mirror of
https://github.com/geekwenjie/SmartJavaAI.git
synced 2026-09-14 05:38:50 +00:00
1、FaceNet 特征提取新增人脸对齐
2、人脸检测新5点人脸关键点定位 3、特征提取接口支持多人脸和最佳人脸提取 4、修复人脸框边界精度问题 5、更新 Maven 发布的 groupId
This commit is contained in:
dengwenjie
@@ -4,9 +4,9 @@
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xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
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<modelVersion>4.0.0</modelVersion>
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<parent>
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<groupId>ink.numberone</groupId>
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<groupId>cn.smartjavaai</groupId>
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<artifactId>smartjavaai-parent</artifactId>
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<version>1.0.10</version>
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<version>1.0.11</version>
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</parent>
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<artifactId>smartjavaai-common</artifactId>
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@@ -1,5 +1,7 @@
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package cn.smartjavaai.common.entity;
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import java.util.List;
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/**
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* 检测结果-矩形区域
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* @author dwj
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@@ -11,9 +13,13 @@ public class DetectionRectangle {
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public int width;
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public int height;
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public float score;
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public String className;
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/**
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* 人脸关键点
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*/
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private List<Point> keyPoints;
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public DetectionRectangle() {
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}
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@@ -81,4 +87,12 @@ public class DetectionRectangle {
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public void setClassName(String className) {
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this.className = className;
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}
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public List<Point> getKeyPoints() {
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return keyPoints;
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}
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public void setKeyPoints(List<Point> keyPoints) {
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this.keyPoints = keyPoints;
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}
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}
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@@ -10,27 +10,27 @@ import java.io.Serializable;
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*/
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public class Point implements Serializable {
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private static final long serialVersionUID = 1L;
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private int x;
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private int y;
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private double x;
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private double y;
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public Point(int x, int y) {
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public Point(double x, double y) {
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this.x = x;
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this.y = y;
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}
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public int getX() {
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public double getX() {
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return x;
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}
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public void setX(int x) {
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public void setX(double x) {
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this.x = x;
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}
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public int getY() {
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public double getY() {
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return y;
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}
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public void setY(int y) {
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public void setY(double y) {
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this.y = y;
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}
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@@ -1,13 +1,23 @@
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package cn.smartjavaai.common.utils;
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import ai.djl.modality.cv.BufferedImageFactory;
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import ai.djl.modality.cv.Image;
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import ai.djl.ndarray.NDArray;
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import cn.smartjavaai.common.entity.DetectionRectangle;
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import cn.smartjavaai.common.entity.DetectionResponse;
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import javax.imageio.ImageIO;
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import java.awt.*;
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import java.awt.image.BufferedImage;
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//import java.awt.image.ColorConvertOp;
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import java.awt.image.ComponentSampleModel;
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import java.awt.image.ImageObserver;
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import java.io.File;
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import java.io.IOException;
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import java.util.ArrayList;
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import java.util.Arrays;
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import java.util.List;
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import java.util.Objects;
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/**
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* 图片处理工具类
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@@ -88,6 +98,86 @@ public class ImageUtils {
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return image != null && image.getWidth() > 0 && image.getHeight() > 0;
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}
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/**
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* 画检测框
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*
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* @param image
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* @param x
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* @param y
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* @param width
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* @param height
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*/
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public static void drawImageRect(BufferedImage image, int x, int y, int width, int height) {
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// 将绘制图像转换为Graphics2D
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Graphics2D g = (Graphics2D) image.getGraphics();
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try {
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g.setColor(new Color(0, 255, 0));
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// 声明画笔属性 ：粗 细（单位像素）末端无修饰 折线处呈尖角
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BasicStroke bStroke = new BasicStroke(2, BasicStroke.CAP_BUTT, BasicStroke.JOIN_MITER);
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g.setStroke(bStroke);
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g.drawRect(x, y, width, height);
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} finally {
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g.dispose();
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}
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}
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/**
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* 画检测框
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*
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* @param image
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* @param x
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* @param y
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* @param width
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* @param height
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*/
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public static void drawImageRect(Image image, int x, int y, int width, int height) {
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// 将绘制图像转换为Graphics2D
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BufferedImage bufferedImage = (BufferedImage)image.getWrappedImage();
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Graphics2D g = (Graphics2D) bufferedImage.getGraphics();
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try {
