public void templateMatching() {
System.loadLibrary(Core.NATIVE_LIBRARY_NAME);
int match_method = 5;
int max_Trackbar = 5;
Mat data = Highgui.imread("images/training_data/1" + "/data (" + 1 + ").jpg");
Mat temp = Highgui.imread("images/template.jpg");
Mat img = data.clone();
int result_cols = img.cols() - temp.cols() + 1;
int result_rows = img.rows() - temp.rows() + 1;
Mat result = new Mat(result_rows, result_cols, CvType.CV_32FC1);
Imgproc.matchTemplate(img, temp, result, match_method);
Core.normalize(result, result, 0, 1, Core.NORM_MINMAX, -1, new Mat());
double minVal;
double maxVal;
Point minLoc;
Point maxLoc;
Point matchLoc;
// minMaxLoc( result, &minVal, &maxVal, &minLoc, &maxLoc, Mat() );
Core.MinMaxLocResult res = Core.minMaxLoc(result);
if (match_method == Imgproc.TM_SQDIFF || match_method == Imgproc.TM_SQDIFF_NORMED) {
matchLoc = res.minLoc;
} else {
matchLoc = res.maxLoc;
}
// / Show me what you got
Core.rectangle(
img,
matchLoc,
new Point(matchLoc.x + temp.cols(), matchLoc.y + temp.rows()),
new Scalar(0, 255, 0));
// Save the visualized detection.
Highgui.imwrite("images/samp.jpg", img);
}
// Transform the json-type feature to mat-type
public static Mat json2mat(String json) {
JsonParser parser = new JsonParser();
JsonElement parseTree = parser.parse(json);
// Verify the input is JSON type
if (!parseTree.isJsonObject()) {
System.out.println("The input is not a JSON type...\nExiting...");
System.exit(1);
}
JsonObject jobj = parser.parse(json).getAsJsonObject();
if (jobj == null || !jobj.isJsonObject() || jobj.isJsonNull()) {
return null;
}
// Detect broken/null features
JsonElement r = jobj.get("rows");
if (r == null) {
return null;
}
int rows = jobj.get("rows").getAsInt();
int cols = jobj.get("cols").getAsInt();
int type = jobj.get("type").getAsInt();
String data = jobj.get("data").getAsString();
String[] pixs = data.split(",");
Mat descriptor = new Mat(rows, cols, type);
for (String pix : pixs) {
String[] tmp = pix.split(" ");
int r_pos = Integer.valueOf(tmp[0]);
int c_pos = Integer.valueOf(tmp[1]);
double rgb = Double.valueOf(tmp[2]);
descriptor.put(r_pos, c_pos, rgb);
}
return descriptor;
}
public static void extractQueryFeatures2HDFS(String filename, Job job) throws IOException {
// Read the local image.jpg as a Mat
Mat query_mat_float =
Highgui.imread(LOCAL_USER_DIR + ID + INPUT + "/" + filename, CvType.CV_32FC3);
// Convert RGB to GRAY
Mat query_gray = new Mat();
Imgproc.cvtColor(query_mat_float, query_gray, Imgproc.COLOR_RGB2GRAY);
// Convert the float type to unsigned integer(required by SIFT)
Mat query_mat_byte = new Mat();
query_gray.convertTo(query_mat_byte, CvType.CV_8UC3);
// // Resize the image to 1/FACTOR both width and height
// Mat query_mat_byte = FeatureExtraction.resize(query_mat_byte);
// Extract the feature from the (Mat)image
Mat query_features = FeatureExtraction.extractFeature(query_mat_byte);
System.out.println(PREFIX + "Extracting the query image feature...");
System.out.println("query_mat(float,color):" + query_mat_float);
System.out.println("query_mat(float,gray):" + query_gray);
System.out.println("query_mat(byte,gray):" + query_mat_byte);
System.out.println("query_mat_features:" + query_features);
System.out.println();
// Store the feature to the hdfs in order to use it later in different map tasks
System.out.println(PREFIX + "Generating the feature file for the query image in HDFS...");
FileSystem fs = FileSystem.get(job.getConfiguration());
