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增加二维码识别

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物流展会算法
LAPTOP-S9HJSOEB\昊天 3 days ago
parent 92380171e4
commit 3d69f32fae
8 changed files with 875 additions and 13 deletions
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7
.gitignore vendored
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@ -31,3 +31,10 @@ build/
### VS Code ###
.vscode/
### Logs ###
logs/
log/
### ONNX models ###
*.onnx

38
pom.xml
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@ -70,6 +70,30 @@
<groupId>org.springframework.boot</groupId>
<artifactId>spring-boot-starter-web-services</artifactId>
</dependency>
<dependency>
<groupId>org.bytedeco</groupId>
<artifactId>javacpp</artifactId>
<version>1.5.7</version>
<classifier>windows-x86_64</classifier>
</dependency>
<dependency>
<groupId>org.bytedeco</groupId>
<artifactId>openblas</artifactId>
<version>0.3.19-1.5.7</version>
<classifier>windows-x86_64</classifier>
</dependency>
<dependency>
<groupId>org.bytedeco</groupId>
<artifactId>opencv</artifactId>
<version>4.5.5-1.5.7</version>
</dependency>
<dependency>
<groupId>org.bytedeco</groupId>
<artifactId>opencv</artifactId>
<version>4.5.5-1.5.7</version>
<classifier>windows-x86_64</classifier>
</dependency>
<dependency>
<groupId>com.microsoft.onnxruntime</groupId>
<artifactId>onnxruntime</artifactId>
@ -127,13 +151,13 @@
<artifactId>javase</artifactId>
<version>3.5.0</version>
</dependency>
<dependency>
<groupId>org</groupId>
<artifactId>opencv</artifactId>
<scope>system</scope>
<version>1.0</version>
<systemPath>${project.basedir}/src/main/resources/libs/opencv-480.jar</systemPath>
</dependency>
<!-- <dependency>-->
<!-- <groupId>org</groupId>-->
<!-- <artifactId>opencv</artifactId>-->
<!-- <scope>system</scope>-->
<!-- <version>1.0</version>-->
<!-- <systemPath>${project.basedir}/src/main/resources/libs/opencv-480.jar</systemPath>-->
<!-- </dependency>-->
<!-- <dependency>-->

2
src/main/java/com/example/lxcameraapi/controller/CategoryController.java
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@ -57,7 +57,7 @@ public class CategoryController {
}
boolean success = imageCaptureService.captureImage(ip, path);
List<BoundingBox> detectResults = onnxServiceNew.detect(path, null);
List<BoundingBox> detectResults = onnxServiceNew.detect26(path, null);
log.info("检测结果: {}", detectResults);
boxCountResponse.setImagePath(url);

341
src/main/java/com/example/lxcameraapi/controller/QtCodeController.java
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@ -0,0 +1,341 @@
package com.example.lxcameraapi.controller;
import ai.onnxruntime.OrtException;
import com.example.lxcameraapi.conf.AppConfig;
import com.example.lxcameraapi.service.IndustrialCamera.QrCode.WeChatDeCode;
import com.example.lxcameraapi.service.IndustrialCamera.algorithm.ONNXServiceNew;
import com.example.lxcameraapi.service.IndustrialCamera.camera.hik.ImageCaptureService;
import com.example.lxcameraapi.service.IndustrialCamera.camera.lx.config.BoxCountRequest;
import com.example.lxcameraapi.service.IndustrialCamera.camera.lx.config.BoxCountResponse;
import com.example.lxcameraapi.service.IndustrialCamera.opencv.OpencvService;
import com.example.lxcameraapi.service.IndustrialCamera.yolo.BoundingBox;
import com.google.zxing.*;