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g.setColor(new Color(0, 255, 0));
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// 声明画笔属性 ：粗 细（单位像素）末端无修饰 折线处呈尖角
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BasicStroke bStroke = new BasicStroke(2, BasicStroke.CAP_BUTT, BasicStroke.JOIN_MITER);
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g.setStroke(bStroke);
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g.drawRect(x, y, width, height);
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} finally {
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g.dispose();
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}
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}
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/**
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* 画检测框
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*
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* @param image
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* @param x
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* @param y
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* @param width
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* @param height
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*/
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public static void drawImageRect(Image image, DetectionResponse detectionResponse) {
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if(Objects.nonNull(detectionResponse) && Objects.nonNull(detectionResponse.getRectangleList()) && !detectionResponse.getRectangleList().isEmpty()){
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// 将绘制图像转换为Graphics2D'
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BufferedImage bufferedImage = (BufferedImage)image.getWrappedImage();
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Graphics2D g = (Graphics2D) bufferedImage.getGraphics();
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try {
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g.setColor(new Color(0, 255, 0));
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// 声明画笔属性 ：粗 细（单位像素）末端无修饰 折线处呈尖角
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BasicStroke bStroke = new BasicStroke(2, BasicStroke.CAP_BUTT, BasicStroke.JOIN_MITER);
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g.setStroke(bStroke);
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for(DetectionRectangle detectionRectangle : detectionResponse.getRectangleList()){
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g.drawRect(detectionRectangle.getX(), detectionRectangle.getY(), detectionRectangle.getWidth(), detectionRectangle.getHeight());
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}
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} finally {
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g.dispose();
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}
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}
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}
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@@ -0,0 +1,119 @@
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package cn.smartjavaai.common.utils;
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import ai.djl.ndarray.NDArray;
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import ai.djl.ndarray.NDManager;
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import org.opencv.core.CvType;
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import org.opencv.core.Mat;
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import org.opencv.core.Point;
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import org.opencv.core.Scalar;
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import org.opencv.imgproc.Imgproc;
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import java.awt.image.BufferedImage;
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import java.awt.image.DataBufferByte;
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/**
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* OpenCV 工具类
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*/
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public class OpenCVUtils {
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/**
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* canny算法，边缘检测
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*
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* @param src
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* @return
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*/
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public static Mat canny(Mat src) {
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Mat mat = src.clone();
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Imgproc.Canny(src, mat, 100, 200);
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return mat;
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}
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/**
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* 画线
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*
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* @param mat
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* @param point1
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* @param point2
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*/
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public static void line(Mat mat, Point point1, Point point2) {
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Imgproc.line(mat, point1, point2, new Scalar(255, 255, 255), 1);
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}
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/**
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* NDArray to opencv_core.Mat
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*
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* @param manager
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* @param srcPoints
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* @param dstPoints
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* @return
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*/
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public static Mat toOpenCVMat(NDManager manager, NDArray srcPoints, NDArray dstPoints) {
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NDArray svdMat = SVDUtils.transformationFromPoints(manager, srcPoints, dstPoints);
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double[] doubleArray = svdMat.toDoubleArray();
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Mat newSvdMat = new Mat(2, 3, CvType.CV_64F);
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for (int i = 0; i < 2; i++) {
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for (int j = 0; j < 3; j++) {
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newSvdMat.put(i, j, doubleArray[i * 3 + j]);
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}
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}
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return newSvdMat;
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}
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/**
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* double[][] points array to Mat
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* @param points
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* @return
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*/
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public static Mat toOpenCVMat(double[][] points) {
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Mat mat = new Mat(5, 2, CvType.CV_64F);
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for (int i = 0; i < 5; i++) {
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for (int j = 0; j < 2; j++) {
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mat.put(i, j, points[i * 5 + j]);
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}
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}
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return mat;
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}
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/**
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* 变换矩阵的逆矩阵
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*
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* @param src
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* @return
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*/
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public static Mat invertAffineTransform(Mat src) {
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Mat dst = src.clone();
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Imgproc.invertAffineTransform(src, dst);
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return dst;
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}
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/**
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* Mat to BufferedImage
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*
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* @param mat
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* @return
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*/
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public static BufferedImage mat2Image(Mat mat) {
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int width = mat.width();
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int height = mat.height();
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byte[] data = new byte[width * height * (int) mat.elemSize()];
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Imgproc.cvtColor(mat, mat, 4);
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mat.get(0, 0, data);
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BufferedImage ret = new BufferedImage(width, height, 5);
