diff --git a/.gitignore b/.gitignore
index 549e00a..485b4ac 100644
--- a/.gitignore
+++ b/.gitignore
@@ -31,3 +31,10 @@ build/
### VS Code ###
.vscode/
+
+### Logs ###
+logs/
+log/
+
+### ONNX models ###
+*.onnx
diff --git a/pom.xml b/pom.xml
index d39b3a6..19dd8f6 100644
--- a/pom.xml
+++ b/pom.xml
@@ -70,7 +70,31 @@
org.springframework.boot
spring-boot-starter-web-services
-
+
+ org.bytedeco
+ javacpp
+ 1.5.7
+ windows-x86_64
+
+
+ org.bytedeco
+ openblas
+ 0.3.19-1.5.7
+ windows-x86_64
+
+
+ org.bytedeco
+ opencv
+ 4.5.5-1.5.7
+
+
+ org.bytedeco
+ opencv
+ 4.5.5-1.5.7
+ windows-x86_64
+
+
+
com.microsoft.onnxruntime
onnxruntime
1.16.0
@@ -127,13 +151,13 @@
javase
3.5.0
-
- org
- opencv
- system
- 1.0
- ${project.basedir}/src/main/resources/libs/opencv-480.jar
-
+
+
+
+
+
+
+
diff --git a/src/main/java/com/example/lxcameraapi/controller/CategoryController.java b/src/main/java/com/example/lxcameraapi/controller/CategoryController.java
index 397de50..77a3d12 100644
--- a/src/main/java/com/example/lxcameraapi/controller/CategoryController.java
+++ b/src/main/java/com/example/lxcameraapi/controller/CategoryController.java
@@ -57,7 +57,7 @@ public class CategoryController {
}
boolean success = imageCaptureService.captureImage(ip, path);
- List detectResults = onnxServiceNew.detect(path, null);
+ List detectResults = onnxServiceNew.detect26(path, null);
log.info("检测结果: {}", detectResults);
boxCountResponse.setImagePath(url);
diff --git a/src/main/java/com/example/lxcameraapi/controller/QtCodeController.java b/src/main/java/com/example/lxcameraapi/controller/QtCodeController.java
new file mode 100644
index 0000000..ac18873
--- /dev/null
+++ b/src/main/java/com/example/lxcameraapi/controller/QtCodeController.java
@@ -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 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 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 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 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 points = new ArrayList();
+ // 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;
+ }
+ }
+
+
+}
diff --git a/src/main/java/com/example/lxcameraapi/service/IndustrialCamera/QrCode/WeChatDeCode.java b/src/main/java/com/example/lxcameraapi/service/IndustrialCamera/QrCode/WeChatDeCode.java
index 6af3722..d93d4ea 100644
--- a/src/main/java/com/example/lxcameraapi/service/IndustrialCamera/QrCode/WeChatDeCode.java
+++ b/src/main/java/com/example/lxcameraapi/service/IndustrialCamera/QrCode/WeChatDeCode.java
@@ -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();
diff --git a/src/main/java/com/example/lxcameraapi/service/IndustrialCamera/algorithm/ONNXServiceNew.java b/src/main/java/com/example/lxcameraapi/service/IndustrialCamera/algorithm/ONNXServiceNew.java
index 708ad93..58e68f8 100644
--- a/src/main/java/com/example/lxcameraapi/service/IndustrialCamera/algorithm/ONNXServiceNew.java
+++ b/src/main/java/com/example/lxcameraapi/service/IndustrialCamera/algorithm/ONNXServiceNew.java
@@ -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 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 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 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 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 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
*/
diff --git a/src/main/java/com/example/lxcameraapi/service/IndustrialCamera/camera/lx/config/BoxCountResponse.java b/src/main/java/com/example/lxcameraapi/service/IndustrialCamera/camera/lx/config/BoxCountResponse.java
index 7809dd1..0a99335 100644
--- a/src/main/java/com/example/lxcameraapi/service/IndustrialCamera/camera/lx/config/BoxCountResponse.java
+++ b/src/main/java/com/example/lxcameraapi/service/IndustrialCamera/camera/lx/config/BoxCountResponse.java
@@ -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 qrCodeResults;
+
/**
* 从 BoxCountResult 创建响应对象
*/
diff --git a/src/main/resources/application.yml b/src/main/resources/application.yml
index 0929a3c..6f7a701 100644
--- a/src/main/resources/application.yml
+++ b/src/main/resources/application.yml
@@ -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']
+