import com.google.zxing.client.j2se.BufferedImageLuminanceSource;
import com.google.zxing.common.HybridBinarizer;
import com.google.zxing.qrcode.QRCodeReader;
import jakarta.annotation.Resource;
import lombok.extern.slf4j.Slf4j;
import org.bytedeco.opencv.opencv_core.Mat;
import org.bytedeco.opencv.opencv_core.StringVector;
import org.bytedeco.opencv.opencv_wechat_qrcode.WeChatQRCode;
import org.opencv.imgcodecs.Imgcodecs;
import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.stereotype.Service;
import org.springframework.web.bind.annotation.*;
import javax.imageio.ImageIO;
import java.awt.*;
import java.awt.image.BufferedImage;
import java.io.File;
import java.io.IOException;
import java.nio.charset.StandardCharsets;
import java.time.LocalDateTime;
import java.time.format.DateTimeFormatter;
import java.util.ArrayList;
import java.util.HashMap;
import java.util.List;
import java.util.Map;
import java.util.stream.Collectors;
@Slf4j
@RestController
@RequestMapping("/api/qrcode")
public class QtCodeController {
@Resource
ONNXServiceNew onnxServiceNew;
@Resource
HikCaptureController hikCaptureController;
@Resource
private ImageCaptureService imageCaptureService;
@Resource
private AppConfig appConfig;
@Autowired
private OpencvService opencvService;
/**
* 品规识别
*/
@PostMapping("/single")
public BoxCountResponse captureSingle(@RequestBody BoxCountRequest request) {
BoxCountResponse boxCountResponse = new BoxCountResponse();
// 获取当前日期格式为 yyyy-MM-dd
LocalDateTime currentDate = LocalDateTime.now();
DateTimeFormatter formatter = DateTimeFormatter.ofPattern("yyyy-MM-dd");
String formattedDate = currentDate.format(formatter);
String path = appConfig.getPicPath()+ formattedDate + "/" + request.getCameraId() + "/" + System.currentTimeMillis() + ".png";
String url = appConfig.getPicUrl() + formattedDate + "/" + request.getCameraId() + "/" + System.currentTimeMillis() + ".png";
try {
log.info("开始拍照，IP: {}, 路径: {}", request.getCameraId(), path);
String ip = null;
for (AppConfig.Camera camera : appConfig.getHikCamera()) {
if (camera.getId().equals(request.getCameraId()))
ip = camera.getIp();
}
boolean success = imageCaptureService.captureImage(ip, path);
List<BoundingBox> detectResults = onnxServiceNew.detect26(path, "qrCode");
opencvService.drawBoundingBoxesOnImage(detectResults, path,path+".jpg");
log.info("检测结果: {}", detectResults);
// 读取原始图片
BufferedImage originalImage = ImageIO.read(new File(path));
if (originalImage == null) {
log.error("无法读取图片: {}", path);
boxCountResponse.setResult("读取图片失败");
return boxCountResponse;
}
// 存储解码结果
List<String> qrCodeResults = new ArrayList<>();
for (BoundingBox box : detectResults){
try {
// 根据 BoundingBox 裁剪图片
BufferedImage croppedImage = cropBoundingBox(originalImage, box,30);
ImageIO.write(croppedImage, "jpg", new File(path+"_"+box.getIndex()+".jpg"));
// 使用 ZXing 读取二维码
String qrCodeContent = WeChatDeCode.deCode(croppedImage);
qrCodeResults.add(qrCodeContent);
if (qrCodeContent != null) {
log.info("解码成功: {}, 置信度: {}", qrCodeContent, box.getConfidence());
} else {
log.warn("解码失败，box: {}", box);
}
} catch (Exception e) {
log.error("解码二维码异常，box: {}", box, e);
}
}
boxCountResponse.setQrCodeResults(qrCodeResults);
} catch (OrtException e) {
log.error("检测异常", e);
return boxCountResponse;
} catch (Exception e) {
