Update app.py

app.py

@@ -40,7 +40,7 @@ AG_REPO_ID    = "emkessle/2024-24679-fencing-touch-predictor"
 AG_ZIP_NAME   = "autogluon_predictor_dir.zip"
 
 FRAME_SKIP = 2
-KEEP_CONF  = 0.80
+KEEP_CONF  = 0.85
 YOLO_CONF  = 0.25
 YOLO_IOU   = 0.50
 CLIP_PAD_S = 2.0
@@ -128,34 +128,76 @@ def isolate_scoreboard_color(frame_bgr: np.ndarray,
                              conf: float = YOLO_CONF,
                              iou: float = YOLO_IOU,
                              keep_conf: float = KEEP_CONF,
-                             debug: bool = DEBUG_MODE,
+                             debug: bool = False,
                              frame_id: int = None) -> np.ndarray:
-    """Grayscale everything except the largest YOLO box ≥ keep_conf."""
+    """
+    Improved version:
+      - Choose the largest bbox among candidates meeting confidence.
+      - Primary threshold: >= max(0.80, keep_conf)
+      - Fallback threshold: >= (primary - 0.05)
+      - Entire chosen bbox is restored to color; everything else is grayscale.
+      - Rejects low-saturation ROIs (flat/neutral areas).
+    """
     H, W = frame_bgr.shape[:2]
+
+    # Start fully grayscale
     gray = cv2.cvtColor(frame_bgr, cv2.COLOR_BGR2GRAY)
     gray = cv2.cvtColor(gray, cv2.COLOR_GRAY2BGR)
 
+    primary_thr  = max(0.80, keep_conf)
+    fallback_thr = max(0.7, primary_thr - 0.05)
+
     chosen_box = None
     res = yolo().predict(frame_bgr, conf=conf, iou=iou, verbose=False)
     if len(res):
         r = res[0]
         if getattr(r, "boxes", None) is not None and len(r.boxes) > 0:
-            boxes = r.boxes.xyxy.cpu().numpy()
+            boxes  = r.boxes.xyxy.cpu().numpy()
             scores = r.boxes.conf.cpu().numpy()
-            valid = [(b, s) for b, s in zip(boxes, scores) if float(s) >= keep_conf]
-            if valid:
-                chosen_box, _ = max(valid, key=lambda bs: (bs[0][2]-bs[0][0])*(bs[0][3]-bs[0][1]))
-                x1, y1, x2, y2 = [int(v) for v in chosen_box]
-                gray[y1:y2, x1:x2] = frame_bgr[y1:y2, x1:x2]
+            candidates = list(zip(boxes, scores))
 
+            # Prefer largest box meeting primary threshold
+            strong = [(b, s) for (b, s) in candidates if float(s) >= primary_thr]
+            if strong:
+                chosen_box, _ = max(strong, key=lambda bs: (bs[0][2]-bs[0][0]) * (bs[0][3]-bs[0][1]))
+            else:
+                # Fallback: largest box meeting fallback threshold
+                medium = [(b, s) for (b, s) in candidates if float(s) >= fallback_thr]
+                if medium:
+                    chosen_box, _ = max(medium, key=lambda bs: (bs[0][2]-bs[0][0]) * (bs[0][3]-bs[0][1]))
+
+            if chosen_box is not None:
+                x1, y1, x2, y2 = [int(round(v)) for v in chosen_box]
+                x1, y1 = max(0, x1), max(0, y1)
+                x2, y2 = min(W-1, x2), min(H-1, y2)
+
+                if x2 > x1 and y2 > y1:
+                    # Single safeguard: reject very low-saturation ROIs
+                    roi_color = frame_bgr[y1:y2, x1:x2]
+                    if roi_color.size > 0:
+                        hsv = cv2.cvtColor(roi_color, cv2.COLOR_BGR2HSV)
+                        sat_mean = hsv[:, :, 1].mean()
+                        if sat_mean < 25:
+                            print(f"[WARN] Rejected bbox due to low saturation (mean={sat_mean:.1f})")
+                            chosen_box = None
+
+                    # If accepted, restore whole bbox to color
+                    if chosen_box is not None:
+                        gray[y1:y2, x1:x2] = frame_bgr[y1:y2, x1:x2]
+
+    # Optional debug save
     if debug and frame_id is not None:
         dbg = gray.copy()
         if chosen_box is not None:
-            x1, y1, x2, y2 = [int(v) for v in chosen_box]
-            cv2.rectangle(dbg, (x1, y1), (x2, y2), (0,255,0), 2)
-        cv2.imwrite(str(DEBUG_DIR / f"frame_{frame_id:06d}.jpg"), dbg)
+            x1, y1, x2, y2 = [int(round(v)) for v in chosen_box]
+            cv2.rectangle(dbg, (x1, y1), (x2, y2), (0, 255, 0), 2)
+        out_path = DEBUG_DIR / f"frame_{frame_id:06d}.jpg"
+        cv2.imwrite(str(out_path), dbg)
+        print(f"[DEBUG] Saved debug frame → {out_path}")
+
     return gray
 
+
 def _count_color_pixels(rgb, ch):
     R, G, B = rgb[:,:,0], rgb[:,:,1], rgb[:,:,2]
     if ch==0: mask=(R>150)&(R>1.2*G)&(R>1.2*B)