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## 目录：
https://aias.top/
# 图像预处理SDK
在OCR文字识别的时候，我们得到的图像一般情况下都不是正的，多少都会有一定的倾斜。
所以需要将图片转正。并且图片有可能是透视视角拍摄，需要重新矫正。
### SDK功能
-图像转正
-图像二值化，灰度化，去燥等经典算法。
### 完善中的功能：
-完善透视矫正
-完善文字方向检测算法，判断转正后的图片角度，以便进一步旋转图片使得文字水平。
## 运行例子
运行成功后，命令行应该看到下面的信息:
```text
319.0 , 865.0
319.0 , 113.0
785.0 , 113.0
785.0 , 865.0
startLeft = 319
startUp = 113
width = 467
height = 753
```
输出图片效果如下：
![ocr_result](https://aias-home.oss-cn-beijing.aliyuncs.com/AIAS/image_sdk/images/rotation.png)
### 帮助
引擎定制化配置，可以提升首次运行的引擎下载速度，解决外网无法访问或者带宽过低的问题。
[引擎定制化配置](https://aias.top/engine_cpu.html)
### 官网：
[官网链接](https://www.aias.top/)
### Git地址：
[Github链接](https://github.com/mymagicpower/AIAS)
[Gitee链接](https://gitee.com/mymagicpower/AIAS)

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</component>
</module>

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<?xml version="1.0" encoding="UTF-8"?>
<!--
~ Licensed to the Apache Software Foundation (ASF) under one
~ or more contributor license agreements. See the NOTICE file
~ distributed with this work for additional information
~ regarding copyright ownership. The ASF licenses this file
~ to you under the Apache License, Version 2.0 (the
~ "License"); you may not use this file except in compliance
~ with the License. You may obtain a copy of the License at
~
~ http://www.apache.org/licenses/LICENSE-2.0
~
~ Unless required by applicable law or agreed to in writing,
~ software distributed under the License is distributed on an
~ "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
~ KIND, either express or implied. See the License for the
~ specific language governing permissions and limitations
~ under the License.
-->
<project xmlns="http://maven.apache.org/POM/4.0.0"
xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
<modelVersion>4.0.0</modelVersion>
<groupId>aias</groupId>
<artifactId>image-sdk</artifactId>
<version>0.17.0</version>
<properties>
<project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
<maven.compiler.source>1.8</maven.compiler.source>
<maven.compiler.target>1.8</maven.compiler.target>
</properties>
<build>
<plugins>
<plugin>
<groupId>org.apache.maven.plugins</groupId>
<artifactId>maven-compiler-plugin</artifactId>
<configuration>
<source>8</source>
<target>8</target>
</configuration>
<version>3.8.1</version>
</plugin>
</plugins>
</build>
<dependencies>
<dependency>
<groupId>com.google.code.gson</groupId>
<artifactId>gson</artifactId>
<version>2.8.6</version>
</dependency>
<dependency>
<groupId>commons-cli</groupId>
<artifactId>commons-cli</artifactId>
<version>1.4</version>
</dependency>
<dependency>
<groupId>org.apache.logging.log4j</groupId>
<artifactId>log4j-slf4j-impl</artifactId>
<version>2.17.2</version>
</dependency>
<dependency>
<groupId>org.bytedeco</groupId>
<artifactId>javacv-platform</artifactId>
<version>1.5.7</version>
</dependency>
<dependency>
<groupId>org.projectlombok</groupId>
<artifactId>lombok</artifactId>
<version>1.18.18</version>
<scope>provided</scope>
</dependency>
<dependency>
<groupId>junit</groupId>
<artifactId>junit</artifactId>
<version>4.13.1</version>
</dependency>
</dependencies>
</project>

47
archive/1_image_sdks/image_kit/imagekit_java/src/main/java/me/aias/TestBinaryUtils.java Normal file
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package me.aias;
import me.aias.util.BinaryUtils;
import me.aias.util.GeneralUtils;
import me.aias.util.GrayUtils;
import org.bytedeco.opencv.opencv_core.Mat;
import org.junit.Test;
public class TestBinaryUtils {
@Test
/**
* 测试opencv自带的二值化
* Test OpenCV's built-in binary method
*/
public void testBinaryNative(){
String imgPath = "src/test/resources/1.png";
String destPath = "build/output/binary/";
Mat src = GeneralUtils.matFactory(imgPath);
src = GrayUtils.grayColByAdapThreshold(src);
src = BinaryUtils.binaryNative(src);
GeneralUtils.saveImg(src , destPath + "binaryNative.png");
}
@Test
/**
* 测试自定义二值化
* Test custom binary method
*/
public void testBinaryzation(){
String imgPath = "src/test/resources/1.png";
String destPath = "build/output/binary/";
Mat src = GeneralUtils.matFactory(imgPath);
src = GrayUtils.grayColByAdapThreshold(src);
src = BinaryUtils.binaryzation(src);
GeneralUtils.saveImg(src , destPath + "binaryzation.png");
}
}

105
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package me.aias;
import me.aias.util.GeneralUtils;
import me.aias.util.GrayUtils;
import org.bytedeco.opencv.opencv_core.Mat;
import org.junit.Test;
/**
* 测试灰度化
* Testing grayscale
*/
public class TestGrayUtils {
@Test
/**
* 测试opencv自带的灰度化方法
* Testing OpenCV's built-in grayscale method
*/
public void testGrayNative(){
String imgPath = "src/test/resources/1.png";
String destPath = "build/output/gray/";
Mat src = GeneralUtils.matFactory(imgPath);
src = GrayUtils.grayNative(src);
GeneralUtils.saveImg(src , destPath + "grayNative.png");
}
@Test
/**
* 测试细粒度灰度化方法
* Testing fine-grained grayscale method
* 均值灰度化减噪
* Mean grayscale denoising
*/
public void testGrayColByMidle() {
String imgPath = "src/test/resources/1.png";
String destPath = "build/output/gray/";
Mat src = GeneralUtils.matFactory(imgPath);
src = GrayUtils.grayColByMidle(src);
GeneralUtils.saveImg(src , destPath + "grayRowByMidle.png");
}
@Test
/**
* 测试细粒度灰度化方法
* Testing fine-grained grayscale method
* k值灰度化减噪
* K-value grayscale denoising
*/
public void testGrayColByKLargest() {
String imgPath = "src/test/resources/1.png";
String destPath = "build/output/gray/";
Mat src = GeneralUtils.matFactory(imgPath);
src = GrayUtils.grayColByKLargest(src);
GeneralUtils.saveImg(src , destPath + "grayRowByKLargest.png");
}
@Test
/**
* 测试细粒度灰度化方法
* Testing fine-grained grayscale method
* 局部自适应阀值灰度化减噪
* Local adaptive threshold grayscale denoising
*/
public void testGrayColByPartAdapThreshold() {
String imgPath = "src/test/resources/1.png";
String destPath = "build/output/gray/";
Mat src = GeneralUtils.matFactory(imgPath);
src = GrayUtils.grayColByPartAdapThreshold(src);
GeneralUtils.saveImg(src , destPath + "grayColByPartAdapThreshold.png");
}
@Test
/**
* 测试细粒度灰度化方法
* Testing fine-grained grayscale method
* 全局自适应阀值灰度化减噪
* Global adaptive threshold grayscale denoising
*/
public void testGrayColByAdapThreshold() {
String imgPath = "src/test/resources/1.png";
String destPath = "build/output/gray/";
Mat src = GeneralUtils.matFactory(imgPath);
src = GrayUtils.grayColByAdapThreshold(src);
GeneralUtils.saveImg(src , destPath + "grayColByAdapThreshold.png");
}
}

60
archive/1_image_sdks/image_kit/imagekit_java/src/main/java/me/aias/TestRemoveNoiseUtils.java Normal file
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package me.aias;
import me.aias.util.BinaryUtils;
import me.aias.util.GeneralUtils;
import me.aias.util.GrayUtils;
import me.aias.util.NoiseUtils;
import org.bytedeco.opencv.opencv_core.Mat;
import org.junit.Test;
/**
* 测试降噪
* Test denoising
*/
public class TestRemoveNoiseUtils {
@Test
/**
* 测试8邻域降噪
* Test 8-neighborhood denoising
*/
public void testNativeRemoveNoise(){
String imgPath = "src/test/resources/1.png";
String destPath = "build/output/noise/";
Mat src = GeneralUtils.matFactory(imgPath);
src = GrayUtils.grayColByPartAdapThreshold(src);
src = BinaryUtils.binaryzation(src);
// 8邻域降噪
// 8-neighborhood denoising
src = NoiseUtils.navieRemoveNoise(src , 1);
GeneralUtils.saveImg(src , destPath + "nativeRemoveNoise.png");
}
@Test
/**
* 连通域降噪
* Connected domain denoising
*/
public void testConnectedRemoveNoise(){
String imgPath = "src/test/resources/1.png";
String destPath = "build/output/noise/";
Mat src = GeneralUtils.matFactory(imgPath);
src = GrayUtils.grayColByPartAdapThreshold(src);
src = BinaryUtils.binaryzation(src);
// 连通域降噪 - Connected domain denoising
src = NoiseUtils.connectedRemoveNoise(src , 1);
GeneralUtils.saveImg(src , destPath + "connectedRemoveNoise.png");
}
}

