Vuforia.java

package archive_21;

import com.qualcomm.robotcore.eventloop.opmode.Autonomous;
import com.qualcomm.robotcore.eventloop.opmode.Disabled;
import com.qualcomm.robotcore.eventloop.opmode.LinearOpMode;
import com.qualcomm.robotcore.eventloop.opmode.TeleOp;
import com.qualcomm.robotcore.hardware.DcMotor;
import com.qualcomm.robotcore.hardware.Servo;
import com.qualcomm.robotcore.util.ElapsedTime;

import org.firstinspires.ftc.robotcore.external.ClassFactory;
import org.firstinspires.ftc.robotcore.external.hardware.camera.WebcamName;
import org.firstinspires.ftc.robotcore.external.matrices.OpenGLMatrix;
import org.firstinspires.ftc.robotcore.external.navigation.VuforiaLocalizer;
import org.firstinspires.ftc.robotcore.external.navigation.VuforiaTrackable;
import org.firstinspires.ftc.robotcore.external.navigation.VuforiaTrackableDefaultListener;
import org.firstinspires.ftc.robotcore.external.navigation.VuforiaTrackables;
import org.firstinspires.ftc.robotcore.external.tfod.Recognition;
import org.firstinspires.ftc.robotcore.external.tfod.TFObjectDetector;

import java.util.List;

@Autonomous(name = "Vuforia", group = "Concept")
public class Vuforia extends LinearOpMode
{

/*{
    VuforiaLocalizer vurforiaLocalizer;
    VuforiaLocalizer.Parameters parameters;
    //VuforiaTrackables visionTargets = //vuforiaLocalizer.loa;
    VuforiaTrackable target;
    VuforiaTrackableDefaultListener listener;

    OpenGLMatrix lastknownLocation;
    OpenGLMatrix driverhubLocation;

    public static final String VUFORIA_KEY = "AQFvn1X/////AAABmfD5QbW72U/KuuZnETqfRvxtsghR7zWjrmjy6vu4H4g1VnCMWG5HPq3XhUD1kyMrbjP5lRruLzhYzgHtr9dK4TCjv0K0GTEK8Ww52Y++exIFmBRDTm84bjzBw0CPo0oeSx82fTU+c0S8B5Q4QFYWSCP5FD1pJESKDz3H5WnA0LoEFMkjpjwhMEbYqpVW6PdvO+QqSHBKBQjzKKstLDbEiZI+7A+A++dEaNehTtpy3cL3Jz0Jui6w6Fu3M/Dij9EwVHJTEEOJS4LEQdDV4U3lfkAuVxVxb0SGChr/1qcd+YzKQ/Cv3a5rKmMja54LkCfQ2dgRI3FapdPVZglZ2rr1nc0KTKI5T+Q3lzzhpc1brGZS";
    //need key

    public void runOpMode () throws InterruptedException
    {


        waitForStart();

        while(opModeIsActive())
        {


            telemetry.update();
            idle();
        }
    }

    public void setupVuforia()
    {

        parameters = new VuforiaLocalizer.Parameters(R.id.cameraMonitorViewId);
        parameters.vuforiaLicenseKey = VUFORIA_KEY;
        parameters.cameraDirection = VuforiaLocalizer.CameraDirection.BACK;
        // vuforiaLocalizer = ClassFactory.createVuforiaLocalizer(parameters);

    }
}

*/
    /**
     * This 2020-2021 OpMode illustrates the basics of using the TensorFlow Object Detection API to
     * determine the position of the Freight Frenzy game elements.
     * <p>
     * Use Android Studio to Copy this Class, and Paste it into your team's code folder with a new name.
     * Remove or comment out the @Disabled line to add this opmode to the Driver Station OpMode list.
     * <p>
     * IMPORTANT: In order to use this OpMode, you need to obtain your own Vuforia license key as
     * is explained below.
     */

    /* Note: This sample uses the all-objects Tensor Flow model (FreightFrenzy_BCDM.tflite), which contains
     * the following 4 detectable objects
     *  0: Ball,
     *  1: Cube,
     *  2: Duck,
     *  3: Marker (duck location tape marker)
     *
     *  Two additional model assets are available which only contain a subset of the objects:
     *  FreightFrenzy_BC.tflite  0: Ball,  1: Cube
     *  FreightFrenzy_DM.tflite  0: Duck,  1: Marker
     */
    private static final String TFOD_MODEL_ASSET = "FreightFrenzy_BCDM.tflite";
    private static final String[] LABELS = {
            "Ball",
            "Cube",
            "Duck",
            "Marker"
    };

