Java Code Examples for org.opencv.core.MatOfRect#toArray()
The following examples show how to use
org.opencv.core.MatOfRect#toArray() .
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Example 1
| Source File: MainActivity.java From MOAAP with MIT License | 7 votes |
|
@Override
public Mat onCameraFrame(CameraBridgeViewBase.CvCameraViewFrame inputFrame) {
//Rotating the input frame
Mat mGray = inputFrame.gray();
mRgba = inputFrame.rgba();
if (mIsFrontCamera)
{
Core.flip(mRgba, mRgba, 1);
Core.flip(mGray, mGray, 1);
}
//Detecting face in the frame
MatOfRect faces = new MatOfRect();
if(haarCascade != null)
{
haarCascade.detectMultiScale(mGray, faces, 1.1, 2, 2, new Size(200,200), new Size());
}
Rect[] facesArray = faces.toArray();
for (int i = 0; i < facesArray.length; i++)
Imgproc.rectangle(mRgba, facesArray[i].tl(), facesArray[i].br(), new Scalar(100), 3);
return mRgba;
}
Example 2
| Source File: DetectObject.java From classchecks with Apache License 2.0 | 7 votes |
|
/**
*
* @Title: detectMany
* @Description: 检测多人图片，返回检测的人脸区域对象
* @param mImgSRC
* @return
* MatOfRect
*/
public static MatOfRect detectMany(Mat mImgSRC) {
if(mImgSRC.empty()) {
LOG.info("检测多人图片检测时没有找到图片");
return null;
}
// 人脸检测器文件的所在路径的文件夹名称数组
String [] pathKey = {ServletContextHolder.getOpenCVHaarcascades(), "haarcascade_frontalface_alt.xml"};
CascadeClassifier cascade = new CascadeClassifier(FileUtils.buildFilePath(pathKey));
if(cascade.empty()) {
LOG.info("人脸检测级联加载器为null");
return null;
}
// 记录搜索到的人脸区域
MatOfRect mOfRect = new MatOfRect();
// 用于计算缩放比例
int scaledWidth = mImgSRC.width();
detectManyObject(mImgSRC, cascade, mOfRect, scaledWidth);
if(mOfRect.toArray().length <= 0) {
LOG.info("没有检测到人脸...");
return null;
}
return mOfRect;
}
Example 3
| Source File: FaceDetectionTest.java From opencv-fun with GNU Affero General Public License v3.0 | 6 votes |
|
public static void loop (CascadeClassifier classifier, Mat mat, ImgWindow window, VideoCapture video) {
video.read(mat);
System.out.println(mat);
if (!mat.empty()) {
MatOfRect rects = new MatOfRect();
long start = System.nanoTime();
classifier.detectMultiScale(mat, rects);
System.out.println((System.nanoTime()-start)/1000000000.0);
window.setImage(mat);
Graphics2D g = window.begin();
g.setColor(Color.RED);
for(Rect r: rects.toArray()) {
g.drawRect(r.x, r.y, r.width, r.height);
}
window.end();
}
}
Example 4
| Source File: VideoFaceTests.java From super-cloudops with Apache License 2.0 | 6 votes |
|
/**
* OpenCV-4.0.0 人脸识别
*
* @date: 2019年5月7日12:16:55
* @param image
* 待处理Mat图片(视频中的某一帧)
* @return 处理后的图片
* @throws IOException
*/
public static Mat getFace(Mat image) throws IOException {
// 1 读取OpenCV自带的人脸识别特征XML文件
File faceFile = new ClassPathResourcePatternResolver().getResource("opencv/data/haarcascade_frontalface_alt.xml")
.getFile();
CascadeClassifier facebook = new CascadeClassifier(faceFile.getAbsolutePath());
// 2 特征匹配类
MatOfRect face = new MatOfRect();
// 3 特征匹配
facebook.detectMultiScale(image, face);
Rect[] rects = face.toArray();
System.out.println("匹配到 " + rects.length + " 个人脸");