62
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package me.aias;
import me.aias.util.*;
import org.bytedeco.opencv.opencv_core.*;
import java.io.IOException;
import java.util.stream.LongStream;
import static org.bytedeco.opencv.global.opencv_imgproc.*;
import static org.bytedeco.opencv.helper.opencv_imgcodecs.cvLoadImage;
public class TestRotation {
public static void main(String[] args) throws IOException {
IplImage marcel_ = cvLoadImage("src/test/resources/ticket_r2.png");
Mat marcel = new Mat(marcel_);
GeneralUtils.saveImg(marcel, "build/output/srcImg.jpg");
// 边缘检测
// Edge detection
Mat cannyMat = GeneralUtils.canny(marcel);
GeneralUtils.saveImg(cannyMat, "build/output/canny.jpg");
// 获取所有轮廓
// Get all the contours
MatVector contours = ContourUtils.getContours(cannyMat);
Mat resultImage = cannyMat.clone();
LongStream.range(0, contours.size())
.mapToObj(contours::get)
.forEach(
contour -> {
Mat points = new Mat();
approxPolyDP(contour, points, arcLength(contour, true) * 0.02, true);
drawContours(resultImage, new MatVector(points), -1, Scalar.BLUE);
});
GeneralUtils.saveImg(resultImage, "build/output/contours.jpg");
// 获取最大外接矩形
// Get the maximum bounding rectangle
RotatedRect rect = RectUtils.getMaxRect(contours);
Mat rectMat = marcel.clone();
Scalar scalar = new Scalar(255, 0, 0, 1);
Rect r = rect.boundingRect();
rectMat = DrawUtils.drawRect(rectMat, r, scalar);
GeneralUtils.saveImg(rectMat, "build/output/maxRect.jpg");
// 旋转矩形
// Rotate the rectangle
Mat rotatedImg = RotationUtils.rotation(cannyMat, rect);
GeneralUtils.saveImg(rotatedImg, "build/output/rotatedImg.jpg");
Mat nativeRotatedImg = RotationUtils.rotation(marcel, rect);
GeneralUtils.saveImg(nativeRotatedImg, "build/output/nativeRotatedImg.jpg");
// 裁剪矩形
// Crop the rectangle
Mat cutMat = RectUtils.cutRect(rotatedImg, nativeRotatedImg);
GeneralUtils.saveImg(cutMat, "build/output/cutRect.jpg");
}
}

198
archive/1_image_sdks/image_kit/imagekit_java/src/main/java/me/aias/util/BinaryUtils.java Normal file
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package me.aias.util;
import org.bytedeco.opencv.global.opencv_imgproc;
import org.bytedeco.opencv.opencv_core.Mat;
/**
* 二值化的工具类
*/
public class BinaryUtils {
/**
* opencv自带的二值化
* @param src
* @return
*/
public static Mat binaryNative(Mat src){
Mat dst = src.clone();
opencv_imgproc.adaptiveThreshold(src, dst, 255, opencv_imgproc.ADAPTIVE_THRESH_MEAN_C, opencv_imgproc.THRESH_BINARY, 25, 10);
return dst;
}
/**
* 图像二值化 阀值自适应确定
*
* @param src
* Mat矩阵图像
* @return
*/
public static Mat binaryzation(Mat src) {
if (src.channels() != 1) {
throw new RuntimeException("不是单通道图，需要先灰度话！！！");
}
int threshold = getAdapThreshold(src);
return binaryzation(src, threshold);
}
/**
* 图像二值化
*
* @param src
* Mat矩阵图像
* @param b
* [true/false] true：表示白底黑字，false表示黑底白字
* @return
*/
public static Mat binaryzation(Mat src, boolean b) {
if (src.channels() != 1) {
throw new RuntimeException("不是单通道图，需要先灰度话！！！");
}
int threshold = getAdapThreshold(src);
return binaryzation(src, threshold, b);
}
/**
* 图像二值化
*
* @param src
* Mat矩阵图像
* @param threshold
* 阀值
* @return
*/
public static Mat binaryzation(Mat src, int threshold) {
if (src.channels() != 1) {
throw new RuntimeException("不是单通道图，需要先灰度化");
}
return binaryzation(src, threshold, true);
}
/**
* 图像二值化
*
* @param src
* Mat矩阵图像
* @param threshold
* 阀值
* @param b
* [true/false] true：表示白底黑字，false表示黑底白字
* @return
*/
public static Mat binaryzation(Mat src, int threshold, boolean b) {
if (src.channels() != 1 || threshold < 0) {
System.out.println("不是单通道图像或者阀值异常");
return src;
}
int nWhite_sum = 0, nBlack_sum = 0;
int i, j;
int width = GeneralUtils.getImgWidth(src), height = GeneralUtils.getImgHeight(src);
int value;
for (j = 0; j < height; j++) {
for (i = 0; i < width; i++) {
value = GeneralUtils.getPixel(src, j, i);
if (value > threshold) {
GeneralUtils.setPixel(src, j, i, GeneralUtils.getWHITE());
nWhite_sum++;
} else {
GeneralUtils.setPixel(src, j, i, GeneralUtils.getBLACK());
nBlack_sum++;
}
}
}
if (b) {
// 白底黑字
if (nBlack_sum > nWhite_sum) {
src = GeneralUtils.turnPixel(src);
}
} else {
// 黑底白字
if (nWhite_sum > nBlack_sum) {
src = GeneralUtils.turnPixel(src);
}
}
return src;
}
/**
* 作用：自适应选取阀值
*
* @param src
* Mat矩阵图像
* @return
*/
public static int getAdapThreshold(Mat src) {
int threshold = 0, threshold_new = 127;
int nWhite_count, nBlack_count;
int nWhite_sum, nBlack_sum;
int value, i, j;
int width = GeneralUtils.getImgWidth(src), height = GeneralUtils.getImgHeight(src);
if(width == 0 || height == 0){
System.out.println("图像加载异常");
return -1;
}
while (threshold != threshold_new) {
nWhite_sum = nBlack_sum = 0;
nWhite_count = nBlack_count = 0;
for (j = 0; j < height; j++) {
for (i = 0; i < width; i++) {
value = GeneralUtils.getPixel(src, j, i);
if (value > threshold_new) {
nWhite_count++;
nWhite_sum += value;
} else {
nBlack_count++;
nBlack_sum += value;
}
}
}
threshold = threshold_new;
if(nWhite_count == 0 || nBlack_count == 0){
threshold_new = (nWhite_sum + nBlack_sum) / (nWhite_count + nBlack_count);
}else{
threshold_new = (nWhite_sum / nWhite_count + nBlack_sum / nBlack_count) / 2;
}
}
return threshold;
}
/**
* 局部自适应二值化
* @param src
* @return
*/
public static Mat partBinaryzation(Mat src){
int width = GeneralUtils.getImgWidth(src);
int height = GeneralUtils.getImgHeight(src);
int value;
for(int i = 0 ; i < width ; i++){
Mat partMat = src.col(i);
int thresold = getAdapThreshold(partMat);
int black_num = 0 , white_num = 0;
for(int j = 0 ; j < height ; j++){
value = GeneralUtils.getPixel(src , j , i);
if(value > thresold){
GeneralUtils.setPixel(src , j , i , GeneralUtils.getWHITE());
white_num++;
}else{
GeneralUtils.setPixel(src, j, i, GeneralUtils.getBLACK());
black_num++;
}
}
}
return src;
}
}

112
archive/1_image_sdks/image_kit/imagekit_java/src/main/java/me/aias/util/ContourUtils.java Normal file
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package me.aias.util;
import org.bytedeco.opencv.global.opencv_imgproc;
import org.bytedeco.opencv.opencv_core.Mat;
import org.bytedeco.opencv.opencv_core.MatVector;
import org.bytedeco.opencv.opencv_core.Point;
import org.bytedeco.opencv.opencv_core.RotatedRect;
import static org.bytedeco.opencv.global.opencv_imgproc.*;
/** 轮廓工具类 */
public class ContourUtils {
/**
* 寻找轮廓
*
* @param cannyMat
* @return
*/
public static MatVector getContours(Mat cannyMat) {
MatVector contours = new MatVector();
Mat hierarchy = new Mat();
// 寻找轮廓
// CV_RETR_EXTERNAL, CV_CHAIN_APPROX_NONE
// CV_RETR_TREE
findContours(
cannyMat,
contours,
hierarchy,
opencv_imgproc.RETR_LIST,
opencv_imgproc.CHAIN_APPROX_SIMPLE,
new Point(0, 0));
if (contours.size() <= 0) {
throw new RuntimeException("未找到图像轮廓");
} else {
return contours;
}
}
/**
* 作用：返回边缘检测之后的最大轮廓
*
* @param contours
* @return
*/
public static Mat getMaxContour(MatVector contours) {
// 找出匹配到的最大轮廓
double area = boundingRect(contours.get(0)).area();
int index = 0;
// 找出匹配到的最大轮廓
for (int i = 0; i < contours.size(); i++) {
double tempArea = boundingRect(contours.get(i)).area();
if (tempArea > area) {
area = tempArea;
index = i;
}
}
return contours.get(index);
}
/**
* 作用：返回边缘检测之后的最大轮廓
*
* @param cannyMat Canny之后的Mat矩阵
* @return
*/
public static Mat getMaxContour(Mat cannyMat) {
MatVector contours = getContours(cannyMat);
// 找出匹配到的最大轮廓
double area = boundingRect(contours.get(0)).area();
int index = 0;
// 找出匹配到的最大轮廓
for (int i = 0; i < contours.size(); i++) {
double tempArea = boundingRect(contours.get(i)).area();
if (tempArea > area) {
area = tempArea;
index = i;
}
}
return contours.get(index);
}
/**
* 利用函数approxPolyDP来对指定的点集进行逼近 精确度设置好，效果还是比较好的
*
* @param cannyMat
*/
public static Mat useApproxPolyDPFindPoints(Mat cannyMat) {
return useApproxPolyDPFindPoints(cannyMat, 0.01);
}
/**
* 利用函数approxPolyDP来对指定的点集进行逼近 精确度设置好，效果还是比较好的
*
* @param cannyMat
* @param threshold 阀值(精确度)
* @return
*/
public static Mat useApproxPolyDPFindPoints(Mat cannyMat, double threshold) {
Mat maxContour = getMaxContour(cannyMat);
Mat approxCurve = new Mat();
// 原始曲线与近似曲线之间的最大距离设置为0.01，true表示是闭合的曲线
opencv_imgproc.approxPolyDP(maxContour, approxCurve, threshold, true);
return approxCurve;
}
}

