public static boolean checkAngleL(Mat binary) {
int leftPointX = -1;
double pixel[];
for (int i = 0; i < binary.cols(); i++) {
pixel = binary.get(0, i);
if (pixel[0] != 0.0) {
leftPointX = i;
}
}
int rightPointY = -1;
for (int i = 0; i < binary.rows(); i++) {
pixel = binary.get(i, binary.cols() - 1);
if (pixel[0] != 0.0) {
rightPointY = i;
break;
}
}
double opposite = rightPointY;
double adjacent = binary.cols() - 1 - leftPointX;
double angle = Math.atan2(opposite, adjacent) * (180 / Math.PI);
if (angle >= 50.0 & angle < 70.0) {
return true; // angle indicates L
} else {
return false; // angle indicates not L
}
}
private static double perceptualHash(String sourceImagePath, String targetImagePath) {
System.loadLibrary(Core.NATIVE_LIBRARY_NAME);
Mat matSrc = Imgcodecs.imread(sourceImagePath, Imgcodecs.CV_LOAD_IMAGE_COLOR);
Mat matTrgt = Imgcodecs.imread(targetImagePath, Imgcodecs.CV_LOAD_IMAGE_COLOR);
Mat matSrcResize = new Mat();
Mat matTrgtResize = new Mat();
Imgproc.resize(
matSrc,
matSrcResize,
new Size(matSrc.width(), matSrc.height()),
0,
0,
Imgproc.INTER_NEAREST);
Imgproc.resize(
matTrgt,
matTrgtResize,
new Size(matTrgt.width(), matTrgt.height()),
0,
0,
Imgproc.INTER_LANCZOS4);
Mat matSrcDst = new Mat();
Mat matTrgtDst = new Mat();
Imgproc.resize(matSrcResize, matSrcDst, new Size(8, 8), 0, 0, Imgproc.INTER_CUBIC);
Imgproc.resize(matTrgtResize, matTrgtDst, new Size(8, 8), 0, 0, Imgproc.INTER_CUBIC);
Imgproc.cvtColor(matSrcDst, matSrcDst, Imgproc.COLOR_BGR2GRAY);
Imgproc.cvtColor(matTrgtDst, matTrgtDst, Imgproc.COLOR_BGR2GRAY);
int iAvgSrc = 0, iAvgTrgt = 0;
int[] arrSrc = new int[64];
int[] arrTrgt = new int[64];
for (int i = 0; i < 8; i++) {
byte[] dataSrc = new byte[8];
matSrcDst.get(i, 0, dataSrc);
byte[] dataTrgt = new byte[8];
matTrgtDst.get(i, 0, dataTrgt);
int tmp = i * 8;
for (int j = 0; j < 8; j++) {
int tmpSrc = tmp + j;
arrSrc[tmpSrc] = dataSrc[j] / 4 * 4;
arrTrgt[tmpSrc] = dataTrgt[j] / 4 * 4;
iAvgSrc += arrSrc[tmpSrc];
iAvgTrgt += arrTrgt[tmpSrc];
}
}
iAvgSrc /= 64;
iAvgTrgt /= 64;
for (int i = 0; i < 64; i++) {
arrSrc[i] = (arrSrc[i] >= iAvgSrc) ? 1 : 0;
arrTrgt[i] = (arrTrgt[i] >= iAvgTrgt) ? 1 : 0;
}
int iDiffNum = 0;
for (int i = 0; i < 64; i++) if (arrSrc[i] != arrTrgt[i]) ++iDiffNum;
return 1.0 - (double) iDiffNum / 64;
}
Mat setupMatrix(double hSmallToGlass[], double hGlassToSmall[]) {
Mat hSmallToGlassMat = HMatFromArray(hSmallToGlass);
Mat hGlassToSmallMat = hSmallToGlassMat.inv();
double denominator = hGlassToSmallMat.get(2, 2)[0];
for (int i = 0; i < 3; i++)
for (int j = 0; j < 3; j++)
hGlassToSmall[i * 3 + j] = hGlassToSmallMat.get(i, j)[0] / denominator;
return hSmallToGlassMat;
}
public SaliencyResult saliencyalgorithmInterface(ImageObj imgobj, String method) {
// TODO Auto-generated method stub
float min = Float.MAX_VALUE;
float max = Float.MIN_VALUE;
String imgpath = imgobj.getSourcePath();
int k_num = imgobj.getK_num();
SaliencyResult result = new SaliencyResult();
Mat img = Highgui.imread(imgpath, Highgui.CV_LOAD_IMAGE_GRAYSCALE);
Mat saliencyMap = new Mat();
saliencyMap.create(img.rows(), img.cols(), CvType.CV_16U);
int HistGram[] = new int[256];
int Gray[] = new int[img.cols() * img.rows()];
int Dist[] = new int[256];
float DistMap[] = new float[img.rows() * img.cols()];
for (int row = 0; row < img.rows(); row++) {
int CurIndex = row * img.cols();
for (int col = 0; col < img.cols(); col++) {
HistGram[(int) (img.get(row, col)[0])]++;
Gray[CurIndex] = (int) (img.get(row, col)[0]);
CurIndex++;
}
}
for (int Y = 0; Y < 256; Y++) {
int Value = 0;
for (int X = 0; X < 256; X++) Value += Math.abs(Y - X) * HistGram[X];
Dist[Y] = Value;
}
for (int row = 0; row < img.rows(); row++) {
int CurIndex = row * img.cols();
for (int col = 0; col < img.cols(); col++) {
DistMap[CurIndex] = Dist[Gray[CurIndex]];
if (DistMap[CurIndex] < min) min = DistMap[CurIndex];
if (DistMap[CurIndex] > max) max = DistMap[CurIndex];
CurIndex++;
}
}
for (int row = 0; row < img.rows(); row++) {
int CurIndex = row * img.cols();
for (int col = 0; col < img.cols(); col++) {
saliencyMap.put(row, col, partTwo((DistMap[CurIndex] - min) / (max - min) * 255));
CurIndex++;
}
}
new findMarkUtil();
int nums[] = null;
if (method == "kmeans") {
nums = findMarkUtil.findMarkUtil_kmeans(saliencyMap, k_num, 255, 0, 5);
} else if (method == "random") {
nums = findMarkUtil.findMarkUtil_random(saliencyMap, k_num, 255);
}
result.setK_num(k_num);
result.setSource(imgpath);
result.setResult(nums);
result.setSaliency(saliencyMap);
return result;
}
private float derivImodelAlpha(int i, int idx) {
float Gx, Gy;
float res;
int x = model.get(idx).getX();
int y = model.get(idx).getY();
Gx = (float) dxModel.get(x, y)[0];
Gy = (float) dyModel.get(x, y)[0];
res = Gx * s[idx * 3][i] + Gy * s[idx * 3 + 2][i];
return res;
}
/**
* Helper function to convert a Mat into a BufferedImage. Taken from
* http://answers.opencv.org/question/10344/opencv-java-load-image-to-gui Author: 'Lucky Luke'
*
* @param matrix Mat of type CV_8UC3 or CV_8UC1
* @return BufferedImage of type TYPE_3BYTE_BGR or TYPE_BYTE_GRAY
*/
private static BufferedImage matToBufferedImage(Mat matrix) {
int cols = matrix.cols();
int rows = matrix.rows();
int elemSize = (int) matrix.elemSize();
byte[] data = new byte[cols * rows * elemSize];
int type;
matrix.get(0, 0, data);
switch (matrix.channels()) {
case 1:
type = BufferedImage.TYPE_BYTE_GRAY;
break;
case 3:
type = BufferedImage.TYPE_3BYTE_BGR;
// bgr to rgb
byte b;
for (int i = 0; i < data.length; i = i + 3) {
b = data[i];
data[i] = data[i + 2];
data[i + 2] = b;
}
break;
default:
return null;
}
BufferedImage image = new BufferedImage(cols, rows, type);
