public static void detect(IplImage src) {
CvHaarClassifierCascade cascade = new CvHaarClassifierCascade(cvLoad(XML_FILE));
CvMemStorage storage = CvMemStorage.create();
CvSeq sign = cvHaarDetectObjects(src, cascade, storage, 1.5, 3, CV_HAAR_DO_CANNY_PRUNING);
cvClearMemStorage(storage);
int total_Faces = sign.total();
for (int i = 0; i < total_Faces; i++) {
CvRect r = new CvRect(cvGetSeqElem(sign, i));
cvRectangle(
src,
cvPoint(r.x(), r.y()),
cvPoint(r.width() + r.x(), r.height() + r.y()),
CvScalar.RED,
2,
CV_AA,
0);
}
cvShowImage("Result", src);
cvSaveImage("D:\\asd\\a.jpg", src);
cvWaitKey(0);
}
/**
* Receives frame and uses face recognition algorithms to set coordinates of faces
*
* @param originalImage
* @return list of integer arrays, with coordinates and size of faces
*/
public static List<Integer[]> detect(IplImage originalImage) {
List<Integer[]> facesList = new ArrayList<Integer[]>();
IplImage grayImage =
IplImage.create(originalImage.width(), originalImage.height(), IPL_DEPTH_8U, 1);
cvCvtColor(originalImage, grayImage, CV_BGR2GRAY);
CvMemStorage storage = CvMemStorage.create();
opencv_objdetect.CvHaarClassifierCascade cascade =
new opencv_objdetect.CvHaarClassifierCascade(cvLoad(CASCADE_FILE));
CvSeq faces = cvHaarDetectObjects(grayImage, cascade, storage, 1.1, 1, 0);
Integer[] coordinates = null;
for (int i = 0; i < faces.total(); i++) {
CvRect r = new CvRect(cvGetSeqElem(faces, i));
coordinates = new Integer[4];
coordinates[0] = r.x();
coordinates[1] = r.y();
coordinates[2] = r.height();
coordinates[3] = r.width();
facesList.add(coordinates);
}
return facesList;
}
public void generatePGMFromPic(String srcPath, String file, String destPath) throws Exception {
String srcFilePath = srcPath + "/" + file;
System.out.println("Loading image from " + srcFilePath);
IplImage origImg = cvLoadImage(srcFilePath);
// convert to grayscale
IplImage grayImg = IplImage.create(origImg.width(), origImg.height(), IPL_DEPTH_8U, 1);
cvCvtColor(origImg, grayImg, CV_BGR2GRAY);
// scale the grayscale (to speed up face detection)
IplImage smallImg =
IplImage.create(grayImg.width() / SCALE, grayImg.height() / SCALE, IPL_DEPTH_8U, 1);
cvResize(grayImg, smallImg, CV_INTER_LINEAR);
// equalize the small grayscale
IplImage equImg = IplImage.create(smallImg.width(), smallImg.height(), IPL_DEPTH_8U, 1);
cvEqualizeHist(smallImg, equImg);
CvMemStorage storage = CvMemStorage.create();
CvHaarClassifierCascade cascade = new CvHaarClassifierCascade(cvLoad(CASCADE_FILE));
System.out.println("Detecting faces...");
CvSeq faces = cvHaarDetectObjects(equImg, cascade, storage, 1.1, 3, CV_HAAR_DO_CANNY_PRUNING);
cvClearMemStorage(storage);
int total = faces.total();
System.out.println("Found " + total + " face(s)");
for (int i = 0; i < total; i++) {
CvRect r = new CvRect(cvGetSeqElem(faces, i));
cvSetImageROI(
origImg, cvRect(r.x() * SCALE, r.y() * SCALE, r.width() * SCALE, r.height() * SCALE));
IplImage origface = cvCreateImage(cvSize(r.width() * SCALE, r.height() * SCALE), 8, 3);
IplImage smallface = cvCreateImage(cvSize(120, 120), 8, 3);
cvCopy(origImg, origface);
cvResize(origface, smallface, CV_INTER_LINEAR);
cvSaveImage(destPath + "/" + file + i + ".pgm", smallface);
cvResetImageROI(origImg);
}
}
public IplImage DetectFaces(IplImage image) throws Exception {
// Converts the image to gray scale for detection to work, using the same dimensions as the
// original.
IplImage grayImage = IplImage.createFrom(convertColorToGray(image.getBufferedImage()));
CvMemStorage storage = CvMemStorage.create();
// Using the cascade file, this creates a classification for what objects to detect. In our case
// it is the anterior of the face.
CvHaarClassifierCascade classifier = new CvHaarClassifierCascade(cvLoad(CASCADE_FILE));
// Detect Haar-like objects, depending on the classifier. In this case we use a classifier for
// detecting the anterior of the face.
CvSeq faces = cvHaarDetectObjects(grayImage, classifier, storage, 1.1, 1, 0);
// Initialize the static variables in FaceScanner for determining the area to crop the largest
// detected face.
FaceScanner.height = 0;
FaceScanner.width = 0;
FaceScanner.x = 0;
FaceScanner.y = 0;
// Loop through all detected faces and save the largest (closest) face.
for (int i = 0; i < faces.total(); i++) {
CvRect rect = new CvRect(cvGetSeqElem(faces, i));
if (FaceScanner.width < rect.width()) {
FaceScanner.width = rect.width();
FaceScanner.height = rect.height();
FaceScanner.x = rect.x();
FaceScanner.y = rect.y();
}
if (FaceScanner.displayRects) {
/*Uncomment to draw the rectangles around the detected faces.*/
// if(rect.width() > 130 && rect.height() > 130){
// Draw a square around the detected face.
cvRectangle(
image,
cvPoint(rect.x(), rect.y()),
cvPoint(rect.x() + rect.width(), rect.y() + rect.height()),
CvScalar.GREEN,
2,
CV_AA,
0);
// }
/*-----------------------------------------------------------*/
}
}
// Checks that there was a detected face in the image before saving. Also, the detected "face"
// must be large enough to be considered
// a detected face. This is to limit the amount of erroneous detections. This saves the full
// size image with detections drawn on
// whole image before cropping.
if (!(FaceScanner.height == 0 && FaceScanner.width == 0)
&& !(FaceScanner.height < 130 && FaceScanner.width < 130)) {
// Save the image with rectangles.
// cvSaveImage(filename.replace(".png", "-Rect.png"), image);
} else {
return null;
}
return image;
}