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package me.aias.util;
import org.bytedeco.opencv.global.opencv_imgproc;
import org.bytedeco.opencv.opencv_core.*;
import static org.bytedeco.opencv.global.opencv_imgproc.minAreaRect;
/** 画图工具类 */
public class DrawUtils {
/**
* 画出所有的矩形
*
* @param src
* @return
*/
public static Mat drawContours(Mat src, String filePath) {
Mat cannyMat = GeneralUtils.canny(src);
MatVector contours = ContourUtils.getContours(cannyMat);
Mat rectMat = src.clone();
Scalar scalar = new Scalar(255, 0, 0, 1);
for (long i = contours.size() - 1; i >= 0; i--) {
Mat mat = contours.get(i);
RotatedRect rect = minAreaRect(mat);
Rect r = rect.boundingRect();
System.out.println(r.area() + " --- " + i);
rectMat = drawRect(rectMat, r, scalar);
}
GeneralUtils.saveImg(rectMat, filePath);
return rectMat;
}
/**
* 画出最大的矩形
*
* @param src
* @return
*/
public static void drawMaxRect(Mat src, String filePath) {
Mat cannyMat = GeneralUtils.canny(src);
RotatedRect rect = RectUtils.getMaxRect(cannyMat);
Rect r = rect.boundingRect();
Mat rectMat = src.clone();
Scalar scalar = new Scalar(255, 0, 0, 1);
rectMat = drawRect(rectMat, r, scalar);
GeneralUtils.saveImg(rectMat, filePath);
}
/**
* 画矩形
*
* @param src
* @param r
* @param scalar
* @return
*/
public static Mat drawRect(Mat src, Rect r, Scalar scalar) {
Point pt1 = new Point(r.x(), r.y());
Point pt2 = new Point(r.x() + r.width(), r.y());
Point pt3 = new Point(r.x() + r.width(), r.y() + r.height());
Point pt4 = new Point(r.x(), r.y() + r.height());
opencv_imgproc.line(src, pt1, pt2, scalar);
opencv_imgproc.line(src, pt2, pt3, scalar);
opencv_imgproc.line(src, pt3, pt4, scalar);
opencv_imgproc.line(src, pt4, pt1, scalar);
return src;
}
/**
* 画实心圆
*
* @param src
* @param point 点
* @param size 点的尺寸
* @param scalar 颜色
* @param path 保存路径
*/
public static boolean drawCircle(Mat src, Point[] point, int size, Scalar scalar, String path) {
if (src == null || point == null) {
throw new RuntimeException("Mat 或者 Point 数组不能为NULL");
}
for (Point p : point) {
opencv_imgproc.circle(src, p, size, scalar);
}
if (path != null && !"".equals(path)) {
return GeneralUtils.saveImg(src, path);
}
return false;
}
}

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package me.aias.util;
import org.bytedeco.javacpp.indexer.UByteRawIndexer;
import org.bytedeco.opencv.global.opencv_imgcodecs;
import org.bytedeco.opencv.global.opencv_imgproc;
import org.bytedeco.opencv.opencv_core.Mat;
import org.bytedeco.opencv.opencv_core.Size;
/** opencv的一些通用方法工具类 */
public class GeneralUtils {
private static final int BLACK = 0;
private static final int WHITE = 255;
// 设置归一化图像的固定大小
private static final Size dsize = new Size(32, 32);
/**
* 作用：输入图像路径，返回mat矩阵
*
* @param imgPath 图像路径
* @return
*/
public static Mat matFactory(String imgPath) {
return opencv_imgcodecs.imread(imgPath);
}
/**
* 作用：输入图像Mat矩阵对象，返回图像的宽度
*
* @param src Mat矩阵图像
* @return
*/
public static int getImgWidth(Mat src) {
return src.cols();
}
/**
* 作用：输入图像Mat矩阵，返回图像的高度
*
* @param src Mat矩阵图像
* @return
*/
public static int getImgHeight(Mat src) {
return src.rows();
}
/**
* 作用：获取图像(y,x)点的像素，我们只针对单通道(灰度图)
*
* @param src Mat矩阵图像
* @param y y坐标轴
* @param x x坐标轴
* @return
*/
public static int getPixel(Mat src, int y, int x) {
UByteRawIndexer ldIdx = src.createIndexer();
int result = ldIdx.get(y, x);
ldIdx.release();
return result;
}
/**
* 作用：设置图像(y,x)点的像素，我们只针对单通道(灰度图)
*
* @param src Mat矩阵图像
* @param y y坐标轴 row
* @param x x坐标轴 col
* @param color 颜色值[0-255]
*/
public static void setPixel(Mat src, int y, int x, int color) {
UByteRawIndexer ldIdx = src.createIndexer();
ldIdx.put(y, x, color);
ldIdx.release();
}
/**
* 作用：保存图像
*
* @param src Mat矩阵图像
* @param filePath 要保存图像的路径及名字
* @return
*/
public static boolean saveImg(Mat src, String filePath) {
return opencv_imgcodecs.imwrite(filePath, src);
}
/**
* 作用：翻转图像像素
* 确保白底黑字或者黑底白字
*
* @param src
* @param b true：表示白底黑字 ， false相反
* @return
*/
public static Mat turnPixel(Mat src, boolean b) {
if (src != null) {
int width = GeneralUtils.getImgWidth(src);
int height = GeneralUtils.getImgHeight(src);
int value;
int black_num = 0;
int white_num = 0;
int i, j;
for (i = 0; i < width; i++) {
for (j = 0; j < height; j++) {
value = GeneralUtils.getPixel(src, j, i);
if (value == GeneralUtils.getWHITE()) {
white_num++;
} else if (value == GeneralUtils.getBLACK()) {
black_num++;
}
}
}
if (b && black_num > white_num) {
// 反转
src = turnPixel(src);
} else if (!b && white_num > black_num) {
// 反转
src = turnPixel(src);
}
}
return src;
}
/**
* 作用：翻转图像像素
*
* @param src Mat矩阵图像
* @return
*/
public static Mat turnPixel(Mat src) {
if (src.channels() != 1) {
throw new RuntimeException("不是单通道图，需要先灰度化");
}
int j, i, value;
int width = getImgWidth(src), height = getImgHeight(src);
for (j = 0; j < height; j++) {
for (i = 0; i < width; i++) {
value = getPixel(src, j, i);
if (value == 0) {
setPixel(src, j, i, WHITE);
} else {
setPixel(src, j, i, BLACK);
}
}
}
return src;
}
/** 图像腐蚀/膨胀处理 腐蚀和膨胀对处理没有噪声的图像很有利，慎用 */
public static Mat erodeDilateImg(Mat src) {
Mat outImage = new Mat();
// size 越小，腐蚀的单位越小，图片越接近原图
Mat structImage =
opencv_imgproc.getStructuringElement(opencv_imgproc.MORPH_RECT, new Size(2, 2));
/**
* 图像腐蚀 腐蚀说明： 图像的一部分区域与指定的核进行卷积， 求核的最`小`值并赋值给指定区域。 腐蚀可以理解为图像中`高亮区域`的'领域缩小'。
* 意思是高亮部分会被不是高亮部分的像素侵蚀掉，使高亮部分越来越少。
*/
opencv_imgproc.erode(src, outImage, structImage);
// opencv_imgproc.erode(src, outImage, structImage, new Point(-1, -1), 2);
src = outImage;
/**
* 膨胀 膨胀说明： 图像的一部分区域与指定的核进行卷积， 求核的最`大`值并赋值给指定区域。 膨胀可以理解为图像中`高亮区域`的'领域扩大'。
* 意思是高亮部分会侵蚀不是高亮的部分，使高亮部分越来越多。
*/
opencv_imgproc.dilate(src, outImage, structImage);
// opencv_imgproc.dilate(src, outImage, structImage, new Point(-1, -1), 2);
src = outImage;
return src;
}
/**
* canny算法，边缘检测
*
* @param src
* @return
*/
public static Mat canny(Mat src) {
Mat mat = src.clone();
opencv_imgproc.Canny(src, mat, 60, 200);
return mat;
}
public static int getBLACK() {
return BLACK;
}
public static int getWHITE() {
return WHITE;
}
public static Size getDsize() {
return dsize;
}
}