image.getRaster().setDataElements(0, 0, cols, rows, data);
return image;
}
private Mat rotateImage(Mat src, double angle) {
Point center = new Point(src.cols() / 2, src.rows() / 2);
double scale = 1.0;
Mat rotateMat = Imgproc.getRotationMatrix2D(center, angle, scale);
Mat dst = new Mat();
Size frameSize = new Size(src.rows(), src.cols());
// adjust dst center point
double m[] = new double[6];
rotateMat.get(0, 0, m);
m[2] += (frameSize.width - src.cols()) / 2;
m[5] += (frameSize.height - src.rows()) / 2;
rotateMat.put(0, 0, m);
Imgproc.warpAffine(
src,
dst,
rotateMat,
frameSize,
Imgproc.INTER_LINEAR,
Imgproc.BORDER_CONSTANT,
Scalar.all(0));
return dst;
}
private Scalar converScalarHsv2Rgba(Scalar hsvColor) {
Mat pointMatRgba = new Mat();
Mat pointMatHsv = new Mat(1, 1, CvType.CV_8UC3, hsvColor);
Imgproc.cvtColor(pointMatHsv, pointMatRgba, Imgproc.COLOR_HSV2RGB_FULL, 4);
return new Scalar(pointMatRgba.get(0, 0));
}
protected static void rotateXAxis(Mat rotation) {
// get the matrix corresponding to the rotation vector
Mat R = new Mat(3, 3, CvType.CV_64FC1);
Calib3d.Rodrigues(rotation, R);
// create the matrix to rotate 90º around the X axis
// 1, 0, 0
// 0 cos -sin
// 0 sin cos
double[] rot = {
1, 0, 0,
0, 0, -1,
0, 1, 0
};
// multiply both matrix
Mat res = new Mat(3, 3, CvType.CV_64FC1);
double[] prod = new double[9];
double[] a = new double[9];
R.get(0, 0, a);
for (int i = 0; i < 3; i++)
for (int j = 0; j < 3; j++) {
prod[3 * i + j] = 0;
for (int k = 0; k < 3; k++) {
prod[3 * i + j] += a[3 * i + k] * rot[3 * k + j];
}
}
// convert the matrix to a vector with rodrigues back
res.put(0, 0, prod);
Calib3d.Rodrigues(res, rotation);
}
public static void main(String[] args) {
try {
System.loadLibrary(Core.NATIVE_LIBRARY_NAME);
File input = new File("traffic_signal.jpg");
BufferedImage image = ImageIO.read(input);
byte[] data = ((DataBufferByte) image.getRaster().getDataBuffer()).getData();
Mat mat = new Mat(image.getHeight(), image.getWidth(), CvType.CV_8UC3);
mat.put(0, 0, data);
Mat mat1 = new Mat(image.getHeight(), image.getWidth(), CvType.CV_8UC1);
Imgproc.cvtColor(mat, mat1, Imgproc.COLOR_RGB2GRAY);
byte[] data1 = new byte[mat1.rows() * mat1.cols() * (int) (mat1.elemSize())];
mat1.get(0, 0, data1);
BufferedImage image1 =
new BufferedImage(mat1.cols(), mat1.rows(), BufferedImage.TYPE_BYTE_GRAY);
image1.getRaster().setDataElements(0, 0, mat1.cols(), mat1.rows(), data1);
File ouptut = new File("output\\grayscale_" + new Date().getTime() + ".jpg");
ImageIO.write(image1, "jpg", ouptut);
} catch (Exception e) {
System.out.println("Error: " + e.getMessage());
}
}
private static void printMatI(Mat mat) {
int[] data = new int[mat.channels()];
for (int r = 0; r < mat.rows(); r++) {
for (int c = 0; c < mat.cols(); c++) {
mat.get(r, c, data);
log(lvl, "(%d, %d) %s", r, c, Arrays.toString(data));
}
}
}
public static BufferedImage bufferedImage(Mat m) {
int type = BufferedImage.TYPE_BYTE_GRAY;
if (m.channels() > 1) {
type = BufferedImage.TYPE_3BYTE_BGR;
}
BufferedImage image = new BufferedImage(m.cols(), m.rows(), type);
m.get(
0, 0, ((DataBufferByte) image.getRaster().getDataBuffer()).getData()); // get all the pixels
return image;
}
public void predictOutput() {
network.load(Settings.networkFile);
for (int ntestSample = 0; ntestSample < networkInput.rows(); ntestSample++) {
Mat testSample = networkInput.row(ntestSample);
Mat classificationResult = new Mat(1, Settings.CLASSES, CvType.CV_32F);
// System.out.println(testSample.dump());
network.predict(testSample, classificationResult);
// System.out.println(classificationResult.dump());
int maxIndex = 0;
double value = 0;
double maxValue = classificationResult.get(0, 0)[0];
for (int index = 1; index < Settings.CLASSES; index++) {
value = classificationResult.get(0, index)[0];
if (value > maxValue) {
maxValue = value;
maxIndex = index;
}
}
if (networkOutput.get(ntestSample, maxIndex)[0] != 1) {
for (int classIndex = 0; classIndex < Settings.CLASSES; classIndex++) {
if (networkOutput.get(ntestSample, classIndex)[0] == 1) {
results[classIndex][1]++;
}
}
} else {
results[maxIndex][0]++;
results[maxIndex][1]++;
}
}
}
public static Image convertMatToImage(Mat m) {
int type = BufferedImage.TYPE_BYTE_GRAY;
if (m.channels() > 1) {
type = BufferedImage.TYPE_3BYTE_BGR;
}
int bufferSize = m.channels() * m.cols() * m.rows();
byte[] b = new byte[bufferSize];
m.get(0, 0, b);
BufferedImage image = new BufferedImage(m.cols(), m.rows(), type);
final byte[] targetPixels = ((DataBufferByte) image.getRaster().getDataBuffer()).getData();
System.arraycopy(b, 0, targetPixels, 0, b.length);
return image;
}
/** Capture images and run color processing through here */
public void capture() {
VideoCapture camera = new VideoCapture();
camera.set(12, -20); // change contrast, might not be necessary
// CaptureImage image = new CaptureImage();
camera.open(0); // Useless
if (!camera.isOpened()) {
System.out.println("Camera Error");
// Determine whether to use System.exit(0) or return
} else {
System.out.println("Camera OK");
}
boolean success = camera.read(capturedFrame);
if (success) {
try {
processWithContours(capturedFrame, processedFrame);
} catch (Exception e) {
System.out.println(e);
}
// image.processFrame(capturedFrame, processedFrame);
// processedFrame should be CV_8UC3
// image.findCaptured(processedFrame);
// image.determineKings(capturedFrame);
int bufferSize = processedFrame.channels() * processedFrame.cols() * processedFrame.rows();
byte[] b = new byte[bufferSize];
processedFrame.get(0, 0, b); // get all the pixels
// This might need to be BufferedImage.TYPE_INT_ARGB
