前提
采集的图片有淡淡的水印，为了避免不必要的麻烦，需要淡化或去除水印。图片如下所示：
Java自带的工具可以对图片指定位置(x,y)的颜色(r,g,b)进行替换。如果图片上下左右颜色一致，则可进行颜色区间的简单替换。
如上图所示，图片是手机拍摄的，颜色不一致。这就需要算法来识别图片中的水印部分。
方案
- 取图片四周的点位建立二元线性回归方程RGB(r,g,b)=f(x,y)
- 对各个点位值偏差在方程值阈值范围内的进行统一替换
代码
多元线性回归
public class LinearRegressionTest {
public static final Logger logger = LoggerFactory.getLogger(LinearRegressionTest.class);
/**
* 多元线性回归分析
*
* @param x 每一列存放m个自变量的观察值
* @param y 存放随即变量y的n个观察值
* @param m 自变量的个数
* @param n 观察数据的组数
* @param a 返回回归系数a0,...,am
* @param dt dt[0]偏差平方和q,dt[1]平均标准偏差s,dt[2]复相关系数r,dt[3]回归平方和u
* @param v 返回m个自变量的偏相关系数
*/
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)) {
logger.warn("Fail!");
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; // 自变量的个数
int n = x[0].length; // 观察数据的组数
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;
}
public static void main(String[] args) {
double[][] x = {{1.1, 1.0, 1.2, 1.1, 0.9},
{2.0, 2.0, 1.8, 1.9, 2.1}};
double[] y = {10.1, 10.2, 10.0, 10.1, 10.0};
int m = x.length; // 自变量的个数
int n = x[0].length; // 观察数据的组数
double[] a = new double[m + 1];
double[] v = new double[m];
double[] dt = new double[4];
sqt2(x, y, m, n, a, dt, v);
logger.info("回归系数a0,...,am");
for (int i = 0; i <= m; i++) {
logger.info("a(" + i + ")=" + a[i]);
}
logger.info("自变量的偏相关系数");
for (int i = 0; i <= m - 1; i++) {
logger.info("v(" + i + ")=" + v[i]);
}
logger.info("dt[0]偏差平方和q,dt[1]平均标准偏差s,dt[2]复相关系数r,dt[3]回归平方和u");
logger.info("q=" + dt[0] + " s=" + dt[1] + " r=" + dt[2] + " u=" + dt[3]);
}
}
图片去水印
public class WatermarkRemoveTest {
public static final Logger logger = LoggerFactory.getLogger(WatermarkRemoveTest.class);
private static List<File> fileList = new ArrayList<>();
public static void main(String[] args) {
convertAllImages("D:\\fan.zhou\\可行性研究\\正时\\Tmp-图片去水印\\timing_pic\\",
"D:\\fan.zhou\\可行性研究\\正时\\Tmp-图片去水印\\timing_pic_handle\\"); // 支持批量去除图片水印
}
private static void convertAllImages(String dir, String saveDir) {
File dirFile = new File(dir);
dir = dirFile.getAbsolutePath();
File saveDirFile = new File(saveDir);
saveDir = saveDirFile.getAbsolutePath();
loadImages(new File(dir));
for (File file : fileList) {
String filePath = file.getAbsolutePath();
String dstPath = saveDir + filePath.substring(filePath.indexOf(dir) + dir.length(), filePath.length());
logger.info("Converting: {}", filePath);
replaceColor(file.getAbsolutePath(), dstPath);
}
}
public static void loadImages(File f) {
if (f != null) {
if (f.isDirectory()) {
File[] fileArray = f.listFiles();
if (fileArray != null) {
for (int i = 0; i < fileArray.length; i++) {
loadImages(fileArray[i]);
}
}
} else {
String name = f.getName();
if (name.endsWith("png") || name.endsWith("jpg")) {
fileList.add(f);
}
}
}
}
private static void replaceColor(String srcFile, String dstFile) {
try {
replaceImageColor(srcFile, dstFile);
} catch (IOException e) {
e.printStackTrace();
}
}
public static void replaceImageColor(String file, String dstFile) throws IOException {
URL http;
if (file.trim().startsWith("https")) {
http = new URL(file);
HttpsURLConnection conn = (HttpsURLConnection) http.openConnection();
conn.setRequestMethod("GET");
} else if (file.trim().startsWith("http")) {
http = new URL(file);
HttpURLConnection conn = (HttpURLConnection) http.openConnection();
conn.setRequestMethod("GET");
} else {
http = new File(file).toURI().toURL();
}
BufferedImage bi = ImageIO.read(http.openStream());
if (bi == null) {
return;
}
int pointNum = 30;
double[][] xy = new double[2][4 * pointNum];
double[][] rgb = new double[3][4 * pointNum];
int widthBorder = 30;
int width = bi.getWidth();
double widthStep = (double) (width - widthBorder * 2) / (pointNum - 1);
int heightBorder = 30;
int height = bi.getHeight();
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] = width - 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 = 20;
for (int i = 0; i < bi.getWidth(); i++) {
for (int j = 0; j < bi.getHeight(); j++) {
int redStd = (int) (i * ar[0] + j * ar[1] + ar[2]);
redStd = redStd <= 255 ? redStd : 255;
int greedStd = (int) (i * ag[0] + j * ag[1] + ag[2]);
greedStd = greedStd <= 255 ? greedStd : 255;
int blueStd = (int) (i * ab[0] + j * ab[1] + ab[2]);
blueStd = blueStd <= 255 ? blueStd : 255;
Color oriColor = new Color(bi.getRGB(i, j));
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) {
logger.error("", ex);
continue;
}
bi.setRGB(i, j, wColor.getRGB());
}
}
}
String type = file.substring(file.lastIndexOf(".") + 1, file.length());
Iterator<ImageWriter> it = ImageIO.getImageWritersByFormatName(type);
ImageWriter writer = it.next();
File f = new File(dstFile);
f.getParentFile().mkdirs();
ImageOutputStream ios = ImageIO.createImageOutputStream(f);
writer.setOutput(ios);
writer.write(bi);
bi.flush();
ios.flush();
ios.close();
}
}
效果
处理前
处理后
总结
图片去水印主要的难点在于识别图片中的水印，本文基于多元线性回归识别；实际应用时，可根据需要调整识别算法。
版权声明：本文为sinat_32501475原创文章，遵循 CC 4.0 BY-SA 版权协议，转载请附上原文出处链接和本声明。