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package me.aias.util;
import org.bytedeco.javacpp.indexer.UByteRawIndexer;
import org.bytedeco.opencv.global.opencv_imgproc;
import org.bytedeco.opencv.opencv_core.Mat;
import java.util.List;
import java.util.Random;
/** 对图像灰度化处理的工具类 默认有效数据的灰度值小于底色，即有效数据更清晰 */
public class GrayUtils {
/**
* 作用：灰度化
*
* @param src
* @return
*/
public static Mat grayNative(Mat src) {
Mat gray = src.clone();
if (src.channels() == 3) {
// 进行灰度化
opencv_imgproc.cvtColor(src, gray, opencv_imgproc.COLOR_BGR2GRAY);
src = gray;
}
return src;
}
/**
* 均值灰度化减噪 细粒度灰度化，只降低噪声，不对有效数据做任何的加强的处理 根据灰度化后的图像每一列的像素值的平均值(默认)或者其他表达式值作为阀值，把大于阀值的像素都改为255
* 可以在一定程度上降低噪声，而不对有效数据造成任何影响
*
* @param src
* @return
*/
public static Mat grayColByMidle(Mat src) {
if (src.channels() != 1) {
src = grayNative(src);
}
List<List<Double>> data = MathUtils.MatPixelToList(src);
return grayColByMidle(src, data);
}
/**
* 根据灰度化后的图像每一列的像素值的平均值作为阀值，把大于阀值的像素都改为255
*
* @param data List<Double>中存储图像灰度化后的每一列的像素值
* @return
*/
public static Mat grayColByMidle(Mat src, List<List<Double>> data) {
if (src.channels() != 1) {
src = grayNative(src);
}
UByteRawIndexer ldIdx = src.createIndexer();
// IntRawIndexer ldIdx = src.createIndexer();
for (int j = 0; j < data.size(); j++) {
List<Double> list = data.get(j);
int avg = (int) ((MathUtils.getSumInList(list) / list.size()) * 0.95);
// 随机的更新像素值
int count = 3 * list.size() / 4;
int time = 0;
while (count > 0) {
int index = new Random().nextInt(list.size());
if (list.get(index) >= avg) {
ldIdx.put(index, j, 255);
count--;
}
if (count == count) {
time++;
}
// 避免程序进入死循环
if (time == list.size() / 2) {
break;
}
}
}
ldIdx.release();
return src;
}
/**
* k值灰度化减噪 根据灰度化后的图像每一列的像素值的第k大值作为阀值，把大于阀值的像素都改为255 默认选取第1/3大的值作为阀值
*
* @param src
* @return
*/
public static Mat grayColByKLargest(Mat src) {
if (src.channels() != 1) {
src = grayNative(src);
}
List<List<Double>> data = MathUtils.MatPixelToList(src);
return grayColByKLargest(src, 3, data);
}
/**
* 根据灰度化后的图像每一列的像素值的第k大值作为阀值，把大于阀值的像素都改为255
*
* @param src
* @param k 分母
* @return
*/
public static Mat grayColByKLargest(Mat src, int k) {
if (k == 0) {
throw new RuntimeException("k不能为0");
}
if (src.channels() != 1) {
src = grayNative(src);
}
List<List<Double>> data = MathUtils.MatPixelToList(src);
return grayColByKLargest(src, k, data);
}
/**
* 根据灰度化后的图像每一列的像素值的第k大值作为阀值，把大于阀值的像素都改为255
*
* @param src
* @param k
* @param data
* @return
*/
public static Mat grayColByKLargest(Mat src, int k, List<List<Double>> data) {
if (k == 0) {
throw new RuntimeException("k不能为0");
}
if (src.channels() != 1) {
src = grayNative(src);
}
UByteRawIndexer ldIdx = src.createIndexer();
// IntRawIndexer ldIdx = src.createIndexer();
for (int j = 0; j < data.size(); j++) {
List<Double> list = data.get(j);
Object[] doubles = list.toArray();
Double[] doubles1 = new Double[doubles.length];
for (int i = 0; i < doubles.length; i++) {
doubles1[i] = (Double) doubles[i];
}
double d = MathUtils.findKthLargest(doubles1, list.size() / k);
// 随机的更新像素值
int count = 3 * doubles.length / 4;
int time = 0;
while (count > 0) {
int index = new Random().nextInt(list.size());
if (list.get(index) >= d) {
ldIdx.put(index, j, 255);
count--;
}
if (count == count) {
time++;
}
// 避免程序进入死循环
if (time == list.size() / 2) {
break;
}
}
}
ldIdx.release();
return src;
}
/**
* 作用：自适应选取阀值
*
* @param src Mat矩阵图像
* @return
*/
public static int getAdapThreshold(Mat src) {
int threshold = 0, threshold_new = 127;
int nWhite_count, nBlack_count;
int nWhite_sum, nBlack_sum;
int value, i, j;
int width = GeneralUtils.getImgWidth(src), height = GeneralUtils.getImgHeight(src);
if (width == 0 || height == 0) {
System.out.println("图像加载异常");
return -1;
}
while (threshold != threshold_new) {
nWhite_sum = nBlack_sum = 0;
nWhite_count = nBlack_count = 0;
for (j = 0; j < height; j++) {
for (i = 0; i < width; i++) {
value = GeneralUtils.getPixel(src, j, i);
if (value > threshold_new) {
nWhite_count++;
nWhite_sum += value;
} else {
nBlack_count++;
nBlack_sum += value;
}
}
}
threshold = threshold_new;
if (nWhite_count == 0 || nBlack_count == 0) {
threshold_new = (nWhite_sum + nBlack_sum) / (nWhite_count + nBlack_count);
} else {
threshold_new = (nWhite_sum / nWhite_count + nBlack_sum / nBlack_count) / 2;
}
}
return threshold;
}
/**
* 局部自适应阀值灰度化降噪
*
* @param src
* @return
*/
public static Mat grayColByPartAdapThreshold(Mat src) {
if (src.channels() != 1) {
src = grayNative(src);
}
int width = GeneralUtils.getImgWidth(src);
int height = GeneralUtils.getImgHeight(src);
int value;
for (int i = 0; i < width; i++) {
Mat partMat = src.col(i);
int thresold = getAdapThreshold(partMat);
for (int j = 0; j < height; j++) {
value = GeneralUtils.getPixel(src, j, i);
if (value > thresold) {
GeneralUtils.setPixel(src, j, i, GeneralUtils.getWHITE());
}
}
}
return src;
}
/**
* 全局自适应阀值灰度化降噪
*
* @param src
* @return
*/
public static Mat grayColByAdapThreshold(Mat src) {
if (src.channels() != 1) {
src = grayNative(src);
}
int i, j;
int width = GeneralUtils.getImgWidth(src), height = GeneralUtils.getImgHeight(src);
int value;
int threshold = getAdapThreshold(src);
for (j = 0; j < height; j++) {
for (i = 0; i < width; i++) {
value = GeneralUtils.getPixel(src, j, i);
if (value > threshold) {
GeneralUtils.setPixel(src, j, i, GeneralUtils.getWHITE());
}
}
}
return src;
}
}

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package me.aias.util;
import org.bytedeco.javacv.Java2DFrameConverter;
import org.bytedeco.javacv.OpenCVFrameConverter;
import org.bytedeco.opencv.opencv_core.Mat;
import java.awt.*;
import java.awt.image.BufferedImage;
import java.awt.image.DataBufferByte;
/**
* 图片类型转换
*
* @author Calvin
*/
public class ImageUtils {
/**
* 将 BufferedImage 转 Mat
*
* @param original
*/
public static Mat bufferedImage2Mat(BufferedImage original) {
OpenCVFrameConverter.ToMat cv = new OpenCVFrameConverter.ToMat();
return cv.convertToMat(new Java2DFrameConverter().convert(original));
}
/**
* 将mat转BufferedImage
*
* @param matrix
*/
public static BufferedImage mat2BufferedImage(Mat matrix) {
int cols = matrix.cols();
int rows = matrix.rows();
int elemSize = (int) matrix.elemSize();
byte[] data = new byte[cols * rows * elemSize];
matrix.data().get(data);
int type = 0;
switch (matrix.channels()) {
case 1:
type = BufferedImage.TYPE_BYTE_GRAY;
break;
case 3:
type = BufferedImage.TYPE_3BYTE_BGR;
byte b;
for (int i = 0; i < data.length; i = i + 3) {
b = data[i];
data[i] = data[i + 2];
data[i + 2] = b;
}
break;
default:
return null;
}
BufferedImage image = new BufferedImage(cols, rows, type);
// BufferedImage对象中最重要的两个组件为Raster和ColorModel，分别用于存储图像的像素数据与颜色数据。
// 表示像素矩形数组的类。Raster 封装存储样本值的 DataBuffer，以及描述如何在 DataBuffer 中定位给定样本值的 SampleModel。
// 由于Raster对象是BufferedImage对象中的像素数据存储对象，因此，BufferedImage支持从Raster对象中获取任意位置（x，y）点的像素值p（x，y）。
image.getRaster().setDataElements(0, 0, cols, rows, data);
return image;
}
/**
* 将bufferImage转Mat
*
* @param original
* @param matType
* @param msg
* @param x
* @param y
*/
public static Mat bufferedImage2Mat(
BufferedImage original, int matType, String msg, int x, int y) {
Graphics2D g = original.createGraphics();
try {
g.setComposite(AlphaComposite.Src);
g.drawImage(original, 0, 0, null);
g.drawString(msg, x, y);
} finally {
g.dispose();
}
Mat mat = new Mat(original.getHeight(), original.getWidth(), matType);
mat.data().put(((DataBufferByte) original.getRaster().getDataBuffer()).getData());
return mat;
}
}

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package me.aias.util;
import org.bytedeco.javacpp.indexer.DoubleRawIndexer;
import org.bytedeco.javacpp.indexer.UByteRawIndexer;
import org.bytedeco.opencv.opencv_core.Mat;
import java.util.ArrayList;
import java.util.List;
/**
* 一些数学计算工具类
*/
public class MathUtils {
/**
* 把opencv的灰度图Mat转化为List<List<Double>> , 即数组对象
* List<Double>是每一列的灰度值
* @param gray
* @return
*/
public static List<List<Double>> MatPixelToList(Mat gray){
if(gray == null){
throw new RuntimeException("不能传入空对象");
}
return MatPixelToList(gray , true);
}
/**
* 把opencv的灰度图Mat转化为List<List<Double>> , 即数组对象
* @param gray
* @param b true 表示List<Double>是每一列的灰度值，否则List<Double>是每一行的灰度值
* @return
*/
public static List<List<Double>> MatPixelToList(Mat gray , boolean b){
if(gray == null){
throw new RuntimeException("不能传入空对象");
}
List<List<Double>> result = new ArrayList<>();
int x , y;
int i , j;
double value;
if(b){
//List<Double>是每一列的灰度值
x = gray.cols();
y = gray.rows();
}else{
//List<Double>是每一行的灰度值
x = gray.rows();
y = gray.cols();
}
UByteRawIndexer ldIdx = gray.createIndexer();
for(i = 0 ; i < x ; i++){
List<Double> oneLine = new ArrayList<>();
for(j = 0 ; j < y ; j++){
if(b){
value = ldIdx.get(j, i);
}else{
value = ldIdx.get(i , j);
}
oneLine.add(value);
}
result.add(oneLine);
}
ldIdx.release();
return result;
}
/**
* 返回Mat每一列的平均值
* @param grayMat
* @return
*/
public static List<Double> avgColMat(Mat grayMat){
List<List<Double>> data = MatPixelToList(grayMat);
List<Double> result = new ArrayList<>();
for(List<Double> col : data) {
double sum = getSumInList(col);
result.add(sum / col.size());
}
return result;
}
/**
* 返回Mat每一行的平均值
* @param grayMat
* @return
*/
public static List<Double> avgRowMat(Mat grayMat){
List<List<Double>> data = MatPixelToList(grayMat , false);
List<Double> result = new ArrayList<>();
for(List<Double> row : data) {
double sum = getSumInList(row);
result.add(sum / row.size());
}
return result;
}
/**
* 返回list集合中最大的那个数
* @param list
* @return
*/
public static double getMaxInList(List<Double> list){
if(list == null){
throw new RuntimeException("不能传入空对象");
}
double max = list.get(0);
for(double d : list){
if(d > max){
max = d;
}
}
return max;
}
/**
* 返回list集合中最大的那个数
* @param list
* @return
*/
public static double getMinInList(List<Double> list){
if(list == null){
throw new RuntimeException("不能传入空对象");
}
double min = list.get(0);
for(double d : list){
if(d < min){
min = d;
}
}
return min;
}
/**
* 返回list集合中总和
* @param list
* @return
*/
public static double getSumInList(List<Double> list){
if(list == null){
throw new RuntimeException("不能传入空对象");
}
double sum = 0;
for(double d : list){
sum += d;
}
return sum;
}
/**
* 寻找第k大的元素，快速排序实现
* @param nums
* @param k
* @return
*/
public static double findKthLargest(List<Double> nums , int k){
int len = nums.size();
Double[] doubles1 = new Double[len];
for(int i = 0 ; i < len ; i++){
doubles1[i] = (Double)nums.get(i);
}
return findKthLargest(doubles1 , k);
}
/**
* 寻找第k大的元素，快速排序实现
* @param nums
* @param k
* @return
*/
public static double findKthLargest(Double[] nums, int k) {
if (k < 1 || nums == null || k > nums.length) {
return -1;
}
return getKth(nums.length - k + 1, nums, 0, nums.length - 1);
}
public static double getKth(int k, Double[] nums, int start, int end) {
double pivot = nums[end];
int left = start;
int right = end;
while (true) {
while (nums[left] < pivot && left < right) {
left++;
}
while (nums[right] >= pivot && right > left) {
right--;
}
if (left == right) {
break;
}
swap(left, right , nums);
}
swap(left, end , nums);
if (k == left + 1) { return pivot; }
else if (k < left + 1) { return getKth(k, nums, start, left - 1); }
else { return getKth(k, nums, left + 1, end); }
}
/**
* 交换数组中l1和l2的数据
* @param l1
* @param l2
* @param arr
* @return
*/
private static void swap(int l1 , int l2 , Double[] arr){
double temp = arr[l1];
arr[l1] = arr[l2];
arr[l2] = temp;
}
/**
* 计算指定范围的List的和
* @param data
* @param start
* @param end
* @return
*/
public static int sumList(List<Integer> data, int start , int end){
if(start < 0 || start >= data.size() || end < start || end > data.size()){
throw new RuntimeException("参数不合法");
}
int sum = 0;
for(int i = start ; i < end ; i++){
sum += data.get(i);
}
return sum;
}
/**
* 计算指定范围List的最大值
* @param data
* @param start
* @param end
* @return
*/
public static int maxList(List<Integer> data, int start , int end){
if(start < 0 || start >= data.size() || end < start || end > data.size()){
throw new RuntimeException("参数不合法");
}
int max = data.get(start);
for(int i = start + 1; i < end ; i++){
int value = data.get(i);
if(max < value){
max = value;
}
}
return max;
}
/**
* 计算指定范围List的最小值
* @param data
* @param start
* @param end
* @return
*/
public static int minList(List<Integer> data, int start , int end){
if(start < 0 || start >= data.size() || end < start || end > data.size()){
throw new RuntimeException("参数不合法");
}
int min = data.get(start);
for(int i = start + 1; i < end ; i++){
int value = data.get(i);
if(min > value){
min = value;
}
}
return min;
}
//。。。。。。。。。。。。。。。。。。。。。。。。TODO
/**
* 返回切割片段中最小的片段的起始下标
* @param cutPoint 图像上的切割点
* @param width 图像宽度
* @return
*/
public static int minChoicedPart(List<Integer> cutPoint , int width){
int preValue = cutPoint.get(0);
int curValue = -1;
int minPart = Integer.MAX_VALUE;
int preIndex = -1;
int partWidth = -1;
for(int i = 1 ; i <= cutPoint.size() ; i++){
if(i == cutPoint.size()){
curValue = width;
}else{
curValue = cutPoint.get(i);
}
partWidth = curValue - preValue;
preValue = curValue;
if(partWidth < minPart){
minPart = partWidth;
preIndex = i - 1;
}
}
return preIndex;
}
/**
* 返回切割片段中最大的片段的起始下标
* @param cutPoint 图像上的切割点
* @param width 图像的宽度
* @return
*/
public static int maxChoicedPart(List<Integer> cutPoint , int width){
int preValue = cutPoint.get(0);
int curValue = -1;
int maxPart = Integer.MIN_VALUE;
int preIndex = -1;
int partWidth = -1;
for(int i = 1 ; i <= cutPoint.size() ; i++){
if(i == cutPoint.size()){
curValue = width;
}else{
curValue = cutPoint.get(i);
}
partWidth = curValue - preValue;
preValue = curValue;
if(partWidth > maxPart){
maxPart = partWidth;
preIndex = i - 1;
}
}
return preIndex;
}
/**
* 计算指定范围的List中大于avg的值的个数
* @param data
* @param start
* @param end
* @param avg
* @return
*/
public static int countLargeThanAvgInList(List<Integer> data, int start , int end , int avg){
if(start < 0 || start >= data.size() || end < start || end > data.size()){
throw new RuntimeException("参数不合法");
}
int count = 0;
for(int i = start ; i < end ; i++){
if(data.get(i) > avg){
count++;
}
}
return count;
}
}

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package me.aias.util;
import org.bytedeco.opencv.global.opencv_imgproc;
import org.bytedeco.opencv.opencv_core.Mat;
import org.bytedeco.opencv.opencv_core.Point;
import org.bytedeco.opencv.opencv_core.Scalar;
/**
* 降噪工具类
* Noise reduction utility class
*/
public class NoiseUtils {
private static final int WHITE = 255;
/**
* 作用：给单通道的图像边缘预处理，降噪(默认白底黑字)
* Function: Preprocess the edges of a single-channel image and reduce noise (default white background and black text)
*
* @param src Mat矩阵对象 - Mat matrix object
* @return
*/
public static Mat strokeWhite(Mat src) {
if (src.channels() != 1) {
throw new RuntimeException("不是单通道图，需要先灰度化 - It is not a single-channel image and needs to be grayscale first");
}
int i, width = GeneralUtils.getImgWidth(src), height = GeneralUtils.getImgHeight(src);
for (i = 0; i < width; i++) {
GeneralUtils.setPixel(src, i, 0, WHITE);
GeneralUtils.setPixel(src, i, height - 1, WHITE);
}
for (i = 0; i < height; i++) {
GeneralUtils.setPixel(src, 0, i, WHITE);
GeneralUtils.setPixel(src, width - 1, i, WHITE);
}
return src;
}
/**
* 8邻域降噪，又有点像9宫格降噪;即如果9宫格中心被异色包围，则同化 作用：降噪(默认白底黑字)
* 8-neighborhood noise reduction, similar to 9-grid noise reduction; that is, if the center of the 9-grid is surrounded by different colors, it is homogenized. Function: Noise reduction (default white background and black text)
*
* @param src Mat矩阵对象 - Mat matrix object
* @param pNum 阀值 默认取1即可 - Threshold, the default value is 1
* @return
*/
public static Mat navieRemoveNoise(Mat src, int pNum) {
int i, j, m, n, nValue, nCount;
int width = GeneralUtils.getImgWidth(src), height = GeneralUtils.getImgHeight(src);
// 如果一个点的周围都是白色的，自己确实黑色的，同化
// If the surroundings of a point are all white and it is indeed black, homogenize
for (j = 1; j < height - 1; j++) {
for (i = 1; i < width - 1; i++) {
nValue = GeneralUtils.getPixel(src, j, i);
if (nValue == 0) {
nCount = 0;
// 比较(j , i)周围的9宫格，如果周围都是白色，同化
// Compare the 9-grid around (j, i), and homogenize if the surrounding is white
for (m = j - 1; m <= j + 1; m++) {
for (n = i - 1; n <= i + 1; n++) {
if (GeneralUtils.getPixel(src, m, n) == 0) {
nCount++;
}
}
}
if (nCount <= pNum) {
// 周围黑色点的个数小于阀值pNum,把自己设置成白色
// If the number of black points around is less than the threshold pNum, set it to white
GeneralUtils.setPixel(src, j, i, GeneralUtils.getWHITE());
}
} else {
nCount = 0;
// 比较(j , i)周围的9宫格，如果周围都是黑色，同化
// Compare the 9-grid around (j, i), and homogenize if the surrounding is black
for (m = j - 1; m <= j + 1; m++) {
for (n = i - 1; n <= i + 1; n++) {
if (GeneralUtils.getPixel(src, m, n) == 0) {
nCount++;
}
}
}
if (nCount >= 8 - pNum) {
// 周围黑色点的个数大于等于(8 - pNum),把自己设置成黑色
// If the number of black points around is greater than or equal to (8 - pNum), set it to black
GeneralUtils.setPixel(src, j, i, GeneralUtils.getBLACK());
}
}
}
}
return src;
}
/**
* 连通域降噪 作用：降噪(默认白底黑字)
* Connected domain noise reduction Function: Noise reduction (default white background and black text)
*
* @param src Mat矩阵对象 - Mat matrix object
* @param pArea 阀值 默认取1即可 - Threshold, the default value is 1
* @return
*/
public static Mat connectedRemoveNoise(Mat src, double pArea) {
int i, j, color = 1;
int width = GeneralUtils.getImgWidth(src), height = GeneralUtils.getImgHeight(src);
Result result = floodFill(new Result(src), pArea);
src = result.mat;
// 二值化 - Binaryzation
for (i = 0; i < width; i++) {
for (j = 0; j < height; j++) {
if (GeneralUtils.getPixel(src, j, i) < GeneralUtils.getWHITE()) {
GeneralUtils.setPixel(src, j, i, GeneralUtils.getBLACK());
}
}
}
if (result.status == false && result.count <= 100) {
connectedRemoveNoise(src, pArea);
}
return src;
}
/**
* 连通域填充颜色
* Flood fill color of connected domains
*
* @param result
* @return
*/
public static Result floodFill(Result result, double pArea) {
Mat src = result.mat;
if (src == null) {
return null;
}
int i, j, color = 1;
int width = GeneralUtils.getImgWidth(src), height = GeneralUtils.getImgHeight(src);
for (i = 0; i < width; i++) {
for (j = 0; j < height; j++) {
if (GeneralUtils.getPixel(src, j, i) == GeneralUtils.getBLACK()) {
// 用不同的颜色填充连接区域中的每个黑色点
// Fill each black point in the connected area with a different color
// floodFill就是把与点(i , j)的所有相连通的区域都涂上color颜色
// floodFill is to color all areas that are connected with point (i, j) with color
int area = opencv_imgproc.floodFill(src, new Mat(), new Point(i, j), new Scalar(color));
if (area <= pArea) {
opencv_imgproc.floodFill(src, new Mat(), new Point(i, j), new Scalar(255));
} else {
color++;
}
if (color == 255) {
result.status = false; // 连通域还没填充完 - The connected area is not completely filled
result.mat = src;
result.count = result.count + 1;
return result;
}
}
}
}
result.mat = src;
result.status = true; // 表示所有的连通域都已填充完毕 - Indicates that all connected domains have been filled
return result;
}
/**
* 连通域填充颜色
* Flood fill color of connected domains
*
* @param src
* @return
*/
public static Mat floodFill(Mat src, double pArea) {
if (src == null) {
return null;
}
int i, j, color = 1;
int width = GeneralUtils.getImgWidth(src), height = GeneralUtils.getImgHeight(src);
for (i = 0; i < width; i++) {
for (j = 0; j < height; j++) {
if (GeneralUtils.getPixel(src, j, i) == GeneralUtils.getBLACK()) {
// 用不同的颜色填充连接区域中的每个黑色点
// Fill each black point in the connected area with a different color
// floodFill就是把与点(i , j)的所有相连通的区域都涂上color颜色
// floodFill is to color all areas that are connected with point (i, j) with color
int area = opencv_imgproc.floodFill(src, new Mat(), new Point(i, j), new Scalar(color));
if (area <= pArea) {
System.out.println(color);
opencv_imgproc.floodFill(src, new Mat(), new Point(i, j), new Scalar(255));
} else {
color++;
}
System.out.println(color);
}
}
}
return src;
}
// 只填充最大的连通域
// Only fill the largest connected area
public static Mat findMaxConnected(Mat src) {
int i, j, color = 127;
int width = GeneralUtils.getImgWidth(src), height = GeneralUtils.getImgHeight(src);
int maxArea = Integer.MAX_VALUE;
int maxI = -1, maxJ = -1;
for (i = 0; i < width; i++) {
for (j = 0; j < height; j++) {
if (GeneralUtils.getPixel(src, j, i) == GeneralUtils.getBLACK()) {
// 用不同的颜色填充连接区域中的每个黑色点
// Fill each black point in the connected area with a different color
// floodFill就是把与点(i , j)的所有相连通的区域都涂上color颜色
// floodFill is to color all areas that are connected with point (i, j) with color
int area = opencv_imgproc.floodFill(src, new Mat(), new Point(i, j), new Scalar(color));
if (maxI != -1 && maxJ != -1) {
if (area > maxArea) {
maxArea = area;
opencv_imgproc.floodFill(src, new Mat(), new Point(maxI, maxJ), new Scalar(255));
maxI = i;
maxJ = j;
} else {
opencv_imgproc.floodFill(src, new Mat(), new Point(i, j), new Scalar(255));
}
} else {
maxI = i;
maxJ = j;
maxArea = area;
}
}
}
}
return src;
}
private static class Result {
Mat mat; // Mat对象 - Mat object
boolean status; // 是否填充完毕 - Whether it is filled
int count; // 记录填充的次数 - Number of fillings
int height; // 记录上一次填充的height位置 - height position of the last filling
int width; // 记录上一次填充的width位置 - width position of the last filling
public Result() {
}
public Result(Mat src) {
this.mat = src;
}
}
}

