/* * This example OpMode shows how existing webcam controls can affect * TensorFlow Object Detection (TFOD) of FTC Freight Frenzy game elements. * It's a companion to the FTC wiki tutorial on Webcam Controls. * * Put the Driver Station in Landscape Mode for this telemetry. * * The FTC SDK 7.0 includes up to 7 ways of controlling the preview image, * depending on webcam capability. This OpMode uses 2 of those controls, * Exposure and Gain, available on most webcams and offering good * potential for affecting TFOD recognition. * * This OpMode simply adds ExposureControl and GainControl methods to the FTC * sample called "ConceptTensorFlowObjectDetectionWebcam.java". Here, you can * use a gamepad to directly change the preview image and observe TFOD results. * * Teams can use this to seek better recognition results from their existing * TFOD model -- whether the basic version in the 7.0 release, or their own * custom model created with the FTC Machine Learning toolchain. * * Exposure, gain and other CameraControl values could be pre-programmed in * team autonomous OpModes. It's also possible to manually enter such values * before a match begins, based on anticipated lighting, starting position and * other game-time factors. * * Add your own Vuforia key, where shown below. * * Questions, comments and corrections to westsiderobotics@verizon.net * * from v04 11/11/21 */ package org.firstinspires.ftc.teamcode; import org.firstinspires.ftc.robotcore.external.hardware.camera.controls.ExposureControl; import org.firstinspires.ftc.robotcore.external.hardware.camera.controls.GainControl; import java.util.concurrent.TimeUnit; import com.qualcomm.robotcore.eventloop.opmode.TeleOp; import com.qualcomm.robotcore.eventloop.opmode.LinearOpMode; import org.firstinspires.ftc.robotcore.external.navigation.VuforiaLocalizer; import org.firstinspires.ftc.robotcore.external.tfod.TFObjectDetector; import org.firstinspires.ftc.robotcore.external.tfod.Recognition; import org.firstinspires.ftc.robotcore.external.hardware.camera.WebcamName; import org.firstinspires.ftc.robotcore.external.ClassFactory; import java.util.List; @TeleOp(name = "W TFOD Webcam Exposure & Gain v04", group = "Webcam Controls") public class W_TFOD_WebcamExpGain_v04 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 = //" INSERT YOUR VUFORIA KEY 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; // *** ADD WEBCAM CONTROLS -- SECTION START *** ExposureControl myExposureControl; // declare exposure control object long minExp; long maxExp; long curExp; // exposure is duration, in time units specified GainControl myGainControl; // declare gain control object int minGain; int maxGain; int curGain; boolean wasSetGainSuccessful; // returned from setGain() boolean isAEPriorityOn = false; // *** ADD WEBCAM CONTROLS -- SECTION END *** @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(1.0, 16.0/9.0); // modified for testing Exposure & Gain // tfod.setZoom(2.5, 16.0/9.0); // original settings in Concept OpMode } // *** ADD WEBCAM CONTROLS -- SECTION START *** // Assign the exposure and gain control objects, to use their methods. myExposureControl = vuforia.getCamera().getControl(ExposureControl.class); myGainControl = vuforia.getCamera().getControl(GainControl.class); // get webcam exposure limits minExp = myExposureControl.getMinExposure(TimeUnit.MILLISECONDS); maxExp = myExposureControl.getMaxExposure(TimeUnit.MILLISECONDS); // get webcam gain limits minGain = myGainControl.getMinGain(); maxGain = myGainControl.getMaxGain(); // Change mode to Manual, in order to control directly. // A non-default setting may persist in the camera, until changed again. myExposureControl.setMode(ExposureControl.Mode.Manual); // Retrieve from webcam its current exposure and gain values curExp = myExposureControl.getExposure(TimeUnit.MILLISECONDS); curGain = myGainControl.getGain(); // display exposure mode and starting values to user telemetry.addLine("\nTouch Start arrow to control webcam Exposure and Gain"); telemetry.addData("\nCurrent exposure mode", myExposureControl.getMode()); telemetry.addData("Current exposure value", curExp); telemetry.addData("Current gain value", curGain); telemetry.update(); // *** ADD WEBCAM CONTROLS -- SECTION END *** waitForStart(); if (opModeIsActive()) { while (opModeIsActive()) { // *** ADD WEBCAM CONTROLS -- SECTION START *** // Driver Station in Landscape Mode for this telemetry. telemetry.addLine("Exposure: left stick Y; Gain: right stick Y"); telemetry.addData("Exposure", "Min:%d, Max:%d, Current:%d", minExp, maxExp, curExp); telemetry.addData("Gain", "Min:%d, Max:%d, Current:%d", minGain, maxGain, curGain); telemetry.addLine("\nAutoExposure Priority: green A ON; red B OFF"); telemetry.addData("AE Priority on?", isAEPriorityOn); // *** ADD WEBCAM CONTROLS -- SECTION END *** if (tfod != null) { // getUpdatedRecognitions() will return null if no new information is available since // the last time that call was made. List updatedRecognitions = tfod.getUpdatedRecognitions(); if (updatedRecognitions != null) { telemetry.addData("\n# 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++; } // end for() loop } // end if (updatedRecognitions) } // end if (tfod) telemetry.update(); // *** ADD WEBCAM CONTROLS -- SECTION START *** // manually adjust the webcam exposure & gain variables float changeExp = -gamepad1.left_stick_y; float changeGain = -gamepad1.right_stick_y; int changeExpInt = (int) (changeExp*2); // was *5 int changeGainInt = (int) (changeGain*2); // was *5 curExp += changeExpInt; curGain += changeGainInt; if (gamepad1.a) { // AE Priority ON with green A myExposureControl.setAePriority(true); isAEPriorityOn = true; } else if (gamepad1.b) { // AE Priority OFF with red B myExposureControl.setAePriority(false); isAEPriorityOn = false; } // ensure inputs are within webcam limits, if provided curExp = Math.max(curExp, minExp); curExp = Math.min(curExp, maxExp); curGain = Math.max(curGain, minGain); curGain = Math.min(curGain, maxGain); // update the webcam's settings myExposureControl.setExposure(curExp, TimeUnit.MILLISECONDS); wasSetGainSuccessful = myGainControl.setGain(curGain); sleep(50); // slow down the main while() loop // *** ADD WEBCAM CONTROLS -- SECTION END *** } // end main while() loop } // end if opModeIsActive() } // end runOpMode() /** * 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. } // end method initVuforia() /** * 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); } // end method initTfod() } //end class