src/main/java/org/jdetect/CameraApp.java

@@ -663,12 +663,14 @@ class DetectionThread extends SwingWorker<Mat, Void> {
             return frame;
         }
 
+        try {
             Size frameSize = frame.size();
             int height = (int) frameSize.height;
             int width = (int) frameSize.width;
 
             // Create blob from image
-        Mat blob = Dnn.blobFromImage(frame, 1.0/255.0, new Size(416, 416), new Scalar(0), true, false);
+            Mat blob = Dnn.blobFromImage(frame, 1.0/255.0, new Size(416, 416),
+                    new Scalar(0), true, false);
 
             // Set input to the network
             net.setInput(blob);
@@ -683,33 +685,46 @@ class DetectionThread extends SwingWorker<Mat, Void> {
             List<Rect2d> boxes = new ArrayList<>();
 
             for (Mat out : outs) {
+                // Convert Mat to float array
+                float[] data = new float[(int) out.total() * out.channels()];
+                out.get(0, 0, data);
+
+                // Process each detection (85 elements per detection: [x, y, w, h, confidence, class_probs...])
                 for (int i = 0; i < out.rows(); i++) {
-                Mat row = out.row(i);
+                    int offset = i * out.cols();
-                Mat scores = row.colRange(5, row.cols());
+                    float confidence = data[offset + 4];
-                Core.MinMaxLocResult mmr = Core.minMaxLoc(scores);
-                double confidence = mmr.maxVal;
 
                     if (confidence > 0.5) {
-                    Point classIdPoint = mmr.maxLoc;
+                        // Find class with maximum score
-                    int classId = (int) classIdPoint.x;
+                        int classId = 0;
+                        float maxScore = 0;
+                        for (int j = 5; j < out.cols(); j++) {
+                            if (data[offset + j] > maxScore) {
+                                maxScore = data[offset + j];
+                                classId = j - 5;
+                            }
+                        }
 
-                    double[] detection = row.get(0, 0);
+                        // Only add detection if classId is valid
-                    double centerX = detection[0] * width;
+                        if (classId >= 0 && classId < classes.size()) {
-                    double centerY = detection[1] * height;
+                            float centerX = data[offset] * width;
-                    double boxWidth = detection[2] * width;
+                            float centerY = data[offset + 1] * height;
-                    double boxHeight = detection[3] * height;
+                            float boxWidth = data[offset + 2] * width;
+                            float boxHeight = data[offset + 3] * height;
 
-                    double x = centerX - (boxWidth / 2);
+                            float x = centerX - (boxWidth / 2);
-                    double y = centerY - (boxHeight / 2);
+                            float y = centerY - (boxHeight / 2);
 
                             boxes.add(new Rect2d(x, y, boxWidth, boxHeight));
-                    confidences.add((float) confidence);
+                            confidences.add(confidence);
                             classIds.add(classId);
                         }
                     }
                 }
+            }
 
             // Apply Non-Maximum Suppression
+            if (!boxes.isEmpty()) {
                 MatOfRect2d boxesMat = new MatOfRect2d();
                 boxesMat.fromList(boxes);
                 MatOfFloat confidencesMat = new MatOfFloat();
@@ -721,7 +736,7 @@ class DetectionThread extends SwingWorker<Mat, Void> {
                 // Draw detections
                 int[] indicesArray = indices.toArray();
                 for (int i : indicesArray) {
-            if (i < boxes.size() && i < classIds.size() && i < confidences.size()) {
+                    if (i >= 0 && i < boxes.size() && i < classIds.size() && i < confidences.size()) {
                         Rect2d box = boxes.get(i);
                         String label = classes.get(classIds.get(i));
                         float confidence = confidences.get(i);
@@ -734,11 +749,17 @@ class DetectionThread extends SwingWorker<Mat, Void> {
 
                         String text = String.format("%s: %.2f", label, confidence);
                         Point textPoint = new Point(box.x, box.y - 5);
-                Imgproc.putText(frame, text, textPoint, Imgproc.FONT_HERSHEY_SIMPLEX, 0.5, color, 2);
+                        Imgproc.putText(frame, text, textPoint,
+                                Imgproc.FONT_HERSHEY_SIMPLEX, 0.5, color, 2);
+                    }
                 }
             }
 
             return frame;
+        } catch (Exception e) {
+            e.printStackTrace();
+            return frame; // Return original frame if detection fails
+        }
     }
 
     @Override