Java tutorial
/* * To change this license header, choose License Headers in Project Properties. * To change this template file, choose Tools | Templates * and open the template in the editor. */ package classes; /** * * @author ggmendez */ import java.awt.image.BufferedImage; import java.util.ArrayList; import java.util.HashMap; import java.util.Iterator; import java.util.List; import java.util.Map; import java.util.Set; import javax.servlet.http.HttpServletRequest; import org.opencv.core.Core; import org.opencv.core.CvType; import org.opencv.core.Mat; import org.opencv.core.Point; import org.opencv.core.Rect; import org.opencv.core.Scalar; import org.opencv.core.Size; import org.opencv.core.TermCriteria; import org.opencv.highgui.Highgui; import org.opencv.imgproc.Imgproc; import org.opencv.video.BackgroundSubtractorMOG2; public class TextRecognitionPreparer { public static void prepareImage(String fileName, Scalar backgroundColor, Scalar userPickedColor) { Mat img = Highgui.imread(fileName); generateRecognizableImages(img, backgroundColor, userPickedColor); } public static ArrayList<String> generateRecognizableImagesNames(Mat img, Scalar userPickedColor, String imageID, HttpServletRequest request) { ArrayList<String> imageNames = new ArrayList<String>(); Mat filledImage = img.clone(); Scalar newVal = new Scalar(userPickedColor.val[2], userPickedColor.val[1], userPickedColor.val[0]); Imgproc.floodFill(filledImage, new Mat(), new Point(0, 0), newVal); String file1 = imageID + "_filledImage.png"; // Highgui.imwrite(file1, filledImage); imageNames.add(ImageUtils.saveImage(filledImage, file1, request)); Mat filledGrayImage = new Mat(); Imgproc.cvtColor(filledImage, filledGrayImage, Imgproc.COLOR_BGR2GRAY); String file2 = imageID + "_filledGrayImage.png"; // Highgui.imwrite(file2, filledGrayImage); imageNames.add(ImageUtils.saveImage(filledGrayImage, file2, request)); Mat gaussianGrayImage = new Mat(); Imgproc.GaussianBlur(filledGrayImage, gaussianGrayImage, new Size(0, 0), 3); Core.addWeighted(filledGrayImage, 3.5, gaussianGrayImage, -1, 0, gaussianGrayImage); String file3 = imageID + "_sharpenedImage.png"; // Highgui.imwrite(file3, gaussianGrayImage); imageNames.add(ImageUtils.saveImage(gaussianGrayImage, file3, request)); // Mat filledBinarizedImage2 = new Mat(); // Imgproc.adaptiveThreshold(filledGrayImage, filledBinarizedImage2, 255, Imgproc.ADAPTIVE_THRESH_MEAN_C, Imgproc.THRESH_BINARY, 75, 10); // String file5 = imageID + "_filledBinarizedImage2.png"; //// Highgui.imwrite(file11, filledBinarizedImage2); // imageNames.add(ImageUtils.saveImage(filledBinarizedImage2, file5)); // // Mat filledBinarizedImage1 = new Mat(); // Imgproc.adaptiveThreshold(filledGrayImage, filledBinarizedImage1, 255, Imgproc.ADAPTIVE_THRESH_MEAN_C, Imgproc.THRESH_BINARY, 15, 4); // String file4 = imageID + "_filledBinarizedImage1.png"; //// Highgui.imwrite(file4, filledBinarizedImage1); // imageNames.add(ImageUtils.saveImage(filledBinarizedImage1, file4)); return imageNames; } public static ArrayList<BufferedImage> generateRecognizableBufferedImages(Mat img, Scalar backgroundColor, Scalar userPickedColor) { ArrayList<BufferedImage> images = new ArrayList<BufferedImage>(); Mat filledImage = img.clone(); Scalar newVal = new Scalar(userPickedColor.val[2], userPickedColor.val[1], userPickedColor.val[0]); Imgproc.floodFill(filledImage, new Mat(), new Point(0, 0), newVal); images.add(Util.mat2Img(filledImage)); Mat filledGrayImage = new Mat(); Imgproc.cvtColor(filledImage, filledGrayImage, Imgproc.COLOR_BGR2GRAY); images.add(Util.mat2Img(filledGrayImage)); Mat gaussianGrayImage = new Mat(); Imgproc.GaussianBlur(filledGrayImage, gaussianGrayImage, new Size(0, 0), 3); Core.addWeighted(filledGrayImage, 3.5, gaussianGrayImage, -1, 0, gaussianGrayImage); images.add(Util.mat2Img(gaussianGrayImage)); Mat filledBinarizedImage2 = new Mat(); Imgproc.adaptiveThreshold(filledGrayImage, filledBinarizedImage2, 255, Imgproc.ADAPTIVE_THRESH_MEAN_C, Imgproc.THRESH_BINARY, 75, 10); images.add(Util.mat2Img(filledBinarizedImage2)); Mat filledBinarizedImage1 = new Mat(); Imgproc.adaptiveThreshold(filledGrayImage, filledBinarizedImage1, 255, Imgproc.ADAPTIVE_THRESH_MEAN_C, Imgproc.THRESH_BINARY, 15, 4); images.add(Util.mat2Img(filledBinarizedImage1)); return images; } public static ArrayList<Mat> generateRecognizableImages(Mat img, Scalar backgroundColor, Scalar userPickedColor) { ArrayList<Mat> images = new ArrayList<Mat>(); Mat filledImage = img.clone(); Scalar newVal = new Scalar(userPickedColor.val[2], userPickedColor.val[1], userPickedColor.val[0]); Imgproc.floodFill(filledImage, new Mat(), new Point(0, 0), newVal); String file1 = "filledImage.png"; // Highgui.imwrite(file1, filledImage); images.add(filledImage); Mat filledGrayImage = new Mat(); Imgproc.cvtColor(filledImage, filledGrayImage, Imgproc.COLOR_BGR2GRAY); String file2 = "filledGrayImage.png"; // Highgui.imwrite(file2, filledGrayImage); images.add(filledGrayImage); Mat gaussianGrayImage = new Mat(); Imgproc.GaussianBlur(filledGrayImage, gaussianGrayImage, new Size(0, 0), 3); Core.addWeighted(filledGrayImage, 3.5, gaussianGrayImage, -1, 0, gaussianGrayImage); // Core.addWeighted(filledGrayImage, 2.5, gaussianGrayImage, -0.5, 0, gaussianGrayImage); String file3 = "sharpenedImage.png"; // Highgui.imwrite(file3, gaussianGrayImage); images.add(gaussianGrayImage); Mat filledBinarizedImage = new Mat(); Imgproc.adaptiveThreshold(filledGrayImage, filledBinarizedImage, 255, Imgproc.ADAPTIVE_THRESH_MEAN_C, Imgproc.THRESH_BINARY, 15, 4); String file4 = "filledBinarizedImage.png"; // Highgui.imwrite(file4, filledBinarizedImage); images.add(filledBinarizedImage); // BackgroundSubtractorMOG2 backgroundSubtractorMOG2 = new BackgroundSubtractorMOG2(); // Mat foregroundMask = new Mat(); // backgroundSubtractorMOG2.apply(img, foregroundMask); // Highgui.imwrite("mFGMask.png", foregroundMask); Scalar fillingColor = cluster(userPickedColor, img, 3); Mat replacedColor = replaceColor(img, backgroundColor, fillingColor); String file5 = "replacedColor.png"; // Highgui.imwrite(file5, replacedColor); images.add(replacedColor); Mat grayImage = new Mat(); Imgproc.cvtColor(replacedColor, grayImage, Imgproc.COLOR_BGR2GRAY); String file6 = "grayImage.png"; // Highgui.imwrite(file6, grayImage); images.add(grayImage); Mat binarized = new Mat(); Imgproc.adaptiveThreshold(grayImage, binarized, 255, Imgproc.ADAPTIVE_THRESH_MEAN_C, Imgproc.THRESH_BINARY, 15, 4); String file7 = "binarized.png"; // Highgui.imwrite(file7, binarized); images.add(binarized); Mat colorReplacedEqualized = equalizeIntensity(replacedColor); String file8 = "colorReplacedEqualized.png"; // Highgui.imwrite(file8, colorReplacedEqualized); images.add(colorReplacedEqualized); Mat colorReducedImage = reduceColor(replacedColor, 64); String file9 = "replacedColorColorReduced.png"; // Highgui.imwrite(file9, colorReducedImage); images.add(colorReducedImage); // Equalizing image Mat colorReducedEqualized = equalizeIntensity(colorReducedImage); String file10 = "colorReducedEqualized.png"; // Highgui.imwrite(file10, colorReducedEqualized); images.add(colorReducedEqualized); return images; } public static Scalar cluster(Scalar userColor, Mat cutout, int k) { Mat samples = cutout.reshape(1, cutout.cols() * cutout.rows()); Mat samples32f = new Mat(); samples.convertTo(samples32f, CvType.CV_32F, 1.0 / 255.0); Mat labels = new Mat(); TermCriteria criteria = new TermCriteria(TermCriteria.COUNT, 100, 1); Mat centers = new Mat(); Core.kmeans(samples32f, k, labels, criteria, 1, Core.KMEANS_PP_CENTERS, centers); Scalar fillingColor = getFillingColor(userColor, cutout, labels, centers); return fillingColor; } private static Scalar getFillingColor(Scalar userColor, Mat cutout, Mat labels, Mat centers) { double minDistance = 1000000; Scalar fillingColor = null; centers.convertTo(centers, CvType.CV_8UC1, 255.0); centers.reshape(3); List<Mat> clusters = new ArrayList<Mat>(); for (int i = 0; i < centers.rows(); i++) { clusters.add(Mat.zeros(cutout.size(), cutout.type())); } Map<Integer, Integer> counts = new HashMap<Integer, Integer>(); for (int i = 0; i < centers.rows(); i++) { counts.put(i, 0); } int rows = 0; for (int y = 0; y < cutout.rows(); y++) { for (int x = 0; x < cutout.cols(); x++) { int label = (int) labels.get(rows, 0)[0]; int r = (int) centers.get(label, 2)[0]; int g = (int) centers.get(label, 1)[0]; int b = (int) centers.get(label, 0)[0]; counts.put(label, counts.get(label) + 1); clusters.get(label).put(y, x, b, g, r); rows++; } } Set<Integer> keySet = counts.keySet(); Iterator<Integer> iterator = keySet.iterator(); while (iterator.hasNext()) { int label = (int) iterator.next(); int r = (int) centers.get(label, 2)[0]; int g = (int) centers.get(label, 1)[0]; int b = (int) centers.get(label, 0)[0]; Scalar currentColor = new Scalar(r, g, b); double distance = getColorDistance(currentColor, userColor); if (distance < minDistance) { minDistance = distance; fillingColor = currentColor; } } return fillingColor; } static double getColorDistance(Scalar color1, Scalar color2) { return Math.abs(color1.val[0] - color2.val[0]) + Math.abs(color1.val[1] - color2.val[1]) + Math.abs(color1.val[2] - color2.val[2]); } static Mat replaceColor(Mat image, Scalar color1, Scalar color2) { Mat replaced = image.clone(); for (int y = 0; y < image.rows(); y++) { for (int x = 0; x < image.cols(); x++) { double[] values = image.get(y, x); double r = values[0]; double g = values[1]; double b = values[2]; if (b == color1.val[0] && g == color1.val[1] && r == color1.val[2]) { values[0] = color2.val[2]; values[1] = color2.val[1]; values[2] = color2.val[0]; } replaced.put(y, x, values); } } return replaced; } public static Mat equalizeIntensity(Mat inputImage) { if (inputImage.channels() >= 3) { Mat ycrcb = new Mat(); Imgproc.cvtColor(inputImage, ycrcb, Imgproc.COLOR_BGR2YUV); ArrayList<Mat> channels = new ArrayList<Mat>(); Core.split(ycrcb, channels); Mat equalized = new Mat(); Imgproc.equalizeHist(channels.get(0), equalized); channels.set(0, equalized); Core.merge(channels, ycrcb); Mat result = new Mat(); Imgproc.cvtColor(ycrcb, result, Imgproc.COLOR_YUV2BGR); return result; } return null; } private static Mat reduceColor(Mat image, int div) { Mat result = new Mat(image.size(), image.type()); int rows = image.rows(); // number of lines int cols = image.cols(); // number of elements per line for (int j = 0; j < rows; j++) { for (int i = 0; i < cols; i++) { double[] data = image.get(j, i); for (int k = 0; k < 3; k++) { data[k] = ((int) data[k] / div) * div + div / 2; } int put = result.put(j, i, data); } } return result; } }