增加计算层高正态分布等算法

src/main/java/com/example/lxcameraapi/controller/YoloController.java

@@ -0,0 +1,205 @@
+package com.example.lxcameraapi.controller;
+
+import ai.onnxruntime.OrtException;
+import com.example.lxcameraapi.conf.AppConfig;
+import com.example.lxcameraapi.service.IndustrialCamera.algorithm.ONNXServiceNew;
+import com.example.lxcameraapi.service.IndustrialCamera.camera.lx.LxPointCloudSaveImage;
+import com.example.lxcameraapi.service.IndustrialCamera.algorithm.BoxPositionConf;
+import com.example.lxcameraapi.service.IndustrialCamera.algorithm.PcdPojo;
+import com.example.lxcameraapi.service.IndustrialCamera.algorithm.PointCloudProcessor;
+import com.example.lxcameraapi.service.IndustrialCamera.pojo.Pojo;
+import com.example.lxcameraapi.yolo.BoundingBox;
+import com.example.lxcameraapi.yolo.ClassifyEntity;
+import io.swagger.models.auth.In;
+import lombok.extern.slf4j.Slf4j;
+import org.springframework.web.bind.annotation.GetMapping;
+import org.springframework.web.bind.annotation.RequestMapping;
+import org.springframework.web.bind.annotation.RestController;
+
+import javax.annotation.Resource;
+import java.io.IOException;
+import java.time.LocalDate;
+import java.time.LocalDateTime;
+import java.time.format.DateTimeFormatter;
+import java.util.*;
+
+import static com.example.lxcameraapi.controller.HikController.DATE_FORMATTER;
+import static com.example.lxcameraapi.controller.HikController.TIME_FORMATTER;
+
+@Slf4j
+@RestController
+@RequestMapping("/yolo")
+public class YoloController {
+    @Resource
+    AppConfig appConfig;
+
+    @Resource
+    ONNXServiceNew onnxServiceNew;
+
+    @GetMapping("/detect")
+    public Pojo detect(String pic,String type) throws IOException {
+        Pojo pojo = new Pojo();
+        try {
+            List<BoundingBox>  list =  onnxServiceNew.detect(pic,type);
+            Integer index = ONNXServiceNew.getMaxClassIndex(list);
+            if (appConfig.getYoloModelConfig().getModelMap().containsKey(type)){
+
+                pojo.setCode(appConfig.getYoloModelConfig().getModelMap().get(type).getNames()[index]);
+            }else {
+                pojo.setCode(appConfig.getYoloModelConfig().getNames()[index]);
+            }
+        } catch (OrtException e) {
+            throw new RuntimeException(e);
+        }
+
+
+        return pojo;
+    }
+
+    @GetMapping("/detect")
+    public ClassifyEntity classifyOne(String pic,String type) throws IOException {
+        ClassifyEntity classifyEntity = null;
+        try {
+            classifyEntity =  onnxServiceNew.classifyOne(pic,type);
+        } catch (OrtException e) {
+            throw new RuntimeException(e);
+        }
+
+
+        return classifyEntity;
+    }
+
+    @GetMapping("/getBoxCountByCategory")
+    public int getBoxCountByCategory(String streetId,String category) throws IOException {
+
+        if(category.equals("20")){
+            return 1;
+        }
+
+        String path =streetId+"\\"
+                + LocalDate.now().format(DATE_FORMATTER)
+                +"\\"+category+"_"+ LocalDateTime.now().format(TIME_FORMATTER) +"_";
+        List<AppConfig.Camera> cameras = appConfig.getLxCamera();
+        int i = 0;
+        Map<Integer, String> map = new HashMap<>();
+        System.out.println(appConfig.getSpecialBox().toString());
+        for (AppConfig.SpecialBox specialBox:appConfig.getSpecialBox()){
+            System.out.println(category.equals(specialBox.getCategory()));
+            if (category.equals(specialBox.getCategory())){
+                map.put(specialBox.getCameraId(),specialBox.getComputeConfPath());
+                System.out.println(specialBox.getCameraId()+" "+specialBox.getComputeConfPath());
+            }
+        }
+        if (!map.isEmpty()){
+            log.info("map: {}", map);
+            System.out.println("map: "+map);
+            for (Integer cameraId:map.keySet()) {
+                for (AppConfig.Camera c : cameras) {
+                    if (Objects.equals(c.getId(), cameraId)) {
+
+                        log.info("cameraId: {}, path: {}", cameraId, path +"_"+c.getId()+ ".pcd");
+                        LxPointCloudSaveImage.saveImage(c.getIp(), path +"_"+c.getId()+ ".pcd");
+                        PcdPojo pojo = new PcdPojo();
+                        pojo.setPcd1(path + "_"+c.getId()+ ".pcd");
+                        pojo.setConfigPath(c.getConvertConfPath());
