update

python_server/app/routes/segment.py

@@ -75,7 +75,7 @@ def segment_sam_prompt(req: SamPromptSegmentRequest) -> Dict[str, Any]:
             return {"success": False, "error": "SAM 未产生有效掩膜", "contour": []}
 
         mask = expand_mask(mask, int(req.expand_px))
-        contour = mask_to_contour_xy(mask, epsilon_px=2.0)
+        contour = mask_to_contour_xy(mask, epsilon_px=3.5)
         if len(contour) < 3:
             return {"success": False, "error": "轮廓点数不足", "contour": []}
 

python_server/model/Seg/seg_loader.py

@@ -81,13 +81,23 @@ def run_sam_prompt(
         b = np.asarray(box_xyxy, dtype=np.float32).reshape(4)
         box_arg = b
 
-    masks, scores, _low = predictor.predict(
+    masks3, scores3, _low3 = predictor.predict(
         point_coords=pc,
         point_labels=pl,
         box=box_arg,
         multimask_output=True,
     )
-    m = _pick_best_sam_mask(masks, scores, pc, pl)
+    m_multi = _union_sam_masks_covering_foreground(masks3, scores3, pc, pl)
+
+    masks1, scores1, _low1 = predictor.predict(
+        point_coords=pc,
+        point_labels=pl,
+        box=box_arg,
+        multimask_output=False,
+    )
+    m_single = _to_bool_mask(masks1[0])
+
+    m = _prefer_larger_foreground_mask(m_multi, m_single, pc, pl)
     m = _refine_sam_instance_mask(m)
     return m
 
@@ -96,15 +106,26 @@ def _mask_contains_foreground_points(
     mask_bool: np.ndarray,
     point_coords: np.ndarray,
     point_labels: np.ndarray,
+    *,
+    dilate_for_hit: int = 0,
 ) -> bool:
+    """dilate_for_hit>0 时：前景点落在膨胀后的掩膜内即算覆盖（缓解边缘亚像素偏差）。"""
+    m = np.asarray(mask_bool, dtype=bool)
+    if dilate_for_hit > 0:
+        try:
+            from scipy import ndimage as ndi  # type: ignore[import]
+
+            m = ndi.binary_dilation(m, structure=np.ones((3, 3), dtype=bool), iterations=dilate_for_hit)
+        except Exception:
+            pass
     for (x, y), lab in zip(point_coords, point_labels):
         if int(lab) != 1:
             continue
         xi = int(round(float(x)))
         yi = int(round(float(y)))
-        if yi < 0 or xi < 0 or yi >= mask_bool.shape[0] or xi >= mask_bool.shape[1]:
+        if yi < 0 or xi < 0 or yi >= m.shape[0] or xi >= m.shape[1]:
             return False
-        if not bool(mask_bool[yi, xi]):
+        if not bool(m[yi, xi]):
             return False
     return True
 
@@ -115,45 +136,74 @@ def _to_bool_mask(m: np.ndarray) -> np.ndarray:
     return m > 0.5
 