String featureFileName = filename.substring(0, filename.lastIndexOf(".")) + ".json";
FSDataOutputStream fsDataOutputStream =
fs.create(new Path(HDFS_HOME + USER + ID + INPUT + "/" + featureFileName));
BufferedWriter bw =
new BufferedWriter(new OutputStreamWriter(fsDataOutputStream, StandardCharsets.UTF_8));
bw.write(FeatureExtraction.mat2json(query_features));
bw.close();
System.out.println(PREFIX + "Query feature extraction finished...");
System.out.println();
}
/** 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();
}
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);
}
}
}
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;
}
/**
* Determines where captured pieces are
*
* @param in Mat image of the board
*/
public void findCaptured(Mat in) {
int vsegment = in.rows() / 8; // only accounts 8 playable
int hsegment = in.cols() / 12; // 8 playable, 2 capture, 2 extra
int offset; // offset for playable board
int capSquares = 12; // number of capture squares
int rowNum = 1; // starting row number for capture squares
int rightdx = 48;
int leftdx = 0;
offset = hsegment;
int count = 0;
// keep track of captured squares
// left: end user, right: system
for (int i = 0; i < capSquares; i++) {
// find where roi should be
Point p1 =
new Point(
offset + count * hsegment, rowNum * vsegment); // top left point of rectangle (x,y)
Point p2 =
new Point(
offset + (count + 1) * hsegment,
(rowNum + 1) * vsegment); // bottom right point of rectangle (x,y)
// create rectangle that is board square
Rect bound = new Rect(p1, p2);
char color;
// frame only includes rectangle
Mat roi = new Mat(in, bound);
// get the color
color = identifyColor(roi);
switch (color) {
case COLOR_BLUE:
// Imgproc.rectangle(in, p1, p2, new Scalar(255, 0, 0), 3);
Core.rectangle(in, p1, p2, new Scalar(255, 0, 0), 2);
captured[i] = 1;
break;
case COLOR_ORANGE:
// Imgproc.rectangle(in, p1, p2, new Scalar(0, 128, 255), 3);
Core.rectangle(in, p1, p2, new Scalar(0, 128, 255), 2);
captured[i] = 1;
break;
case COLOR_WHITE:
// Imgproc.rectangle(in, p1, p2, new Scalar(255, 255, 255), 3);
Core.rectangle(in, p1, p2, new Scalar(255, 255, 255), 2);
captured[i] = 0;
break;
case COLOR_BLACK:
// Imgproc.rectangle(in, p1, p2, new Scalar(0, 0, 0), 3);
Core.rectangle(in, p1, p2, new Scalar(255, 255, 255), 2);
captured[i] = 0;
break;
}
count++;
if (count == 1) {
offset = hsegment * 10 - rightdx;
} else if (count == 2) {
count = 0;
rightdx -= 6;
leftdx += 6;
offset = hsegment - leftdx;
rowNum++;
}
}
}
/**
* Identifies the color in the frame
*
* @param in the Mat image in the region of interest
* @return the color
*/
public char identifyColor(Mat in) {
// Mat blue = new Mat(in.rows(), in.cols(), CvType.CV_8UC1);
// Mat green = new Mat(in.rows(), in.cols(), CvType.CV_8UC1);
// Mat red = new Mat(in.rows(), in.cols(), CvType.CV_8UC1);
// split the channels of the image
Mat blue = new Mat(); // default is CV_8UC3
Mat green = new Mat();
Mat red = new Mat();
List<Mat> channels = new ArrayList<Mat>(3);
Core.split(in, channels);
blue = channels.get(0); // makes all 3 CV_8UC1
green = channels.get(1);
red = channels.get(2);
// System.out.println(blue.toString());
// add the intensities
Mat intensity = new Mat(in.rows(), in.cols(), CvType.CV_32F);
// Mat mask = new Mat();
Core.add(blue, green, intensity); // , mask, CvType.CV_32F);
Core.add(intensity, red, intensity); // , mask, CvType.CV_32F);
// not sure if correct from here to ...