log.error("处理异常", e);
}
if (boxCountResponse.getQrCodeResults().size() > 0){
boxCountResponse.setResult(boxCountResponse.getQrCodeResults().get(0).substring(0,8));
boxCountResponse.setImagePath(url+".jpg");
}else {
boxCountResponse.setResult("Unknown");
}
return boxCountResponse;
}
/**
* 品规识别
*/
@PostMapping("/w")
public BoxCountResponse captureSingleWhite(@RequestBody BoxCountRequest request) {
for (int i = 0; i < 100; i++){
BoxCountResponse boxCountResponse = new BoxCountResponse();
try {
// 获取当前日期格式为 yyyy-MM-dd
LocalDateTime currentDate = LocalDateTime.now();
DateTimeFormatter formatter = DateTimeFormatter.ofPattern("yyyy-MM-dd");
String formattedDate = currentDate.format(formatter);
String path = appConfig.getPicPath()+ formattedDate + "/" + request.getCameraId() + "/" + System.currentTimeMillis() + ".png";
String url = appConfig.getPicUrl() + formattedDate + "/" + request.getCameraId() + "/" + System.currentTimeMillis() + ".png";
log.info("开始拍照，IP: {}, 路径: {}", request.getCameraId(), path);
String ip = null;
for (AppConfig.Camera camera : appConfig.getHikCamera()) {
if (camera.getId().equals(request.getCameraId()))
ip = camera.getIp();
}
boolean success = imageCaptureService.captureImage(ip, path);
List<BoundingBox> detectResults = onnxServiceNew.detect26(path, "qrCode");
opencvService.drawBoundingBoxesOnImage(detectResults, path,path+".jpg");
log.info("检测结果: {}", detectResults);
// 读取原始图片
BufferedImage originalImage = ImageIO.read(new File(path));
if (originalImage == null) {
log.error("无法读取图片: {}", path);
boxCountResponse.setResult("读取图片失败");
}
// 存储解码结果
List<String> qrCodeResults = new ArrayList<>();
for (BoundingBox box : detectResults){
try {
// 根据 BoundingBox 裁剪图片
BufferedImage croppedImage = cropBoundingBox(originalImage, box,30);
ImageIO.write(croppedImage, "jpg", new File(path+"_"+box.getIndex()+".jpg"));
// 使用 ZXing 读取二维码
String qrCodeContent = WeChatDeCode.deCode(croppedImage);
qrCodeResults.add(qrCodeContent);
if (qrCodeContent != null) {
log.info("解码成功: {}, 置信度: {}", qrCodeContent, box.getConfidence());
} else {
log.warn("解码失败，box: {}", box);
}
} catch (Exception e) {
log.error("解码二维码异常，box: {}", box, e);
}
}
boxCountResponse.setQrCodeResults(qrCodeResults);
} catch (OrtException e) {
log.error("检测异常", e);
} catch (Exception e) {
log.error("处理异常", e);
}
boxCountResponse.setResult("Unknown");
}
return null;
}
//
// public static void main(String[] args) {
// String imagePath = "D:\\data\\media\\2026-04-17\\1\\QQ20260417-134439.jpg";
// BufferedImage image = null;
// try {
// image = ImageIO.read(new File(imagePath));
// } catch (IOException e) {
// throw new RuntimeException(e);
// }
// BufferedImageLuminanceSource source = new BufferedImageLuminanceSource(image);
// HybridBinarizer binarizer = new HybridBinarizer(source);
// BinaryBitmap bitmap = new BinaryBitmap(binarizer);
// MultiFormatReader multiFormatReader = new MultiFormatReader();
// try {
// System.out.println(multiFormatReader.decode(bitmap).getText()); ;
// } catch (NotFoundException e) {
// throw new RuntimeException("二维码未找到或无法解码");
// }
// }
// public static void main(String... args) {
//
// String imagePath = "D:\\data\\media\\2026-04-17\\1\\QQ20260417-134439.jpg";
// Mat img = Imgcodecs.imread(imagePath);
// System.out.println(deCode(img));
