修改截图逻辑 加入模型测试使用

ruoyi-video/pom.xml

@@ -38,6 +38,7 @@
             <version>1.5.10</version>
         </dependency>
 
+
         <!-- 解析 models.json-->
         <dependency>
             <groupId>com.fasterxml.jackson.core</groupId>

ruoyi-video/src/main/java/com/ruoyi/video/common/ModelManager.java

@@ -32,7 +32,7 @@ public final class ModelManager implements AutoCloseable {
             int rgb = palette[i % palette.length]; i++;
             int bgr = ((rgb & 0xFF) << 16) | (rgb & 0xFF00) | ((rgb >> 16) & 0xFF);
 
-            YoloDetector det = new OpenVinoYoloDetector(name, dir, w, h, backend, bgr);
+            YoloDetector det = new OpenCvYoloDetector(name, dir, w, h, backend, bgr);
             map.put(name, det);
         }
     }

ruoyi-video/src/main/java/com/ruoyi/video/thread/detector/OpenCvYoloDetector.java

@@ -5,14 +5,21 @@ import org.bytedeco.javacpp.indexer.FloatRawIndexer;
 import org.bytedeco.opencv.opencv_core.*;
 import org.bytedeco.opencv.opencv_dnn.Net;
 
-import java.nio.file.*;
+import java.nio.file.Files;
-import java.util.*;
+import java.nio.file.Path;
+import java.util.ArrayList;
+import java.util.Collections;
+import java.util.List;
 
-import static org.bytedeco.opencv.global.opencv_dnn.*;   // DNN API（readNetFromModelOptimizer / blobFromImage 等）
+import static org.bytedeco.opencv.global.opencv_core.*;
-import static org.bytedeco.opencv.global.opencv_core.*;  // Mat/Size/Scalar/transpose 等
+import static org.bytedeco.opencv.global.opencv_dnn.*;
-import static org.bytedeco.opencv.global.opencv_imgproc.*; // cvtColor 等
+import static org.bytedeco.opencv.global.opencv_imgproc.*;
 
-public final class OpenVinoYoloDetector implements YoloDetector {
+/**
+ * 纯 OpenCV CPU 后端的 YOLO IR 检测器（.xml/.bin）
+ * 与原 OpenVinoYoloDetector 代码保持一致，只是固定：DNN_BACKEND_OPENCV + DNN_TARGET_CPU。
+ */
+public final class OpenCvYoloDetector implements YoloDetector {
     private final String modelName;
     private final Net net;
     private final Size input;
@@ -20,7 +27,15 @@ public final class OpenVinoYoloDetector implements YoloDetector {
     private final String[] classes;
     private final int colorBGR;
 
-    public OpenVinoYoloDetector(String name, Path dir, int inW, int inH, String backend, int colorBGR) throws Exception {
+    /**
+     * @param name    模型别名
+     * @param dir     模型目录（含 model.xml / model.bin / classes.txt）
+     * @param inW     推理输入宽
+     * @param inH     推理输入高
+     * @param backend 兼容参数，忽略（统一使用 OpenCV CPU）
+     * @param colorBGR 画框颜色（BGR packed int）
+     */
+    public OpenCvYoloDetector(String name, Path dir, int inW, int inH, String backend, int colorBGR) throws Exception {
         this.modelName = name;
         this.input = new Size(inW, inH);
         this.colorBGR = colorBGR;
@@ -30,27 +45,19 @@ public final class OpenVinoYoloDetector implements YoloDetector {
 
         Path clsPath = dir.resolve("classes.txt");
         if (Files.exists(clsPath)) {
-            this.classes = Files.readAllLines(clsPath).stream().map(String::trim)
+            this.classes = Files.readAllLines(clsPath).stream()
-                    .filter(s -> !s.isEmpty()).toArray(String[]::new);
+                    .map(String::trim).filter(s -> !s.isEmpty())
+                    .toArray(String[]::new);
         } else {
             this.classes = new String[0];
         }
 
         this.net = readNetFromModelOptimizer(xml, bin);
 
-        boolean set = false;
+        // ✅ 固定使用 OpenCV CPU 后端（不再尝试 OpenVINO）
-        if ("openvino".equalsIgnoreCase(backend)) {
-            try {
-                net.setPreferableBackend(DNN_BACKEND_INFERENCE_ENGINE);
-                net.setPreferableTarget(DNN_TARGET_CPU);
-                set = true;
-            } catch (Throwable ignore) { /* 回退 */ }
-        }
-        if (!set) {
         net.setPreferableBackend(DNN_BACKEND_OPENCV);
         net.setPreferableTarget(DNN_TARGET_CPU);
     }
-    }
 
     @Override public String name() { return modelName; }
 
@@ -58,7 +65,7 @@ public final class OpenVinoYoloDetector implements YoloDetector {
     public List<Detection> detect(Mat bgr) {
         if (bgr == null || bgr.empty()) return Collections.emptyList();
 