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package me.aias.util;
import org.bytedeco.opencv.opencv_core.*;
import static org.bytedeco.opencv.global.opencv_imgproc.minAreaRect;
/**
* 矩形框工具类
* Rectangle tool class
*/
public class RectUtils {
/**
* 返回边缘检测之后的最大矩形
* Returns the maximum rectangle after edge detection
*
* @param contours
* @return
*/
public static RotatedRect getMaxRect(MatVector contours) {
// 找出匹配到的最大轮廓 - Find the largest contour
Mat maxContour = ContourUtils.getMaxContour(contours);
RotatedRect rect = minAreaRect(maxContour);
return rect;
}
/**
* 返回边缘检测之后的最大矩形
* Returns the maximum rectangle after edge detection
*
* @param cannyMat Canny之后的mat矩阵 - Canny's mat matrix
* @return
*/
public static RotatedRect getMaxRect(Mat cannyMat) {
// 找出匹配到的最大轮廓 - Find the largest contour
Mat maxContour = ContourUtils.getMaxContour(cannyMat);
RotatedRect rect = minAreaRect(maxContour);
return rect;
}
/**
* 把矫正后的图像切割出来
* Cut the corrected image
*
* @param correctMat 图像矫正后的Mat矩阵 - The corrected Mat matrix of the image
*/
public static Mat cutRect(Mat correctMat, Mat nativeCorrectMat) {
// 获取所有轮廓 - Obtain all contours
MatVector contours = ContourUtils.getContours(correctMat);
// 获取最大矩形 - Obtain the largest rectangle
RotatedRect rect = getMaxRect(contours);
Point2f rectPoint = new Point2f(4);
rect.points(rectPoint);
for (int i = 0; i < rectPoint.limit(); i++) {
System.out.println(rectPoint.position(i).x() + " , " + rectPoint.position(i).y());
}
int x = rect.boundingRect().x();
int y = rect.boundingRect().y();
int width = rect.boundingRect().width();
int height = rect.boundingRect().height();
// int x = (int) Math.abs(rectPoint.position(2).x());
// int y = (int) Math.abs(rectPoint.position(2).y() < rectPoint.position(3).y() ?
// rectPoint.position(2).y() : rectPoint.position(3).y());
// int width = (int) Math.abs(rectPoint.position(3).x() - rectPoint.position(2).x());
// int height = (int) Math.abs(rectPoint.position(1).y() - rectPoint.position(2).y());
System.out.println("startLeft = " + x);
System.out.println("startUp = " + y);
System.out.println("width = " + width);
System.out.println("height = " + height);
Mat temp = new Mat(nativeCorrectMat, new Rect(x, y, width, height));
Mat t = new Mat();
temp.copyTo(t);
return t;
}
/**
* 把矫正后的图像切割出来--辅助函数(修复)
* Cut the corrected image -- auxiliary function (repair)
*
* @param rectPoint 矩形的四个点 - The four points of the rectangle
* @return int[startLeft , startUp , width , height]
*/
public static int[] cutRectHelp(Point2f rectPoint) {
double minX = rectPoint.position(0).x();
double maxX = rectPoint.position(0).x();
double minY = rectPoint.position(0).y();
double maxY = rectPoint.position(0).y();
for (int i = 1; i < 4; i++) {
minX = rectPoint.position(i).x() < minX ? rectPoint.position(i).x() : minX;
maxX = rectPoint.position(i).x() > maxX ? rectPoint.position(i).x() : maxX;
minY = rectPoint.position(i).y() < minY ? rectPoint.position(i).y() : minY;
maxY = rectPoint.position(i).y() > maxY ? rectPoint.position(i).y() : maxY;
}
int[] roi = {
(int) Math.abs(minX),
(int) Math.abs(minY),
(int) Math.abs(maxX - minX),
(int) Math.abs(maxY - minY)
};
return roi;
}
}

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package me.aias.util;
import org.bytedeco.opencv.global.opencv_imgproc;
import org.bytedeco.opencv.opencv_core.*;
import org.bytedeco.opencv.opencv_core.Mat;
import org.bytedeco.opencv.opencv_core.Rect;
import org.opencv.imgproc.Imgproc;
import java.util.List;
/**
* 归一化工具类
* Normalization utility class
*/
public class ResizeUtils {
// 设置归一化图像的固定大小
// Set the fixed size of the normalized image
private static final Size defaultDsize = new Size(32, 32);
/**
* 把图片归一化到相同的大小
* Normalize the image to the same size
* @param src Mat矩阵对象 - Mat matrix object
* @return
*/
public static Mat resize(Mat src) {
return resize(src, defaultDsize);
}
/**
* 把图片归一化到相同的大小
* Normalize the image to the same size
*
* @param src Mat矩阵对象 - Mat matrix object
* @return
*/
public static Mat resize(Mat src, Size dsize) {
src = trimImg(src);
Mat dst = new Mat();
// 区域插值(INTER_AREA):图像放大时类似于线性插值，图像缩小时可以避免波纹出现。
// Area interpolation (INTER_AREA): similar to linear interpolation when the image is enlarged, and can avoid ripples when the image is reduced.
opencv_imgproc.resize(src, dst, dsize, 0, 0, Imgproc.INTER_AREA);
return dst;
}
/**
* 去除图像中的空白
* Remove the blank space in the image
*
* @param src
* @return
*/
public static Mat trimImg(Mat src) {
List<Double> colList = MathUtils.avgColMat(src); // 每一列的平均值 - Average value of each column
List<Double> rowList = MathUtils.avgRowMat(src); // 每一行的平均值 - Average value of each row
double colAvg = MathUtils.getSumInList(colList) / colList.size();
double rowAvg = MathUtils.getSumInList(rowList) / rowList.size();
int blankCol1 = -1; // 空白列的关键分割点(左) - Key segmentation point (left) of blank column
int blankCol2 = -1; // 空白列的关键分割点(右) - Key segmentation point (right) of blank column
int blankRow1 = -1; // 空白行的关键分割点(上) - Key segmentation point (up) of blank row
int blankRow2 = -1; // 空白行的关键分割点(下) - Key segmentation point (down) of blank row
int preValue = -1;
int curValue = -1;
int count = 0;
boolean b = true;
for (int i = 0; i < colList.size(); i++) {
if (b == false) {
break;
}
if (colList.get(i) > colAvg) {
// 求空白列的关键分割点(左) - Calculate the key segmentation point (left) of the blank column
curValue = i;
if (preValue != -1) {
if (curValue - preValue == 1) {
// 连续 - Continuous
count++;
} else {
// 不连续 - Not continuous
if (count > 10) {
blankCol1 = 2 * i / 3;
b = false;
}
}
}
preValue = i;
}
}
preValue = -1;
curValue = -1;
count = 0;
b = true;
for (int i = colList.size() - 1; i >= 0; i--) {
if (b == false) {
break;
}
if (colList.get(i) > colAvg) {
// 求空白列的关键分割点(右)
// Calculate the key segmentation point (right) of the blank column
curValue = i;
if (preValue != -1) {
if (curValue - preValue == -1) {
// 连续 - Continuous
count++;
} else {
// 不连续 - Not continuous
if (count > 10) {
blankCol2 = i + (colList.size() - i) / 3;
b = false;
}
}
}
preValue = i;
}
}
preValue = -1;
curValue = -1;
count = 0;
b = true;
for (int i = 0; i < rowList.size(); i++) {
if (rowList.get(i) > rowAvg) {
// 空白行的关键分割点(上)
// Key segmentation point (up) of blank row
if (b == false) {
break;
}
if (rowList.get(i) > rowAvg) {
curValue = i;
if (preValue != -1) {
if (curValue - preValue == 1) {
// 连续 - Continuous
count++;
} else {
// 不连续 - Not continuous
if (count > 10) {
blankRow1 = i / 2;
b = false;
}
}
}
preValue = i;
}
}
}
preValue = -1;
curValue = -1;
count = 0;
b = true;
for (int i = rowList.size() - 1; i >= 0; i--) {
if (rowList.get(i) > rowAvg) {
// 空白行的关键分割点(下)
// Key segmentation point (down) of blank row
if (b == false) {
break;
}
if (rowList.get(i) > rowAvg) {
curValue = i;
if (preValue != -1) {
if (curValue - preValue == -1) {
// 连续 - Continuous
count++;
} else {
// 不连续 - Not continuous
if (count > 10) {
blankRow2 = i + 2 * (rowList.size() - i) / 3;
b = false;
}
}
}
preValue = i;
}
}
}
// int blankCol1 = -1; 空白列的关键分割点(左) - Key splitting point (left) of the blank column
// int blankCol2 = -1; 空白列的关键分割点(右) - Key splitting point (right) of the blank column
// int blankRow1 = -1; 空白行的关键分割点(上) - Key splitting point (top) of the blank row
// int blankRow2 = -1; 空白行的关键分割点(下) - Key splitting point (bottom) of the blank row
// Select the area of interest
blankCol1 = blankCol1 == -1 ? 0 : blankCol1;
blankCol2 = blankCol2 == -1 ? colList.size() : blankCol2;
blankRow1 = blankRow1 == -1 ? 0 : blankRow1;
blankRow2 = blankRow2 == -1 ? rowList.size() : blankRow2;
Mat temp =
new Mat(
src, new Rect(blankCol1, blankRow1, blankCol2 - blankCol1, blankRow2 - blankRow1));
Mat t = new Mat();
temp.copyTo(t);
return t;
}
}