// }
private static String deCode(Mat img) {
// 微信二维码对象，要返回二维码坐标前2个参数必传；后2个在二维码小或不清晰时必传。
WeChatQRCode we = new WeChatQRCode();
// List<Mat> points = new ArrayList<Mat>();
// 微信二维码引擎解码，返回的valList中存放的是解码后的数据，points中Mat存放的是二维码4个角的坐标
StringVector stringVector = we.detectAndDecode(img);
if (stringVector.empty()) {
return "0";
}
return stringVector.get(0).getString(StandardCharsets.UTF_8);
}
/**
* 根据 BoundingBox 裁剪图片
*
* @param originalImage 原始图片
* @param box 边界框（detect26 输出：左上角坐标 x, y 和宽高 w, h，都是绝对像素坐标）
* @return 裁剪后的图片
*/
private BufferedImage cropBoundingBox(BufferedImage originalImage, BoundingBox box) {
return cropBoundingBox(originalImage, box, 0);
}
/**
* 根据 BoundingBox 裁剪图片（支持扩展区域）
*
* @param originalImage 原始图片
* @param box 边界框（detect26 输出：左上角坐标 x, y 和宽高 w, h，都是绝对像素坐标）
* @param expandPixels 扩展像素数（上下左右各扩展的像素值）
* @return 裁剪后的图片
*/
private BufferedImage cropBoundingBox(BufferedImage originalImage, BoundingBox box, int expandPixels) {
int imageWidth = originalImage.getWidth();
int imageHeight = originalImage.getHeight();
// detect26 输出的 x, y 已经是左上角坐标，w, h 是宽高，都是绝对像素坐标
int x = (int) box.getX();
int y = (int) box.getY();
int width = (int) box.getW();
int height = (int) box.getH();
// 应用扩展：向左上角扩展
x = x - expandPixels;
y = y - expandPixels;
// 应用扩展：向右下角扩展（增加宽高）
width = width + (expandPixels * 2);
height = height + (expandPixels * 2);
// 确保坐标在图片范围内
x = Math.max(0, Math.min(x, imageWidth - 1));
y = Math.max(0, Math.min(y, imageHeight - 1));
// 确保宽高不超出图片边界
width = Math.min(width, imageWidth - x);
height = Math.min(height, imageHeight - y);
// 确保宽高至少为 1
width = Math.max(1, width);
height = Math.max(1, height);
log.info("裁剪区域: x={}, y={}, width={}, height={}, 扩展像素={}, 原图尺寸: {}x{}",
x, y, width, height, expandPixels, imageWidth, imageHeight);
// 裁剪图片
return originalImage.getSubimage(x, y, width, height);
}
/**
* 使用 ZXing 读取二维码
*
* @param image 图片
* @return 二维码内容，如果解码失败返回 null
*/
private String decodeQRCode(BufferedImage image) {
try {
// 创建一个二进制图像
LuminanceSource source = new BufferedImageLuminanceSource(image);
BinaryBitmap binaryBitmap = new BinaryBitmap(new HybridBinarizer(source));
// 创建解码器
QRCodeReader reader = new QRCodeReader();
// 解码二维码
Result result = reader.decode(binaryBitmap);
return result.getText();
} catch (NotFoundException e) {
log.debug("未找到二维码");
return null;
} catch (ChecksumException e) {
log.warn("二维码校验失败: {}", e.getMessage());
return null;
} catch (FormatException e) {
log.warn("二维码格式错误: {}", e.getMessage());
return null;
} catch (Exception e) {
log.error("解码二维码异常", e);
return null;
}
}
}

48
src/main/java/com/example/lxcameraapi/service/IndustrialCamera/QrCode/WeChatDeCode.java
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@ -20,6 +20,7 @@ import java.util.UUID;
import javax.imageio.ImageIO;
import static org.bytedeco.opencv.global.opencv_imgcodecs.imread;
import static org.opencv.core.CvType.CV_8UC3;
/**
*
@ -27,7 +28,8 @@ import static org.bytedeco.opencv.global.opencv_imgcodecs.imread;
public class WeChatDeCode {
public static void main(String... args) {
Mat img = imread("D:\\data\\QQ20260331-175110.jpg");
String imagePath = "D:\\data\\media\\2026-04-17\\1\\1776408379598.png_0.jpg";
Mat img = imread(imagePath);
System.out.println(deCode(img));
//下载二维码到本地识别
@ -122,8 +124,50 @@ public class WeChatDeCode {
