身份证敏感信息处理 图片添加蒙版

实现效果

需要的jar包

<!-- https://mvnrepository.com/artifact/com.jhlabs/filters --><dependency><groupId>com.jhlabs</groupId><artifactId>filters</artifactId><version>2.0.235-1</version></dependency>

调用

public static void main(String[] args) {try {byte[] bytes = PDFbox.pdf2Jpg(new BASE64Decoder().decodeBuffer(GetImageStr("E:/workspace/fline_work/js/2.pdf")));String filterBytes = MyGaussianFilterUtil.getFilterBytes(bytes);FileUtil.decoderBase64File(filterBytes,"E:\\workspace\\fline_work\\js\\file\\"+UUID.randomUUID().toString()+".jpg");
//            System.out.println(filterBytes);} catch (Exception e) {e.printStackTrace();}
}

根据坐标裁剪图片

import javax.imageio.ImageIO;
import javax.imageio.ImageReadParam;
import javax.imageio.ImageReader;
import javax.imageio.stream.ImageInputStream;
import java.awt.*;
import java.awt.image.BufferedImage;
import java.io.*;
import java.util.Iterator;/*** 更具坐标裁剪图片*/
public class OperateImageUtils {// ===源图片路径名称如:c:\1.jpgprivate String srcpath;// ===剪切图片存放路径名称.如:c:\2.jpgprivate String subpath;// ===剪切点x坐标private int x;private int y;// ===剪切点宽度private int width;private int height;public OperateImageUtils() {}public OperateImageUtils(int x, int y, int width, int height) {this.x = x;this.y = y;this.width = width;this.height = height;}/*** 对图片裁剪，并把裁剪完蛋新图片保存 。*/public void cut() throws IOException {InputStream is = null;ImageInputStream iis = null;try {// 读取图片文件is = new FileInputStream(srcpath);/** 返回包含所有当前已注册 ImageReader 的 Iterator，这些 ImageReader* 声称能够解码指定格式。 参数：formatName - 包含非正式格式名称 .*（例如 "jpeg" 或 "tiff"）等 。*/Iterator<ImageReader> it = ImageIO.getImageReadersByFormatName("jpg");ImageReader reader = it.next();// 获取图片流iis = ImageIO.createImageInputStream(is);/** <p>iis:读取源.true:只向前搜索 </p>.将它标记为 ‘只向前搜索'。* 此设置意味着包含在输入源中的图像将只按顺序读取，可能允许 reader* 避免缓存包含与以前已经读取的图像关联的数据的那些输入部分。*/reader.setInput(iis, true);/** <p>描述如何对流进行解码的类<p>.用于指定如何在输入时从 Java Image I/O* 框架的上下文中的流转换一幅图像或一组图像。用于特定图像格式的插件* 将从其 ImageReader 实现的 getDefaultReadParam 方法中返回* ImageReadParam 的实例。*/ImageReadParam param = reader.getDefaultReadParam();/** 图片裁剪区域。Rectangle 指定了坐标空间中的一个区域，通过 Rectangle 对象* 的左上顶点的坐标（x，y）、宽度和高度可以定义这个区域。*/Rectangle rect = new Rectangle(x, y, width, height);// 提供一个 BufferedImage，将其用作解码像素数据的目标。param.setSourceRegion(rect);/** 使用所提供的 ImageReadParam 读取通过索引 imageIndex 指定的对象，并将* 它作为一个完整的 BufferedImage 返回。*/BufferedImage bi = reader.read(0, param);// 保存新图片ImageIO.write(bi, "jpg", new File(subpath));} finally {if (is != null)is.close();if (iis != null)iis.close();}}/*** 对图片裁剪，并把裁剪完蛋新图片保存 。*/public void cut(byte[] bytes,String subpath) throws IOException {InputStream is = null;ImageInputStream iis = null;try {// 读取图片文件is = byte2InputStream(bytes);/** 返回包含所有当前已注册 ImageReader 的 Iterator，这些 ImageReader* 声称能够解码指定格式。 参数：formatName - 包含非正式格式名称 .*（例如 "jpeg" 或 "tiff"）等 。*/Iterator<ImageReader> it = ImageIO.getImageReadersByFormatName("jpg");ImageReader reader = it.next();// 获取图片流iis = ImageIO.createImageInputStream(is);/** <p>iis:读取源.true:只向前搜索 </p>.将它标记为 ‘只向前搜索'。* 此设置意味着包含在输入源中的图像将只按顺序读取，可能允许 reader* 避免缓存包含与以前已经读取的图像关联的数据的那些输入部分。*/reader.setInput(iis, true);/** <p>描述如何对流进行解码的类<p>.用于指定如何在输入时从 Java Image I/O* 框架的上下文中的流转换一幅图像或一组图像。用于特定图像格式的插件* 将从其 ImageReader 实现的 getDefaultReadParam 方法中返回* ImageReadParam 的实例。*/ImageReadParam param = reader.getDefaultReadParam();/** 图片裁剪区域。Rectangle 指定了坐标空间中的一个区域，通过 Rectangle 对象* 的左上顶点的坐标（x，y）、宽度和高度可以定义这个区域。*/Rectangle rect = new Rectangle(x, y, width, height);// 提供一个 BufferedImage，将其用作解码像素数据的目标。param.setSourceRegion(rect);/** 使用所提供的 ImageReadParam 读取通过索引 imageIndex 指定的对象，并将* 它作为一个完整的 BufferedImage 返回。*/BufferedImage bi = reader.read(0, param);// 保存新图片ImageIO.write(bi, "jpg", new File(subpath));} finally {if (is != null)is.close();if (iis != null)iis.close();}}public static InputStream byte2InputStream(byte[] bytes) {return new ByteArrayInputStream(bytes);}public int getHeight() {return height;}public void setHeight(int height) {this.height = height;}public String getSrcpath() {return srcpath;}public void setSrcpath(String srcpath) {this.srcpath = srcpath;}public String getSubpath() {return subpath;}public void setSubpath(String subpath) {this.subpath = subpath;}public int getWidth() {return width;}public void setWidth(int width) {this.width = width;}public int getX() {return x;}public void setX(int x) {this.x = x;}public int getY() {return y;}public void setY(int y) {this.y = y;}
}

