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ObjDetect.java
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ObjDetect.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.teamcode;
import com.qualcomm.robotcore.eventloop.opmode.Autonomous;
import com.qualcomm.robotcore.eventloop.opmode.LinearOpMode;
import java.util.ArrayList;
import java.util.List;
import java.util.Locale;
import org.firstinspires.ftc.robotcore.external.ClassFactory;
import org.firstinspires.ftc.robotcore.external.hardware.camera.WebcamName;
import org.firstinspires.ftc.robotcore.external.navigation.AngleUnit;
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.internal.system.AppUtil;
/**
* This 2020-2021 OpMode illustrates the basics of using the TensorFlow Object Detection API to
* determine the position of the Freight Frenzy game elements.
* <p>
* 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.
* <p>
* IMPORTANT: In order to use this OpMode, you need to obtain your own Vuforia license key as
* is explained below.
*/
public class ObjDetect {
public static double RIGHT_BOUNDARY = 0.67;
public static double LEFT_BOUNDARY = 0.33;
public static double WIDTH_IGNORE = 1;
public static double HEIGHT_IGNORE = 1;
private final TFObjectDetector tfod;
private ProcessingParameters processingParameters;
public ObjDetect(TFObjectDetector tfod) {
this.tfod = tfod;
tfod.activate();
}
public ObjDetect(TFObjectDetector tfod, ProcessingParameters processingParameters) {
this(tfod);
this.processingParameters = processingParameters;
}
public List<RecognitionPercent> getRecognitions() {
List<Recognition> recognitions = tfod.getRecognitions();
if (recognitions == null) {
return null;
}
List<RecognitionPercent> percentRecognitions = new ArrayList<>();
for (Recognition recognition : recognitions) {
percentRecognitions.add(new RecognitionPercent(recognition));
}
return percentRecognitions;
}
public List<RecognitionPercent> getApprovedRecognitions() {
List<RecognitionPercent> recognitions = getRecognitions();
if (recognitions == null) {
return null;
}
List<RecognitionPercent> approvedRecognitions = new ArrayList<>();
for (RecognitionPercent recognition : recognitions) {
if (recognition.getWidth() < processingParameters.widthIgnore && recognition.getHeight() < processingParameters.heightIgnore) {
approvedRecognitions.add(recognition);
}
}
return approvedRecognitions;
}
public RecognitionPercent getAverageApprovedRecognitions() {
List<RecognitionPercent> recognitions = getApprovedRecognitions();
if (recognitions == null) {
return null;
}
int i = 0;
float left = 0, right = 0, top = 0, bottom = 0;
float confidence = 0;
double angle = 0;
for (RecognitionPercent recognition : recognitions) {
left += recognition.getLeft();
right += recognition.getRight();
top += recognition.getTop();
bottom += recognition.getBottom();
confidence += recognition.getConfidence();
angle += recognition.estimateAngleToObject(AngleUnit.RADIANS);
i++;
}
left /= i;
right /= i;
top /= i;
bottom /= i;
confidence /= i;
angle /= i;
return new RecognitionPercent(new RecognitionStore(left, right, top, bottom, confidence, 1, 1, angle, AngleUnit.RADIANS));
}
public int getPosition() {
RecognitionPercent recognition = getAverageApprovedRecognitions();
if (recognition == null)
return 0;
if (recognition.getX() < processingParameters.leftBoundary)
return 1;
else if (recognition.getX() < processingParameters.rightBoundary)
return 2;
else if (recognition.getX() > processingParameters.rightBoundary)
return 3;
else
return -1;
}
public static class ProcessingParameters {
public double rightBoundary = RIGHT_BOUNDARY;
public double leftBoundary = LEFT_BOUNDARY;
public double widthIgnore = WIDTH_IGNORE;
public double heightIgnore = HEIGHT_IGNORE;
}
public static class RecognitionPercent implements org.firstinspires.ftc.robotcore.external.tfod.Recognition {
private final Recognition recognition;
@Override
public String getLabel() {
return recognition.getLabel();
}
@Override
public float getConfidence() {
return recognition.getConfidence();
}
@Override
public float getLeft() {
return recognition.getLeft() / recognition.getImageWidth();
}
@Override
public float getRight() {
return recognition.getRight() / recognition.getImageWidth();
}
@Override
public float getTop() {
return recognition.getTop() / recognition.getImageHeight();
}
@Override
public float getBottom() {
return recognition.getBottom() / recognition.getImageHeight();
}
@Override
public float getWidth() {
return Math.abs(getRight() - getLeft());
}
@Override
public float getHeight() {
return Math.abs(getBottom() - getTop());
}
public float getX() {
return (getLeft() + getRight()) / 2;
}
public float getY() {
return (getTop() + getBottom()) / 2;
}
public Recognition getOriginalRecognition() {
return recognition;
}
@Override
public int getImageWidth() {
return 1;
}
@Override
public int getImageHeight() {
return 1;
}
@Override
public double estimateAngleToObject(AngleUnit angleUnit) {
return recognition.estimateAngleToObject(angleUnit);
}
public RecognitionPercent(Recognition recognition) {
this.recognition = recognition;
}
}
public static class RecognitionStore implements Recognition{
private final float left;
private final float right;
private final float top;
private final float bottom;
private final float confidence;
private final int imageWidth;
private final int imageHeight;
private final double angleInRadians;
@Override
public String getLabel() {
return null;
}
@Override
public float getConfidence() {
return confidence;
}
@Override
public float getLeft() {
return left;
}
@Override
public float getRight() {
return right;
}
@Override
public float getTop() {
return top;
}
@Override
public float getBottom() {
return bottom;
}
@Override
public float getWidth() {
return Math.abs(getRight() - getLeft());
}
@Override
public float getHeight() {
return Math.abs(getBottom() - getTop());
}
@Override
public int getImageWidth() {
return imageWidth;
}
@Override
public int getImageHeight() {
return imageHeight;
}
@Override
public double estimateAngleToObject(AngleUnit angleUnit) {
return angleUnit.fromRadians(angleInRadians);
}
public RecognitionStore(float left, float right, float top, float bottom, float confidence, int imageWidth, int imageHeight, double angle, AngleUnit angleUnit) {
this.left = left;
this.right = right;
this.top = top;
this.bottom = bottom;
this.confidence = confidence;
this.imageWidth = imageWidth;
this.imageHeight = imageHeight;
this.angleInRadians = angleUnit.toRadians(angle);
}
}
}