+                        try {
+                            pojo.setPCDInfo(pojo);
+                        } catch (IOException e) {
+                            throw new RuntimeException(e);
+                        }
+                        PointCloudProcessor pointCloudProcessor = new PointCloudProcessor();
+                        List<BoxPositionConf> confs = pointCloudProcessor.getBoxPositionConf(map.get(c.getId()));
+                        i += pointCloudProcessor.slicing(pojo, confs);
+                    }
+                }
+            }
+        }else {
+            for (AppConfig.Camera c : cameras) {
+                if (streetId.equals(c.getStreetId())) {
+
+                    log.info("cameraId: {}, path: {}", c.getId(), path +"_"+c.getId()+ ".pcd");
+                    LxPointCloudSaveImage.saveImage(c.getIp(), path +"_"+c.getId()+ ".pcd");
+                    PcdPojo pojo = new PcdPojo();
+                    pojo.setPcd1(path + "_"+c.getId()+ ".pcd");
+                    pojo.setConfigPath(c.getConvertConfPath());
+                    try {
+                        pojo.setPCDInfo(pojo);
+                    } catch (IOException e) {
+                        throw new RuntimeException(e);
+                    }
+                    PointCloudProcessor pointCloudProcessor = new PointCloudProcessor();
+                    List<BoxPositionConf> confs = pointCloudProcessor.getBoxPositionConf(c.getComputeConfPath());
+                    i += pointCloudProcessor.slicing(pojo, confs);
+                }
+            }
+        }
+
+        return i;
+    }
+
+
+    @GetMapping("/getBoxCountByCategoryTest")
+    public int getBoxCountByCategoryTest(String streetId,String category,String path1,String path2) throws IOException {
+        if(category.equals("20")){
+            return 1;
+        }
+
+        List<AppConfig.Camera> cameras = appConfig.getLxCamera();
+        int i = 0;
+        Map<Integer, String> map = new HashMap();
+        for (AppConfig.SpecialBox specialBox:appConfig.getSpecialBox()){
+            if (category.equals(specialBox.getCategory())){
+                map.put(specialBox.getCameraId(),specialBox.getComputeConfPath());
+            }
+        }
+        if (!map.isEmpty()){
+            for (Integer cameraId:map.keySet()) {
+                for (AppConfig.Camera c : cameras) {
+                    if (Objects.equals(c.getId(), cameraId)) {
+//                        LxPointCloudSaveImage.saveImage(c.getIp(), path1);
+                        PcdPojo pojo = new PcdPojo();
+                        if (c.getId()==1) {
+                            pojo.setPcd1(path1);
+                        }else {
+                            pojo.setPcd1(path2);
+                        }
+                        pojo.setConfigPath(c.getConvertConfPath());
+                        try {
+                            pojo.setPCDInfo(pojo);
+                        } catch (IOException e) {
+                            throw new RuntimeException(e);
+                        }
+                        PointCloudProcessor pointCloudProcessor = new PointCloudProcessor();
+                        List<BoxPositionConf> confs = pointCloudProcessor.getBoxPositionConf(map.get(c.getId()));
+                        i += pointCloudProcessor.slicing(pojo, confs);
+                    }
+                }
+            }
+        }else {
+            for (AppConfig.Camera c : cameras) {
+                if (streetId.equals(c.getStreetId())) {
+                    String sn = UUID.randomUUID().toString();
+
+                    PcdPojo pojo = new PcdPojo();
+                    if (c.getId()==1) {
+                        pojo.setPcd1(path1);
+                    }else {
+                        pojo.setPcd1(path2);
+                    }
+                    pojo.setConfigPath(c.getConvertConfPath());
+                    try {
+                        pojo.setPCDInfo(pojo);
+                    } catch (IOException e) {
+                        throw new RuntimeException(e);
+                    }
+                    PointCloudProcessor pointCloudProcessor = new PointCloudProcessor();
+                    List<BoxPositionConf> confs = pointCloudProcessor.getBoxPositionConf(c.getComputeConfPath());
+                    i += pointCloudProcessor.slicing(pojo, confs);
+                }
+            }
+        }
+
+        return i;
+    }
+
+}