图片添加高斯模糊

import com.jhlabs.image.PixelUtils;
import sun.misc.BASE64Encoder;import javax.imageio.ImageIO;
import java.awt.image.BufferedImage;
import java.awt.image.ColorModel;
import java.awt.image.Kernel;
import java.io.*;
import java.util.Hashtable;
import java.util.UUID;/*** 给图片添加蒙版方法*/
public class MyGaussianFilterUtil {public static int CLAMP_EDGES = 1;public static int WRAP_EDGES = 2;protected Kernel kernel;protected boolean alpha = false;protected boolean premultiplyAlpha = false;protected float radius;public MyGaussianFilterUtil() {this(2.0F);}public MyGaussianFilterUtil(float radius) {this.setRadius(radius);}public void setRadius(float radius) {this.radius = radius;this.kernel = makeKernel(radius);}public float getRadius() {return this.radius;}public BufferedImage filter(BufferedImage src, BufferedImage dst) {int width = src.getWidth();int height = src.getHeight();if (dst == null) {dst = this.createCompatibleDestImage(src, (ColorModel)null);}int[] inPixels = new int[width * height];int[] outPixels = new int[width * height];src.getRGB(0, 0, width, height, inPixels, 0, width);if (this.radius > 0.0F) {//这里是对图片进行处理convolveAndTranspose(this.kernel, inPixels, outPixels, width, height, this.alpha, this.alpha && this.premultiplyAlpha, false, CLAMP_EDGES, 1);convolveAndTranspose(this.kernel, outPixels, inPixels, height, width, this.alpha, false, this.alpha && this.premultiplyAlpha, CLAMP_EDGES, 2);}dst.setRGB(0, 0, width, height, inPixels, 0, width);return dst;}public BufferedImage createCompatibleDestImage(BufferedImage src, ColorModel dstCM) {if (dstCM == null) {dstCM = src.getColorModel();}return new BufferedImage(dstCM, dstCM.createCompatibleWritableRaster(src.getWidth(), src.getHeight()), dstCM.isAlphaPremultiplied(), (Hashtable)null);}public static Kernel makeKernel(float radius) {int r = (int)Math.ceil((double)radius);int rows = r * 2 + 1;float[] matrix = new float[rows];float sigma = radius / 3.0F;float sigma22 = 2.0F * sigma * sigma;float sigmaPi2 = 6.2831855F * sigma;float sqrtSigmaPi2 = (float)Math.sqrt((double)sigmaPi2);float radius2 = radius * radius;float total = 0.0F;int index = 0;int i;for(i = -r; i <= r; ++i) {float distance = (float)(i * i);if (distance > radius2) {matrix[index] = 0.0F;} else {matrix[index] = (float)Math.exp((double)(-distance / sigma22)) / sqrtSigmaPi2;}total += matrix[index];++index;}for(i = 0; i < rows; ++i) {matrix[i] /= total;}return new Kernel(rows, 1, matrix);}public static void convolveAndTranspose(Kernel kernel, int[] inPixels, int[] outPixels, int width, int height, boolean alpha, boolean premultiply, boolean unpremultiply, int edgeAction,int type) {float[] matrix = kernel.getKernelData((float[])null);int cols = kernel.getWidth();int cols2 = cols / 2;int y = 0;//这里的height相当于要模糊图片的width（x轴），默认从0开始，我们修改这里的开始值，就能定义模糊位置if (type == 2) {y = height/3;}for(; y < height; ++y) {int index = y;int ioffset = y * width;for(int x = 0; x < width; ++x) {float r = 0.0F;float g = 0.0F;float b = 0.0F;float a = 0.0F;int moffset = cols2;int ia;int ix;int rgb;for(ia = -cols2; ia <= cols2; ++ia) {float f = matrix[moffset + ia];if (f != 0.0F) {ix = x + ia;if (ix < 0) {if (edgeAction == CLAMP_EDGES) {ix = 0;} else if (edgeAction == WRAP_EDGES) {ix = (x + width) % width;}} else if (ix >= width) {if (edgeAction == CLAMP_EDGES) {ix = width - 1;} else if (edgeAction == WRAP_EDGES) {ix = (x + width) % width;}}rgb = inPixels[ioffset + ix];int pa = rgb >> 24 & 255;int pr = rgb >> 16 & 255;int pg = rgb >> 8 & 255;int pb = rgb & 255;if (premultiply) {float a255 = (float)pa * 0.003921569F;pr = (int)((float)pr * a255);pg = (int)((float)pg * a255);pb = (int)((float)pb * a255);}a += f * (float)pa;r += f * (float)pr;g += f * (float)pg;b += f * (float)pb;}}if (unpremultiply && a != 0.0F && a != 255.0F) {float f = 255.0F / a;r *= f;g *= f;b *= f;}ia = alpha ? PixelUtils.clamp((int)((double)a + 0.5D)) : 255;int ir = PixelUtils.clamp((int)((double)r + 0.5D));ix = PixelUtils.clamp((int)((double)g + 0.5D));rgb = PixelUtils.clamp((int)((double)b + 0.5D));outPixels[index] = ia << 24 | ir << 16 | ix << 8 | rgb;index += height;}}}public static void  filter(String path,String savepath){try {MyGaussianFilterUtil gaussianFilter = new MyGaussianFilterUtil();BufferedImage fromImage = ImageIO.read(new File(path));BufferedImage toImage = new BufferedImage(fromImage.getWidth(), fromImage.getHeight(),BufferedImage.TYPE_INT_RGB);gaussianFilter.setRadius(40);gaussianFilter.filter(fromImage, toImage);ImageIO.write(toImage, "jpg", new File(savepath));} catch (IOException e) {e.printStackTrace();}}/*** 递归删除目录下的所有文件及子目录下所有文件* @param dir 将要删除的文件目录* @return boolean Returns "true" if all deletions were successful.*                 If a deletion fails, the method stops attempting to*                 delete and returns "false".*/public static boolean deleteDir(File dir) {if (dir.isDirectory()) {String[] children = dir.list();for (int i=0; i<children.length; i++) {boolean success = deleteDir(new File(dir, children[i]));if (!success) {return false;}}}// 目录此时为空，可以删除return dir.delete();}public static String getImageEncode(String imgPath,String filePath) {// 将图片文件转化为字节数组字符串，并对其进行Base64编码处理String imgFile = imgPath;// 待处理的图片InputStream in = null;byte[] data = null;String encode = null; // 返回Base64编码过的字节数组字符串// 对字节数组Base64编码BASE64Encoder encoder = new BASE64Encoder();try {// 读取图片字节数组in = new FileInputStream(imgFile);data = new byte[in.available()];in.read(data);encode = encoder.encode(data);} catch (IOException e) {e.printStackTrace();} finally {try {in.close();} catch (IOException e) {// TODO Auto-generated catch blocke.printStackTrace();}}deleteDir(new File(filePath));return encode;}public static String getFilterBytes(byte[] bytes){try {String filePath = System.getProperty("user.dir") + File.separator + "tempfile"+ File.separator + UUID.randomUUID().toString()+File.separator;File file = new File(filePath);if(!file.exists()){file.mkdirs();}//截取图片OperateImageUtils operateImage = new OperateImageUtils(325, 390, 540, 745);String subPath = filePath+ UUID.randomUUID().toString()+".jpg";operateImage.cut(bytes,subPath);//给图片添加蒙版String savepath =filePath+ UUID.randomUUID().toString()+".jpg";MyGaussianFilterUtil.filter(subPath,savepath);//合并图片String bigPath = filePath+ UUID.randomUUID().toString()+".jpg";MergeImageUtil.mergeImage(bytes,bigPath,savepath,"325","390");return getImageEncode(bigPath,filePath);} catch (Exception e) {e.printStackTrace();}return null;}
}