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ret.getRaster().setDataElements(0, 0, width, height, data);
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return ret;
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}
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/**
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* BufferedImage to Mat
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*
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* @param img
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* @return
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*/
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public static Mat image2Mat(BufferedImage img) {
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int width = img.getWidth();
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int height = img.getHeight();
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byte[] data = ((DataBufferByte) img.getRaster().getDataBuffer()).getData();
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Mat mat = new Mat(height, width, CvType.CV_8UC3);
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mat.put(0, 0, data);
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return mat;
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}
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}
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@@ -0,0 +1,119 @@
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package cn.smartjavaai.common.utils;
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import Jama.Matrix;
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import Jama.SingularValueDecomposition;
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import ai.djl.ndarray.NDArray;
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import ai.djl.ndarray.NDManager;
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/**
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* 仿射变换处理工具
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*/
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public class SVDUtils {
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/**
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* 计算仿射变换矩阵
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* Calculate affine transformation matrix
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*
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* @param manager
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* @param points1
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* @param points2
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* @return
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*/
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public static NDArray transformationFromPoints(
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NDManager manager, NDArray points1, NDArray points2) {
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// 按列计算均值
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// Calculate column-wise mean
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NDArray c1 = points1.mean(new int[]{0}); // axis=0 列操作 - axis=0 column operation
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NDArray c2 = points2.mean(new int[]{0}); // axis=0 列操作 - axis=0 column operation
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// 按列减去均值
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// Subtract column-wise mean
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points1 = points1.sub(c1);
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points2 = points2.sub(c2);
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// 计算全局标准差
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// Calculate global standard deviation
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double s1 = std(points1);
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double s2 = std(points2);
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// 矩阵除以全局标准差
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// Matrix divided by global standard deviation
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NDArray djl_s1 = manager.create(s1);
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NDArray djl_s2 = manager.create(s2);
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points1 = points1.div(djl_s1);
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points2 = points2.div(djl_s2);
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double[] points1D = points1.toDoubleArray();
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double[] points2D = points2.toDoubleArray();
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// DJL 格式转换成Jamma格式
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// Convert DJL format to Jama format
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double[][] m1 = new double[5][2];
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double[][] m2 = new double[5][2];
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for (int i = 0; i < 5; i++) {
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for (int j = 0; j < 2; j++) {
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m1[i][j] = points1D[i * 2 + j];
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}
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}
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for (int i = 0; i < 5; i++) {
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for (int j = 0; j < 2; j++) {
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m2[i][j] = points2D[i * 2 + j];
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}
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}
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Matrix p1 = new Matrix(m1);
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Matrix p2 = new Matrix(m2);
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// 进行奇异值分解
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// Perform singular value decomposition
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Matrix p3 = p1.transpose().times(p2);
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SingularValueDecomposition s = p3.svd();
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Matrix U = s.getU();
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Matrix S = s.getS();
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Matrix V = s.getV();
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// TODO 为什么第2列的符号是反的？
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// Why is the sign of the second column opposite?
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m1 = U.getArray();
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m1[0][1] = -m1[0][1];
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m1[1][1] = -m1[1][1];
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m2 = V.getArray();
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m2[0][1] = -m2[0][1];
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m2[1][1] = -m2[1][1];
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Matrix R = (U.times(V)).transpose();
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double[][] rArray = R.getArray();
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NDArray newR = manager.create(rArray);
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// np.vstack([np.hstack(((s2 / s1) * R, c2.T - (s2 / s1) * R * c1.T)), np.matrix([0.,0., 1.])])
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// (s2 / s1) * R
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NDArray leftPart = djl_s2.div(djl_s1).mul(newR);
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// c2.T - (s2 / s1) * R * c1.T)
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NDArray rightPart = c2.reshape(2, 1).sub(leftPart.matMul(c1.reshape(2, 1)));
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// numpy.hstack(((s2 / s1) * R, c2.T - (s2 / s1) * R * c1.T))
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NDArray upPart = leftPart.concat(rightPart, 1);
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// np.matrix([0.,0., 1.])
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double[] downArray = {0d, 0d, 1d};
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NDArray downPart = manager.create(downArray).reshape(1, 3);
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NDArray all = upPart.concat(downPart, 0);
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// System.out.println("all: " + all);
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return upPart;
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}
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/**
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* 计算全局标准差
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* Calculate global standard deviation
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*
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* @param points
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* @return
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*/
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public static double std(NDArray points) {
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points = points.square();
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double[] doubleResult = points.toDoubleArray();
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double std = 0;
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for (int i = 0; i < doubleResult.length; i++) {
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std = std + doubleResult[i];
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}
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std = (float) Math.sqrt(std / doubleResult.length);
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return std;
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}
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}
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Reference in New Issue
Block a user