    /*
     * IMPORTANT: You need to obtain your own license key to use Vuforia. The string below with which
     * 'parameters.vuforiaLicenseKey' is initialized is for illustration only, and will not function.
     * A Vuforia 'Development' license key, can be obtained free of charge from the Vuforia developer
     * web site at https://developer.vuforia.com/license-manager.
     *
     * Vuforia license keys are always 380 characters long, and look as if they contain mostly
     * random data. As an example, here is a example of a fragment of a valid key:
     *      ... yIgIzTqZ4mWjk9wd3cZO9T1axEqzuhxoGlfOOI2dRzKS4T0hQ8kT ...
     * Once you've obtained a license key, copy the string from the Vuforia web site
     * and paste it in to your code on the next line, between the double quotes.
     */
    private static final String VUFORIA_KEY =
            "AQFvn1X/////AAABmfD5QbW72U/KuuZnETqfRvxtsghR7zWjrmjy6vu4H4g1VnCMWG5HPq3XhUD1kyMrbjP5lRruLzhYzgHtr9dK4TCjv0K0GTEK8Ww52Y++exIFmBRDTm84bjzBw0CPo0oeSx82fTU+c0S8B5Q4QFYWSCP5FD1pJESKDz3H5WnA0LoEFMkjpjwhMEbYqpVW6PdvO+QqSHBKBQjzKKstLDbEiZI+7A+A++dEaNehTtpy3cL3Jz0Jui6w6Fu3M/Dij9EwVHJTEEOJS4LEQdDV4U3lfkAuVxVxb0SGChr/1qcd+YzKQ/Cv3a5rKmMja54LkCfQ2dgRI3FapdPVZglZ2rr1nc0KTKI5T+Q3lzzhpc1brGZS";

    /**
     * {@link #vuforia} is the variable we will use to store our instance of the Vuforia
     * localization engine.
     */
    private VuforiaLocalizer vuforia;

    /**
     * {@link #tfod} is the variable we will use to store our instance of the TensorFlow Object
     * Detection engine.
     */
    private TFObjectDetector tfod;

    @Override
    public void runOpMode() {
        // The TFObjectDetector uses the camera frames from the VuforiaLocalizer, so we create that
        // first.
        initVuforia();
        initTfod();

        /**
         * Activate TensorFlow Object Detection before we wait for the start command.
         * Do it here so that the Camera Stream window will have the TensorFlow annotations visible.
         **/
        if (tfod != null) {
            tfod.activate();

            // The TensorFlow software will scale the input images from the camera to a lower resolution.
            // This can result in lower detection accuracy at longer distances (> 55cm or 22").
            // If your target is at distance greater than 50 cm (20") you can adjust the magnification value
            // to artificially zoom in to the center of image.  For best results, the "aspectRatio" argument
            // should be set to the value of the images used to create the TensorFlow Object Detection model
            // (typically 16/9).
            tfod.setZoom(2.5, 16.0 / 9.0);
        }

        /** Wait for the game to begin */
        telemetry.addData(">", "Press Play to start op mode");
        telemetry.update();
        waitForStart();

        if (opModeIsActive()) {
            while (opModeIsActive()) {
                if (tfod != null) {
                    // getUpdatedRecognitions() will return null if no new information is available since
                    // the last time that call was made.
                    List<Recognition> updatedRecognitions = tfod.getUpdatedRecognitions();
                    if (updatedRecognitions != null) {
                        telemetry.addData("# Object Detected", updatedRecognitions.size());
                        // step through the list of recognitions and display boundary info.
                        int i = 0;
                        for (Recognition recognition : updatedRecognitions) {
                            telemetry.addData(String.format("label (%d)", i), recognition.getLabel());
                            telemetry.addData(String.format("  left,top (%d)", i), "%.03f , %.03f",
                                    recognition.getLeft(), recognition.getTop());
                            telemetry.addData(String.format("  right,bottom (%d)", i), "%.03f , %.03f",
                                    recognition.getRight(), recognition.getBottom());


                            telemetry.addData(String.format("  ImageWidth, ImageHeight (%d)", i), "%d , %d",
                                    recognition.getImageWidth(), recognition.getImageHeight());
                            telemetry.addData(String.format("  width, height (%d)", i), "%.03f , %.03f",
                                    recognition.getWidth(), recognition.getHeight());
                            i++;

                        }


                        telemetry.update();
                    }

                }

            }
        }
    }

    /**
     * Initialize the Vuforia localization engine.
     */
    private void initVuforia() {
        /*
         * Configure Vuforia by creating a Parameter object, and passing it to the Vuforia engine.
         */
        VuforiaLocalizer.Parameters parameters = new VuforiaLocalizer.Parameters();

        parameters.vuforiaLicenseKey = VUFORIA_KEY;
        parameters.cameraName = hardwareMap.get(WebcamName.class, "Webcam 1");

        //  Instantiate the Vuforia engine
        vuforia = ClassFactory.getInstance().createVuforia(parameters);

        // Loading trackables is not necessary for the TensorFlow Object Detection engine.
    }

    /**
     * Initialize the TensorFlow Object Detection engine.
     */
    private void initTfod() {
        int tfodMonitorViewId = hardwareMap.appContext.getResources().getIdentifier(
                "tfodMonitorViewId", "id", hardwareMap.appContext.getPackageName());
        TFObjectDetector.Parameters tfodParameters = new TFObjectDetector.Parameters(tfodMonitorViewId);
        tfodParameters.minResultConfidence = 0.8f;
        tfodParameters.isModelTensorFlow2 = true;
        tfodParameters.inputSize = 320;
        tfod = ClassFactory.getInstance().createTFObjectDetector(tfodParameters, vuforia);
        tfod.loadModelFromAsset(TFOD_MODEL_ASSET, LABELS);
    }
}