img =
new BufferedImage(
processedFrame.cols(), processedFrame.rows(), BufferedImage.TYPE_INT_RGB);
int width = (int) camera.get(Highgui.CV_CAP_PROP_FRAME_WIDTH);
int height = (int) camera.get(Highgui.CV_CAP_PROP_FRAME_HEIGHT);
// img.getRaster().setDataElements(0, 0, width, height, b);
byte[] a = new byte[bufferSize];
System.arraycopy(b, 0, a, 0, bufferSize);
Highgui.imwrite("camera.jpg", processedFrame);
System.out.println("Success");
} else System.out.println("Unable to capture image");
camera.release();
}
private void seamlessEdges(Mat image) {
int imageHeight = image.height();
int imageWidth = image.width();
double smoothRange = smoothValue * (imageWidth / 2000f);
double smoothRangeHalf = smoothRange / 2;
double pixel1[];
double pixel2[];
double tempPixel[] = new double[4];
tempPixel[3] = 255;
int i, j;
for (i = 0; i < smoothRangeHalf; i++) {
for (j = 0; j < imageHeight; j++) {
pixel1 = image.get(j, i);
pixel2 = image.get(j, imageWidth - i - 1);
tempPixel[0] =
pixel1[0] * ((smoothRangeHalf + i) / smoothRange)
+ pixel2[0] * ((smoothRangeHalf - i) / smoothRange);
tempPixel[1] =
pixel1[1] * ((smoothRangeHalf + i) / smoothRange)
+ pixel2[1] * ((smoothRangeHalf - i) / smoothRange);
tempPixel[2] =
pixel1[2] * ((smoothRangeHalf + i) / smoothRange)
+ pixel2[2] * ((smoothRangeHalf - i) / smoothRange);
pixel2[0] =
pixel2[0] * ((smoothRangeHalf + i) / smoothRange)
+ pixel1[0] * ((smoothRangeHalf - i) / smoothRange);
pixel2[1] =
pixel2[1] * ((smoothRangeHalf + i) / smoothRange)
+ pixel1[1] * ((smoothRangeHalf - i) / smoothRange);
pixel2[2] =
pixel2[2] * ((smoothRangeHalf + i) / smoothRange)
+ pixel1[2] * ((smoothRangeHalf - i) / smoothRange);
image.put(j, i, tempPixel);
image.put(j, imageWidth - i - 1, pixel2);
}
}
}
private static Mat quat2rpy(Mat quat) {
double[] q = new double[4];
quat.get(0, 0, q);
double[] rpy = {
Math.atan2(2 * (q[0] * q[1] + q[2] * q[3]), 1 - 2 * (q[1] * q[1] + q[2] * q[2])),
Math.asin(2 * (q[0] * q[2] - q[3] * q[1])),
Math.atan2(2 * (q[0] * q[3] + q[1] * q[2]), 1 - 2 * (q[2] * q[2] + q[3] * q[3]))
};
Mat rpym = new Mat(3, 1, CvType.CV_64F);
rpym.put(0, 0, rpy);
return rpym;
}
public void copyMat(Mat src, Mat dest) {
int srcRows = src.rows();
int srcCols = src.cols();
int destRows = dest.rows();
int destCols = dest.cols();
for (int i = 0; i < srcRows; i++) {
for (int j = 0; j < srcCols; j++) {
double bit = src.get(i, j)[0];
dest.put(i, j, bit);
System.out.println(bit);
}
}
}
private static Mat rodr2quat(Mat rodr) {
double t = Core.norm(rodr);
double[] r = new double[3];
rodr.get(0, 0, r);
double[] quat = {
Math.cos(t / 2),
Math.sin(t / 2) * r[0] / t,
Math.sin(t / 2) * r[1] / t,
Math.sin(t / 2) * r[2] / t
};
Mat quatm = new Mat(4, 1, CvType.CV_64F);
quatm.put(0, 0, quat);
return quatm;
}
public static Mat getCCH(Mat image) {
ArrayList<MatOfPoint> contours = new ArrayList<MatOfPoint>();
Mat hierarchy = new Mat();
Imgproc.findContours(
image, contours, hierarchy, Imgproc.RETR_EXTERNAL, Imgproc.CHAIN_APPROX_NONE);
Mat chainHistogram = Mat.zeros(1, 8, CvType.CV_32F);
int n = 0;
MatOfPoint2f approxCurve = new MatOfPoint2f();
for (MatOfPoint contour : contours) {
// get the freeman chain code from the contours
int rows = contour.rows();
// System.out.println("\nrows"+rows+"\n"+contour.dump());
int direction = 7;
Mat prevPoint = contours.get(0).row(0);
n += rows - 1;
for (int i = 1; i < rows; i++) {
// get the current point
double x1 = contour.get(i - 1, 0)[1];
double y1 = contour.get(i - 1, 0)[0];
// get the second point
double x2 = contour.get(i, 0)[1];
double y2 = contour.get(i, 0)[0];
if (x2 == x1 && y2 == y1 + 1) direction = 0;
else if (x2 == x1 - 1 && y2 == y1 + 1) direction = 1;
else if (x2 == x1 - 1 && y2 == y1) direction = 2;
else if (x2 == x1 - 1 && y2 == y1 - 1) direction = 3;
else if (x2 == x1 && y2 == y1 - 1) direction = 4;
else if (x2 == x1 + 1 && y2 == y1 - 1) direction = 5;
else if (x2 == x1 + 1 && y2 == y1) direction = 6;
else if (x2 == x1 + 1 && y2 == y1 + 1) direction = 7;
else System.out.print("err");
double counter = chainHistogram.get(0, direction)[0];
chainHistogram.put(0, direction, ++counter);
System.out.print(direction);
}
}
System.out.println("\n" + chainHistogram.dump());
Scalar alpha = new Scalar(n); // the factor
Core.divide(chainHistogram, alpha, chainHistogram);
System.out.println("\nrows=" + n + " " + chainHistogram.dump());
return chainHistogram;
}
/**
* fills 'cTable' with the values in confMatrix
*
* @param confMatrix Mat - a result from TestingResult 'res'
*/
public void fillConfTable(Mat confMatrix) {
// initialize table header
JTableHeader header = cTable.getTableHeader();
for (int i = 0; i < cTable.getColumnCount() - 1; i++) {
TableColumn column1 = cTable.getTableHeader().getColumnModel().getColumn(i + 1);
column1.setHeaderValue(wordClasses[i]);
}
System.out.println(cTable.getColumnCount());
// fill the first column with classes
for (int i = 0; i < wordClasses.length; i++) {
for (int j = 0; j < wordClasses.length; j++) {
cTable.setValueAt(wordClasses[i], i, 0);
cTable.setValueAt(confMatrix.get(i, j)[0], i, j + 1);
}
}
}
private boolean setCircleParameters(Mat circles) {
/** Determine which circle in on the left and right */
if (circles.get(0, 0)[0] < circles.get(0, 1)[0]) {
circle1 = circles.get(0, 0)[0];
circle2 = circles.get(0, 1)[0];
} else {
circle1 = circles.get(0, 1)[0];
circle2 = circles.get(0, 0)[0];
}
minRadius = (int) ((circles.get(0, 1)[2] + circles.get(0, 0)[2]) / 2) - 10;
if (minRadius < 5) minRadius = 5;
maxRadius = minRadius + 20;
minDistance = (int) (circle2 - circle1 - 10);
return true;
}
