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修改截图逻辑 加入模型测试使用

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This commit is contained in:
hdka
2025-09-27 19:37:24 +08:00
parent 7527017070
commit 086553b081
4 changed files with 40 additions and 42 deletions
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2
ruoyi-video/src/main/java/com/ruoyi/video/common/ModelManager.java
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@@ -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);
}
}

75
ruoyi-video/src/main/java/com/ruoyi/video/thread/detector/OpenVinoYoloDetector.java → ruoyi-video/src/main/java/com/ruoyi/video/thread/detector/OpenCvYoloDetector.java
View File

@@ -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.util.*;
import java.nio.file.Files;
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.*; // Mat/Size/Scalar/transpose 等
import static org.bytedeco.opencv.global.opencv_imgproc.*; // cvtColor 等
import static org.bytedeco.opencv.global.opencv_core.*;
import static org.bytedeco.opencv.global.opencv_dnn.*;
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;
@@ -30,26 +45,18 @@ 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)
.filter(s -> !s.isEmpty()).toArray(String[]::new);
this.classes = Files.readAllLines(clsPath).stream()
.map(String::trim).filter(s -> !s.isEmpty())
.toArray(String[]::new);
} else {
this.classes = new String[0];
}
this.net = readNetFromModelOptimizer(xml, bin);
boolean set = false;
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);
}
// ✅ 固定使用 OpenCV CPU 后端（不再尝试 OpenVINO）
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(); // ← 直接返回 Mat
Mat out = net.forward();
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
for (long i = 0; i < outBlobs.size(); i++) {
outs.add(outBlobs.get(i));
}
net.forward(outBlobs, outNames);
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<>();