Use person instance numbering labels instead of confidence

app.py

@@ -91,6 +91,46 @@ def _resolve_person_class_id(result: Any) -> int:
     return 0
 
 
+def _annotate_person_instances(image_rgb: np.ndarray, result: Any, person_id: int) -> Tuple[np.ndarray, int]:
+    annotated = image_rgb.copy()
+    if result.boxes is None:
+        return annotated, 0
+
+    classes = result.boxes.cls.cpu().numpy()
+    boxes = result.boxes.xyxy.cpu().numpy()
+    person_idx = np.where(classes == person_id)[0]
+    if len(person_idx) == 0:
+        return annotated, 0
+
+    person_boxes = []
+    for i in person_idx:
+        x1, y1, x2, y2 = boxes[i].tolist()
+        person_boxes.append((int(x1), int(y1), int(x2), int(y2)))
+
+    # Stabilkan urutan nomor: atas-ke-bawah lalu kiri-ke-kanan.
+    person_boxes.sort(key=lambda b: (b[1], b[0]))
+
+    for idx, (x1, y1, x2, y2) in enumerate(person_boxes, start=1):
+        cv2.rectangle(annotated, (x1, y1), (x2, y2), (34, 197, 94), 2)
+        label = f"person {idx}"
+        (tw, th), baseline = cv2.getTextSize(label, cv2.FONT_HERSHEY_SIMPLEX, 0.6, 2)
+        text_y1 = max(0, y1 - th - baseline - 6)
+        text_y2 = y1
+        text_x2 = min(annotated.shape[1], x1 + tw + 10)
+        cv2.rectangle(annotated, (x1, text_y1), (text_x2, text_y2), (34, 197, 94), -1)
+        cv2.putText(
+            annotated,
+            label,
+            (x1 + 4, max(14, y1 - 6)),
+            cv2.FONT_HERSHEY_SIMPLEX,
+            0.6,
+            (0, 0, 0),
+            2,
+            cv2.LINE_AA,
+        )
+    return annotated, len(person_boxes)
+
+
 def infer_image(image: np.ndarray, conf_threshold: float, model_choice: str) -> Tuple[np.ndarray, int]:
     if image is None:
         raise ValueError("Silakan unggah gambar atau gunakan kamera.")
@@ -98,9 +138,7 @@ def infer_image(image: np.ndarray, conf_threshold: float, model_choice: str) ->
     bgr = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
     result = model.predict(source=bgr, conf=conf_threshold, verbose=False)[0]
     person_id = _resolve_person_class_id(result)
-    cls = result.boxes.cls.cpu().numpy() if result.boxes is not None else np.array([])
-    detected_count = int(np.sum(cls == person_id))
-    annotated = cv2.cvtColor(result.plot(), cv2.COLOR_BGR2RGB)
+    annotated, detected_count = _annotate_person_instances(image, result, person_id)
     return annotated, detected_count
 
 
@@ -754,7 +792,8 @@ def run_training(
             f"- {prep_info}\n"
             f"- Run dir: {save_dir}\n"
             f"- best.pt: {best_out if best_out.exists() else best_pt}\n"
-            f"- {hf_sync_msg}"
+            f"- {hf_sync_msg}\n"
+            "- Lanjut uji di inferensi: pilih `Best.pt` atau mode perbandingan untuk melihat peningkatan deteksi person."
         )
         return msg, str(best_out) if best_out.exists() else ""
     except Exception as err: