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Fixed Building Issues and Implemented Custom Object Detection
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FTC 20177 High Velocity Robotics
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Dec 6, 2023
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TeamCode/src/main/java/org/firstinspires/ftc/teamcode/opencv_auton/Red.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. | ||
*/ | ||
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package org.firstinspires.ftc.teamcode.opencv_auton; | ||
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import com.qualcomm.robotcore.eventloop.opmode.Autonomous; | ||
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; | ||
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import java.util.List; | ||
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/* | ||
* 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. | ||
*/ | ||
@Autonomous(name = "Red Cat Detection", group = "Concept") | ||
public class Red extends LinearOpMode { | ||
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private static final boolean USE_WEBCAM = true; // true for webcam, false for phone camera | ||
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// 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 = "model_20231204_155444.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/model_20231204_135821.tflite"; | ||
// Define the labels recognized in the model for TFOD (must be in training order!) | ||
private static final String[] LABELS = { | ||
"Blue Cat", | ||
}; | ||
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/** | ||
* The variable to store our instance of the TensorFlow Object Detection processor. | ||
*/ | ||
private TfodProcessor tfod; | ||
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/** | ||
* The variable to store our instance of the vision portal. | ||
*/ | ||
private VisionPortal visionPortal; | ||
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@Override | ||
public void runOpMode() { | ||
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initTfod(); | ||
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// 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(); | ||
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if (opModeIsActive()) { | ||
while (opModeIsActive()) { | ||
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telemetryTfod(); | ||
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// Push telemetry to the Driver Station. | ||
telemetry.update(); | ||
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// Save CPU resources; can resume streaming when needed. | ||
if (gamepad1.dpad_down) { | ||
visionPortal.stopStreaming(); | ||
} else if (gamepad1.dpad_up) { | ||
visionPortal.resumeStreaming(); | ||
} | ||
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// Share the CPU. | ||
sleep(20); | ||
} | ||
} | ||
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// Save more CPU resources when camera is no longer needed. | ||
visionPortal.close(); | ||
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} // end runOpMode() | ||
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/** | ||
* Initialize the TensorFlow Object Detection processor. | ||
*/ | ||
private void initTfod() { | ||
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// Create the TensorFlow processor by using a builder. | ||
tfod = new TfodProcessor.Builder() | ||
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// 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) | ||
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// 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) | ||
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.build(); | ||
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// Create the vision portal by using a builder. | ||
VisionPortal.Builder builder = new VisionPortal.Builder(); | ||
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// 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); | ||
} | ||
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// Choose a camera resolution. Not all cameras support all resolutions. | ||
//builder.setCameraResolution(new Size(640, 480)); | ||
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// Enable the RC preview (LiveView). Set "false" to omit camera monitoring. | ||
//builder.enableLiveView(true); | ||
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// Set the stream format; MJPEG uses less bandwidth than default YUY2. | ||
//builder.setStreamFormat(VisionPortal.StreamFormat.YUY2); | ||
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// 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); | ||
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// Set and enable the processor. | ||
builder.addProcessor(tfod); | ||
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// Build the Vision Portal, using the above settings. | ||
visionPortal = builder.build(); | ||
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// Set confidence threshold for TFOD recognitions, at any time. | ||
tfod.setMinResultConfidence(0.85f); | ||
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// Disable or re-enable the TFOD processor at any time. | ||
//visionPortal.setProcessorEnabled(tfod, true); | ||
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} // end method initTfod() | ||
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/** | ||
* Add telemetry about TensorFlow Object Detection (TFOD) recognitions. | ||
*/ | ||
private void telemetryTfod() { | ||
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List<Recognition> currentRecognitions = tfod.getRecognitions(); | ||
telemetry.addData("# Objects Detected", currentRecognitions.size()); | ||
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// 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 ; | ||
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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 | ||
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} // end method telemetryTfod() | ||
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} // end class |