnn.Dnn;
import org.opencv.dnn.Net;
import org.opencv.imgproc.Imgproc;
import org.opencv.utils.Converters;

import java.io.File;
import java.util.ArrayList;
import java.util.Arrays;
import java.util.List;






public class MainActivity extends AppCompatActivity implements CameraBridgeViewBase.CvCameraViewListener2 {

    private static String TAG = "MainActivity";
    CameraBridgeViewBase cameraBridgeViewBase;
    BaseLoaderCallback baseLoaderCallback;
    boolean startYolo = false;
    boolean firstTimeYolo = false;
    Net tinyYolo;
    //String config =  getExternalFilesDir("")+ "/deepNeuralNets/yolov3-tiny.cfg";
    //String weights =  getExternalFilesDir("")+ "/deepNeuralNets/yolov3-tiny.weights";

    public void YOLO(View Button){

        if (startYolo == false){




            startYolo = true;

            if (firstTimeYolo == false){

                firstTimeYolo = true;
                //String tinyYoloCfg = Environment.getExternalStorageDirectory() + "/deepNeuralNets/yolov3-tiny.cfg" ;
                //String tinyYoloWeights = Environment.getExternalStorageDirectory() + "/deepNeuralNets/yolov3-tiny.weights";

                tinyYolo = Dnn.readNetFromDarknet("/storage/emulated/0/deepNeuralNets/yolov3-tiny.cfg", "/storage/emulated/0/deepNeuralNets/yolov3-tiny.weights");
                //tinyYolo = Dnn.readNet(weights);








            }



        }

        else{

            startYolo = false;


        }




    }






    @Override
    protected void onCreate(Bundle savedInstanceState) {
        super.onCreate(savedInstanceState);
        setContentView(R.layout.activity_main);


        cameraBridgeViewBase = (JavaCameraView)findViewById(R.id.my_camera_view);
        cameraBridgeViewBase.setVisibility(SurfaceView.VISIBLE);
        cameraBridgeViewBase.setCvCameraViewListener(this);


        //System.loadLibrary(Core.NATIVE_LIBRARY_NAME);
        baseLoaderCallback = new BaseLoaderCallback(this) {
            @Override
            public void onManagerConnected(int status) {
                super.onManagerConnected(status);

                switch(status){

                    case BaseLoaderCallback.SUCCESS:
                        cameraBridgeViewBase.enableView();
                        break;
                    default:
                        super.onManagerConnected(status);
                        break;
                }


            }

        };




    }

    @Override
    public Mat onCameraFrame(CameraBridgeViewBase.CvCameraViewFrame inputFrame) {

        Mat frame = inputFrame.rgba();

        if (startYolo == true) {

            Imgproc.cvtColor(frame, frame, Imgproc.COLOR_RGBA2RGB);



            Mat imageBlob = Dnn.blobFromImage(frame, 0.00392, new Size(416,416),new Scalar(0, 0, 0),/*swapRB*/false, /*crop*/true);


            tinyYolo.setInput(imageBlob);

            //int cols = frame.cols();
            //int rows = frame.rows();
            //double THRESHOLD = 0.5;

            java.util.List<Mat> result = new java.util.ArrayList<Mat>(2);

            List<String> outBlobNames = new java.util.ArrayList<>();
            outBlobNames.add(0, "yolo_16");
            outBlobNames.add(1, "yolo_23");

            //tinyYolo.forward(/*result,outBlobNames*/"detection_out");
            tinyYolo.forward(result,outBlobNames);

            float confThreshold = 0.3f;



            List<Integer> clsIds = new ArrayList<>();
            List<Float> confs = new ArrayList<>();
            List<Rect> rects = new ArrayList<>();




            for (int i = 0; i < result.size(); ++i)
            {

                Mat level = result.get(i);

                for (int j = 0; j < level.rows(); ++j)
                {
                    Mat row = level.row(j);
                    Mat scores = row.colRange(5, level.cols());

                    Core.MinMaxLocResult mm = Core.minMaxLoc(scores);




                    float confidence = (float)mm.maxVal;


                    Point classIdPoint = mm.maxLoc;



                    if (confidence > confThreshold)
                    {
                        int centerX = (int)(row.get(0,0)[0] * frame.cols());
                        int centerY = (int)(row.get(0,1)[0] * frame.rows());
                        int width   = (int)(row.get(0,2)[0] * frame.cols());
                        int height  = (int)(row.get(0,3)[0] * frame.rows());


