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ConceptTensorFlowObjectDetection.java
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ConceptTensorFlowObjectDetection.java
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/* 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.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 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
/**
* 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()
// Use setModelAssetName() if the TF Model is built in as an asset.
// Use setModelFileName() if you have downloaded a custom team model to the Robot Controller.
//.setModelAssetName(TFOD_MODEL_ASSET)
//.setModelFileName(TFOD_MODEL_FILE)
//.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.enableCameraMonitoring(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