// 4 为每张识别到的人脸画一个圈
for (int i = 0; i < rects.length; i++) {
Imgproc.rectangle(image, new Point(rects[i].x, rects[i].y),
new Point(rects[i].x + rects[i].width, rects[i].y + rects[i].height), new Scalar(0, 255, 0));
Imgproc.putText(image, "Human", new Point(rects[i].x, rects[i].y), Imgproc.FONT_HERSHEY_SCRIPT_SIMPLEX, 1.0,
new Scalar(0, 255, 0), 1, Imgproc.LINE_AA, false);
// Mat dst=image.clone();
// Imgproc.resize(image, image, new Size(300,300));
}
return image;
}
Example 5
| Source File: MainActivity.java From MOAAP with MIT License | 6 votes |
|
void HOGDescriptor() {
Mat grayMat = new Mat();
Mat people = new Mat();
//Converting the image to grayscale
Imgproc.cvtColor(originalMat, grayMat, Imgproc.COLOR_BGR2GRAY);
HOGDescriptor hog = new HOGDescriptor();
hog.setSVMDetector(HOGDescriptor.getDefaultPeopleDetector());
MatOfRect faces = new MatOfRect();
MatOfDouble weights = new MatOfDouble();
hog.detectMultiScale(grayMat, faces, weights);
originalMat.copyTo(people);
//Draw faces on the image
Rect[] facesArray = faces.toArray();
for (int i = 0; i < facesArray.length; i++)
Imgproc.rectangle(people, facesArray[i].tl(), facesArray[i].br(), new Scalar(100), 3);
//Converting Mat back to Bitmap
Utils.matToBitmap(people, currentBitmap);
imageView.setImageBitmap(currentBitmap);
}
Example 6
| Source File: DetectFaceDemo.java From Java-for-Data-Science with MIT License | 6 votes |
|
public void run() {
System.loadLibrary(Core.NATIVE_LIBRARY_NAME);
String base = "C:/Books in Progress/Java for Data Science/Chapter 10/OpenCVExamples/src/resources";
CascadeClassifier faceDetector =
new CascadeClassifier(base + "/lbpcascade_frontalface.xml");
Mat image = Imgcodecs.imread(base + "/images.jpg");
MatOfRect faceVectors = new MatOfRect();
faceDetector.detectMultiScale(image, faceVectors);
out.println(faceVectors.toArray().length + " faces found");
for (Rect rect : faceVectors.toArray()) {
Imgproc.rectangle(image, new Point(rect.x, rect.y),
new Point(rect.x + rect.width, rect.y + rect.height),
new Scalar(0, 255, 0));
}
Imgcodecs.imwrite("faceDetection.png", image);
}
Example 7
| Source File: FaceDetectionController.java From ExoVisix with MIT License | 5 votes |
|
/**
* Method for face detection and tracking
*
* @param frame
* it looks for faces in this frame
*/
private void detectAndDisplay(Mat frame)
{
MatOfRect faces = new MatOfRect();
Mat grayFrame = new Mat();
// convert the frame in gray scale
Imgproc.cvtColor(frame, grayFrame, Imgproc.COLOR_BGR2GRAY);
// equalize the frame histogram to improve the result
Imgproc.equalizeHist(grayFrame, grayFrame);
// compute minimum face size (20% of the frame height, in our case)
if (this.absoluteFaceSize == 0)
{
int height = grayFrame.rows();
if (Math.round(height * 0.2f) > 0)
{
this.absoluteFaceSize = Math.round(height * 0.2f);
}
}
// detect faces
this.faceCascade.detectMultiScale(grayFrame, faces, 1.1, 2, 0 | Objdetect.CASCADE_SCALE_IMAGE,
new Size(this.absoluteFaceSize, this.absoluteFaceSize), new Size());
// each rectangle in faces is a face: draw them!