protected static void glGetModelViewMatrix(double[] modelview_matrix, Mat Rvec, Mat Tvec)
throws ExtParamException {
// check if parameters are valid
boolean invalid = false;
double[] tvec = new double[3];
double[] rvec = new double[3];
Rvec.get(0, 0, rvec);
Tvec.get(0, 0, tvec);
for (int i = 0; i < 3 && !invalid; i++) {
if (tvec[i] != -999999) invalid |= false;
if (rvec[i] != -999999) invalid |= false;
}
if (invalid)
throw new ExtParamException("extrinsic parameters are not set Marker.getModelViewMatrix");
Mat Rot = new Mat(3, 3, CvType.CV_32FC1);
Mat Jacob = new Mat();
Calib3d.Rodrigues(Rvec, Rot, Jacob); // TODO jacob no se vuelve a usar
double[][] para = new double[3][4];
double[] rotvec = new double[9];
Rot.get(0, 0, rotvec);
for (int i = 0; i < 3; i++) for (int j = 0; j < 3; j++) para[i][j] = rotvec[3 * i + j];
// now, add the translation
para[0][3] = tvec[0];
para[1][3] = tvec[1];
para[2][3] = tvec[2];
double scale = 1;
// R1C2
modelview_matrix[0 + 0 * 4] = para[0][0];
modelview_matrix[0 + 1 * 4] = para[0][1];
modelview_matrix[0 + 2 * 4] = para[0][2];
modelview_matrix[0 + 3 * 4] = para[0][3];
// R2
modelview_matrix[1 + 0 * 4] = para[1][0];
modelview_matrix[1 + 1 * 4] = para[1][1];
modelview_matrix[1 + 2 * 4] = para[1][2];
modelview_matrix[1 + 3 * 4] = para[1][3];
// R3
modelview_matrix[2 + 0 * 4] = -para[2][0];
modelview_matrix[2 + 1 * 4] = -para[2][1];
modelview_matrix[2 + 2 * 4] = -para[2][2];
modelview_matrix[2 + 3 * 4] = -para[2][3];
modelview_matrix[3 + 0 * 4] = 0.0f;
modelview_matrix[3 + 1 * 4] = 0.0f;
modelview_matrix[3 + 2 * 4] = 0.0f;
modelview_matrix[3 + 3 * 4] = 1.0f;
if (scale != 0.0) {
modelview_matrix[12] *= scale;
modelview_matrix[13] *= scale;
modelview_matrix[14] *= scale;
}
// rotate 90º around the x axis
// rotating around x axis in OpenGL is equivalent to
// multiply the model matrix by the matrix:
// 1, 0, 0, 0, 0, cos(a), sin(a), 0, 0, -sin(a), cos(a), 0, 0, 0, 0, 1
// double[] auxRotMat = new double[]{
// 1, 0, 0, 0,
// 0, 0, 1, 0,
// 0, -1, 0, 0,
// 0, 0, 0, 1
// };
// Utils.matrixProduct(modelview_matrix1, auxRotMat, modelview_matrix);
}
public Mat onCameraFrame(Mat inputFrame) {
inputFrame.copyTo(mRgba);
switch (ImageManipulationsActivity.viewMode) {
case ImageManipulationsActivity.VIEW_MODE_RGBA:
break;
case ImageManipulationsActivity.VIEW_MODE_HIST:
if ((mSizeRgba == null)
|| (mRgba.cols() != mSizeRgba.width)
|| (mRgba.height() != mSizeRgba.height)) CreateAuxiliaryMats();
int thikness = (int) (mSizeRgba.width / (mHistSizeNum + 10) / 5);
if (thikness > 5) thikness = 5;
int offset = (int) ((mSizeRgba.width - (5 * mHistSizeNum + 4 * 10) * thikness) / 2);
// RGB
for (int c = 0; c < 3; c++) {
Imgproc.calcHist(Arrays.asList(mRgba), mChannels[c], mMat0, mHist, mHistSize, mRanges);
Core.normalize(mHist, mHist, mSizeRgba.height / 2, 0, Core.NORM_INF);
mHist.get(0, 0, mBuff);
for (int h = 0; h < mHistSizeNum; h++) {
mP1.x = mP2.x = offset + (c * (mHistSizeNum + 10) + h) * thikness;
mP1.y = mSizeRgba.height - 1;
mP2.y = mP1.y - 2 - (int) mBuff[h];
Core.line(mRgba, mP1, mP2, mColorsRGB[c], thikness);
}
}
// Value and Hue
Imgproc.cvtColor(mRgba, mIntermediateMat, Imgproc.COLOR_RGB2HSV_FULL);
// Value
Imgproc.calcHist(
Arrays.asList(mIntermediateMat), mChannels[2], mMat0, mHist, mHistSize, mRanges);
Core.normalize(mHist, mHist, mSizeRgba.height / 2, 0, Core.NORM_INF);
mHist.get(0, 0, mBuff);
for (int h = 0; h < mHistSizeNum; h++) {
mP1.x = mP2.x = offset + (3 * (mHistSizeNum + 10) + h) * thikness;
mP1.y = mSizeRgba.height - 1;
mP2.y = mP1.y - 2 - (int) mBuff[h];
Core.line(mRgba, mP1, mP2, mWhilte, thikness);
}
// Hue
Imgproc.calcHist(
Arrays.asList(mIntermediateMat), mChannels[0], mMat0, mHist, mHistSize, mRanges);
Core.normalize(mHist, mHist, mSizeRgba.height / 2, 0, Core.NORM_INF);
mHist.get(0, 0, mBuff);
for (int h = 0; h < mHistSizeNum; h++) {
mP1.x = mP2.x = offset + (4 * (mHistSizeNum + 10) + h) * thikness;
mP1.y = mSizeRgba.height - 1;
mP2.y = mP1.y - 2 - (int) mBuff[h];
Core.line(mRgba, mP1, mP2, mColorsHue[h], thikness);
}
break;
case ImageManipulationsActivity.VIEW_MODE_CANNY:
if ((mRgbaInnerWindow == null)
|| (mGrayInnerWindow == null)
|| (mRgba.cols() != mSizeRgba.width)
|| (mRgba.height() != mSizeRgba.height)) CreateAuxiliaryMats();
Imgproc.Canny(mRgbaInnerWindow, mIntermediateMat, 80, 90);
Imgproc.cvtColor(mIntermediateMat, mRgbaInnerWindow, Imgproc.COLOR_GRAY2BGRA, 4);
break;
case ImageManipulationsActivity.VIEW_MODE_SOBEL:
Imgproc.cvtColor(mRgba, mGray, Imgproc.COLOR_RGBA2GRAY);
if ((mRgbaInnerWindow == null)
|| (mGrayInnerWindow == null)
|| (mRgba.cols() != mSizeRgba.width)
|| (mRgba.height() != mSizeRgba.height)) CreateAuxiliaryMats();
Imgproc.Sobel(mGrayInnerWindow, mIntermediateMat, CvType.CV_8U, 1, 1);
Core.convertScaleAbs(mIntermediateMat, mIntermediateMat, 10, 0);
Imgproc.cvtColor(mIntermediateMat, mRgbaInnerWindow, Imgproc.COLOR_GRAY2BGRA, 4);
break;
case ImageManipulationsActivity.VIEW_MODE_SEPIA:
Core.transform(mRgba, mRgba, mSepiaKernel);
break;
case ImageManipulationsActivity.VIEW_MODE_ZOOM:
if ((mZoomCorner == null)
|| (mZoomWindow == null)
|| (mRgba.cols() != mSizeRgba.width)
|| (mRgba.height() != mSizeRgba.height)) CreateAuxiliaryMats();
Imgproc.resize(mZoomWindow, mZoomCorner, mZoomCorner.size());
Size wsize = mZoomWindow.size();
Core.rectangle(
mZoomWindow,
new Point(1, 1),
new Point(wsize.width - 2, wsize.height - 2),
new Scalar(255, 0, 0, 255),
2);
break;
case ImageManipulationsActivity.VIEW_MODE_PIXELIZE:
if ((mRgbaInnerWindow == null)
|| (mRgba.cols() != mSizeRgba.width)
|| (mRgba.height() != mSizeRgba.height)) CreateAuxiliaryMats();