e.printStackTrace();
}
}
public static String deCode(BufferedImage croppedImage) {
Mat img = bufferedImageToMat(croppedImage);
try {
return deCode(img);
} finally {
if (img != null) {
img.close();
}
}
}
private static Mat bufferedImageToMat(BufferedImage image) {
if (image == null) {
return null;
}
int width = image.getWidth();
int height = image.getHeight();
private static String deCode(Mat img) {
// 创建 Mat 对象
Mat mat = new Mat(height, width, CV_8UC3);
// 获取像素数据
int[] pixels = new int[width * height];
image.getRGB(0, 0, width, height, pixels, 0, width);
// 将像素数据复制到 Mat
byte[] data = new byte[width * height * 3];
for (int i = 0; i < width * height; i++) {
int pixel = pixels[i];
int r = (pixel >> 16) & 0xFF;
int g = (pixel >> 8) & 0xFF;
int b = pixel & 0xFF;
// OpenCV 使用 BGR 格式
data[i * 3] = (byte) b;
data[i * 3 + 1] = (byte) g;
data[i * 3 + 2] = (byte) r;
}
mat.data().put(data);
return mat;
}
public static String deCode(Mat img) {
// 微信二维码对象，要返回二维码坐标前2个参数必传；后2个在二维码小或不清晰时必传。
WeChatQRCode we = new WeChatQRCode();

433
src/main/java/com/example/lxcameraapi/service/IndustrialCamera/algorithm/ONNXServiceNew.java
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@ -664,7 +664,7 @@ public class ONNXServiceNew {
detection.setH(h);
detection.setConfidence(conf);
detection.setIndex(label);
detection.setName(config.getNames()[label]);
// detection.setName(config.getNames()[label]);
detections.add(detection);
}
@ -686,6 +686,437 @@ public class ONNXServiceNew {
return arg;
}
/**
* YOLO26 专用检测方法
* 处理 YOLO26 的两种输出格式：
* 1. [1, 300, 6]: NMS 后的输出，格式为 [x1, y1, x2, y2, conf, class_id]
* 2. [1, 84, 8400]: YOLOv8 原始输出，84 = 4(xywh) + 1(conf) + 79(classes)
*
* @param imagePath 输入图片路径
* @param type 模型类型
* @return 检测结果列表
* @throws OrtException
*/
public List<BoundingBox> detect26(String imagePath, String type) throws OrtException {
OrtSession session = getSession(type);
AppConfig.YoloModelConfig config = getConfig(type);
// 处理图像
float[] imageData = new float[0];
try {
imageData = processImageFromURL(imagePath, config.getImageSize());
} catch (IOException ex) {
log.error("处理图像时出错: {}", ex.getMessage(), ex);
return new ArrayList<>();
}
// 构建输入张量
long[] shape = new long[]{1, 3, config.getImageSize(), config.getImageSize()};
OnnxTensor inputTensor = null;
OrtSession.Result result = null;
try {
inputTensor = OnnxTensor.createTensor(environment, FloatBuffer.wrap(imageData), shape);
HashMap<String, OnnxTensor> stringOnnxTensorHashMap = new HashMap<>();
stringOnnxTensorHashMap.put(session.getInputInfo().keySet().iterator().next(), inputTensor);
// 执行推理
result = session.run(stringOnnxTensorHashMap);
log.info("YOLO26 输出数量: {}", result.size());
// 获取第一个输出（检测输出）
float[][][] detOutput = (float[][][]) result.get(0).getValue();
// 调试：打印输出维度
int batch = detOutput.length;
int dim1 = detOutput[0].length;
int dim2 = detOutput[0][0].length;
log.info("YOLO26 输出维度: [{}, {}, {}]", batch, dim1, dim2);
// 判断输出格式
// [1, 300, 6]: NMS 后的输出，格式为 [x1, y1, x2, y2, conf, class_id]
// [1, 84, 8400]: YOLOv8 原始输出格式
boolean isNMSOutput = (dim2 == 6); // 第三个维度是 6，说明是 NMS 后的输出
List<BoundingBox> detections = new ArrayList<>();
if (isNMSOutput) {
// [1, 300, 6] -> NMS 后的输出
// 格式: [x1, y1, x2, y2, conf, class_id]
log.info("检测到 NMS 后的输出格式 [1, {}, 6]，直接解析", dim1);
// 获取原始图像尺寸，用于坐标转换
int originalWidth = 0;