Rect[] facesArray = faces.toArray();
for (int i = 0; i < facesArray.length; i++)
{
Imgproc.rectangle(frame, facesArray[i].tl(), facesArray[i].br(), new Scalar(7, 255, 90), 4);
System.out.println(facesArray[i].tl());
System.out.println(facesArray[i].br());
}
}
Example 8
| Source File: DetectActivity.java From FaceDetectDemo with Apache License 2.0 | 5 votes |
|
@Override
// 这里执行人脸检测的逻辑, 根据OpenCV提供的例子实现(face-detection)
public Mat onCameraFrame(CameraBridgeViewBase.CvCameraViewFrame inputFrame) {
mRgba = inputFrame.rgba();
mGray = inputFrame.gray();
// 翻转矩阵以适配前后置摄像头
if (isFrontCamera) {
Core.flip(mRgba, mRgba, 1);
Core.flip(mGray, mGray, 1);
} else {
Core.flip(mRgba, mRgba, -1);
Core.flip(mGray, mGray, -1);
}
float mRelativeFaceSize = 0.2f;
if (mAbsoluteFaceSize == 0) {
int height = mGray.rows();
if (Math.round(height * mRelativeFaceSize) > 0) {
mAbsoluteFaceSize = Math.round(height * mRelativeFaceSize);
}
}
MatOfRect faces = new MatOfRect();
if (classifier != null)
classifier.detectMultiScale(mGray, faces, 1.1, 2, 2,
new Size(mAbsoluteFaceSize, mAbsoluteFaceSize), new Size());
Rect[] facesArray = faces.toArray();
Scalar faceRectColor = new Scalar(0, 255, 0, 255);
for (Rect faceRect : facesArray)
Imgproc.rectangle(mRgba, faceRect.tl(), faceRect.br(), faceRectColor, 3);
return mRgba;
}
Example 9
| Source File: ImgprocessUtils.java From classchecks with Apache License 2.0 | 5 votes |
|
/**
*
* @Title: cutOutImage
* @Description: 以Rect坐标裁剪图片
* @param src
* @param mOfRect
* @return
* List<Mat>
*/
public static List<Mat> cutOutImage(Mat src, MatOfRect mOfRect) {
List<Mat> outs = new ArrayList<Mat>();
if(src.empty() || mOfRect.toArray().length <= 0) {
return null;
}
int enlarge = 50; // 将图片的x，y坐标加大
for(Rect r : mOfRect.toArray()) {
int enlargeW = (r.x + enlarge) / r.x;
int enlargeH = (r.y + enlarge) / r.y;
r.x -= enlarge;
r.y -= enlarge;
r.width += (enlargeW * r.width);
r.height += (enlargeH * r.height);
// 确定人脸在图像中的整个边界，防止人脸在边框之外
if (r.x < 0) {
r.x = 0;
}
if (r.y < 0) {
r.y = 0;
}
if (r.x + r.width > src.cols()) {
r.x = src.cols() - r.width;
}
if (r.y + r.height > src.rows()) {
r.y = src.rows() - r.height;
}
Mat cutting = new Mat(src, r); // 裁剪图片
// System.out.println(cutting);
outs.add(cutting);
}
return outs;
}
Example 10
| Source File: DetectObjectTest.java From classchecks with Apache License 2.0 | 5 votes |
|
@Test
public void testDetectManyObject() {
String opencvDLL = "G:/java/JavaProjectRelease/classchecks/src/main/webapp/WEB-INF/dll/x64/opencv_java320.dll";
System.load(opencvDLL);
String haarcascade = "haarcascade_frontalface_alt.xml";
CascadeClassifier cascade = new CascadeClassifier(XMLFilePath + haarcascade);
Mat src = Imgcodecs.imread(imageDir + "/split/14.jpg");
MatOfRect objects = new MatOfRect();
int scaledWidth = src.width();
DetectObject.detectManyObject(src, cascade, objects, scaledWidth);
Rect [] rects = objects.toArray();
int i = 0;
for(Rect r : rects) {
/*Imgproc.rectangle(src, new Point(r.x-100 , r.y-100 ),
new Point(r.x + r.width + 80,