Mat inten = new Mat();
Core.divide(intensity, Scalar.all(3.0), inten);
// System.out.println(intensity.toString());
// Core.divide(3.0, intensity, inten);
// if intensity = intensity / 3.0; means element-wise division
// use intensity.muls(Mat m)
// so make new Mat m of same size that has each element of 1/3
/*
* or
* About per-element division you can use Core.divide()
Core.divide(A,Scalar.all(d), B);
It's equivalent to B=A/d
*/
// find normalized values
Mat bnorm = new Mat();
Mat gnorm = new Mat();
Mat rnorm = new Mat();
// blue.convertTo(blue, CvType.CV_32F);
// green.convertTo(green, CvType.CV_32F);
// red.convertTo(red, CvType.CV_32F);
Core.divide(blue, inten, bnorm);
Core.divide(green, inten, gnorm);
Core.divide(red, inten, rnorm);
// find average norm values
Scalar val = new Scalar(0);
val = Core.mean(bnorm);
String value[] = val.toString().split(",");
String s = value[0].substring(1);
double bavg = Double.parseDouble(s);
val = Core.mean(gnorm);
String value1[] = val.toString().split(",");
String s1 = value1[0].substring(1);
double gavg = Double.parseDouble(s1);
val = Core.mean(rnorm);
String value2[] = val.toString().split(",");
String s2 = value2[0].substring(1);
double ravg = Double.parseDouble(s2);
// ... here
// original values
/*
// define the reference color values
//double RED[] = {0.4, 0.5, 1.8};
//double GREEN[] = {1.0, 1.2, 1.0};
double BLUE[] = {1.75, 1.0, 0.5};
//double YELLOW[] = {0.82, 1.7, 1.7};
double ORANGE[] = {0.2, 1.0, 2.0};
double WHITE[] = {2.0, 1.7, 1.7};
//double BLACK[] = {0.0, 0.3, 0.3};
*/
// define the reference color values
// double RED[] = {0.4, 0.5, 1.8};
// double GREEN[] = {1.0, 1.2, 1.0};
double BLUE[] = {1.75, 1.0, 0.5};
// double YELLOW[] = {0.82, 1.7, 1.7};
double ORANGE[] = {0.2, 1.0, 2.0};
double WHITE[] = {2.0, 1.7, 1.7};
// double BLACK[] = {0.0, 0.3, 0.3};
// compute the square error relative to the reference color values
// double minError = 3.0;
double minError = 2.0;
double errorSqr;
char bestFit = 'x';
// test++;
// System.out.print("\n\n" + test + "\n\n");
// check BLUE fitness
errorSqr = normSqr(BLUE[0], BLUE[1], BLUE[2], bavg, gavg, ravg);
System.out.println("Blue: " + errorSqr);
if (errorSqr < minError) {
minError = errorSqr;
bestFit = COLOR_BLUE;
}
// check ORANGE fitness
errorSqr = normSqr(ORANGE[0], ORANGE[1], ORANGE[2], bavg, gavg, ravg);
System.out.println("Orange: " + errorSqr);
if (errorSqr < minError) {
minError = errorSqr;
bestFit = COLOR_ORANGE;
}
// check WHITE fitness
errorSqr = normSqr(WHITE[0], WHITE[1], WHITE[2], bavg, gavg, ravg);
System.out.println("White: " + errorSqr);
if (errorSqr < minError) {
minError = errorSqr;
bestFit = COLOR_WHITE;
}
// check BLACK fitness
/*errorSqr = normSqr(BLACK[0], BLACK[1], BLACK[2], bavg, gavg, ravg);
System.out.println("Black: " + errorSqr);
if(errorSqr < minError)
{
minError = errorSqr;
bestFit = COLOR_BLACK;
}*/
// return the best fit color label
return bestFit;
}
public void processWithContours(Mat in, Mat out) {
int playSquares = 32; // number of playable game board squares
// keep track of starting row square
int parity = 0; // 0 is even, 1 is odd, tied to row number
int count = 0; // row square
int rowNum = 0; // row number, starting at 0