Imgproc.resize(mRgbaInnerWindow, mIntermediateMat, mSize0, 0.1, 0.1, Imgproc.INTER_NEAREST);
Imgproc.resize(
mIntermediateMat, mRgbaInnerWindow, mSizeRgbaInner, 0., 0., Imgproc.INTER_NEAREST);
break;
case ImageManipulationsActivity.VIEW_MODE_POSTERIZE:
if ((mRgbaInnerWindow == null)
|| (mRgba.cols() != mSizeRgba.width)
|| (mRgba.height() != mSizeRgba.height)) CreateAuxiliaryMats();
/*
Imgproc.cvtColor(mRgbaInnerWindow, mIntermediateMat, Imgproc.COLOR_RGBA2RGB);
Imgproc.pyrMeanShiftFiltering(mIntermediateMat, mIntermediateMat, 5, 50);
Imgproc.cvtColor(mIntermediateMat, mRgbaInnerWindow, Imgproc.COLOR_RGB2RGBA);
*/
Imgproc.Canny(mRgbaInnerWindow, mIntermediateMat, 80, 90);
mRgbaInnerWindow.setTo(new Scalar(0, 0, 0, 255), mIntermediateMat);
Core.convertScaleAbs(mRgbaInnerWindow, mIntermediateMat, 1. / 16, 0);
Core.convertScaleAbs(mIntermediateMat, mRgbaInnerWindow, 16, 0);
break;
}
return mRgba;
}
boolean decodeJPEGAndAnalyze(Context context, CalibrationEntries calibrationEntries) {
boolean result = false;
BitmapFactory.Options opt = new BitmapFactory.Options();
opt.inPreferQualityOverSpeed = true;
BitmapRegionDecoder brd;
byte[] data = mData;
try {
brd = BitmapRegionDecoder.newInstance(data, 0, data.length, false);
Rect rect;
rect = new Rect(0, 0, brd.getWidth(), brd.getHeight());
Bitmap bitmap = brd.decodeRegion(rect, opt);
MatBitmapHolder mRgbaMatHolder = new MatBitmapHolder(bitmap);
Mat rgbaMat = mRgbaMatHolder.pin();
Mat grayData = new Mat();
Imgproc.cvtColor(rgbaMat, grayData, Imgproc.COLOR_RGB2GRAY, 1);
Mat centersCalibCircles = new Mat();
Size patternSize = CalibrationEntries.Pattern_ASYMMETRIC_CIRCLES_GRID_SIZE;
Size imageSize = new Size(grayData.cols(), grayData.rows());
boolean patternWasFound =
Calib3d.findCirclesGridDefault(
grayData, patternSize, centersCalibCircles, Calib3d.CALIB_CB_ASYMMETRIC_GRID);
if (patternWasFound && this.orientation != null) {
int addedIdx = calibrationEntries.addEntry(this.orientation, centersCalibCircles);
if (addedIdx >= 0) {
Log.d(
"CALIB",
String.format(
"PictureCapture: Added calibration entry at %d tot: %d",
addedIdx, calibrationEntries.getNumEntries()));
if (calibrationEntries.getNewlyAdded() > 5) {
List<Mat> imagePoints = calibrationEntries.getPoints();
List<Mat> objectPoints =
calibrationEntries.getObjectPointsAsymmentricList(imagePoints.size());
if (CalibrationEntries.isEnoughCalibrationPoints(imagePoints.size())) {
calibrationEntries.resetNewlyAdded();
CameraCalibrationData cameraCalibrationData = new CameraCalibrationData();
List<Mat> rvecs = new Vector<Mat>(imagePoints.size());
List<Mat> tvecs = new Vector<Mat>(imagePoints.size());
int flags = 0;
flags |= Calib3d.CALIB_FIX_K4 | Calib3d.CALIB_FIX_K5;
Log.d("CALIB", String.format("PictureCapture: Calling Calib3d.calibrateCamera"));
Mat K = new Mat();
Mat kdist = new Mat();
double rms =
Calib3d.calibrateCamera(
objectPoints, imagePoints, imageSize, K, kdist, rvecs, tvecs, flags);
double[] Karray = new double[9];
double[] distcoeffs_array = new double[5];
K.get(0, 0, Karray);
kdist.get(0, 0, distcoeffs_array);
cameraCalibrationData.setData(
grayData.cols(), grayData.rows(), Karray, distcoeffs_array, rms);
Log.d(
"CALIB",
String.format(
"PictureCapture: Calibration data: %s",
cameraCalibrationData.formatCalibrationDataString()));
calibrationEntries.setCalibrationData(cameraCalibrationData);
result = true;
}
}
}
}
// mRightBitmap = brd.decodeRegion(rect, opt);
rect = new Rect(brd.getWidth() - brd.getWidth() / 3, 0, brd.getWidth(), brd.getHeight());
// mLeftBitmap = brd.decodeRegion(rect, opt);
mRgbaMatHolder.unpin(rgbaMat);
} catch (IOException e) {
XLog.e("Region decoder doesn't want to cooperate", e);
}
return result;
}
public static void Circle(List<MatOfPoint> contours, int index) {
int i = index;
Mat mRGBA = new Mat();
Utils.bitmapToMat(image, mRGBA);
// cyklus s podmienkou na konci
do {
int buff[] = new int[4];
hierarchy.get(0, i, buff);
// Get contour form list
Mat contour = contours.get(i);
// id kont�ry
int id = i;
// dostaneme �a��ie id kont�ry
i = buff[0];
// zis�ujeme �i m�me dostato�ne ve�k� kont�ru aby sme sa �ou v�bec zaoberali
if (Imgproc.contourArea(contour) > 500) {
List<Point> points = new ArrayList<Point>();
// dostaneme celkov� po�et kont�r
int num = (int) contour.total();
// vytvor�me si pole o dvojn�sobnej ve�kosti samotnej kontury
int temp[] = new int[num * 2];
// na��tame si kont�ru do do�asnej premennej
contour.get(0, 0, temp);
// konvertujeme List<Point> do MatOfPoint2f pre pou�itie fitEllipse
for (int j = 0; j < num * 2; j = j + 2) {
points.add(new Point(temp[j], temp[j + 1]));
}
MatOfPoint2f specialPointMtx = new MatOfPoint2f(points.toArray(new Point[0]));
// do premennej bound uklad�me dokonal� elipsu
RotatedRect bound = Imgproc.fitEllipse(specialPointMtx);
// Vypo��ta sa hodnota pi
double pi =
Imgproc.contourArea(contour) / ((bound.size.height / 2) * (bound.size.width / 2));
// zis�ujeme toleranciu pi - zaoplenie
if (Math.abs(pi - 3.14) > 0.03) {
int k = buff[2];
// zis�ujeme �i existuje nejak� rodi� kont�ry
if (k != -1) {
Circle(contours, k);
}
continue;
}
// konvertujeme MatOfPoint2f do MatOfPoint pre funckiu fitEllipse - rozdie� je len v 32-bit
// float a 32-bit int
MatOfPoint NewMtx = new MatOfPoint(specialPointMtx.toArray());
// dostaneme s�radnice najmen�ieho mo�n�ho �tvorca
Rect box = Imgproc.boundingRect(NewMtx);
// nacita obrazok znova
Mat mat_for_count = new Mat();
Utils.bitmapToMat(image, mat_for_count);
// vytvori sa klon stvorca - dobry kandidat pre vyhladanie
Mat candidate = ((mat_for_count).submat(box)).clone();
// napln maticu binarnou ciernou
Mat mask = new Mat(box.size(), candidate.type(), new Scalar(0, 0, 0));