                        int left    = centerX - width  / 2;
                        int top     = centerY - height / 2;

                        clsIds.add((int)classIdPoint.x);
                        confs.add((float)confidence);




                        rects.add(new Rect(left, top, width, height));
                    }
                }
            }
            int ArrayLength = confs.size();

            if (ArrayLength>=1) {
                // Apply non-maximum suppression procedure.
                float nmsThresh = 0.2f;




                MatOfFloat confidences = new MatOfFloat(Converters.vector_float_to_Mat(confs));


                Rect[] boxesArray = rects.toArray(new Rect[0]);

                MatOfRect boxes = new MatOfRect(boxesArray);

                MatOfInt indices = new MatOfInt();



                Dnn.NMSBoxes(boxes, confidences, confThreshold, nmsThresh, indices);


                // Draw result boxes:
                int[] ind = indices.toArray();
                for (int i = 0; i < ind.length; ++i) {

                    int idx = ind[i];
                    Rect box = boxesArray[idx];

                    int idGuy = clsIds.get(idx);

                    float conf = confs.get(idx);


                    List<String> cocoNames = Arrays.asList("a person", "a bicycle", "a motorbike", "an airplane", "a bus", "a train", "a truck", "a boat", "a traffic light", "a fire hydrant", "a stop sign", "a parking meter", "a car", "a bench", "a bird", "a cat", "a dog", "a horse", "a sheep", "a cow", "an elephant", "a bear", "a zebra", "a giraffe", "a backpack", "an umbrella", "a handbag", "a tie", "a suitcase", "a frisbee", "skis", "a snowboard", "a sports ball", "a kite", "a baseball bat", "a baseball glove", "a skateboard", "a surfboard", "a tennis racket", "a bottle", "a wine glass", "a cup", "a fork", "a knife", "a spoon", "a bowl", "a banana", "an apple", "a sandwich", "an orange", "broccoli", "a carrot", "a hot dog", "a pizza", "a doughnut", "a cake", "a chair", "a sofa", "a potted plant", "a bed", "a dining table", "a toilet", "a TV monitor", "a laptop", "a computer mouse", "a remote control", "a keyboard", "a cell phone", "a microwave", "an oven", "a toaster", "a sink", "a refrigerator", "a book", "a clock", "a vase", "a pair of scissors", "a teddy bear", "a hair drier", "a toothbrush");



                    int intConf = (int) (conf * 100);



                    Imgproc.putText(frame,cocoNames.get(idGuy) + " " + intConf + "%",box.tl(),Core.FONT_HERSHEY_SIMPLEX, 2, new Scalar(255,255,0),2);



                    Imgproc.rectangle(frame, box.tl(), box.br(), new Scalar(255, 0, 0), 2);





                }
            }









        }



        return frame;
    }


    @Override
    public void onCameraViewStarted(int width, int height) {


        if (startYolo == true){

            String tinyYoloCfg = Environment.getExternalStorageDirectory() + "/dnns/yolov3-tiny.cfg" ;
            String tinyYoloWeights = Environment.getExternalStorageDirectory() + "/dnns/yolov3-tiny.weights";

            tinyYolo = Dnn.readNetFromDarknet(tinyYoloCfg, tinyYoloWeights);


        }



    }


    @Override
    public void onCameraViewStopped() {

    }


    @Override
    protected void onResume() {
        super.onResume();

        if (OpenCVLoader.initDebug()){
            Log.d(TAG, "----------OpenCV is configured or connected successfully----------");
            baseLoaderCallback.onManagerConnected(BaseLoaderCallback.SUCCESS);
        }
        else{
            Log.d(TAG, "----------OpenCV not working or loades----------");
            OpenCVLoader.initAsync(OpenCVLoader.OPENCV_VERSION, this, baseLoaderCallback);
        }



    }

    @Override
    protected void onPause() {
        super.onPause();
        if(cameraBridgeViewBase!=null){

            cameraBridgeViewBase.disableView();
        }

    }


    @Override
    protected void onDestroy() {
        super.onDestroy();
        if (cameraBridgeViewBase!=null){
            cameraBridgeViewBase.disableView();
        }
    }
}