int vsegment = in.rows() / 8; // only accounts 8 playable
int hsegment = in.cols() / 10; // 8 playable, 2 capture
int hOffset = hsegment * 2; // offset for playable board
int vOffset = vsegment + 40;
// For angle of camera
int dx = 80;
int ddx = 0;
hsegment -= 16;
int dy = 20;
vsegment -= 24;
int ddy = 0;
// Go through all playable squares
for (int i = 0; i < playSquares; i++) {
// change offset depending on the row
if (parity == 0) // playable squares start on 2nd square from left
{
if (rowNum >= 5) dx -= 3;
hOffset = hsegment * 2 + dx;
} else // playable squares start on immediate left
{
if (rowNum >= 5) dx -= 3;
hOffset = hsegment + dx;
}
if (rowNum == 0) ddy = 5;
if (rowNum == 4) if (count == 6) ddx = 10;
if (rowNum == 5) {
if (count == 0) ddx = -6;
else if (count == 2) ddx = 6;
else if (count == 4) ddx = 12;
else if (count == 6) ddx = 20;
}
if (rowNum == 6) {
if (count == 0) ddx = 0;
else if (count == 2) ddx = 16;
else if (count == 4) ddx = 32;
else if (count == 6) ddx = 40;
}
if (rowNum == 7) {
if (count == 0) ddx = 6;
else if (count == 2) ddx = 24;
else if (count == 4) ddx = 40;
else ddx = 52;
}
// find where roi should be
// System.out.println("" + vOffset);
Point p1 =
new Point(
hOffset + count * hsegment + ddx + 5,
vOffset + rowNum * vsegment - dy - 5 - ddy); // top left point of rectangle (x,y)
Point p2 =
new Point(
hOffset + (count + 1) * hsegment + ddx - 5,
vOffset
+ (rowNum + 1) * vsegment
- dy
- 5
- ddy); // bottom right point of rectangle (x,y)
// create rectangle that is board square
Rect bound = new Rect(p1, p2);
Mat roi;
char color;
if (i == 0) {
// frame only includes rectangle
roi = new Mat(in, bound);
// get the color
color = identifyColor(roi);
// copy input image to output image
in.copyTo(out);
} else {
// frame only includes rectangle
roi = new Mat(out, bound);
// get the color
color = identifyColor(roi);
}
Imgproc.cvtColor(roi, roi, Imgproc.COLOR_BGR2GRAY); // change to single color
Mat canny = new Mat();
Imgproc.Canny(roi, canny, 20, 40); // make image a canny image that is only edges; 2,4
// lower threshold values find more edges
List<MatOfPoint> contours = new ArrayList<MatOfPoint>();
Mat hierarchy = new Mat(); // holds nested contour information
Imgproc.findContours(
canny,
contours,
hierarchy,
Imgproc.RETR_LIST,
Imgproc.CHAIN_APPROX_SIMPLE); // Imgproc.RETR_LIST, TREE
System.out.println(++test + "\t" + contours.size());
if (contours.size() > 3) // or error value for color is below 1
{
switch (color) {
case COLOR_BLUE:
// Imgproc.rectangle(out, p1, p2, new Scalar(255, 0, 0), 2);
Core.rectangle(out, p1, p2, new Scalar(255, 0, 0), 2);
board[i] = CheckersBoard.BLACK; // end user's piece
break;
case COLOR_ORANGE:
// Imgproc.rectangle(out, p1, p2, new Scalar(0, 128, 255), 2);
Core.rectangle(out, p1, p2, new Scalar(0, 128, 255), 2);
board[i] = CheckersBoard.WHITE; // system's piece
break;
case COLOR_WHITE:
// Imgproc.rectangle(out, p1, p2, new Scalar(255, 255, 255), 2);
Core.rectangle(out, p1, p2, new Scalar(255, 255, 255), 2);
board[i] = CheckersBoard.EMPTY;
break;
case COLOR_BLACK: // this is black
// Imgproc.rectangle(out, p1, p2, new Scalar(0, 0, 0), 2);
Core.rectangle(
out,
p1,
p2,
new Scalar(0, 0, 0),
