ConceptTensorFlowObjectDetectionWebcam.java

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package org.firstinspires.ftc.robotcontroller.external.samples;

import com.qualcomm.robotcore.eventloop.opmode.Disabled;
import com.qualcomm.robotcore.eventloop.opmode.LinearOpMode;
import com.qualcomm.robotcore.eventloop.opmode.TeleOp;
import java.util.List;
import org.firstinspires.ftc.robotcore.external.ClassFactory;
import org.firstinspires.ftc.robotcore.external.hardware.camera.WebcamName;
import org.firstinspires.ftc.robotcore.external.navigation.VuforiaLocalizer;
import org.firstinspires.ftc.robotcore.external.tfod.TFObjectDetector;
import org.firstinspires.ftc.robotcore.external.tfod.Recognition;

/**
 * This 2020-2021 OpMode illustrates the basics of using the TensorFlow Object Detection API to
 * determine the position of the Freight Frenzy game elements.
 *
 * 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.
 *
 * IMPORTANT: In order to use this OpMode, you need to obtain your own Vuforia license key as
 * is explained below.
 */
@TeleOp(name = "Concept: TensorFlow Object Detection Webcam", group = "Concept")
@Disabled
public class ConceptTensorFlowObjectDetectionWebcam extends LinearOpMode {
  /* 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 =
            " -- YOUR NEW VUFORIA KEY GOES HERE  --- ";

    /**
     * {@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());
                        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);
    }
}