int originalHeight = 0;
try {
File file = new File(imagePath);
BufferedImage originalImage = ImageIO.read(file);
if (originalImage != null) {
originalWidth = originalImage.getWidth();
originalHeight = originalImage.getHeight();
log.info("原始图像尺寸: {} x {}", originalWidth, originalHeight);
}
} catch (IOException e) {
log.warn("无法读取原始图像尺寸: {}", e.getMessage());
}
int imageSize = config.getImageSize();
log.info("模型输入尺寸: {} x {}", imageSize, imageSize);
double scale = (originalWidth > 0 && originalHeight > 0)
? Math.min((double) imageSize / originalWidth, (double) imageSize / originalHeight)
: 1.0;
int scaledWidth = (int) (originalWidth * scale);
int scaledHeight = (int) (originalHeight * scale);
int offsetX = (imageSize - scaledWidth) / 2;
int offsetY = (imageSize - scaledHeight) / 2;
log.info("缩放参数: scale={}, scaledWidth={}, scaledHeight={}, offsetX={}, offsetY={}",
scale, scaledWidth, scaledHeight, offsetX, offsetY);
for (int i = 0; i < dim1; i++) {
float x1 = detOutput[0][i][0];
float y1 = detOutput[0][i][1];
float x2 = detOutput[0][i][2];
float y2 = detOutput[0][i][3];
float conf = detOutput[0][i][4];
int classId = (int) detOutput[0][i][5];
// 如果置信度为 0 或低于阈值，跳过（NMS 后的输出，空的框 conf 为 0）
if (conf < config.getConfThreshold()) {
continue;
}
// 将检测框坐标从缩放后图像转换回原始图像
// 先减去偏移，再除以缩放比例
x1 = (float) ((x1 - offsetX) / scale);
y1 = (float) ((y1 - offsetY) / scale);
x2 = (float) ((x2 - offsetX) / scale);
y2 = (float) ((y2 - offsetY) / scale);
// xyxy -> xywh（YOLO 的 xywh 是左上角坐标 + 宽高）
float x = x1;
float y = y1;
float w = x2 - x1;
float h = y2 - y1;
BoundingBox box = new BoundingBox();
box.setX(x);
box.setY(y);
box.setW(w);
box.setH(h);
box.setConfidence(conf);
box.setIndex(classId);
if (classId < config.getNames().length) {
box.setName(config.getNames()[classId]);
}
detections.add(box);
}
} else {
// YOLOv8 原始输出格式 [1, 84, 8400]
// 需要 transpose(1, 0) 变成 [8400, 84]
boolean isYOLOv8Format = (dim1 > dim2);
// 获取原始图像尺寸，用于坐标转换
int originalWidth = 0;
int originalHeight = 0;
try {
File file = new File(imagePath);
BufferedImage originalImage = ImageIO.read(file);
if (originalImage != null) {
originalWidth = originalImage.getWidth();
originalHeight = originalImage.getHeight();
log.info("原始图像尺寸: {} x {}", originalWidth, originalHeight);
}
} catch (IOException e) {
log.warn("无法读取原始图像尺寸: {}", e.getMessage());
}
int imageSize = config.getImageSize();
double scale = (originalWidth > 0 && originalHeight > 0)
? Math.min((double) imageSize / originalWidth, (double) imageSize / originalHeight)
: 1.0;
int scaledWidth = (int) (originalWidth * scale);
int scaledHeight = (int) (originalHeight * scale);
int offsetX = (imageSize - scaledWidth) / 2;
int offsetY = (imageSize - scaledHeight) / 2;
int numPredictions, numFeatures;
if (isYOLOv8Format) {
// [1, 84, 8400] -> 需要转置
numPredictions = dim2; // 8400
numFeatures = dim1; // 84
log.info("检测到 YOLOv8 格式 [1, {}, {}]，转置后 [8400, {}]", dim1, dim2, numFeatures);
} else {
// [1, 8400, 84]
numPredictions = dim1; // 8400
numFeatures = dim2; // 84
log.info("检测到标准格式 [1, {}, {}]", dim1, dim2);
}
log.info("预测框数量: {}, 特征数: {}", numPredictions, numFeatures);
// 解析每个预测框
for (int i = 0; i < numPredictions; i++) {
float x, y, w, h, conf;
if (isYOLOv8Format) {
// detOutput[0] 是 [84, 8400]
// 格式: [x, y, w, h, conf, class_scores...]