r.y + r.height + 80), new Scalar(0, 0, 255), 3);*/
Imgproc.rectangle(src, r.tl(),
r.br(), new Scalar(0, 0, 255), 3);
/*r.width += 120;
r.height += 120;
r.x -= 100;
r.y -= 100;
System.out.println(r);
Mat roi = new Mat(src, r);
Imgcodecs.imwrite("e:/classchecks/2017417/split/"+i+".jpg", roi);
i ++;*/
}
Imgcodecs.imwrite("e:/classchecks/2017417/dectctManyObject.jpg", src);
//Imgcodecs.imwrite("e:/classchecks/dectctManyObject.jpg", src);
}
Example 11
| Source File: FaceDetection.java From Android-Face-Recognition-with-Deep-Learning-Library with Apache License 2.0 | 5 votes |
|
public Eyes getEyes(Mat img){
double halfWidth = img.cols() / 2;
double height = img.rows();
double[] values = new double[4];
values[0] = 0;
values[1] = 0;
values[2] = halfWidth;
values[3] = height;
Rect rightHalf = new Rect(values);
values[0] = halfWidth;
Rect leftHalf = new Rect(values);
MatOfRect rightEyes = new MatOfRect();
MatOfRect leftEyes = new MatOfRect();
Mat rightHalfImg = img.submat(rightHalf);
rightEyeDetector.detectMultiScale(rightHalfImg, rightEyes);
Mat leftHalfImg = img.submat(leftHalf);
leftEyeDetector.detectMultiScale(leftHalfImg, leftEyes);
if (rightEyes.empty() || leftEyes.empty() || rightEyes.toArray().length > 1 || leftEyes.toArray().length > 1){
return null;
}
Rect rightEye = rightEyes.toArray()[0];
Rect leftEye = leftEyes.toArray()[0];
MatOfFloat rightPoint = new MatOfFloat(rightEye.x + rightEye.width / 2, rightEye.y + rightEye.height / 2);
MatOfFloat leftPoint = new MatOfFloat(img.cols() / 2 + leftEye.x + leftEye.width / 2, leftEye.y + leftEye.height / 2);
MatOfFloat diff = new MatOfFloat();
Core.subtract(leftPoint, rightPoint, diff);
double angle = Core.fastAtan2(diff.toArray()[1], diff.toArray()[0]);
double dist = Core.norm(leftPoint, rightPoint, Core.NORM_L2);
Eyes eyes = new Eyes(dist, rightPoint, leftPoint, angle);
return eyes;
}
Example 12
| Source File: FaceDetectionActivity.java From OpenCV-AndroidSamples with MIT License | 5 votes |
|
public Mat onCameraFrame(CvCameraViewFrame inputFrame) {
mRgba = inputFrame.rgba();
mGray = inputFrame.gray();
if (mAbsoluteFaceSize == 0) {
int height = mGray.rows();
if (Math.round(height * mRelativeFaceSize) > 0) {
mAbsoluteFaceSize = Math.round(height * mRelativeFaceSize);
}
mNativeDetector.setMinFaceSize(mAbsoluteFaceSize);
}
MatOfRect faces = new MatOfRect();
if (mDetectorType == JAVA_DETECTOR) {
if (mJavaDetector != null)
mJavaDetector.detectMultiScale(mGray, faces, 1.1, 2, 2, // TODO: objdetect.CV_HAAR_SCALE_IMAGE
new Size(mAbsoluteFaceSize, mAbsoluteFaceSize), new Size());
}
else if (mDetectorType == NATIVE_DETECTOR) {
if (mNativeDetector != null)
mNativeDetector.detect(mGray, faces);
}
else {
Log.e(TAG, "Detection method is not selected!");
}
Rect[] facesArray = faces.toArray();
for (int i = 0; i < facesArray.length; i++)
Imgproc.rectangle(mRgba, facesArray[i].tl(), facesArray[i].br(), FACE_RECT_COLOR, 3);
return mRgba;
}
Example 13
| Source File: FaceDetection.java From AndroidFaceRecognizer with MIT License | 5 votes |