2); // maybe add 8, 0 as line type and fractional bits
board[i] = CheckersBoard.EMPTY;
break;
}
}
System.out.println("in color switch " + board[i]);
count += 2;
if (count == 8) {
parity = ++parity % 2; // change odd or even
count = 0;
rowNum++;
hsegment += 2;
dx -= 10;
dy += 10;
vsegment += 3;
ddy = 0;
}
}
}
/**
* Determines which pieces are kings
*
* @param in Mat image of board
*/
public void determineKings(Mat in) {
int playSquares = 32;
Mat dst = new Mat(in.rows(), in.cols(), in.type());
in.copyTo(dst);
Imgproc.cvtColor(dst, dst, Imgproc.COLOR_BGR2GRAY); // change to single color
Mat canny = new Mat();
Imgproc.Canny(dst, canny, 100, 200); // make image a canny image that is only edges; 2,4
// lower threshold values find more edges
List<MatOfPoint> contours = new ArrayList<MatOfPoint>();
Mat hierarchy = new Mat(); // holds nested contour information
Imgproc.findContours(
canny,
contours,
hierarchy,
Imgproc.RETR_LIST,
Imgproc.CHAIN_APPROX_SIMPLE); // Imgproc.RETR_LIST, TREE
// draw contour image
Mat mask = new Mat();
mask = Mat.zeros(dst.size(), dst.type());
Imgproc.drawContours(
mask, contours, -1, new Scalar(255, 255, 255), 1, 8, hierarchy, 2, new Point());
Highgui.imwrite("contours.jpg", mask);
ArrayList occupied = new ArrayList<Integer>();
for (int i = 0; i < playSquares; i++) {
if (board[i] != 0) occupied.add(i);
}
for (int i = 0; i < contours.size(); i++) // assuming only contours are checker pieces
{
// determine if it should be a king
// use Rect r = Imgproc.boundingRect then find height of it by r.height
// Get bounding rect of contour
Rect bound = Imgproc.boundingRect(contours.get(i));
if (bound.height > in.rows() / 8) {
// board[(int) occupied.get(0)]++; // make it a king
// occupied.remove(0);
}
}
// or apply to each region of interest
/*
// keep track of starting row square
int parity = 0; // 0 is even, 1 is odd, tied to row number
int count = 0; // row square
int rowNum = 0; // row number, starting at 0
int vsegment = in.rows() / 8; // only accounts 8 playable
int hsegment = in.cols() / 12; // 8 playable, 2 capture, 2 extra
int offset = hsegment * 2; // offset for playable board
// For angle of camera
int dx = 48;
hsegment -= 8;
// Go through all playable squares
for (int i = 0; i < playSquares; i++)
{
// change offset depending on the row
if (parity == 0) // playable squares start on immediate left
offset = hsegment * 3 + dx;
else // playable squares start on 2nd square from left
offset = hsegment * 2 + dx;
// find where roi should be
Point p1 = new Point(offset + count * hsegment, rowNum * vsegment); // top left point of rectangle (x,y)
Point p2 = new Point(offset + (count + 1) * hsegment, (rowNum + 1) * vsegment); // bottom right point of rectangle (x,y)
// create rectangle that is board square
Rect bound = new Rect(p1, p2);
// frame only includes rectangle
Mat roi = new Mat(in, bound);
Imgproc.cvtColor(roi, roi, Imgproc.COLOR_BGR2GRAY); // change to single color
Mat canny = new Mat();
Imgproc.Canny(roi, canny, 2, 4); // make image a canny image that is only edges; 2,4
// lower threshold values find more edges
List<MatOfPoint> contours = new ArrayList<MatOfPoint>();
Mat hierarchy = new Mat(); // holds nested contour information