// naplni ciernu plochu bielimi konturami
Imgproc.drawContours(
mask,
contours,
id,
new Scalar(255, 255, 255),
-1,
8,
hierarchy,
0,
new Point(-box.x, -box.y));
// ulozi sa kandidat
Mat roi = new Mat(candidate.size(), candidate.type(), new Scalar(255, 255, 255));
// ulozia sa len informacie o kandidatovi
candidate.copyTo(roi, mask);
double longAxis;
double shortAxis;
// ziska dve osy elipsy
if (bound.size.height < bound.size.width) {
shortAxis = bound.size.height / 2;
longAxis = bound.size.width / 2;
} else {
shortAxis = bound.size.width / 2;
longAxis = bound.size.height / 2;
}
// zastavi sa vyhladavanie pokial je elipsa prilis ovalna
if ((longAxis / shortAxis) < 2.0) {
signList.add(roi);
boxList.add(box);
}
}
// zis�uje sa �i je tam e�te �al�� kandid�t
} while (i != -1);
}
/**
* Extracts and classifies colour bands for each Resistor. Each ColourBand object is instantiated
* and linked to their parent Resistor object.
*
* @param resistorList A list of Resistor objects from which to extract the colour bands
* @param paintDebugInfo If ture, the extracted colour band ROIs are displayed on the GUI
*/
private void extractColourBandsAndClassify(List<Resistor> resistorList, boolean paintDebugInfo) {
if (resistorList.size() > 0) {
for (int r = 0; r < resistorList.size(); r++) {
Mat resImg = resistorList.get(r).resistorMat;
Mat imgHSV = new Mat();
Mat satImg = new Mat();
Mat hueImg = new Mat();
// convert to HSV
Imgproc.cvtColor(resImg, imgHSV, Imgproc.COLOR_BGR2HSV);
ArrayList<Mat> channels = new ArrayList<Mat>();
Core.split(imgHSV, channels);
// extract channels
satImg = channels.get(1); // saturation
hueImg = channels.get(0); // hue
// threshold saturation channel
Mat threshedROISatBands = new Mat(); // ~130 sat thresh val
Imgproc.threshold(satImg, threshedROISatBands, SAT_BAND_THRESH, 255, Imgproc.THRESH_BINARY);
// threshold hue channel
Mat threshedROIHueBands = new Mat(); // ~50 hue thresh val
Imgproc.threshold(hueImg, threshedROIHueBands, HUE_BAND_THRESH, 255, Imgproc.THRESH_BINARY);
// combine the thresholded binary images
Mat bandROI = new Mat();
Core.bitwise_or(threshedROIHueBands, threshedROISatBands, bandROI);
// find contours in binary ROI image
ArrayList<MatOfPoint> contours = new ArrayList<MatOfPoint>();
Mat hierarchy = new Mat();
Imgproc.findContours(
bandROI,
contours,
hierarchy,
Imgproc.RETR_EXTERNAL,
Imgproc.CHAIN_APPROX_SIMPLE,
new Point(0, 0));
// remove any remaining noise by only keeping contours which area > threshold
for (int i = 0; i < contours.size(); i++) {
double area = Imgproc.contourArea(contours.get(i));
if (area < MIN_BAND_AREA) {
contours.remove(i);
i--;
}
}
// create a ColourBand object for each detected band
// storing its center, the contour and the bandROI
for (int i = 0; i < contours.size(); i++) {
MatOfPoint contour = contours.get(i);
// extract this colour band and store in a Mat
Rect boundingRect = Imgproc.boundingRect(contour);
Mat mask = Mat.zeros(bandROI.size(), CvType.CV_8U);
Imgproc.drawContours(mask, contours, i, new Scalar(255), Core.FILLED);
Mat imageROI = new Mat();
resImg.copyTo(imageROI, mask);
Mat colourBandROI = new Mat(imageROI, boundingRect);
// instantiate new ColourBand object
ColourBand cb = new ColourBand(findCenter(contour), contour, colourBandROI);
// cluster the band colour
cb.clusterBandColour(BAND_COLOUR_K_MEANS);
// classify using the Lab colourspace as feature vector
Mat sampleMat =
new Mat(1, 3, CvType.CV_32FC1); // create a Mat contacting the clustered band colour
sampleMat.put(0, 0, cb.clusteredColourLAB[0]);
sampleMat.put(0, 1, cb.clusteredColourLAB[1]);
sampleMat.put(0, 2, cb.clusteredColourLAB[2]);
Mat classifiedValue = new Mat(1, 1, CvType.CV_32FC1);
Mat neighborResponses = new Mat(); // dont actually use this
Mat dists = new Mat(); // dont actually use this
// classify
knn.find_nearest(sampleMat, 3, classifiedValue, neighborResponses, dists);
// cast classified value into Colour enum and store
cb.classifiedColour = ColourEnumVals[(int) classifiedValue.get(0, 0)[0]];
// add the band to the parent resistor
resistorList.get(r).bands.add(cb);
}
// paint the extracted band ROIs
if (paintDebugInfo) {
Mat finalBandROIMask = Mat.zeros(bandROI.size(), CvType.CV_8U);
for (int i = 0; i < contours.size(); i++) {
Scalar color = new Scalar(255, 255, 255);
Imgproc.drawContours(
finalBandROIMask, contours, i, color, -1, 4, hierarchy, 0, new Point());
}
Mat colourROI = new Mat();
resImg.copyTo(colourROI, finalBandROIMask);
paintResistorSubRegion(colourROI, r);
}
}
}
}
public static void main(String[] args) {
System.loadLibrary(Core.NATIVE_LIBRARY_NAME);
/** *** Configuration Variables **** */
int imgWidth = 200;
int imgHeight = 200;
int numPatch = 2000;
int patchWidth = 40;
int patchHeight = 40;
int k = 200; // kmeans number of center
int numBins = 8;
String filePathRed = "base/Red/";
String filePathBlack = "base/Black";
String procPathRed = "base/ProcRed";
String procPathBlack = "base/ProcBlack";
/** ******************************** */
ArrayList<String> fileNames = new ArrayList<String>();
sources = new ArrayList<Mat>();
/* Image IO */
try {
/* Read Red Staplers */
File folder = new File(filePathRed);
BufferedImage currentImage;
for (final File fileEntry : folder.listFiles()) {
if (!fileEntry.isDirectory()) {
// Resize Image
currentImage = ImageProc.resize(ImageIO.read(fileEntry), imgWidth, imgHeight);
File outFile = new File(procPathRed + "/" + fileEntry.getName());
ImageIO.write(currentImage, "JPG", outFile);
sources.add(Highgui.imread(outFile.getPath()));
fileNames.add(outFile.getName());
}
}
/* Read Black Staplers */
folder = new File(filePathBlack);
