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);
}
}