src/main/java/com/example/lxcameraapi/service/IndustrialCamera/algorithm/PcdPojo.java

@@ -29,6 +29,9 @@ public class PcdPojo {
     //随行路径 configProperties.getSavePath().getMediaPath() + street.getLeft3D()+"000/"+ currentDate.format(formatter)+"/"+transmissionPojo.getRow()+".pcd";
     private String pcd1;
     private String pcd2;
+    private Integer functionType = 1;//1 中位数 2 平均值 3 正态分布
+    private Integer fluctuate= 80;
+    private String pcd4;
 
     private String cameraSn1;
     private String cameraSn2;

src/main/java/com/example/lxcameraapi/service/IndustrialCamera/algorithm/PointCloudProcessor.java

@@ -3,6 +3,7 @@ package com.example.lxcameraapi.service.IndustrialCamera.algorithm;
 import com.fasterxml.jackson.core.JsonProcessingException;
 import com.fasterxml.jackson.databind.JsonMappingException;
 import com.fasterxml.jackson.databind.ObjectMapper;
+import lombok.extern.slf4j.Slf4j;
 import org.opencv.core.Core;
 import org.opencv.core.CvType;
 import org.opencv.core.Mat;
@@ -21,6 +22,7 @@ import java.util.*;
 import java.util.List;
 import java.util.stream.Collectors;
 
+@Slf4j
 public class PointCloudProcessor {
 
 //
@@ -235,6 +237,66 @@ public class PointCloudProcessor {
         return sum / (values.size() - 2 * trimCount);
     }
 
