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java代碼多線程批量處理文件內的圖片去水印 1、上效果 2、上代碼 package com.aaaa.yaojian.util; import lombok.extern.slf4j.Slf4j; import javax.imageio.ImageIO; import java.awt.*; import java.awt.image.BufferedImage; import java.awt.image.ConvolveOp; import java.awt.image.Kernel; import java.io.File; import java.util.ArrayList; import java.util.concurrent.ExecutorService; import java.util.concurrent.Executors; /*** * Desc: * @author lisong * */ @Slf4j public class ImageUtil { // 開啟10個線程 private static final int NUM_THREADS = 10; public static void main(String[] args) { File folder = new File("D:\\meituanpic\\meituanpic1\\xianfu"); File[] files = folder.listFiles(); if (files == null) { return; // 文件夾為空或者讀取失敗 } int numFiles = files.length; int batchSize = numFiles / NUM_THREADS; ExecutorService executor = Executors.newFixedThreadPool(NUM_THREADS); for (int i = 0; i < NUM_THREADS; i++) { final int start = i * batchSize; final int end = (i == NUM_THREADS - 1) ? numFiles : (i + 1) * batchSize; executor.submit(new FileProcessorTask(files, start, end)); } executor.shutdown(); } private static class FileProcessorTask implements Runnable { private File[] files; private int start; private int end; public FileProcessorTask(File[] files, int start, int end) { this.files = files; this.start = start; this.end = end; } @Override public void run() { java.util.List<File> list1 = new ArrayList<>(); for (int i = start; i < end; i++) { // File file = files[i]; getmain(files[i].getPath(),files[i].getName()); // 分批處理文件的邏輯 // list1.add(files[i]); // if(list1.size() == 100){ // for(File file1 : list1){ // getmain(file1.getPath(),file1.getName()); // } // list1.clear(); // } } // if(list1.size()>1){ // for(File file1 : list1){ // getmain(file1.getPath(),file1.getName()); // } // list1.clear(); // } } } public static void yidong( File sourceFile,String destinationDirPath){ File destinationDirectory = new File(destinationDirPath); File destinationFile = new File(destinationDirectory, sourceFile.getName()); sourceFile.renameTo(destinationFile); } public static void getmain(String imgUrl,String imgName){ try{ File file = new File(imgUrl); BufferedImage bi = ImageIO.read(file); if(bi!=null){ int pointNum = 5; double[][] xy = new double[2][5 * pointNum]; double[][] rgb = new double[3][5 * pointNum]; int widthBorder = 5; double w = bi.getWidth()*0.35; int width = (int)w; // double widthStep = (double) (width - widthBorder * 2) / (pointNum - 1); double widthStep = (double) (width - widthBorder * 2) / (pointNum - 1); int heightBorder = 5; double h = bi.getHeight()*0.20; int height = (int)h; // double heightStep = (double) (height - heightBorder * 2) / (pointNum - 1); double heightStep = (double) (height - heightBorder * 2) / (pointNum - 1); for (int i = 0; i < pointNum; i++) { xy[0][i] = widthBorder; xy[1][i] = (int) (heightBorder + heightStep * i); Color oriColor = new Color(bi.getRGB((int) xy[0][i], (int) xy[1][i])); rgb[0][i] = oriColor.getRed(); rgb[1][i] = oriColor.getGreen(); rgb[2][i] = oriColor.getBlue(); xy[0][pointNum + i] = bi.getWidth() - widthBorder; xy[1][pointNum + i] = (int) (heightBorder + heightStep * i); oriColor = new Color(bi.getRGB((int) xy[0][pointNum + i], (int) xy[1][pointNum + i])); rgb[0][pointNum + i] = oriColor.getRed(); rgb[1][pointNum + i] = oriColor.getGreen(); rgb[2][pointNum + i] = oriColor.getBlue(); } for (int i = 0; i < pointNum; i++) { xy[0][2 * pointNum + i] = (int) (widthBorder + widthStep * i); xy[1][2 * pointNum + i] = heightBorder; Color oriColor = new Color(bi.getRGB((int) xy[0][2 * pointNum + i], (int) xy[1][2 * pointNum + i])); rgb[0][2 * pointNum + i] = oriColor.getRed(); rgb[1][2 * pointNum + i] = oriColor.getGreen(); rgb[2][2 * pointNum + i] = oriColor.getBlue(); xy[0][3 * pointNum + i] = (int) (widthBorder + widthStep * i); xy[1][3 * pointNum + i] = height - heightBorder; oriColor = new Color(bi.getRGB((int) xy[0][3 * pointNum + i], (int) xy[1][3 * pointNum + i])); rgb[0][3 * pointNum + i] = oriColor.getRed(); rgb[1][3 * pointNum + i] = oriColor.getGreen(); rgb[2][3 * pointNum + i] = oriColor.getBlue(); } double[] ar = LinearRegressionTest.getRegressionCoefficient(xy, rgb[0]); double[] ag = LinearRegressionTest.getRegressionCoefficient(xy, rgb[1]); double[] ab = LinearRegressionTest.getRegressionCoefficient(xy, rgb[2]); int colorDiffInit = 27; for (int x = bi.getHeight() - height; x < bi.getHeight(); x++) { for (int y = bi.getWidth() - width; y < bi.getWidth(); y++) { int redStd = (int) (y * ar[0] + x * ar[1] + ar[2]); redStd = redStd <= 255 ? redStd : 255; int greedStd = (int) (y * ag[0] + x * ag[1] + ag[2]); greedStd = greedStd <= 255 ? greedStd : 255; int blueStd = (int) (y * ab[0] + x * ab[1] + ab[2]); blueStd = blueStd <= 255 ? blueStd : 255; Color oriColor = new Color(bi.getRGB(y, x)); int red = oriColor.getRed(); int greed = oriColor.getGreen(); int blue = oriColor.getBlue(); int colorDiffRed = redStd > red ? redStd - red : red - redStd; int colorDiffGreed = greedStd > greed ? greedStd - greed : greed - greedStd; int colorDiffBlue = blueStd > blue ? blueStd - blue : blue - blueStd; if (colorDiffRed < colorDiffInit && colorDiffGreed < colorDiffInit && colorDiffBlue < colorDiffInit) { Color wColor; try { wColor = new Color(redStd, greedStd, blueStd); } catch (Exception ex) { continue; } bi.setRGB(y, x, wColor.getRGB()); } } } // 去完水印的圖片保存 File output = new File("D:\\meituanpic\\meituanpic1\\去水印\\xianfu6\\"+imgName); if (output.getName().toLowerCase().endsWith(".jpg")){ ImageIO.write(bi, "jpg", output); } else if(output.getName().toLowerCase().endsWith(".png")){ ImageIO.write(bi, "png", output); } else if(output.getName().toLowerCase().endsWith(".webp")){ ImageIO.write(bi, "webp", output); }else { ImageIO.write(bi, "jpeg", output); } // 原圖移動到另一文件夾 防止找不到進行到哪個位置 yidong(file,"D:\\meituanpic\\meituanpic1\\完成\\xianfu6"); } }catch (Exception e){ e.printStackTrace(); } } } package com.aaa.yaojian.util; import lombok.extern.slf4j.Slf4j; @Slf4j public class LinearRegressionTest { /** * 