x = detOutput[0][0][i];
y = detOutput[0][1][i];
w = detOutput[0][2][i];
h = detOutput[0][3][i];
conf = detOutput[0][4][i];
if (conf < config.getConfThreshold()) {
continue;
}
// 将检测框坐标从缩放后图像转换回原始图像
// YOLOv8 的 xywh 是中心点坐标，需要转换为左上角坐标
// 然后减去偏移，再除以缩放比例
x = (float) ((x - w / 2 - offsetX) / scale);
y = (float) ((y - h / 2 - offsetY) / scale);
w = (float) (w / scale);
h = (float) (h / scale);
float[] clsScores = new float[numFeatures - 5];
for (int j = 0; j < clsScores.length; j++) {
clsScores[j] = detOutput[0][5 + j][i];
}
int classId = argmax(clsScores, 0.0f);
if (classId != -1) {
BoundingBox box = new BoundingBox();
box.setX(x);
box.setY(y);
box.setW(w);
box.setH(h);
box.setConfidence(conf);
box.setIndex(classId);
if (classId < config.getNames().length) {
box.setName(config.getNames()[classId]);
}
detections.add(box);
}
} else {
// detOutput[0] 是 [8400, 84]
x = detOutput[0][i][0];
y = detOutput[0][i][1];
w = detOutput[0][i][2];
h = detOutput[0][i][3];
conf = detOutput[0][i][4];
if (conf < config.getConfThreshold()) {
continue;
}
// 将检测框坐标从缩放后图像转换回原始图像
// YOLOv8 的 xywh 是中心点坐标，需要转换为左上角坐标
// 然后减去偏移，再除以缩放比例
x = (float) ((x - w / 2 - offsetX) / scale);
y = (float) ((y - h / 2 - offsetY) / scale);
w = (float) (w / scale);
h = (float) (h / scale);
float[] clsScores = new float[numFeatures - 5];
for (int j = 0; j < clsScores.length; j++) {
clsScores[j] = detOutput[0][i][5 + j];
}
int classId = argmax(clsScores, 0.0f);
if (classId != -1) {
BoundingBox box = new BoundingBox();
box.setX(x);
box.setY(y);
box.setW(w);
box.setH(h);
box.setConfidence(conf);
box.setIndex(classId);
if (classId < config.getNames().length) {
box.setName(config.getNames()[classId]);
}
detections.add(box);
}
}
}
}
// NMS 过滤
List<BoundingBox> filteredDetections = nonMaxSuppression(detections, 0.5f);
log.info("YOLO26 检测结果: {} 个", filteredDetections.size());
return filteredDetections;
} finally {
if (inputTensor != null) {
inputTensor.close();
}
if (result != null) {
result.close();
}
}
}
/**
* YOLO26 专用分类方法
* 处理 YOLO26 的两种输出格式：
* 1. [1, 300, 6]: NMS 后的输出，格式为 [x1, y1, x2, y2, conf, class_id]
* 2. [1, 84, 8400]: YOLOv8 原始输出，84 = 4(xywh) + 1(conf) + 79(classes)
*
* @param imagePath 输入图片路径
* @param type 模型类型
* @return 分类结果
* @throws OrtException
*/
public ClassifyEntity classify26(String imagePath, String type) throws OrtException {
OrtSession session = getSession(type);
AppConfig.YoloModelConfig config = getConfig(type);
// 处理图像
float[] imageData = new float[0];
try {
imageData = processImageFromURL(imagePath, config.getImageSize());
} catch (IOException ex) {
log.error("处理图像时出错: {}", ex.getMessage(), ex);
ClassifyEntity errorResult = new ClassifyEntity();
errorResult.setName("Unknown");
return errorResult;
}
// 构建输入张量
long[] shape = new long[]{1, 3, config.getImageSize(), config.getImageSize()};
OnnxTensor inputTensor = null;
OrtSession.Result result = null;
try {
inputTensor = OnnxTensor.createTensor(environment, FloatBuffer.wrap(imageData), shape);
HashMap<String, OnnxTensor> stringOnnxTensorHashMap = new HashMap<>();
stringOnnxTensorHashMap.put(session.getInputInfo().keySet().iterator().next(), inputTensor);
// 执行推理
result = session.run(stringOnnxTensorHashMap);
log.info("YOLO26 输出数量: {}", result.size());
// YOLO26 第一个输出是检测输出
float[][][] detOutput = (float[][][]) result.get(0).getValue();
// 判断输出格式
int dim1 = detOutput[0].length;
int dim2 = detOutput[0][0].length;
boolean isNMSOutput = (dim2 == 6); // NMS 后的输出
float bestConfidence = -Float.MAX_VALUE;
int bestClassId = -1;
if (isNMSOutput) {
// [1, 300, 6] -> NMS 后的输出，格式为 [x1, y1, x2, y2, conf, class_id]