for (final File fileEntry : folder.listFiles()) {
if (!fileEntry.isDirectory()) {
// Resize Image
currentImage = ImageProc.resize(ImageIO.read(fileEntry), imgWidth, imgHeight);
File outFile = new File(procPathBlack + "/" + fileEntry.getName());
ImageIO.write(currentImage, "JPG", outFile);
sources.add(Highgui.imread(outFile.getPath()));
fileNames.add(outFile.getName());
}
}
} catch (IOException e) {
e.printStackTrace();
}
/** ************************************* */
float[] p1 = new float[30];
float[] p2 = new float[30];
/* Create Image Patches and calculate color feature vector for each patch */
Iterator<Mat> imgIter = sources.iterator();
Mat thisImage;
Mat featureMat = new Mat();
List<Mat> imagePatches = null;
Iterator<Mat> patchIter = null;
while (imgIter.hasNext()) {
thisImage = imgIter.next();
// Randomly Sample Patches
imagePatches = ImageProc.sampleImage(thisImage, patchWidth, patchHeight, numPatch);
patchIter = imagePatches.iterator();
// Create color feature vector for each patch
while (patchIter.hasNext()) {
featureMat.push_back(ImageProc.calBGRFeature(patchIter.next(), numBins));
}
}
Mat centers = new Mat();
Mat bestLabels = new Mat();
Core.kmeans(
featureMat,
k,
bestLabels,
new TermCriteria(TermCriteria.EPS, 0, Math.pow(10, -5)),
0,
Core.KMEANS_RANDOM_CENTERS,
centers);
MatOfFloat bestLabelRange = new MatOfFloat(0, k);
ArrayList<Mat> centerHist = new ArrayList<Mat>();
Mat centerHistMat = new Mat(0, k, CvType.CV_32FC1);
imgIter = sources.listIterator();
Iterator<String> nameIter = fileNames.iterator();
int ptr = 0;
int cnt = 0;
// Output CSV
try {
File outCSV = new File("output/res.csv");
FileWriter fstream = new FileWriter(outCSV);
BufferedWriter out = new BufferedWriter(fstream);
StringBuilder sb;
out.write("@relation staplers\n");
for (int n = 0; n < 200; n++) {
out.write("@attribute " + "a" + n + " real\n");
}
out.write("@attribute class {RedStapler, BlackStapler}\n\n");
out.write("@data\n\n");
while (imgIter.hasNext()) {
Mat thisMat = new Mat(bestLabels, new Range(ptr, ptr + numPatch), new Range(0, 1));
Mat mat = new Mat();
thisMat.convertTo(mat, CvType.CV_32F);
ArrayList<Mat> bestLabelList = new ArrayList<Mat>();
bestLabelList.add(mat);
Mat thisHist = new Mat();
Imgproc.calcHist(
bestLabelList, new MatOfInt(0), new Mat(), thisHist, new MatOfInt(k), bestLabelRange);
centerHist.add(thisHist);
// Create file
sb = new StringBuilder();
float[] histArr = new float[(int) thisHist.total()];
thisHist.get(0, 0, histArr);
for (int m = 0; m < histArr.length; m++) {
sb.append(histArr[m] + ",");
}
if (cnt++ < 10) sb.append("RedStapler");
else sb.append("BlackStapler");
sb.append("\n");
out.write(sb.toString());
// Close the output stream
centerHistMat.push_back(thisHist.t());
ptr += numPatch;
imgIter.next();
}
out.close();
} catch (IOException e) { // Catch exception if any
System.err.println("Error: " + e.getMessage());
System.exit(-1);
}
/* Support Vector Machine Validation */
Mat labelMat = new Mat(sources.size(), 1, CvType.CV_32FC1);
double[] labels = new double[20];
for (int i = 0; i < 10; i++) {
labels[i] = 1;
labels[i + 10] = -1;
}
labelMat.put(0, 0, labels);
CvSVMParams params = new CvSVMParams();
params.set_kernel_type(CvSVM.LINEAR);
CvSVM svm = new CvSVM();
svm.train(centerHistMat, labelMat, new Mat(), new Mat(), params);
svm.save("base/haha.txt");
String basePath = "base/predict/";
try {
File testCSV = new File("output/test.arff");
FileWriter testStream = new FileWriter(testCSV);
BufferedWriter testOut = new BufferedWriter(testStream);
testOut.write("@relation staplers\n");
for (int n = 0; n < 200; n++) {
testOut.write("@attribute " + "a" + n + " real\n");
}
testOut.write("@attribute class {RedStapler, BlackStapler}\n\n");
testOut.write("@data\n\n");
for (int m = 0; m < 21; m++) {
// System.out.println(basePath + m + ".jpg");
Mat testImg = Highgui.imread(basePath + m + ".jpg");
List<Mat> patches = ImageProc.sampleImage(testImg, patchWidth, patchHeight, numPatch);
List<Mat> features = new ArrayList<Mat>();
for (int i = 0; i < patches.size(); i++) {
Mat testVector = ImageProc.calBGRFeature(patches.get(i), numBins);
features.add(testVector);
}
Mat testData = ImageProc.calFeatureVector(features, centers);
StringBuilder testsb = new StringBuilder();
// String name = nameIter.next();
// sb.append(name + ",");
float[] data = new float[testData.cols()];
testData.get(0, 0, data);
for (int o = 0; o < data.length; o++) {
testsb.append(data[o] + ",");
}
if (m < 6) testsb.append("RedStapler");
else testsb.append("BlackStapler");
testsb.append("\n");
testOut.write(testsb.toString());
System.out.println("Img" + m + " " + svm.predict(testData));
}
} catch (IOException e) {
e.printStackTrace();
System.exit(-1);
}
}
/** new algo */
public static String findTips(Mat image) {
Mat binary = new Mat();
// Convert to B&W image
Imgproc.threshold(image, binary, 0, 255, Imgproc.THRESH_BINARY);
// Convert to binary image, 1 channel
Imgproc.cvtColor(binary, binary, Imgproc.COLOR_RGB2GRAY);
boolean flag_bg = false;
boolean handInQuarterOfImg = false;
int count_finger = 0;
int start = -1;
int end = -1;
int tester = 0; // test variable CAN BE REMOVED
double startPercent = 0, endPercent = 0;
int leftHeight = -1;
int firstHandPixel = -1;
int lastHandPixel = -1;
int KV_midFinger = -1;
int KV_indxFinger = -1;
double pixel[];
for (int i = 0; i < binary.cols(); i++) {
pixel = binary.get((int) (binary.rows() * 0.23), i);
if (pixel[0] != 0.0) {
if (firstHandPixel == -1) firstHandPixel = i;
if (i < image.cols() / 4) handInQuarterOfImg = true;
lastHandPixel = i;
leftHeight = i;
}
if (pixel[0] != 0.0) {