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archive/1_image_sdks/image_kit/imagekit_java/src/main/java/me/aias/util/RotationUtils.java Normal file
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package me.aias.util;
import org.bytedeco.opencv.opencv_core.Mat;
import org.bytedeco.opencv.opencv_core.Point2f;
import org.bytedeco.opencv.opencv_core.RotatedRect;
import org.bytedeco.opencv.opencv_core.Scalar;
import static org.bytedeco.opencv.global.opencv_imgproc.getRotationMatrix2D;
import static org.bytedeco.opencv.global.opencv_imgproc.warpAffine;
/**
* 旋转矩形工具类
* Rotation Rectangle Tool Class
*/
public class RotationUtils {
/**
* 旋转矩形 返回旋转后的Mat
* Rotate rectangle and return rotated Mat
* @param cannyMat mat矩阵 - matrix
* @param rect 矩形 - rectangle
* @return
*/
public static Mat rotation(Mat cannyMat, RotatedRect rect) {
double angle = rect.angle();
Point2f center = rect.center();
Mat correctImg = new Mat(cannyMat.size(), cannyMat.type());
cannyMat.copyTo(correctImg);
// 得到旋转矩阵算子 - Get the rotation matrix operator
Mat matrix = getRotationMatrix2D(center, angle, 0.8);
warpAffine(correctImg, correctImg, matrix, correctImg.size(), 1, 0, new Scalar(0, 0, 0, 0));
return correctImg;
}
}