|
private static Rect detectEyes(DetectionBasedTracker detector, Mat image, boolean left){
Rect result = null;
if(detector == null){
return result;
}
detector.setMinFaceSize(30);
MatOfRect eyes = new MatOfRect();
detector.detect(image, eyes);
Rect[] eyesArray = eyes.toArray();
int possibleEyeIndex = -1;
int minEyeRectSize = 1000000;
for (int j = 0; j < eyesArray.length; j++){
if(left && (eyesArray[j].x + eyesArray[j].width) > (image.cols()/2)) {
continue;
}
if(!left && (eyesArray[j].x + eyesArray[j].width) < (image.cols()/2)) {
continue;
}
if(eyesArray[j].x > 0
&& eyesArray[j].y > (image.rows() /4) && (eyesArray[j].y + eyesArray[j].height) < (image.rows() /2)){
int rectSize = eyesArray[j].height * eyesArray[j].width;
if(rectSize < minEyeRectSize){
possibleEyeIndex = j;
rectSize = minEyeRectSize;
}
}
}
if(possibleEyeIndex != -1) {
result = eyesArray[possibleEyeIndex];
}
return result;
}
Example 14
| Source File: HaarCascadeOp.java From StormCV with Apache License 2.0 | 5 votes |
|
@Override
public List<CVParticle> execute(CVParticle input) throws Exception {
ArrayList<CVParticle> result = new ArrayList<CVParticle>();
Frame frame = (Frame)input;
if(frame.getImageType().equals(Frame.NO_IMAGE)) return result;
MatOfByte mob = new MatOfByte(frame.getImageBytes());
Mat image = Highgui.imdecode(mob, Highgui.CV_LOAD_IMAGE_COLOR);
/*
mob = new MatOfByte();
Highgui.imencode(".png", image, mob);
BufferedImage bi = ImageUtils.bytesToImage(mob.toArray());
ImageIO.write(bi, "png", new File("testOutput/"+sf.getStreamId()+"_"+sf.getSequenceNr()+".png"));
*/
MatOfRect haarDetections = new MatOfRect();
haarDetector.detectMultiScale(image, haarDetections, scaleFactor, minNeighbors, flags, new Size(minSize[0], minSize[1]), new Size(maxSize[0], maxSize[1]));
ArrayList<Descriptor> descriptors = new ArrayList<Descriptor>();
for(Rect rect : haarDetections.toArray()){
Rectangle box = new Rectangle(rect.x, rect.y, rect.width, rect.height);
descriptors.add(new Descriptor(input.getStreamId(), input.getSequenceNr(), box, 0, new float[0]));
}
Feature feature = new Feature(input.getStreamId(), input.getSequenceNr(), name, 0, descriptors, null);
if(outputFrame){
frame.getFeatures().add(feature);
result.add(frame);
}else{
result.add(feature);
}
return result;
}
Example 15
| Source File: CascadeClassifierProcessor.java From Camdroid with Apache License 2.0 | 5 votes |
|
protected void execute() {
if (!classifierPath.equals(lastUsedClassifierPath)) {
detector.load(classifierPath);
lastUsedClassifierPath = classifierPath;
}
if (detector.empty()) {
return;
}
out = gray();
double min = (double) this.in.height() * object_min_size / 100;
double max = (double) this.in.height() * object_max_size / 100;
MatOfRect rects = new MatOfRect();
detector.detectMultiScale(out, rects,
1 + (double) scale_factor / 100, min_neighbors, 0,
new Size(min, min), new Size(max, max));
for (Rect rect : rects.toArray()) {
Imgproc.rectangle(out, rect.tl(), rect.br(),
new Scalar(255, 0, 0), 1);
}
}
Example 16