多元線性回歸分析 * * @param x 每一列存放m個自變量的觀察值 * @param y 存放隨即變量y的n個觀察值 * @param m 自變量的個數 * @param n 觀察數據的組數 * @param a 返回回歸系數a0,...,am * @param dt dt[0]偏差平方和q,dt[1]平均標準偏差s,dt[2]復相關系數r,dt[3]回歸平方和u * @param v 返回m個自變量的偏相關系數 */ public static void sqt2(double[][] x, double[] y, int m, int n, double[] a, double[] dt, double[] v) { int i, j, k, mm; double q, e, u, p, yy, s, r, pp; double[] b = new double[(m + 1) * (m + 1)]; mm = m + 1; b[mm * mm - 1] = n; for (j = 0; j <= m - 1; j++) { p = 0.0; for (i = 0; i <= n - 1; i++){ p = p + x[j][i]; } b[m * mm + j] = p; b[j * mm + m] = p; } for (i = 0; i <= m - 1; i++){ for (j = i; j <= m - 1; j++) { p = 0.0; for (k = 0; k <= n - 1; k++){ p = p + x[i][k] * x[j][k]; b[j * mm + i] = p; b[i * mm + j] = p; } } } a[m] = 0.0; for (i = 0; i <= n - 1; i++) { a[m] = a[m] + y[i]; } for (i = 0; i <= m - 1; i++) { a[i] = 0.0; for (j = 0; j <= n - 1; j++){ a[i] = a[i] + x[i][j] * y[j]; } } chlk(b, mm, 1, a); yy = 0.0; for (i = 0; i <= n - 1; i++){ yy = yy + y[i] / n; } q = 0.0; e = 0.0; u = 0.0; for (i = 0; i <= n - 1; i++) { p = a[m]; for (j = 0; j <= m - 1; j++){ p = p + a[j] * x[j][i]; } q = q + (y[i] - p) * (y[i] - p); e = e + (y[i] - yy) * (y[i] - yy); u = u + (yy - p) * (yy - p); } s = Math.sqrt(q / n); r = Math.sqrt(1.0 - q / e); for (j = 0; j <= m - 1; j++) { p = 0.0; for (i = 0; i <= n - 1; i++) { pp = a[m]; for (k = 0; k <= m - 1; k++){ if (k != j){ pp = pp + a[k] * x[k][i]; } } p = p + (y[i] - pp) * (y[i] - pp); } v[j] = Math.sqrt(1.0 - q / p); } dt[0] = q; dt[1] = s; dt[2] = r; dt[3] = u; } private static int chlk(double[] a, int n, int m, double[] d) { int i, j, k, u, v; if ((a[0] + 1.0 == 1.0) || (a[0] < 0.0)) { return (-2); } a[0] = Math.sqrt(a[0]); for (j = 1; j <= n - 1; j++){ a[j] = a[j] / a[0]; } for (i = 1; i <= n - 1; i++) { u = i * n + i; for (j = 1; j <= i; j++) { v = (j - 1) * n + i; a[u] = a[u] - a[v] * a[v]; } if ((a[u] + 1.0 == 1.0) || (a[u] < 0.0)) { return (-2); } a[u] = Math.sqrt(a[u]); if (i != (n - 1)) { for (j = i + 1; j <= n - 1; j++) { v = i * n + j; for (k = 1; k <= i; k++){ a[v] = a[v] - a[(k - 1) * n + i] * a[(k - 1) * n + j]; } a[v] = a[v] / a[u]; } } } for (j = 0; j <= m - 1; j++) { d[j] = d[j] / a[0]; for (i = 1; i <= n - 1; i++) { u = i * n + i; v = i * m + j; for (k = 1; k <= i; k++){ d[v] = d[v] - a[(k - 1) * n + i] * d[(k - 1) * m + j]; } d[v] = d[v] / a[u]; } } for (j = 0; j <= m - 1; j++) { u = (n - 1) * m + j; d[u] = d[u] / a[n * n - 1]; for (k = n - 1; k >= 1; k--) { u = (k - 1) * m + j; for (i = k; i <= n - 1; i++) { v = (k - 1) * n + i; d[u] = d[u] - a[v] * d[i * m + j]; } v = (k - 1) * n + k - 1; d[u] = d[u] / a[v]; } } return (2); } public static double[] getRegressionCoefficient(double[][] x, double[] y) { int m = x.length; // 自變量的個數 int n = x[0].length; // 觀察數據的組數 double[] a = new double[m + 1]; double[] v = new double[m]; double[] dt = new double[4]; sqt2(x, y, m, n, a, dt, v); return a; } } package com.wangyaow.yaojian.util; import lombok.extern.slf4j.Slf4j; @Slf4j public class LinearRegressionTest { /** * 多元線性回歸分析 * * @param x 每一列存放m個自變量的觀察值 * @param y 存放隨即變量y的n個觀察值 * @param m 自變量的個數 * @param n 觀察數據的組數 * @param a 返回回歸系數a0,...,am * @param dt dt[0]偏差平方和q,dt[1]平均標準偏差s,dt[2]復相關系數r,dt[3]回歸平方和u * @param v 返回m個自變量的偏相關系數 */ public static void sqt2(double[][] x, double[] y, int m, int n, double[] a, double[] dt, double[] v) { int i, j, k, mm; double q, e, u, p, yy, s, r, pp; double[] b = new double[(m + 1) * (m + 1)]; mm = m + 1; b[mm * mm - 1] = n; for (j = 0; j <= m - 1; j++) { p = 0.0; for (i = 0; i <= n - 1; i++){ p = p + x[j][i]; } b[m * mm + j] = p; b[j * mm + m] = p; } for (i = 0; i <= m - 1; i++){ for (j = i; j <= m - 1; j++) { p = 0.0; for (k = 0; k <= n - 1; k++){ p = p + x[i][k] * x[j][k]; b[j * mm + i] = p; b[i * mm + j] = p; } } } a[m] = 0.0; for (i = 0; i <= n - 1; i++) { a[m] = a[m] + y[i]; } for (i = 0; i <= m - 1; i++) { a[i] = 0.0; for (j = 0; j <= n - 1; j++){ a[i] = a[i] + x[i][j] * y[j]; } } chlk(b, mm, 1, a); yy = 0.0; for (i = 0; i <= n - 1; i++){ yy = yy + y[i] / n; } q = 0.0; e = 0.0; u = 0.0; for (i = 0; i <= n - 1; i++) { p = a[m]; for (j = 0; j <= m - 1; j++){ p = p + a[j] * x[j][i]; } q = q + (y[i] - p) * (y[i] - p); e = e + (y[i] - yy) * (y[i] - yy); u = u + (yy - p) * (yy - p); } s = Math.sqrt(q / n); r = Math.sqrt(1.0 - q / e); for (j = 0; j <= m - 1; j++) { p = 0.0; for (i = 0; i <= n - 1; i++) { pp = a[m]; for (k = 0; k <= m - 1; k++){ if (k != j){ pp = pp + a[k] * x[k][i]; } } p = p + (y[i] - pp) * (y[i] - pp); } v[j] = Math.sqrt(1.0 - q / p); } dt[0] = q; dt[1] = s; dt[2] = r; dt[3] = u; } private static int chlk(double[] a, int n, int m, double[] d) { int i, j, k, u, v; if ((a[0] + 1.0 == 1.0) || (a[0] < 0.0)) { return (-2); } a[0] = Math.sqrt(a[0]); for (j = 1; j <= n - 1; j++){ a[j] = a[j] / a[0]; } for (i = 1; i <= n - 1; i++) { u = i * n + i; for (j = 1; j <= i; j++) { v = (j - 1) * n + i; a[u] = a[u] - a[v] * a[v]; } if ((a[u] + 1.0 == 1.0) || (a[u] < 0.0)) { // logger.warn("Fail!"); return (-2); } a[u] = Math.sqrt(a[u]); if (i != (n - 1)) { for (j = i + 1; j <= n - 1; j++) { v = i * n + j; for (k = 1; k <= i; k++){ a[v] = a[v] - a[(k - 1) * n + i] * a[(k - 1) * n + j]; } a[v] = a[v] / a[u]; } } } for (j = 0; j <= m - 1; j++) { d[j] = d[j] / a[0]; for (i = 1; i <= n - 1; i++) { u = i * n + i; v = i * m + j; for (k = 1; k <= i; k++){ d[v] = d[v] - a[(k - 1) * n + i] * d[(k - 1) * m + j]; } d[v] = d[v] / a[u]; } } for (j = 0; j <= m - 1; j++) { u = (n - 1) * m + j; d[u] = d[u] / a[n * n - 1]; for (k = n - 1; k >= 1; k--) { u = (k - 1) * m + j; for (i = k; i <= n - 1; i++) { v = (k - 1) * n + i; d[u] = d[u] - a[v] * d[i * m + j]; } v = (k - 1) * n + k - 1; d[u] = d[u] / a[v]; } } return (2); } public static double[] getRegressionCoefficient(double[][] x, double[] y) { int m = x.length; // 自變量的個數 int n = x[0].length; // 觀察數據的組數 double[] a = new double[m + 1]; double[] v = new double[m]; double[] dt = new double[4]; sqt2(x, y, m, n, a, dt, v); return a; } } ———————————————— |
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