-        // 统一成 BGR 3 通道，避免 blobFromImage 断言失败
+        // 保证 BGR 3通道
         if (bgr.channels() != 3) {
             Mat tmp = new Mat();
             if (bgr.channels() == 1) cvtColor(bgr, tmp, COLOR_GRAY2BGR);
@@ -69,23 +76,19 @@ public final class OpenVinoYoloDetector implements YoloDetector {
 
         try (Mat blob = blobFromImage(bgr, 1.0/255.0, input, new Scalar(0.0), true, false, CV_32F)) {
             net.setInput(blob);
-            // ===== 多输出兼容（Bytedeco 正确写法）=====
+
+            // 兼容单/多输出
             org.bytedeco.opencv.opencv_core.StringVector outNames = net.getUnconnectedOutLayersNames();
             List<Mat> outs = new ArrayList<>();
 
             if (outNames == null || outNames.size() == 0) {
-                // 只有一个默认输出
+                Mat out = net.forward();
-                Mat out = net.forward();      // ← 直接返回 Mat
                 outs.add(out);
             } else {
-                // 多输出：用 MatVector 承接
                 org.bytedeco.opencv.opencv_core.MatVector outBlobs =
                         new org.bytedeco.opencv.opencv_core.MatVector(outNames.size());
-                net.forward(outBlobs, outNames);   // ← 正确的重载
+                net.forward(outBlobs, outNames);
-
+                for (long i = 0; i < outBlobs.size(); i++) outs.add(outBlobs.get(i));
-                for (long i = 0; i < outBlobs.size(); i++) {
-                    outs.add(outBlobs.get(i));
-                }
             }
 
             int fw = bgr.cols(), fh = bgr.rows();
@@ -98,7 +101,6 @@ public final class OpenVinoYoloDetector implements YoloDetector {
             }
             if (boxes.isEmpty()) return Collections.emptyList();
 
-            // 纯 Java NMS，避免 MatOf* / Vector API 兼容问题
             List<Integer> keep = nmsIndices(boxes, scores, nmsTh);
 
             List<Detection> result = new ArrayList<>(keep.size());
@@ -111,7 +113,6 @@ public final class OpenVinoYoloDetector implements YoloDetector {
             }
             return result;
         } catch (Throwable e) {
-            // 单帧失败不影响整体
             return Collections.emptyList();
         }
     }
@@ -123,7 +124,6 @@ public final class OpenVinoYoloDetector implements YoloDetector {
         Mat m;
 
         if (dims == 2) {
-            // NxC 或 CxN
             if (out.cols() >= 6) {
                 m = out;
             } else {
@@ -132,7 +132,6 @@ public final class OpenVinoYoloDetector implements YoloDetector {
                 m = tmp;
             }
         } else if (dims == 3) {
-            // [1,N,C] 或 [1,C,N]
             if (out.size(2) >= 6) {
                 m = out.reshape(1, out.size(1)); // -> N×C
             } else {
@@ -142,7 +141,6 @@ public final class OpenVinoYoloDetector implements YoloDetector {
                 m = tmp;
             }
         } else if (dims == 4) {
-            // [1,1,N,C] 或 [1,1,C,N]
             int a = out.size(2), b = out.size(3);
             if (b >= 6) {
                 m = out.reshape(1, a).clone(); // -> N×C
@@ -153,7 +151,7 @@ public final class OpenVinoYoloDetector implements YoloDetector {
                 m = tmp.clone();
             }
         } else {
-            return; // 不支持的形状
+            return;
         }
 
         int N = m.rows(), C = m.cols();
@@ -172,7 +170,7 @@ public final class OpenVinoYoloDetector implements YoloDetector {
             float conf = obj * bestScore;
             if (conf < confTh) continue;
 
-            // 默认假设归一化中心点格式 (cx,cy,w,h)；若你的 IR 是 x1,y1,x2,y2，请把这里换算改掉
+            // 假定 (cx,cy,w,h) 为归一化中心点格式；如是 x1,y1,x2,y2，请按需改这里
             int bx = Math.max(0, Math.round(cx * fw - (w * fw) / 2f));
             int by = Math.max(0, Math.round(cy * fh - (h * fh) / 2f));
             int bw = Math.min(fw - bx, Math.round(w * fw));
@@ -189,7 +187,6 @@ public final class OpenVinoYoloDetector implements YoloDetector {
     private List<Integer> nmsIndices(List<Rect2d> boxes, List<Float> scores, float nmsThreshold) {
         List<Integer> order = new ArrayList<>(boxes.size());
         for (int i = 0; i < boxes.size(); i++) order.add(i);
-        // 按分数降序
         order.sort((i, j) -> Float.compare(scores.get(j), scores.get(i)));
 
         List<Integer> keep = new ArrayList<>();

ruoyi-video/src/main/resources/libs/models/models.json

@@ -1,4 +1,4 @@
 [
-  {"name":"smoke","path":"models/smoke","size":[640,640],"backend":"openvino"},
+  {"name":"smoke","path":"libs/models/smoke","size":[640,640],"backend":"OpenCV"},
-  {"name":"garbage","path":"models/garbage","size":[640,640],"backend":"openvino"}
+  {"name":"garbage","path":"libs/models/garbage","size":[640,640],"backend":"OpenCV"}
 ]