| Source File: ObjectDetector.java From OpenCV-android with Apache License 2.0 | 5 votes |
|
/**
* 目标检测
*
* @param gray 灰度图像
* @param object 识别结果的容器
* @return 检测到的目标位置集合
*/
public Rect[] detectObject(Mat gray, MatOfRect object) {
// 使用Java人脸检测
mCascadeClassifier.detectMultiScale(
gray, // 要检查的灰度图像
object, // 检测到的人脸
1.1, // 表示在前后两次相继的扫描中，搜索窗口的比例系数。默认为1.1即每次搜索窗口依次扩大10%;
mMinNeighbors, // 默认是3 控制误检测，表示默认几次重叠检测到人脸，才认为人脸存在
Objdetect.CASCADE_SCALE_IMAGE,
getSize(gray, mRelativeObjectWidth, mRelativeObjectHeight), // 目标最小可能的大小
gray.size()); // 目标最大可能的大小
return object.toArray();
}
Example 17
| Source File: EyesDetectionInteractorImpl.java From Image-Detection-Samples with Apache License 2.0 | 4 votes |
|
/**
* <p>Build a template from a specific eye area previously substracted
* uses detectMultiScale for this area, then uses minMaxLoc method to
* detect iris from the detected eye</p>
*
* @param area Preformatted Area
* @param size minimum iris size
* @param grayMat image in gray
* @param rgbaMat image in color
* @param detectorEye Haar Cascade classifier
* @return built template
*/
@NonNull
private static Mat buildTemplate(Rect area, final int size,
@NonNull Mat grayMat,
@NonNull Mat rgbaMat,
CascadeClassifier detectorEye) {
Mat template = new Mat();
Mat graySubMatEye = grayMat.submat(area);
MatOfRect eyes = new MatOfRect();
Rect eyeTemplate;
detectorEye.detectMultiScale(graySubMatEye, eyes, 1.15, 2,
Objdetect.CASCADE_FIND_BIGGEST_OBJECT
| Objdetect.CASCADE_SCALE_IMAGE, new Size(EYE_MIN_SIZE, EYE_MIN_SIZE),
new Size());
Rect[] eyesArray = eyes.toArray();
if (eyesArray.length > 0) {
Rect e = eyesArray[0];
e.x = area.x + e.x;
e.y = area.y + e.y;
Rect eyeRectangle = getEyeArea((int) e.tl().x,
(int) (e.tl().y + e.height * 0.4),
e.width,
(int) (e.height * 0.6));
graySubMatEye = grayMat.submat(eyeRectangle);
Mat rgbaMatEye = rgbaMat.submat(eyeRectangle);
Core.MinMaxLocResult minMaxLoc = Core.minMaxLoc(graySubMatEye);
FaceDrawerOpenCV.drawIrisCircle(rgbaMatEye, minMaxLoc);
Point iris = new Point();
iris.x = minMaxLoc.minLoc.x + eyeRectangle.x;
iris.y = minMaxLoc.minLoc.y + eyeRectangle.y;
eyeTemplate = getEyeArea((int) iris.x - size / 2,
(int) iris.y
- size / 2, size, size);
FaceDrawerOpenCV.drawEyeRectangle(eyeTemplate, rgbaMat);
template = (grayMat.submat(eyeTemplate)).clone();
}
return template;
}
Example 18
| Source File: ShowCameraViewActivity.java From FaceT with Mozilla Public License 2.0 | 4 votes |
|
@Override
public Mat onCameraFrame(CvCameraViewFrame cvCameraViewFrame) {
mRgba = cvCameraViewFrame.rgba();
mGray = cvCameraViewFrame.gray();
if (mAbsoluteFaceSize == 0) {
int height = mGray.rows();
if (Math.round(height * mRelativeFaceSize) > 0) {
mAbsoluteFaceSize = Math.round(height * mRelativeFaceSize);
}
}
MatOfRect faces = new MatOfRect();
if (mDetectorType == JAVA_DETECTOR) {
if (mJavaDetector != null) {
mJavaDetector.detectMultiScale(mGray, faces, 1.1, 2, 2, // TODO: objdetect.CV_HAAR_SCALE_IMAGE
new Size(mAbsoluteFaceSize, mAbsoluteFaceSize), new Size());