log.info("YOLO26 分类检测到 NMS 后的输出格式 [1, {}, 6]", dim1);
for (int i = 0; i < dim1; i++) {
float conf = detOutput[0][i][4];
int classId = (int) detOutput[0][i][5];
if (conf < config.getConfThreshold()) {
continue;
}
if (conf > bestConfidence) {
bestConfidence = conf;
bestClassId = classId;
}
}
} else {
// YOLOv8 原始输出格式
boolean isYOLOv8Format = (dim1 > dim2);
int numPredictions = isYOLOv8Format ? dim2 : dim1;
int numFeatures = isYOLOv8Format ? dim1 : dim2;
log.info("YOLO26 分类预测框数量: {}, 特征数: {}, YOLOv8格式: {}",
numPredictions, numFeatures, isYOLOv8Format);
// 找到置信度最高的类别
for (int i = 0; i < numPredictions; i++) {
float conf;
int classId;
float[] clsScores;
if (isYOLOv8Format) {
// detOutput[0] 是 [84, 8400]
conf = detOutput[0][4][i];
if (conf < config.getConfThreshold()) {
continue;
}
// 获取类别分数
clsScores = new float[numFeatures - 5];
for (int j = 0; j < clsScores.length; j++) {
clsScores[j] = detOutput[0][5 + j][i];
}
classId = argmax(clsScores, 0.0f);
float classScore = clsScores[classId];
if (classScore > bestConfidence) {
bestConfidence = classScore;
bestClassId = classId;
}
} else {
// detOutput[0] 是 [8400, 84]
conf = detOutput[0][i][4];
if (conf < config.getConfThreshold()) {
continue;
}
clsScores = new float[numFeatures - 5];
for (int j = 0; j < clsScores.length; j++) {
clsScores[j] = detOutput[0][i][5 + j];
}
classId = argmax(clsScores, 0.0f);
float classScore = clsScores[classId];
if (classScore > bestConfidence) {
bestConfidence = classScore;
bestClassId = classId;
}
}
}
}
ClassifyEntity classifyEntity = new ClassifyEntity();
classifyEntity.setIndex(bestClassId);
if (bestClassId != -1 && bestClassId < config.getNames().length) {
String className = config.getNames()[bestClassId];
classifyEntity.setName(className);
classifyEntity.setConfidence(bestConfidence);
log.info("YOLO26 识别模版：{}，置信度：{}", className, bestConfidence);
} else {
log.info("YOLO26 未识别到模版");
classifyEntity.setName("Unknown");
}
return classifyEntity;
} finally {
if (inputTensor != null) {
inputTensor.close();
}
if (result != null) {
result.close();
}
}
}
/**
* 销毁时清理资源
*/

6
src/main/java/com/example/lxcameraapi/service/IndustrialCamera/camera/lx/config/BoxCountResponse.java
Unescape Escape View File

@ -5,6 +5,7 @@ import lombok.Builder;
import lombok.Data;
import lombok.NoArgsConstructor;
import java.util.List;
import java.util.Map;
/**
@ -84,6 +85,11 @@ public class BoxCountResponse {
private String imagePath;
private String result;
/**
* 二维码解码结果列表
*/
private List<String> qrCodeResults;
/**
* 从 BoxCountResult 创建响应对象
*/

11
src/main/resources/application.yml
Unescape Escape View File

@ -57,9 +57,18 @@ picUrl: "http://127.0.0.1:9012/pic/"
yoloModelConfig:
# 模型地址
modelPath: "D:/PycharmProjects/yolo/runs/detect/train22/weights/best.onnx"
modelPath: "D:/git/测试/lxCameraApi/yolo/qrcode.onnx"
# 图片大小
imgSize: 640
# 置信度
confThreshold: 0.5
names: ['0143','0153','0173','0177','0191','0253','0256','0266','0268','0286','0302','0304','0305','0307','0320','0326','0336','0339','0343','0352','0458','0461','0462','0473','0477','0486','0490','0492','0930','1101','1102','1104','1262','1269','1302','1308','1359','1366','1622','1625','1919','1976','20','2165','2188','2210','2224','2445','2476','2611','2730','2731','2910','2914','2943','3027','3028','3029','3212','3226','3344','3501','3509','3538','3725','3741','3751','3754','3763','3766','3808']
modelMap:
qrCode: # 模型地址
modelPath: "D:/git/测试/lxCameraApi/yolo/best.onnx"
# 图片大小
imgSize: 2048
# 置信度
confThreshold: 0.95
names: ['code']

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