Imgproc.findContours(canny, contours, hierarchy, Imgproc.RETR_EXTERNAL, Imgproc.CHAIN_APPROX_SIMPLE); // Imgproc.RETR_LIST, TREE
// Get bounding rect of contour
Rect rect = Imgproc.boundingRect(contours.get(0));
if (rect.height > in.rows() / 8)
{
board[i]++; // make it a king
}
count += 2;
if (count == 8)
{
parity = ++parity % 2; // change odd or even
count = 0;
rowNum++;
hsegment += 1;
dx -= 6;
}
}*/
}
/**
* Processes the board image
*
* @param in image captured of board
* @param out processed image of board
*/
public void processFrame(Mat in, Mat out) {
// multiple regions of interest
int playSquares = 32; // number of playable game board squares
// keep track of starting row square
int parity = 0; // 0 is even, 1 is odd, tied to row number
int count = 0; // row square
int rowNum = 0; // row number, starting at 0
int vsegment = in.rows() / 8; // only accounts 8 playable
int hsegment = in.cols() / 10; // 8 playable, 2 capture
int hOffset = hsegment * 2; // offset for playable board
int vOffset = vsegment + 40;
// For angle of camera
int dx = 80;
int ddx = 0;
hsegment -= 16;
int dy = 20;
vsegment -= 24;
// Go through all playable squares
for (int i = 0; i < playSquares; i++) {
// change offset depending on the row
if (parity == 0) // playable squares start on 2nd square from left
{
if (rowNum >= 5) dx -= 3;
hOffset = hsegment * 2 + dx;
} else // playable squares start on immediate left
{
if (rowNum >= 5) dx -= 3;
hOffset = hsegment + dx;
}
if (rowNum == 4) if (count == 6) ddx = 10;
if (rowNum == 5) {
if (count == 0) ddx = -6;
else if (count == 2) ddx = 6;
else if (count == 4) ddx = 12;
else if (count == 6) ddx = 20;
}
if (rowNum == 6) {
if (count == 0) ddx = 0;
else if (count == 2) ddx = 16;
else if (count == 4) ddx = 32;
else if (count == 6) ddx = 40;
}
if (rowNum == 7) {
if (count == 0) ddx = 0;
else if (count == 2) ddx = 24;
else if (count == 4) ddx = 40;
else ddx = 52;
}
// find where roi should be
// System.out.println("" + vOffset);
Point p1 =
new Point(
hOffset + count * hsegment + ddx,
vOffset + rowNum * vsegment - dy); // top left point of rectangle (x,y)
Point p2 =
new Point(
hOffset + (count + 1) * hsegment + ddx,
vOffset + (rowNum + 1) * vsegment - dy); // bottom right point of rectangle (x,y)
// create rectangle that is board square
Rect bound = new Rect(p1, p2);
char color;
if (i == 0) {
// frame only includes rectangle
Mat roi = new Mat(in, bound);
// get the color
color = identifyColor(roi);
// copy input image to output image
in.copyTo(out);
} else {
// frame only includes rectangle
Mat roi = new Mat(out, bound);
// get the color
color = identifyColor(roi);
}
// annotate the output image
// scalar values as (blue, green, red)
switch (color) {
case COLOR_BLUE:
// Imgproc.rectangle(out, p1, p2, new Scalar(255, 0, 0), 2);
Core.rectangle(out, p1, p2, new Scalar(255, 0, 0), 2);
board[i] = CheckersBoard.BLACK; // end user's piece
break;
case COLOR_ORANGE:
// Imgproc.rectangle(out, p1, p2, new Scalar(0, 128, 255), 2);
Core.rectangle(out, p1, p2, new Scalar(0, 128, 255), 2);
board[i] = CheckersBoard.WHITE; // system's piece
break;
case COLOR_WHITE:
// Imgproc.rectangle(out, p1, p2, new Scalar(255, 255, 255), 2);
