Documentation
Control Intake Using The OpenML
For Low Quality Camera Machine Learning Model (low.tflite) (Will be added support
for high.tflite today)
package org.firstinspires.ftc.teamcode.drive.opmodes;
import android.content.res.AssetFileDescriptor;
import android.content.res.AssetManager;
import com.qualcomm.robotcore.eventloop.opmode.LinearOpMode;
import com.qualcomm.robotcore.eventloop.opmode.TeleOp;
import org.firstinspires.ftc.robotcore.external.hardware.camera.WebcamName;
import org.firstinspires.ftc.robotcore.internal.camera.calibration.CameraCalibration;
import org.firstinspires.ftc.vision.VisionPortal;
import org.firstinspires.ftc.vision.VisionProcessor;
import org.opencv.core.Core;
import org.opencv.core.CvType;
import org.opencv.core.Mat;
import org.opencv.imgproc.Imgproc;
import org.tensorflow.lite.Interpreter;
import android.graphics.Canvas;
import android.util.Size;
import java.io.FileInputStream;
import java.io.IOException;
import java.nio.ByteBuffer;
import java.nio.ByteOrder;
import java.nio.channels.FileChannel;
import java.util.ArrayList;
import java.util.List;
@TeleOp(name = "AI Vision", group = "AITesting")
public class AIVision extends LinearOpMode {
// Configuration
private static final String MODEL_FILE = "best_float32.tflite";
private static final int MODEL_SIZE = 320;
private static final float CONF_THRESHOLD = 0.6f;
private static final float IOU_THRESHOLD = 0.5f;
IntakeControl intake_control; // Intake Control Object
private VisionPortal visionPortal;
@Override
public void runOpMode() {
YoloProcessor frameProcessor = new YoloProcessor(hardwareMap.appContext.getAssets());
intake_control = new IntakeControl(hardwareMap, Gamepad1);
visionPortal = new VisionPortal.Builder()
.setCamera(hardwareMap.get(WebcamName.class, "AICam"))
.setStreamFormat(VisionPortal.StreamFormat.YUY2)
.setCameraResolution(new Size(320, 240))
.addProcessor(frameProcessor)
.build();
waitForStart();
try {
while (opModeIsActive()) {
List detections = frameProcessor.getLatestDetections();
telemetry.addData("Detections", detections.size());
int bestConfidence = -1;
double confCounter=0;
for(int i=0; i 0){
Detection d = detections.get(bestConfidence);
double angle = Math.toDegrees(Math.atan(d.height/d.width));
double x = 6.4131;
double y = 0.7223;
double final_angle = (angle - x)/y;
//example control of intake
intake_control.setIntakePosition(final_angle); // final_ angle is in degrees and will be managed in the IntakeControl class to give the proper position | Example of that is in PYTHON testing code|
telemetry.addData(String.format("Obj %d", bestConfidence),
"%s %.1f%% | W: %.1f H: %.1f Angle: %.1f",
d.className(),
d.confidence * 100,
d.width,
d.height,
final_angle);
}
//!!! This one is to show all the detections. The one from above is to get only the detection with the highest confidence out of all.
