Attempt at webcam currently broken + fixed minor issues based on FIRS…

…T-Tech-Challenge/FtcRobotController#383

FtcRobotController/src/main/java/org/firstinspires/ftc/robotcontroller/internal/FtcOpModeRegister.java

@@ -34,8 +34,6 @@ OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
 import com.qualcomm.robotcore.eventloop.opmode.OpModeManager;
 import com.qualcomm.robotcore.eventloop.opmode.OpModeRegister;
 
-import org.firstinspires.ftc.robotcontroller.external.samples.ConceptNullOp;
-
 /**
  * {@link FtcOpModeRegister} is responsible for registering opmodes for use in an FTC game.
  * @see #register(OpModeManager)
@@ -49,7 +47,7 @@ public class FtcOpModeRegister implements OpModeRegister {
      * There are two mechanisms by which an OpMode may be registered.
      *
      *  1) The preferred method is by means of class annotations in the OpMode itself.
-     *  See, for example the class annotations in {@link ConceptNullOp}.
+     *  See, for example the class annotations in ConceptNullOp.
      *
      *  2) The other, retired,  method is to modify this {@link #register(OpModeManager)}
      *  method to include explicit calls to OpModeManager.register().

MeepMeepTesting/src/main/java/com/example/meepmeeptesting/MeepMeepTesting.java

@@ -22,6 +22,8 @@ public static void main(String[] args) {
                                 .turn(Math.toRadians(90))
                                 .forward(30)
                                 .turn(Math.toRadians(90))
+                                .splineToSplineHeading(new Pose2d(-59, -48, Math.toRadians(45)), 60)
+                                .strafeRight(5)
                                 .build()
                 );
 

TeamCode/src/main/java/org/firstinspires/ftc/teamcode/ConceptTensorFlowObjectDetectionWebcam.java