根据坐标合并图片

import javax.imageio.ImageIO;
import java.awt.*;
import java.awt.image.BufferedImage;
import java.io.ByteArrayInputStream;
import java.io.File;
import java.io.IOException;
import java.io.InputStream;
import java.net.URL;/*** 合并图片*/
public class MergeImageUtil {/**** @param bigPath 需要进行合并的大图* @param smallPath 需要进行合并的小图* @param x 坐标* @param y 坐标* @throws IOException*/public static void mergeImage(String bigPath, String smallPath, String x, String y) throws IOException {try {BufferedImage small;BufferedImage big = ImageIO.read(new File(bigPath));if (smallPath.contains("http")) {URL url = new URL(smallPath);small = ImageIO.read(url);} else {small = ImageIO.read(new File(smallPath));}Graphics2D g = big.createGraphics();float fx = Float.parseFloat(x);float fy = Float.parseFloat(y);int x_i = (int) fx;int y_i = (int) fy;g.drawImage(small, x_i, y_i, small.getWidth(), small.getHeight(), null);g.dispose();ImageIO.write(big, "jpg", new File(bigPath));} catch (Exception e) {e.printStackTrace();}}/*** @param bytes 数据源* @param bigPath 需要进行合并的大图* @param smallPath 需要进行合并的小图* @param x 坐标* @param y 坐标* @throws IOException*/public static void mergeImage(byte[] bytes,String bigPath, String smallPath, String x, String y) throws IOException {try {BufferedImage small;BufferedImage big = ImageIO.read(byte2InputStream(bytes));if (smallPath.contains("http")) {URL url = new URL(smallPath);small = ImageIO.read(url);} else {small = ImageIO.read(new File(smallPath));}Graphics2D g = big.createGraphics();float fx = Float.parseFloat(x);float fy = Float.parseFloat(y);int x_i = (int) fx;int y_i = (int) fy;g.drawImage(small, x_i, y_i, small.getWidth(), small.getHeight(), null);g.dispose();ImageIO.write(big, "jpg", new File(bigPath));} catch (Exception e) {e.printStackTrace();}}public static InputStream byte2InputStream(byte[] bytes) {return new ByteArrayInputStream(bytes);}
}