Core.rectangle(out, p1, p2, new Scalar(255, 255, 255), 2);
board[i] = CheckersBoard.EMPTY;
break;
case COLOR_BLACK: // this is black
// Imgproc.rectangle(out, p1, p2, new Scalar(0, 0, 0), 2);
Core.rectangle(
out,
p1,
p2,
new Scalar(0, 0, 0),
2); // maybe add 8, 0 as line type and fractional bits
board[i] = CheckersBoard.EMPTY;
break;
}
count += 2;
if (count == 8) {
parity = ++parity % 2; // change odd or even
count = 0;
rowNum++;
hsegment += 2;
dx -= 10;
dy += 10;
vsegment += 3;
}
}
}
public void map(Text key, Text value, Context context)
throws InterruptedException, IOException {
String filename = key.toString();
String json = value.toString();
// Make sure the input is valid
if (!(filename.isEmpty() || json.isEmpty())) {
// Change the json-type feature to Mat-type feature
Mat descriptor = json2mat(json);
if (descriptor != null) {
// Read the query feature from the cache in Hadoop
Mat query_features;
String pathStr = context.getConfiguration().get("featureFilePath");
FileSystem fs = FileSystem.get(context.getConfiguration());
FSDataInputStream fsDataInputStream = fs.open(new Path(pathStr));
StringBuilder sb = new StringBuilder();
// Use a buffer to read the query_feature
int remain = fsDataInputStream.available();
while (remain > 0) {
int read;
byte[] buf = new byte[BUF_SIZE];
read = fsDataInputStream.read(buf, fsDataInputStream.available() - remain, BUF_SIZE);
sb.append(new String(buf, 0, read, StandardCharsets.UTF_8));
remain = remain - read;
System.out.println("remain:" + remain + "\tread:" + read + "\tsb.size:" + sb.length());
}
// Read the query_feature line by line
// Scanner sc = new Scanner(fsDataInputStream, "UTF-8");
// StringBuilder sb = new StringBuilder();
// while (sc.hasNextLine()) {
// sb.append(sc.nextLine());
// }
// String query_json = sb.toString();
// String query_json = new String(buf, StandardCharsets.UTF_8);
String query_json = sb.toString();
fsDataInputStream.close();
query_features = json2mat(query_json);
// Get the similarity of the current database image against the query image
DescriptorMatcher matcher = DescriptorMatcher.create(DescriptorMatcher.FLANNBASED);
MatOfDMatch matches = new MatOfDMatch();
// Ensure the two features have same length of cols (the feature extracted are all 128
// cols(at least in this case))
if (query_features.cols() == descriptor.cols()) {
matcher.match(query_features, descriptor, matches);
DMatch[] dMatches = matches.toArray();
// Calculate the max/min distances
// double max_dist = Double.MAX_VALUE;
// double min_dist = Double.MIN_VALUE;
double max_dist = 0;
double min_dist = 100;
for (int i = 0; i < dMatches.length; i++) {
double dist = dMatches[i].distance;
if (min_dist > dist) min_dist = dist;
if (max_dist < dist) max_dist = dist;
}
// Only distances ≤ threshold are good matches
double threshold = max_dist * THRESHOLD_FACTOR;
// double threshold = min_dist * 2;
LinkedList<DMatch> goodMatches = new LinkedList<DMatch>();
for (int i = 0; i < dMatches.length; i++) {
if (dMatches[i].distance <= threshold) {
goodMatches.addLast(dMatches[i]);
}
}
// Get the ratio of good_matches to all_matches
double ratio = (double) goodMatches.size() / (double) dMatches.length;
System.out.println("*** current_record_filename:" + filename + " ***");