470
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package me.aias.util;
import org.bytedeco.opencv.opencv_core.*;
import java.util.ArrayList;
import java.util.Arrays;
import java.util.List;
/**
* 图像处理工具类1.0.0(只针对几乎没有畸变的图像) 灰度话、二值化、降噪、切割、归一化
* Image processing utility class 1.0.0 (only for images with almost no distortion) Grayscale, binarization, denoising, cutting, normalization
*
* @author admin
*/
public class TableUtils {
private static final int BLACK = 0;
private TableUtils() {};
/**
* 统计图像每行/每列黑色像素点的个数
* Count the number of black pixels per row/column in the image
* (n1,n2)=>(height,width),
* b=true; 统计每行 - count per row
* (n1,n2)=>(width,height),
* b=false; 统计每列 - count per column
*
* @param src Mat矩阵对象 - Mat object
* @param n1
* @param n2
* @param b true表示统计每行;false表示统计每列 - true to count each row; false to count each column
* @return
*/
public static int[] countPixel(Mat src, int n1, int n2, boolean b) {
int[] xNum = new int[n1];
for (int i = 0; i < n1; i++) {
for (int j = 0; j < n2; j++) {
if (b) {
if (GeneralUtils.getPixel(src, i, j) == BLACK) {
xNum[i]++;
}
} else {
if (GeneralUtils.getPixel(src, j, i) == BLACK) {
xNum[i]++;
}
}
}
}
return xNum;
}
/**
* 压缩像素值数量；即统计zipLine行像素值的数量为一行
* Compress the number of pixel values; count the number of pixel values of zipLine rows as one row
* @param num
* @param zipLine
*/
public static int[] zipLinePixel(int[] num, int zipLine) {
int len = num.length / zipLine;
int[] result = new int[len];
int sum;
for (int i = 0, j = 0; i < num.length && i + zipLine < num.length; i += zipLine) {
sum = 0;
for (int k = 0; k < zipLine; k++) {
sum += num[i + k];
}
result[j++] = sum;
}
return result;
}
/**
* 水平投影法切割，适用于类似表格的图像(默认白底黑字) 改进
* Horizontal projection cutting, suitable for image like tables (default white background and black text) improvement
* @param src Mat矩阵对象 - Mat object
* @return
*/
public static List<Mat> cutImgX(Mat src) {
int i, j;
int width = GeneralUtils.getImgWidth(src), height = GeneralUtils.getImgHeight(src);
int[] xNum, cNum;
int average = 0; // 记录黑色像素和的平均值 - Record the average value of black pixels
int zipLine = 3;
// 压缩像素值数量；即统计三行像素值的数量为一行
// Compress the number of pixel values;
// 统计出每行黑色像素点的个数
// count three rows of pixel values as one row
xNum = zipLinePixel(countPixel(src, height, width, true), zipLine);
// 排序 - Sort
cNum = Arrays.copyOf(xNum, xNum.length);
Arrays.sort(cNum);
for (i = 31 * cNum.length / 32; i < cNum.length; i++) {
average += cNum[i];
}
average /= (height / 32);
// 把需要切割的y轴点存到cutY中
// Store the y-axis points that need to be cut in cutY
List<Integer> cutY = new ArrayList<Integer>();
for (i = 0; i < xNum.length; i++) {
if (xNum[i] > average) {
cutY.add(i * zipLine + 1);
}
}
// 优化cutY,把距离相差在30以内的都清除掉
// Optimize cutY, remove all those whose distance is within 30
if (cutY.size() != 0) {
int temp = cutY.get(cutY.size() - 1);
// 因为线条有粗细，优化cutY
// Optimize cutY because of the thickness of the line
for (i = cutY.size() - 2; i >= 0; i--) {
int k = temp - cutY.get(i);
if (k <= 30) {
cutY.remove(i + 1);
} else {
temp = cutY.get(i);
}
}
temp = cutY.get(cutY.size() - 1);
// 因为线条有粗细，优化cutY
// Optimize cutY because of the thickness of the line
for (i = cutY.size() - 2; i >= 0; i--) {
int k = temp - cutY.get(i);
if (k <= 30) {
cutY.remove(i + 1);
} else {
temp = cutY.get(i);
}
}
}
// 把切割的图片保存到YMat中
// Store the cut images in YMat
List<Mat> YMat = new ArrayList<Mat>();
for (i = 1; i < cutY.size(); i++) {
// // 设置感兴趣区域 - Set the region of interest
int startY = cutY.get(i - 1);
int h = cutY.get(i) - startY;
Mat temp = new Mat(src, new Rect(0, startY, width, h));
Mat t = new Mat();
temp.copyTo(t);
YMat.add(t);
}
return YMat;
}
/**
* 垂直投影法切割，适用于类似表格的图像(默认白底黑字) 改进
* Vertical projection cutting, suitable for image like tables (default white background and black text) improvement
*
* @param src Mat矩阵对象 - Mat object
* @return
*/
public static List<Mat> cutImgY(Mat src) {
int i, j;
int width = GeneralUtils.getImgWidth(src), height = GeneralUtils.getImgHeight(src);
int[] yNum, cNum;
int average = 0; // 记录黑色像素和的平均值 - Record the average value of black pixels
int zipLine = 2;
// 压缩像素值数量；即统计三行像素值的数量为一行
// Compress the number of pixel values
// 统计出每列黑色像素点的个数
// count three rows of pixel values as one row
yNum = zipLinePixel(countPixel(src, width, height, false), zipLine);
// 经过测试这样得到的平均值最优，平均值的选取很重要
// After testing, this is the optimal way to get the average value. The selection of the average value is very important.
cNum = Arrays.copyOf(yNum, yNum.length);
Arrays.sort(cNum);
for (i = 31 * cNum.length / 32; i < cNum.length; i++) {
average += cNum[i];
}
average /= (cNum.length / 32);
// 把需要切割的x轴的点存到cutX中
// Store the x-axis points that need to be cut in cutX
List<Integer> cutX = new ArrayList<Integer>();
for (i = 0; i < yNum.length; i++) {
if (yNum[i] >= average) {
cutX.add(i * zipLine + 2);
}
}
// 优化cutX - Optimize cutX
if (cutX.size() != 0) {
int temp = cutX.get(cutX.size() - 1);
// 因为线条有粗细，优化cutX
// Optimize cutX because of the thickness of the line
for (i = cutX.size() - 2; i >= 0; i--) {
int k = temp - cutX.get(i);
if (k <= 100) {
cutX.remove(i);
} else {
temp = cutX.get(i);
}
}
temp = cutX.get(cutX.size() - 1);
// 因为线条有粗细，优化cutX
// Optimize cutX because of the thickness of the line
for (i = cutX.size() - 2; i >= 0; i--) {
int k = temp - cutX.get(i);
if (k <= 100) {
cutX.remove(i);
} else {
temp = cutX.get(i);
}
}
}
// 把切割的图片都保存到XMat中
// Store the cut images in XMat
List<Mat> XMat = new ArrayList<Mat>();
for (i = 1; i < cutX.size(); i++) {
// 设置感兴趣的区域
// Set the region of interest
int startX = cutX.get(i - 1);
int w = cutX.get(i) - startX;
Mat temp = new Mat(src, new Rect(startX, 0, w, height));
Mat t = new Mat();
temp.copyTo(t);
XMat.add(t);
}
return XMat;
}
/**
* 切割 因为是表格图像，采用新的切割思路，中和水平切割和垂直切割一次性切割出所有的小格子
* Cutting. Because it is a table image, a new cutting idea is used, which cuts out all small cells in one step by combining horizontal and vertical cutting.
*
* @param src
* @return
*/
public static List<Mat> cut(Mat src) {
if (src.channels() == 3) {
// TODO
}
int i, j, k;
int width = GeneralUtils.getImgWidth(src), height = GeneralUtils.getImgHeight(src);
int[] xNum = new int[height], copy_xNum;
int x_average = 0;
int value = -1;
// 统计每行每列的黑色像素值
// Count the number of black pixels per row/column
for (i = 0; i < width; i++) {
for (j = 0; j < height; j++) {
value = GeneralUtils.getPixel(src, j, i);
if (value == BLACK) {
xNum[j]++;
}
}
}
int zipXLine = 3;
xNum = zipLinePixel(xNum, zipXLine);
// 排序 ............求水平切割点
// Sort ............ to get horizontal cutting points
copy_xNum = Arrays.copyOf(xNum, xNum.length);
Arrays.sort(copy_xNum);
for (i = 31 * copy_xNum.length / 32; i < copy_xNum.length; i++) {
x_average += copy_xNum[i];
}
x_average /= (height / 32);
// 把需要切割的y轴点存到cutY中
// Store the y-axis points that need to be cut in cutY
List<Integer> cutY = new ArrayList<Integer>();
for (i = 0; i < xNum.length; i++) {
if (xNum[i] > x_average) {
cutY.add(i * zipXLine + zipXLine / 2);
}
}
// 优化cutY,把距离相差在30以内的都清除掉
// Optimize cutY, remove all those whose distance is within 30
if (cutY.size() != 0) {
int temp = cutY.get(cutY.size() - 1);
// 因为线条有粗细，优化cutY
// Optimize cutY because of the thickness of the line
for (i = cutY.size() - 2; i >= 0; i--) {
k = temp - cutY.get(i);
if (k <= 10 * zipXLine) {
cutY.remove(i + 1);
} else {
temp = cutY.get(i);
}
}
temp = cutY.get(cutY.size() - 1);
// 因为线条有粗细，优化cutY
// Optimize cutY because of the thickness of the line
for (i = cutY.size() - 2; i >= 0; i--) {
k = temp - cutY.get(i);
if (k <= 10 * zipXLine) {
cutY.remove(i + 1);
} else {
temp = cutY.get(i);
}
}
}
// 把需要切割的x轴的点存到cutX中
// Store the x-axis points that need to be cut in cutX
// 新思路，因为不是很畸变的图像，y轴的割点还是比较好确定的 随机的挑选一个y轴割点，用一个滑动窗口去遍历选中点所在直线，确定x轴割点
// New idea, because it is not a very distorted image, the y-axis cutting point is still relatively easy to determine.
// Randomly select a y-axis cutting point, use a sliding window to traverse the straight line where the selected point is located, and determine the x-axis cutting point.
List<Integer> cutX = new ArrayList<Integer>();
int choiceY = cutY.size() > 1 ? cutY.get(1) : (cutY.size() > 0 ? cutY.get(0) : -1);
if (choiceY == -1) {
throw new RuntimeException("切割失败，没有找到水平切割点 - Cutting failed, no horizontal cutting point found");
}
int winH = 5;
List<Integer> LH1 = new ArrayList<Integer>();
List<Integer> LH2 = new ArrayList<Integer>();
if (choiceY - winH >= 0 && choiceY + winH <= height) {
// 上下 - Up and down
for (i = 0; i < width; i++) {
value = GeneralUtils.getPixel(src, choiceY - winH, i);
if (value == BLACK) {
LH1.add(i);
}
value = GeneralUtils.getPixel(src, choiceY + winH, i);
if (value == BLACK) {
LH2.add(i);
}
}
} else if (choiceY + winH <= height && choiceY + 2 * winH <= height) {
// 下 - Down
for (i = 0; i < width; i++) {
value = GeneralUtils.getPixel(src, choiceY + 2 * winH, i);
if (value == BLACK) {
LH1.add(i);
}
value = GeneralUtils.getPixel(src, choiceY + winH, i);
if (value == BLACK) {
LH2.add(i);
}
}
} else if (choiceY - winH >= 0 && choiceY - 2 * winH >= 0) {
// 上 - Up
for (i = 0; i < width; i++) {
value = GeneralUtils.getPixel(src, choiceY - winH, i);
if (value == BLACK) {
LH1.add(i);
}
value = GeneralUtils.getPixel(src, choiceY - 2 * winH, i);
if (value == BLACK) {
LH2.add(i);
}
}
} else {
throw new RuntimeException("切割失败，图像异常 - Cutting failed, image exception");
}
// 优化LH1、LH2,把距离相差在30以内的都清除掉
// Optimize LH1 and LH2, removing all distances within 30
if (LH1.size() != 0) {
int temp = LH1.get(LH1.size() - 1);
// 因为线条有粗细，优化cutY
// optimize cutY because of line thickness
for (i = LH1.size() - 2; i >= 0; i--) {
k = temp - LH1.get(i);
if (k <= 50) {
LH1.remove(i + 1);
} else {
temp = LH1.get(i);
}
}
temp = LH1.get(LH1.size() - 1);
// 因为线条有粗细，优化cutY
// optimize cutY because of line thickness
for (i = LH1.size() - 2; i >= 0; i--) {
k = temp - LH1.get(i);
if (k <= 50) {
LH1.remove(i + 1);
} else {
temp = LH1.get(i);
}
}
}
if (LH2.size() != 0) {
int temp = LH2.get(LH2.size() - 1);
// 因为线条有粗细，优化cutY
// optimize cutY because of line thickness
for (i = LH2.size() - 2; i >= 0; i--) {
k = temp - LH2.get(i);
if (k <= 50) {
LH2.remove(i + 1);
} else {
temp = LH2.get(i);
}
}
temp = LH2.get(LH2.size() - 1);
// 因为线条有粗细，优化cutY
// optimize cutY because of line thickness
for (i = LH2.size() - 2; i >= 0; i--) {
k = temp - LH2.get(i);
if (k <= 50) {
LH2.remove(i + 1);
} else {
temp = LH2.get(i);
}
}
}
if (LH1.size() < LH2.size()) {
// 进一步优化LH1
// further optimize LH1
int avg = 0;
for (k = 1; k < LH1.size() - 2; k++) {
avg += LH1.get(k + 1) - LH1.get(k);
}
avg /= (LH1.size() - 2);
int temp = LH1.get(LH1.size() - 1);
for (i = LH1.size() - 2; i >= 0; i--) {
k = temp - LH1.get(i);
if (k <= avg) {
LH1.remove(i + 1);
} else {
temp = LH1.get(i);
}
}
cutX = LH1;
} else {
// 进一步优化LH2
// further optimize LH2
int avg = 0;
for (k = 1; k < LH2.size() - 2; k++) {
avg += LH2.get(k + 1) - LH2.get(k);
}
avg /= (LH2.size() - 2);
int temp = LH2.get(LH2.size() - 1);
for (i = LH2.size() - 2; i >= 0; i--) {
k = temp - LH2.get(i);
if (k <= avg) {
LH2.remove(i + 1);
} else {
temp = LH2.get(i);
}
}
cutX = LH2;
}
List<Mat> destMat = new ArrayList<Mat>();
for (i = 1; i < cutY.size(); i++) {
for (j = 1; j < cutX.size(); j++) {
// 设置感兴趣的区域
// set region of interest
int startX = cutX.get(j - 1);
int w = cutX.get(j) - startX;
int startY = cutY.get(i - 1);
int h = cutY.get(i) - startY;
Mat temp = new Mat(src, new Rect(startX + 2, startY + 2, w - 2, h - 2));
Mat t = new Mat();
temp.copyTo(t);
destMat.add(t);
}
}
return destMat;
}
}

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package me.aias.util;
import org.bytedeco.opencv.global.opencv_imgproc;
import org.bytedeco.opencv.opencv_core.Mat;
import org.bytedeco.opencv.opencv_core.Point2f;
import org.bytedeco.opencv.opencv_core.Scalar;
import org.opencv.imgproc.Imgproc;
/**
* 透视变换工具类
* Perspective transformation
*
*/
public class WarpPerspectiveUtils {
/**
* 透视变换
* Perspective transformation
*
* @param src
* @param srcPoints
* @param dstPoints
* @return
*/
public static Mat warpPerspective(Mat src, Mat srcPoints, Mat dstPoints) {
// srcPoints, dstPoints
// 点的顺序[左上 ，右上 ，右下 ，左下]
// Point order [top left, top right, bottom right, bottom left]
Mat perspectiveMmat = opencv_imgproc.getPerspectiveTransform(srcPoints, dstPoints);
Mat dst = new Mat();
opencv_imgproc.warpPerspective(
src,
dst,
perspectiveMmat,
src.size(),
Imgproc.INTER_LINEAR + Imgproc.WARP_INVERSE_MAP,
1,
new Scalar(0));
return dst;
}
public static Mat perspectiveTransform(
Mat src, Point2f srcPoints, Point2f dstPoints) {
Mat dst = src.clone();
Mat warp_mat = opencv_imgproc.getPerspectiveTransform(srcPoints.position(0), dstPoints.position(0));
opencv_imgproc.warpPerspective(src, dst, warp_mat, dst.size());
return dst;
}
}

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<?xml version="1.0" encoding="UTF-8"?>
<Configuration status="INFO">
<Appenders>
<Console name="console" target="SYSTEM_OUT">
<PatternLayout
pattern="[%-5level] - %msg%n"/>
</Console>
</Appenders>
<Loggers>
<Root level="info" additivity="false">
<AppenderRef ref="console"/>
</Root>
<Logger name="me.calvin" level="${sys:me.calvin.logging.level:-info}" additivity="false">
<AppenderRef ref="console"/>
</Logger>
</Loggers>
</Configuration>