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

import android.util.Size;
import com.qualcomm.robotcore.eventloop.opmode.Disabled;
import com.qualcomm.robotcore.eventloop.opmode.LinearOpMode;
import com.qualcomm.robotcore.eventloop.opmode.TeleOp;
import org.firstinspires.ftc.robotcore.external.hardware.camera.BuiltinCameraDirection;
import org.firstinspires.ftc.robotcore.external.hardware.camera.WebcamName;
import org.firstinspires.ftc.robotcore.external.tfod.Recognition;
import org.firstinspires.ftc.vision.VisionPortal;
import org.firstinspires.ftc.vision.tfod.TfodProcessor;

import java.util.List;

/*
 * This OpMode illustrates the basics of TensorFlow Object Detection,
 * including Java Builder structures for specifying Vision parameters.
 *
 * 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.
 */
@TeleOp(name = "Concept: TensorFlow Object Detection", group = "Concept")
@Disabled
public class ConceptTensorFlowObjectDetection extends LinearOpMode {

    private static final boolean USE_WEBCAM = true;  // true for webcam, false for phone camera

    // TFOD_MODEL_ASSET points to a model file stored in the project Asset location,
    // this is only used for Android Studio when using models in Assets.
    private static final String TFOD_MODEL_ASSET = "MyModelStoredAsAsset.tflite";
    // TFOD_MODEL_FILE points to a model file stored onboard the Robot Controller's storage,
    // this is used when uploading models directly to the RC using the model upload interface.
    private static final String TFOD_MODEL_FILE = "/sdcard/FIRST/tflitemodels/myCustomModel.tflite";
    // Define the labels recognized in the model for TFOD (must be in training order!)
    private static final String[] LABELS = {
       "Pixel",
    };

    /**
     * The variable to store our instance of the TensorFlow Object Detection processor.
     */
    private TfodProcessor tfod;

    /**
     * The variable to store our instance of the vision portal.
     */
    private VisionPortal visionPortal;

    @Override
    public void runOpMode() {

        initTfod();

        // Wait for the DS start button to be touched.
        telemetry.addData("DS preview on/off", "3 dots, Camera Stream");
        telemetry.addData(">", "Touch Play to start OpMode");
        telemetry.update();
        waitForStart();

        if (opModeIsActive()) {
            while (opModeIsActive()) {

                telemetryTfod();

                // Push telemetry to the Driver Station.
                telemetry.update();

                // Save CPU resources; can resume streaming when needed.
                if (gamepad1.dpad_down) {
                    visionPortal.stopStreaming();
                } else if (gamepad1.dpad_up) {
                    visionPortal.resumeStreaming();
                }

                // Share the CPU.
                sleep(20);
            }
        }

        // Save more CPU resources when camera is no longer needed.
        visionPortal.close();

    }   // end runOpMode()

    /**
     * Initialize the TensorFlow Object Detection processor.
     */
    private void initTfod() {

        // Create the TensorFlow processor by using a builder.
        tfod = new TfodProcessor.Builder()

            // With the following lines commented out, the default TfodProcessor Builder
            // will load the default model for the season. To define a custom model to load, 
            // choose one of the following:
            //   Use setModelAssetName() if the custom TF Model is built in as an asset (AS only).
            //   Use setModelFileName() if you have downloaded a custom team model to the Robot Controller.
            //.setModelAssetName(TFOD_MODEL_ASSET)
            //.setModelFileName(TFOD_MODEL_FILE)

            // The following default settings are available to un-comment and edit as needed to 
            // set parameters for custom models.
            //.setModelLabels(LABELS)
            //.setIsModelTensorFlow2(true)
            //.setIsModelQuantized(true)
            //.setModelInputSize(300)
            //.setModelAspectRatio(16.0 / 9.0)

            .build();

        // Create the vision portal by using a builder.
        VisionPortal.Builder builder = new VisionPortal.Builder();

        // Set the camera (webcam vs. built-in RC phone camera).
        if (USE_WEBCAM) {
            builder.setCamera(hardwareMap.get(WebcamName.class, "Webcam 1"));
        } else {
            builder.setCamera(BuiltinCameraDirection.BACK);
        }

        // Choose a camera resolution. Not all cameras support all resolutions.
        //builder.setCameraResolution(new Size(640, 480));

        // Enable the RC preview (LiveView).  Set "false" to omit camera monitoring.
        //builder.enableLiveView(true);

        // Set the stream format; MJPEG uses less bandwidth than default YUY2.
        //builder.setStreamFormat(VisionPortal.StreamFormat.YUY2);

        // Choose whether or not LiveView stops if no processors are enabled.
        // If set "true", monitor shows solid orange screen if no processors enabled.
        // If set "false", monitor shows camera view without annotations.
        //builder.setAutoStopLiveView(false);

        // Set and enable the processor.
        builder.addProcessor(tfod);

        // Build the Vision Portal, using the above settings.
        visionPortal = builder.build();

        // Set confidence threshold for TFOD recognitions, at any time.
        //tfod.setMinResultConfidence(0.75f);

        // Disable or re-enable the TFOD processor at any time.
        //visionPortal.setProcessorEnabled(tfod, true);

    }   // end method initTfod()

    /**
     * Add telemetry about TensorFlow Object Detection (TFOD) recognitions.
     */
    private void telemetryTfod() {

        List<Recognition> currentRecognitions = tfod.getRecognitions();
        telemetry.addData("# Objects Detected", currentRecognitions.size());

        // Step through the list of recognitions and display info for each one.
        for (Recognition recognition : currentRecognitions) {
            double x = (recognition.getLeft() + recognition.getRight()) / 2 ;
            double y = (recognition.getTop()  + recognition.getBottom()) / 2 ;

            telemetry.addData(""," ");
            telemetry.addData("Image", "%s (%.0f %% Conf.)", recognition.getLabel(), recognition.getConfidence() * 100);
            telemetry.addData("- Position", "%.0f / %.0f", x, y);
            telemetry.addData("- Size", "%.0f x %.0f", recognition.getWidth(), recognition.getHeight());
        }   // end for() loop

    }   // end method telemetryTfod()

}   // end class