System.out.println("feature:" + descriptor + "\nquery_feature:" + query_features);
System.out.println(
"min_dist of keypoints:" + min_dist + " max_dist of keypoints:" + max_dist);
System.out.println(
"total_matches:" + dMatches.length + "\tgood_matches:" + goodMatches.size());
// System.out.println("type:" + descriptor.type() + " channels:" +
// descriptor.channels() + " rows:" + descriptor.rows() + " cols:" + descriptor.cols());
// System.out.println("qtype:" + query_features.type() + "
// qchannels:" + query_features.channels() + " qrows:" + query_features.rows() + "
// qcols:" + query_features.cols());
System.out.println();
if (ratio > PERCENTAGE_THRESHOLD) {
// Key:1 Value:filename|ratio
context.write(ONE, new Text(filename + "|" + ratio));
// context.write(ONE, new Text(filename + "|" +
// String.valueOf(goodMatches.size())));
}
} else {
System.out.println("The size of the features are not equal");
}
} else {
// a null pointer, do nothing
System.out.println("A broken/null feature:" + filename);
System.out.println();
}
}
}
public static void main(String[] args) {
System.loadLibrary(Core.NATIVE_LIBRARY_NAME);
// Mat mat = Mat.eye( 3, 3, CvType.CV_8UC1 );
// System.out.println( "mat = " + mat.dump() );
Sample n = new Sample();
// n.templateMatching();
// put text in image
// Mat data= Highgui.imread("images/erosion.jpg");
// Core.putText(data, "Sample", new Point(50,80), Core.FONT_HERSHEY_SIMPLEX, 1, new
// Scalar(0,0,0),2);
//
// Highgui.imwrite("images/erosion2.jpg", data);
// getting dct of an image
String path = "images/croppedfeature/go (20).jpg";
path = "images/wordseg/img1.png";
Mat image = Highgui.imread(path, Highgui.IMREAD_GRAYSCALE);
ArrayList<MatOfPoint> contours = new ArrayList<MatOfPoint>();
Imgproc.threshold(image, image, 0, 255, Imgproc.THRESH_OTSU);
Imgproc.threshold(image, image, 220, 128, Imgproc.THRESH_BINARY_INV);
Mat newImg = new Mat(45, 100, image.type());
newImg.setTo(new Scalar(0));
n.copyMat(image, newImg);
int vgap = 25;
int hgap = 45 / 3;
Moments m = Imgproc.moments(image, false);
Mat hu = new Mat();
Imgproc.HuMoments(m, hu);
System.out.println(hu.dump());
// //divide the mat into 12 parts then get the features of each part
// int count=1;
// for(int j=0; j<45; j+=hgap){
// for(int i=0;i<100;i+=vgap){
// Mat result = newImg.submat(j, j+hgap, i, i+vgap);
//
//
// Moments m= Imgproc.moments(result, false);
// double m01= m.get_m01();
// double m00= m.get_m00();
// double m10 = m.get_m10();
// int x= m00!=0? (int)(m10/m00):0;
// int y= m00!=0? (int)(m01/m00):0;
// Mat hu= new Mat();
// Imgproc.HuMoments(m, hu);
// System.out.println(hu.dump());
// System.out.println(count+" :"+x+" and "+y);
// Imgproc.threshold(result, result, 0,254, Imgproc.THRESH_BINARY_INV);
// Highgui.imwrite("images/submat/"+count+".jpg", result);
// count++;
//
// }
// }
//
// for(int i=vgap;i<100;i+=vgap){
// Point pt1= new Point(i, 0);
// Point pt2= new Point(i, 99);
// Core.line(newImg, pt1, pt2, new Scalar(0,0,0));
// }
// for(int i=hgap;i<45;i+=hgap){
// Point pt1= new Point(0, i);
// Point pt2= new Point(99, i);
// Core.line(newImg, pt1, pt2, new Scalar(0,0,0));
// }
// Highgui.imwrite("images/submat/copyto.jpg", newImg);
}