/*for (int i = 0; i < detections.size(); i++) {
Detection d = detections.get(i);
telemetry.addData(String.format("Obj %d", i),
"%s %.1f%% | W: %.1f H: %.1f",
d.className(),
d.confidence * 100,
d.width,
d.height);
}*/
telemetry.update();
}
} finally {
visionPortal.close();
frameProcessor.close();
}
}
static class YoloProcessor implements VisionProcessor {
private final Interpreter tflite;
private final Object syncLock = new Object();
private List detections = new ArrayList<>();
private final float[][][] outputBuffer = new float[1][7][2100];
private final ByteBuffer inputBuffer;
private final Mat resizedMat = new Mat();
private final Mat rgbMat = new Mat();
public YoloProcessor(AssetManager assets) {
try {
Interpreter.Options options = new Interpreter.Options();
options.setNumThreads(4);
options.setUseXNNPACK(true);
tflite = new Interpreter(loadModelFile(assets), options);
inputBuffer = ByteBuffer.allocateDirect(MODEL_SIZE * MODEL_SIZE * 3 * 4);
inputBuffer.order(ByteOrder.nativeOrder());
} catch (IOException e) {
throw new RuntimeException("Model loading failed", e);
}
}
private ByteBuffer loadModelFile(AssetManager assets) throws IOException {
try (AssetFileDescriptor afd = assets.openFd(MODEL_FILE)) {
try (FileInputStream fis = new FileInputStream(afd.getFileDescriptor())) {
FileChannel channel = fis.getChannel();
return channel.map(FileChannel.MapMode.READ_ONLY, afd.getStartOffset(), afd.getDeclaredLength());
}
}
}
@Override
public void init(int width, int height, CameraCalibration calibration) {}
@Override
public Object processFrame(Mat frame, long captureTimeNanos) {
Mat rotated = new Mat();
Core.rotate(frame, rotated, Core.ROTATE_90_CLOCKWISE);
Imgproc.resize(rotated , resizedMat, new org.opencv.core.Size(MODEL_SIZE, MODEL_SIZE));
Imgproc.cvtColor(resizedMat, rgbMat, Imgproc.COLOR_BGR2RGB);
rgbMat.convertTo(rgbMat, CvType.CV_32FC3, 1.0 / 255.0);
float[] floatBuffer = new float[MODEL_SIZE * MODEL_SIZE * 3];
rgbMat.get(0, 0, floatBuffer);
inputBuffer.rewind();
inputBuffer.asFloatBuffer().put(floatBuffer);
tflite.run(inputBuffer, outputBuffer);
List newDetections = processOutput(frame.width(), frame.height());
synchronized (syncLock) {
detections = newDetections;
}
return null;
}
private List processOutput(int frameWidth, int frameHeight) {
List rawDetections = new ArrayList<>();
final float[] xCenterArray = outputBuffer[0][0];
final float[] yCenterArray = outputBuffer[0][1];
final float[] widthArray = outputBuffer[0][2];
final float[] heightArray = outputBuffer[0][3];
final float[] class0Array = outputBuffer[0][4];
final float[] class1Array = outputBuffer[0][5];
final float[] class2Array = outputBuffer[0][6];
for (int j = 0; j < 2100; j++) {
float maxScore = Math.max(class0Array[j], Math.max(class1Array[j], class2Array[j]));
if (maxScore < CONF_THRESHOLD) continue;
int classId = 0;
if (maxScore == class1Array[j]) classId = 1;
else if (maxScore == class2Array[j]) classId = 2;
rawDetections.add(new Detection(
xCenterArray[j] * frameWidth,
yCenterArray[j] * frameHeight,
widthArray[j] * frameWidth,
heightArray[j] * frameHeight,
maxScore,
classId
));
}
return nms(rawDetections);
}
private List nms(List detections) {
List results = new ArrayList<>(10);
detections.sort((d1, d2) -> Float.compare(d2.confidence, d1.confidence));
while (!detections.isEmpty()) {
Detection best = detections.remove(0);
results.add(best);
detections.removeIf(d -> iou(best, d) > IOU_THRESHOLD);
}
return results;
}
private float iou(Detection a, Detection b) {
float intersectionLeft = Math.max(a.x1, b.x1);
float intersectionTop = Math.max(a.y1, b.y1);
float intersectionRight = Math.min(a.x2, b.x2);
float intersectionBottom = Math.min(a.y2, b.y2);
if (intersectionRight < intersectionLeft || intersectionBottom < intersectionTop)
return 0.0f;
float intersectionArea = (intersectionRight - intersectionLeft) * (intersectionBottom - intersectionTop);
float areaA = a.width * a.height;
float areaB = b.width * b.height;
return intersectionArea / (areaA + areaB - intersectionArea);
}
@Override
public void onDrawFrame(Canvas canvas, int width, int height, float scale, float density, Object tag) {}
public List getLatestDetections() {
synchronized (syncLock) {
return new ArrayList<>(detections);
}
}
public void close() {
tflite.close();
resizedMat.release();
rgbMat.release();
}
}
static class Detection {
final float x1, y1, x2, y2;
final float confidence;
final int classId;
public final float width;
public final float height;
public Detection(float cx, float cy, float w, float h, float conf, int cls) {
width = w;
height = h;
x1 = cx - width/2;
y1 = cy - height/2;
x2 = cx + width/2;
y2 = cy + height/2;
confidence = conf;
classId = cls;
}
String className() {
switch (classId) {
case 0: return "Yellow";
case 1: return "Blue";
case 2: return "Red";
default: return "Unknown";
}
}
}
}
This code implements an AI processing system for object detection
using a YOLOv8 model in an FTC competition.
Setup
2D Sample Detection
3D Sample Detection
Training ML
Examples