+    /**
+     * 根据 functionType 计算点云层高
+     *
+     * @param zValues      层匹配范围内所有点的 z 坐标
+     * @param functionType 1 中位数, 2 平均值, 3 正态分布（直方图峰值）
+     * @return 计算得到的层高，无数据时返回 NaN
+     */
+    private static double calculateLayerHeight(List<Double> zValues, Integer functionType) {
+        if (zValues == null || zValues.isEmpty()) {
+            return Double.NaN;
+        }
+        int type = functionType == null ? 1 : functionType;
+        List<Double> sorted = new ArrayList<>(zValues);
+        sorted.sort(Comparator.naturalOrder());
+        switch (type) {
+            case 2: // 平均值
+                return sorted.stream().mapToDouble(Double::doubleValue).average().orElse(Double.NaN);
+            case 3: // 正态分布：直方图峰值
+                return histogramPeak(sorted);
+            default: // 中位数
+                int size = sorted.size();
+                if (size % 2 == 1) {
+                    return sorted.get(size / 2);
+                }
+                return (sorted.get(size / 2 - 1) + sorted.get(size / 2)) / 2.0;
+        }
+    }
+
+    /**
+     * 正态分布：将数据分为多个区间，返回点数最多的区间中心值（近似峰值）
+     */
+    private static double histogramPeak(List<Double> values) {
+        double min = values.get(0);
+        double max = values.get(values.size() - 1);
+        if (max - min < 1e-9) {
+            return min;
+        }
+        int bins = 100; // 直方图区间数
+        double binWidth = (max - min) / bins;
+        int[] counts = new int[bins];
+        double[] binCenter = new double[bins];
+        for (int i = 0; i < bins; i++) {
+            binCenter[i] = min + (i + 0.5) * binWidth;
+        }
+        for (double v : values) {
+            int idx = (int) ((v - min) / binWidth);
+            if (idx >= bins) {
+                idx = bins - 1;
+            }
+            counts[idx]++;
+        }
+        int maxIdx = 0;
+        for (int i = 1; i < bins; i++) {
+            if (counts[i] > counts[maxIdx]) {
+                maxIdx = i;
+            }
+        }
+        return binCenter[maxIdx];
+    }
+
     public static List<double[]> readPCD(String filePath) {
         try {
             if (isBinaryFormat(filePath)) {
@@ -439,9 +501,9 @@ public class PointCloudProcessor {
 //        pojo.setPcd1("D:\\data\\test\\1巷道\\F83C51123126\\3766_173016547__2.pcd");
 //        pojo.setConfigPath("D:\\data\\test\\1巷道\\F83C51123126\\cj.json");
 //        String pz = "D:\\data\\test\\1巷道\\F83C51123126\\pz.json";
-        pojo.setPcd1("D:\\xwechat_files\\wxid_ttkf0xgmyihv22_2388\\msg\\file\\2026-07\\3766_173056141__2.pcd");
+        pojo.setPcd1("D:\\desktop\\新建文件夹\\673\\0184_082322214__1.pcd");
-        pojo.setConfigPath("D:\\xwechat_files\\wxid_ttkf0xgmyihv22_2388\\msg\\file\\2026-07\\cj.json");
+        pojo.setConfigPath("D:\\desktop\\新建文件夹\\670\\cj.json");
-        String pz = "D:\\xwechat_files\\wxid_ttkf0xgmyihv22_2388\\msg\\file\\2026-07\\pz.json";
+        String pz = "D:\\desktop\\新建文件夹\\670\\pz.json";
 
         try {
             pojo.setPCDInfo(pojo);
@@ -616,7 +678,15 @@ public class PointCloudProcessor {
             System.out.println("  第" + i + "层: height=" + conf.getHeight() + ", 盒子数=" + conf.getPosition().size());
         }
 
-        // 计算每层的 Z 轴匹配范围（相邻层取中间值，首尾层留±300边距）
+        // 计算pcd1
+        List<double[]> points = readPCD(pojo.getPcd1());
+        if (points == null || points.isEmpty()) {
+            System.out.println("点云数据为空!");
+            return 0;
+        }
+        System.out.println("点云总点数: " + points.size());
+
+        // 1. 计算每层的初始 Z 轴匹配范围（相邻层取中间值，首尾层留±300边距）
         double[][] zRanges = new double[boxPositionConfs.size()][2];
         for (int i = 0; i < boxPositionConfs.size(); i++) {
             double currentHeight = boxPositionConfs.get(i).getHeight();
@@ -633,16 +703,32 @@ public class PointCloudProcessor {
                 zRanges[i][0] = (boxPositionConfs.get(i - 1).getHeight() + currentHeight) / 2.0;
                 zRanges[i][1] = (currentHeight + boxPositionConfs.get(i + 1).getHeight()) / 2.0;
             }
-            System.out.println("  第" + i + "层 Z范围: [" + zRanges[i][0] + ", " + zRanges[i][1] + "]");
+            System.out.println("  第" + i + "层 初始Z范围: [" + zRanges[i][0] + ", " + zRanges[i][1] + "]");
         }
 