}
} else {
Log.e(TAG, "Detection method is not selected!");
}
Rect[] facesArray = faces.toArray();
for (int i = 0; i < facesArray.length; i++) {
Imgproc.rectangle(mRgba, facesArray[i].tl(), facesArray[i].br(), FACE_RECT_COLOR, 3);
face_middle_x = (int) facesArray[i].tl().x + facesArray[i].width / 2;
face_middle_y = (int) facesArray[i].tl().y + facesArray[i].height / 2;
Log.d("face middle : ", face_middle_x + "," + face_middle_y);
Log.d(TAG, "faces array " + String.valueOf(i));
}
// if(facesArray.length > 0) {
// //auto save face when it detects the face
// //Thread t = getBaseContext().getMainLooper().getThread();
// Thread t = new Thread(new Runnable() {
// @Override
// public void run() {
// SimpleDateFormat sdf = new SimpleDateFormat("yyyy-MM-dd_HH-mm-ss_S");
// String currentDateandTime = sdf.format(new Date());
// String saveDir = Environment.getExternalStorageDirectory().getPath() + "/DCIM/OCV/FDSave";
// File dirCheck = new File(saveDir);
// if(!dirCheck.exists()) {
// dirCheck.mkdirs();
// }
// String fileName = saveDir + "/" + currentDateandTime + ".jpg";
// try {
// mOpenCvCameraView.takePicture(fileName);
// } catch (Exception ex) {
// ex.printStackTrace();
// }
// }
// });
// t.start();
// }
//
// if (mIsColorSelected) {
// mDetector.process(mRgba);
// List<MatOfPoint> contours = mDetector.getContours();
// Log.e(TAG, "Contours count: " + contours.size());
// Imgproc.drawContours(mRgba, contours, -1, CONTOUR_COLOR);
//
// Mat colorLabel = mRgba.submat(4, 68, 4, 68);
// colorLabel.setTo(mBlobColorRgba);
//
// Mat spectrumLabel = mRgba.submat(4, 4 + mSpectrum.rows(), 70, 70 + mSpectrum.cols());
// mSpectrum.copyTo(spectrumLabel);
// }
return mRgba;
}
Example 19
| Source File: Training.java From marvel with MIT License | 4 votes |
|
@Override
public Mat onCameraFrame(CameraBridgeViewBase.CvCameraViewFrame inputFrame) {
mRgba = inputFrame.rgba();
mGray = inputFrame.gray();
if (mAbsoluteFaceSize == 0) {
int height = mGray.rows();
if (Math.round(height * mRelativeFaceSize) > 0) {
mAbsoluteFaceSize = Math.round(height * mRelativeFaceSize);
}
// mNativeDetector.setMinFaceSize(mAbsoluteFaceSize);
}
MatOfRect faces = new MatOfRect();
if (mDetectorType == JAVA_DETECTOR) {
if (mJavaDetector != null)
mJavaDetector.detectMultiScale(mGray, faces, 1.1, 2, 2, // TODO: objdetect.CV_HAAR_SCALE_IMAGE
new Size(mAbsoluteFaceSize, mAbsoluteFaceSize), new Size());
}
else if (mDetectorType == NATIVE_DETECTOR) {
/*if (mNativeDetector != null)
mNativeDetector.detect(mGray, faces);*/
}
else {
Log.e(TAG, "Detection method is not selected!");
}
Rect[] facesArray = faces.toArray();
if ((facesArray.length==1)&&(faceState==TRAINING)&&(countImages<MAXIMG)&&(!text.equals("")))
{
Mat m;
Rect r=facesArray[0];
m=mRgba.submat(r);
mBitmap = Bitmap.createBitmap(m.width(),m.height(), Bitmap.Config.ARGB_8888);
Utils.matToBitmap(m, mBitmap);
Message msg = new Message();
String textTochange = "IMG";
msg.obj = textTochange;
mHandler.sendMessage(msg);
if (countImages<MAXIMG)
{