if (flag_bg == false) { // flag_bg is false
count_finger++;
flag_bg = true;
start = i;
}
end = i;
} else {
if (count_finger == 1) KV_midFinger = end;
else if (count_finger == 2) KV_indxFinger = start;
flag_bg = false;
if (tester != count_finger) { // remove this line -- just for
// testing
startPercent = ((double) start / (double) binary.cols()) * 100;
endPercent = ((double) end / (double) binary.cols()) * 100;
}
tester = count_finger; // remove this line -- just for testing
}
}
if (count_finger == 3) {
if (endPercent > 80.0) {
return "W";
} else {
return "F";
}
} else if (count_finger == 4) {
return "B";
} else if (count_finger == 1) {
if (endPercent > 70.0)
if (handInQuarterOfImg) return "B";
else {
int result = identify_R_U(binary);
switch (result) {
case 1:
return "R";
case 2:
return "U";
default:
return " ";
// return "RU";
}
}
else {
if ((float) (lastHandPixel - firstHandPixel) / image.cols() > 0.35) return "F";
else {
image = Filter.furtherClean(image);
Mat binary2 = new Mat();
// Convert to B&W image
Imgproc.threshold(image, binary2, 0, 255, Imgproc.THRESH_BINARY);
// Convert to binary image, 1 channel
Imgproc.cvtColor(binary2, binary2, Imgproc.COLOR_RGB2GRAY);
flag_bg = false;
int countHandArea = 0;
for (int i = 0; i < binary2.rows(); i++) {
pixel = binary2.get(i, (int) (binary2.cols() * 0.70));
if (pixel[0] != 0.0) {
if (!flag_bg) {
flag_bg = true;
countHandArea++;
}
} else flag_bg = false;
}
if (countHandArea == 1) {
if (checkAngleL(binary)) return "L";
else {
int result = identify_R_U(binary);
switch (result) {
case 1:
return "R";
case 2:
return "U";
default:
return " ";
}
}
} else if (countHandArea == 2) return "D";
else return " ";
}
}
} else if (count_finger == 2) {
// int result = identify_K_V(rgb, binary, new Point(KV_midFinger,
// (int) (binary.rows() * 0.23)), new Point(KV_indxFinger,
// (int) (binary.rows() * 0.23)));
int startX = (int) KV_midFinger;
int startY = (int) (binary.rows() * 0.23);
int lastDir = 1; // 0 up; 1 down
int currDir = 1;
int ctr = 1;
Point KV_PtsArray[] = new Point[3];
KV_PtsArray[0] = new Point(startX, startY);
// Core.circle(rgb,
// new Point(UL.x + startX, UL.y + startY), 5,
// new Scalar(0, 0, 255));
// double pixel[];
Log.i("START", "" + startX);
Log.i("END", "" + startY);
for (int i = startX; i < KV_indxFinger; i++) {
for (int k = (int) (binary.rows() * 0.23); k < binary.rows(); k++) {
pixel = binary.get(k, i + 1);
Log.i("CHECK1", "" + binary.rows());
Log.i("CHECK2", "" + binary.rows() * 0.23);
Log.i("CHECK3", "" + (int) (binary.rows() * 0.23));
Log.i("CHECK4", "" + pixel[0]);
if (pixel[0] != 0.0) {
// Plots the edges from hullPoint[0] to hullPoint[1]
// Core.circle(rgb, new Point(UL.x + startX + 1,
// UL.y
// + k), 5, new Scalar(0, 255, 0));
if (k > startY) currDir = 1;
else if (k < startY) currDir = 0;
if (currDir != lastDir && ctr < 3) {
// Plots 3 significant pts in K and V
// Core.circle(rgb,
// new Point(UL.x + i + 1, UL.y + k), 5,
// new Scalar(0, 0, 255));
KV_PtsArray[ctr] = new Point(i + 1, k);
ctr++;
}
lastDir = currDir;
// startX = startX + 1;
startY = k;
break;
}
}
if (ctr < 3 && i + 1 == (int) KV_indxFinger) {
// Core.circle(rgb, new Point(UL.x + startX, UL.y + startY),
// 5,
// new Scalar(0, 0, 255));
KV_PtsArray[ctr] = new Point(i, startY);
ctr++;
}
if (ctr == 3) break;
}
if (ctr == 3) {
if (KV_PtsArray[0].y == KV_PtsArray[2].y) return "V"; // V
else return "K"; // K
} else return " ";
}
return Integer.toString(count_finger);
}
// Bhattacharyya coefficient for two distributions
// a, b are original data samples
// better if a, b are dimension-reduced Principal Components
public static double Bhattacharyya(Mat a, Mat b) {
// if not dimension-reduced
// int k = a.cols()/2;
// Mat temp1 = new Mat();
// Mat temp2 = new Mat();
// Mat pa = new Mat();
// Mat pb = new Mat();
// Core.PCACompute(a, temp1, temp2, k);
// Core.PCAProject(a, temp1, temp2, pa);
// Core.PCACompute(b, temp1, temp2, k);
// Core.PCAProject(b, temp1, temp2, pb);
//
Mat cova = new Mat();
Mat covb = new Mat();
Mat meana = new Mat();
Mat meanb = new Mat();
Core.calcCovarMatrix(a, cova, meana, Core.COVAR_NORMAL | Core.COVAR_ROWS);
Core.calcCovarMatrix(b, covb, meanb, Core.COVAR_NORMAL | Core.COVAR_ROWS);
// if not dimension-reduced
// Core.calcCovarMatrix(pa, cova, meana, Core.COVAR_NORMAL|Core.COVAR_ROWS);
// Core.calcCovarMatrix(pb, covb, meanb, Core.COVAR_NORMAL|Core.COVAR_ROWS);
// System.out.println("cov");
// System.out.println(cova.dump());
// System.out.println(covb.dump());
double detcova = Core.determinant(cova);
double detcovb = Core.determinant(covb);
Mat cov = new Mat();
Core.add(cova, covb, cov);
// cov = cov.mul(cov, 0.5);
// Core.scaleAdd(cov, 0.5, new Mat(), cov);
for (int i = 0; i < cov.rows(); i++)
for (int j = 0; j < cov.cols(); j++) {
cov.put(i, j, 0.5 * cov.get(i, j)[0]);
}
double detcov = Core.determinant(cov);
Mat inv = new Mat(); // inverse of cov
Core.invert(cov, inv);
Mat mabsub = new Mat();
Core.subtract(meana, meanb, mabsub);
Mat mabsub_tp = new Mat();
Core.transpose(mabsub, mabsub_tp);
Mat temp = new Mat();
Core.gemm(mabsub, inv, 1, new Mat(), 0, temp);
// System.out.println(temp.dump());
// System.out.println(mabsub_tp.dump());
Mat res = new Mat();
Core.gemm(temp, mabsub_tp, 1, new Mat(), 0, res);
// System.out.println(res.dump());
// Mat temp = mabsub_tp.mul(inv);
// System.out.println(temp.dump());
//
// Mat res = temp.mul(mabsub);
// System.out.println(res.dump());
double s2 = Math.sqrt(detcov / (Math.sqrt(detcova * detcovb)));
// s1 = 0.125 * (mabsub_tp*inv*mabsub)
double s1 = 0.125 * res.get(0, 0)[0];
double bdis = 1 / (Math.exp(s1) * s2);
return bdis;
}