-        // 计算pcd1
+        // 2. 根据每层初始匹配范围内的所有点，按 functionType 计算每层实际高度（1 中位数 / 2 平均值 / 3 正态分布）
-        List<double[]> points = readPCD(pojo.getPcd1());
+        // 3. 用计算得到的值 ± fluctuate 构成新的 Z 轴匹配范围
-        if (points == null || points.isEmpty()) {
+        int fluctuate = pojo.getFluctuate() == null ? 80 : pojo.getFluctuate();
-            System.out.println("点云数据为空!");
+        for (int i = 0; i < boxPositionConfs.size(); i++) {
-            return 0;
+            List<Double> zValues = new ArrayList<>();
+            for (double[] point : points) {
+                double[] rotated = rotatePoints(point, pojo.getRotationMatrix());
+                if (rotated[2] >= zRanges[i][0] && rotated[2] <= zRanges[i][1]) {
+                    zValues.add(rotated[2]);
+                }
+            }
+            double layerHeight = calculateLayerHeight(zValues, pojo.getFunctionType());
+            if (Double.isNaN(layerHeight)) {
+                // 该层初始范围内没有点，沿用配置高度
+                layerHeight = boxPositionConfs.get(i).getHeight();
+                log.info("  第" + i + "层 匹配范围内无点，沿用配置高度: " + layerHeight);
+            } else {
+                log.info("  第" + i + "层 匹配点数=" + zValues.size() + ", 计算实际高度=" + layerHeight);
+            }
+            zRanges[i][0] = layerHeight - fluctuate;
+            zRanges[i][1] = layerHeight + fluctuate;
+            log.info("  第" + i + "层 新Z范围(±" + fluctuate + "): [" + zRanges[i][0] + ", " + zRanges[i][1] + "]");
         }
-        System.out.println("点云总点数: " + points.size());
 
         Map<Integer, Integer> map = new HashMap <>();
         Map<Integer, Double[]> listMap = new HashMap<>();
@@ -697,7 +783,7 @@ public class PointCloudProcessor {
             count = baseCount;
             int currentLayerCount = 0;
 
-            System.out.println(">>> 检查第" +i  + "层 (height=" + boxPositionConfs.get(i).getHeight()
+            log.info(">>> 检查第" +i  + "层 (height=" + boxPositionConfs.get(i).getHeight()
                     + ", Z范围=[" + zRanges[i][0] + "," + zRanges[i][1]
                     + "], 盒子数=" + boxPositionConfs.get(i).getPosition().size() + "), baseCount=" + baseCount);
 
@@ -709,11 +795,11 @@ public class PointCloudProcessor {
                     count += 1;
                     currentLayerCount++;
                 }
-                System.out.println("    盒子[" + (j+1 )+ "] position=" + position + ", 点数=" + pointCount
+                log.info("    盒子[" + (j+1 )+ "] position=" + position + ", 点数=" + pointCount
                         + (hasData ? " [有货]" : " [空]"));
             }
 
-            System.out.println("  第" + i + "层有货盒子数: " + currentLayerCount + ", 累计count=" + count);
+            log.info("  第" + i + "层有货盒子数: " + currentLayerCount + ", 累计count=" + count);
 
             if (count > baseCount) {
                 System.out.println(">>> 在第" + i + "层发现货物，停止向上查找 <<<");

src/main/java/com/example/lxcameraapi/service/IndustrialCamera/pojo/Pojo.java

@@ -1,7 +1,10 @@
 package com.example.lxcameraapi.service.IndustrialCamera.pojo;
 
+import com.example.lxcameraapi.yolo.BoundingBox;
 import lombok.Data;
 
+import java.util.List;
+
 @Data
 public class Pojo {
     //旋转路径
@@ -10,4 +13,11 @@ public class Pojo {
     private String determinePath;
     //结果
     private Boolean result;
+    private String code;
+
+    private String type;
+    private List<BoundingBox> detects;
+    private String path;
+    private String streetId;
+    private Integer direction;
 }