@@ -0,0 +1,261 @@
+/* Copyright (c) 2019 FIRST. All rights reserved.
+ *
+ * Redistribution and use in source and binary forms, with or without modification,
+ * are permitted (subject to the limitations in the disclaimer below) provided that
+ * the following conditions are met:
+ *
+ * Redistributions of source code must retain the above copyright notice, this list
+ * of conditions and the following disclaimer.
+ *
+ * Redistributions in binary form must reproduce the above copyright notice, this
+ * list of conditions and the following disclaimer in the documentation and/or
+ * other materials provided with the distribution.
+ *
+ * Neither the name of FIRST nor the names of its contributors may be used to endorse or
+ * promote products derived from this software without specific prior written permission.
+ *
+ * NO EXPRESS OR IMPLIED LICENSES TO ANY PARTY'S PATENT RIGHTS ARE GRANTED BY THIS
+ * LICENSE. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+ * "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO,
+ * THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
+ * ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE
+ * FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+ * DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+ * SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+ * CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+ * OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+ * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+ */
+
+package org.firstinspires.ftc.teamcode;
+
+import com.acmerobotics.dashboard.FtcDashboard;
+import com.acmerobotics.dashboard.telemetry.MultipleTelemetry;
+import com.qualcomm.robotcore.eventloop.opmode.LinearOpMode;
+import com.qualcomm.robotcore.eventloop.opmode.TeleOp;
+import com.qualcomm.robotcore.util.RobotLog;
+
+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.Recognition;
+import org.firstinspires.ftc.robotcore.external.tfod.TFObjectDetector;
+
+import java.io.BufferedReader;
+import java.io.FileReader;
+import java.util.ArrayList;
+import java.util.List;
+
+/**
+ * This 2020-2021 OpMode illustrates the basics of using the TensorFlow Object Detection API to
+ * determine the position of the Ultimate Goal 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 = "TFOD Everyday Objects", group = "Concept")
+//@Disabled
+public class ConceptTensorFlowObjectDetectionWebcam extends LinearOpMode {
+    private static final String TFOD_MODEL_FILE = "/sdcard/FIRST/tflitemodels/detect.tflite";
+    private static final String TFOD_MODEL_LABELS = "/sdcard/FIRST/tflitemodels/labelmap.txt";
+    private String[] labels;
+
+    /*
+     * 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 =
+            "AYIy+wf/////AAABmTogX7sfc00thsy7eGmWjM0t4M0Us8RBEMt1Iirw/kewa0thLqGGvBQ6ywDiCn6A6FxGh8OveZuemqV17zZezDrUWcQ2CNl2hUo0HUm5Lq4X9UPvlqLd7CTp7yWrRkJS7Wz3V2Balxyuq06cRnWDv/IegCK88mlrtMiC677QXo4k5SfBlhKJtmUCF2xCxeudF6tUvsigoYnfW5J924saoNiQJKagpfAxoTey8o2/AaC8Gy3UYaQjs3ye29LpELDyyxTGAWYRgsKWXcpP7jQtbsQMqslY5UUqUIBcI0BcnYZ3iZkgDPf7pfXhs1zyxAnoE+GKPPDg/eOAn7G6Rd+JTasXb+tkhT7v73DcAzJxh0y1";
+
+    /**
+     * {@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() {
+        // read the label map text files.
+        readLabels();
+
+        // The TFObjectDetector uses the camera frames from the VuforiaLocalizer, so we create that
+        // first.
+        /*
+         * Configure Vuforia by creating a Parameter object, and passing it to the Vuforia engine.
+         */
+        VuforiaLocalizer.Parameters parameters = new VuforiaLocalizer.Parameters(R.id.cameraMonitorViewId);
+
+        parameters.vuforiaLicenseKey = VUFORIA_KEY;
+        //Camera Webcam =
+        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.
+        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 1.78 or 16/9).
+
+            // Uncomment the following line if you want to adjust the magnification and/or the aspect ratio of the input images.
+            //tfod.setZoom(2.5, 1.78);
+        }
+        FtcDashboard dashboard = FtcDashboard.getInstance();
+        dashboard.startCameraStream(tfod, 0);
+        telemetry = new MultipleTelemetry(telemetry, dashboard.getTelemetry());
+
+        /** Wait for the game to begin */
+        telemetry.addData(">", "Press Play to start op mode");
+        telemetry.update();
+        waitForStart();
+        int detectedLayer = 0; // 1 is lowest, 2 is middle, 3 is highest
+        if (opModeIsActive() && tfod != null) {
+            while (opModeIsActive()) { // TODO: add "& detectedLayer == 0" after we have the location of the detections set
+                List<Recognition> updatedRecognitions = tfod.getUpdatedRecognitions();
+                if (updatedRecognitions != null) {
+
+
+                    int i = 0;
+                    for (Recognition recognition : updatedRecognitions) {
+                        float leftEdge = recognition.getLeft();
+                        if (leftEdge < 10 && leftEdge > 1) { // TODO: edit to be accurate
+                            detectedLayer = 1;
+                        } else if (leftEdge > 10 && leftEdge < 20) {
+                            detectedLayer = 2;
+                        } else if (leftEdge > 20 && leftEdge < 30) {
+                            detectedLayer = 3;
+
+                        }
+                        telemetry.addData("Detected Layer", detectedLayer);
+                        // step through the list of recognitions and display boundary info
+                        telemetry.addData("# Object Detected", updatedRecognitions.size());
+                        telemetry.addData(String.format("label (%d)", i), recognition.getLabel());
+                        telemetry.addData(String.format("  left,top (%d)", i), "%.03f , %.03f",
+                                leftEdge, recognition.getTop());
+                        telemetry.addData(String.format("  right,bottom (%d)", i), "%.03f , %.03f",
+                                recognition.getRight(), recognition.getBottom());
+                        telemetry.update();
+
+                    }
+                } else {
+                    telemetry.addData("Error", "TFOD has initialized but no objects have been detected");
+                    telemetry.update();
+                }
+            }
+
+
+        }
+        // TODO: roadrunner paths
+
+
+
+        if (tfod != null) {
+            tfod.shutdown();
+        }
+    }
+
+
+    /**
+     * 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 = 0.6f;
+        tfod = ClassFactory.getInstance().createTFObjectDetector(tfodParameters, vuforia);
+        if(labels != null) {
+            tfod.loadModelFromFile(TFOD_MODEL_FILE, labels);
+        }
+    }
+
+    /**
+     * Read the labels for the object detection model from a file.
+     */
+    private void readLabels() {
+        ArrayList<String> labelList = new ArrayList<>();
+
+        // try to read in the the labels.
+        try (BufferedReader br = new BufferedReader(new FileReader(TFOD_MODEL_LABELS))) {
+            int index = 0;
+            while (br.ready()) {
+                /* skip the first row of the labelmap.txt file.
+                 * if you look at the TFOD Android example project (https://github.com/tensorflow/examples/tree/master/lite/examples/object_detection/android)
+                 * you will see that the labels for the inference model are actually extracted (as metadata) from the .tflite model file
+                 * instead of from the labelmap.txt file. if you build and run that example project, you'll see that
+                 * the label list begins with the label "person" and does not include the first line of the labelmap.txt file ("???").
+                 * i suspect that the first line of the labelmap.txt file might be reserved for some future metadata schema
+                 * (or that the generated label map file is incorrect).
+                 * for now, skip the first line of the label map text file so that your label list is in sync with the embedded label list in the .tflite model.
+                 */
+                if(index == 0) {
+                    // skip first line.
+                    br.readLine();
+                } else {
+                    labelList.add(br.readLine());
+                }
+                index++;
+            }
+        } catch (Exception e) {
+            telemetry.addData("Exception", e.getLocalizedMessage());
+            telemetry.update();
+        }
+
+        if (labelList.size() > 0) {
+            labels = getStringArray(labelList);
+            RobotLog.vv("readLabels()", "%d labels read.", labels.length);
+            for (String label : labels) {
+                RobotLog.vv("readLabels()", " " + label);
+            }
+        } else {
+            RobotLog.vv("readLabels()", "No labels read!");
+        }
+    }
+
+    // Function to convert ArrayList<String> to String[]
+    private String[] getStringArray(ArrayList<String> arr)
+    {
+        // declaration and initialize String Array
+        String[] str = new String[arr.size()];
+
+        // Convert ArrayList to object array
+        Object[] objArr = arr.toArray();
+
+        // Iterating and converting to String
+        int i = 0;
+        for (Object obj : objArr) {
+            str[i++] = (String)obj;
+        }
+
+        return str;
+    }
+}