fr.add(m, text);
countImages++;
}
}
for (int i = 0; i < facesArray.length; i++)
Core.rectangle(mRgba, facesArray[i].tl(), facesArray[i].br(), FACE_RECT_COLOR, 3);
return mRgba;
}
Example 20
| Source File: FXController.java From Face-Recognition with Apache License 2.0 | 4 votes |
|
/**
* Method for face detection and tracking
*
* @param frame
* it looks for faces in this frame
*/
private void detectAndDisplay(Mat frame)
{
MatOfRect faces = new MatOfRect();
Mat grayFrame = new Mat();
// convert the frame in gray scale
Imgproc.cvtColor(frame, grayFrame, Imgproc.COLOR_BGR2GRAY);
// equalize the frame histogram to improve the result
Imgproc.equalizeHist(grayFrame, grayFrame);
// compute minimum face size (20% of the frame height, in our case)
if (this.absoluteFaceSize == 0)
{
int height = grayFrame.rows();
if (Math.round(height * 0.2f) > 0)
{
this.absoluteFaceSize = Math.round(height * 0.2f);
}
}
// detect faces
this.faceCascade.detectMultiScale(grayFrame, faces, 1.1, 2, 0 | Objdetect.CASCADE_SCALE_IMAGE,
new Size(this.absoluteFaceSize, this.absoluteFaceSize), new Size());
// each rectangle in faces is a face: draw them!
Rect[] facesArray = faces.toArray();
for (int i = 0; i < facesArray.length; i++) {
Imgproc.rectangle(frame, facesArray[i].tl(), facesArray[i].br(), new Scalar(0, 255, 0), 3);
// Crop the detected faces
Rect rectCrop = new Rect(facesArray[i].tl(), facesArray[i].br());
Mat croppedImage = new Mat(frame, rectCrop);
// Change to gray scale
Imgproc.cvtColor(croppedImage, croppedImage, Imgproc.COLOR_BGR2GRAY);
// Equalize histogram
Imgproc.equalizeHist(croppedImage, croppedImage);
// Resize the image to a default size
Mat resizeImage = new Mat();
Size size = new Size(250,250);
Imgproc.resize(croppedImage, resizeImage, size);
// check if 'New user' checkbox is selected
// if yes start collecting training data (50 images is enough)
if ((newUser.isSelected() && !newname.isEmpty())) {
if (index<20) {
Imgcodecs.imwrite("resources/trainingset/combined/" +
random + "-" + newname + "_" + (index++) + ".png", resizeImage);
}
}
// int prediction = faceRecognition(resizeImage);
double[] returnedResults = faceRecognition(resizeImage);
double prediction = returnedResults[0];
double confidence = returnedResults[1];
// System.out.println("PREDICTED LABEL IS: " + prediction);
int label = (int) prediction;
String name = "";
if (names.containsKey(label)) {
name = names.get(label);
} else {
name = "Unknown";
}
// Create the text we will annotate the box with:
// String box_text = "Prediction = " + prediction + " Confidence = " + confidence;
String box_text = "Prediction = " + name + " Confidence = " + confidence;
// Calculate the position for annotated text (make sure we don't
// put illegal values in there):
double pos_x = Math.max(facesArray[i].tl().x - 10, 0);
double pos_y = Math.max(facesArray[i].tl().y - 10, 0);
// And now put it into the image:
Imgproc.putText(frame, box_text, new Point(pos_x, pos_y),
Core.FONT_HERSHEY_PLAIN, 1.0, new Scalar(0, 255, 0, 2.0));
}
}