 
-def _pick_best_sam_mask(
+def _union_sam_masks_covering_foreground(
     masks: np.ndarray,
     scores: np.ndarray,
     point_coords: np.ndarray,
     point_labels: np.ndarray,
 ) -> np.ndarray:
     """
-    多掩膜时：优先覆盖全部前景点；在高分候选中取面积最大，减少“只切到局部/缺边”的概率。
+    对「包含全部前景点」的候选掩膜做逻辑并，通常比单选一片更接近完整物体（船身+棚+人物等）。
+    若并集过大（误吞整图）则退回「有效候选中面积最大」的一片。
     """
     n = int(masks.shape[0])
-    valid_idx: List[int] = []
+    valid: List[np.ndarray] = []
     for i in range(n):
         mb = _to_bool_mask(masks[i])
-        if _mask_contains_foreground_points(mb, point_coords, point_labels):
+        if _mask_contains_foreground_points(mb, point_coords, point_labels, dilate_for_hit=2):
-            valid_idx.append(i)
+            valid.append(mb)
-    if not valid_idx:
+    if not valid:
         best = int(np.argmax(scores))
         return _to_bool_mask(masks[best])
-    smax = max(float(scores[j]) for j in valid_idx)
+    if len(valid) == 1:
-    near = [j for j in valid_idx if float(scores[j]) >= smax - 0.12]
+        return valid[0]
-    best_area = -1
+    u = np.zeros_like(valid[0], dtype=bool)
-    best_j = near[0]
+    for v in valid:
-    for j in near:
+        u |= v
-        mb = _to_bool_mask(masks[j])
+    area_u = int(np.count_nonzero(u))
-        area = int(np.count_nonzero(mb))
+    h, w = u.shape[:2]
-        if area > best_area:
+    if area_u > int(0.62 * h * w):
-            best_area = area
+        return max(valid, key=lambda x: int(np.count_nonzero(x)))
-            best_j = j
+    return u
-    return _to_bool_mask(masks[best_j])
+
+
+def _prefer_larger_foreground_mask(
+    a: np.ndarray,
+    b: np.ndarray,
+    point_coords: np.ndarray,
+    point_labels: np.ndarray,
+) -> np.ndarray:
+    """在「多掩膜并集」与「单掩膜」之间选：均含前景点时优先面积明显更大者（更接近整物体）。"""
+    a = np.asarray(a, dtype=bool)
+    b = np.asarray(b, dtype=bool)
+    ca = _mask_contains_foreground_points(a, point_coords, point_labels, dilate_for_hit=2)
+    cb = _mask_contains_foreground_points(b, point_coords, point_labels, dilate_for_hit=2)
+    aa = int(np.count_nonzero(a))
+    ab = int(np.count_nonzero(b))
+
+    if ca and cb:
+        if ab > aa * 1.08:
+            return b
+        if aa > ab * 1.08:
+            return a
+        return a if aa >= ab else b
+    if ca:
+        return a
+    if cb:
+        return b
+    return a if aa >= ab else b
 
 
 def _refine_sam_instance_mask(mask_bool: np.ndarray) -> np.ndarray:
-    """填内部孔洞 + 轻微闭运算，使复杂物体轮廓更完整。"""
+    """填孔 + 闭运算连接断裂，再轻微膨胀以包全外轮廓。"""
     m = np.asarray(mask_bool, dtype=bool)
     try:
         from scipy import ndimage as ndi  # type: ignore[import]
 
         m = ndi.binary_fill_holes(m)
+        m = ndi.binary_closing(m, structure=np.ones((5, 5), dtype=bool))
         m = ndi.binary_closing(m, structure=np.ones((3, 3), dtype=bool))
+        m = ndi.binary_dilation(m, structure=np.ones((3, 3), dtype=bool), iterations=1)
         return m
     except Exception:
         pass
@@ -161,8 +211,9 @@ def _refine_sam_instance_mask(mask_bool: np.ndarray) -> np.ndarray:
         import cv2  # type: ignore[import]
 
         u8 = (m.astype(np.uint8) * 255)
-        k = np.ones((3, 3), np.uint8)
+        k5 = np.ones((5, 5), np.uint8)
-        u8 = cv2.morphologyEx(u8, cv2.MORPH_CLOSE, k)
+        u8 = cv2.morphologyEx(u8, cv2.MORPH_CLOSE, k5)
+        u8 = cv2.dilate(u8, np.ones((3, 3), np.uint8), iterations=1)
         return u8 > 127
     except Exception:
         return m
@@ -239,7 +290,7 @@ def mask_to_contour_xy(
     if cv2.contourArea(cnt) < 1.0:
         return []
     peri = cv2.arcLength(cnt, True)
-    eps = max(epsilon_px, 0.001 * peri)
+    eps = max(epsilon_px, 0.0025 * peri)
     approx = cv2.approxPolyDP(cnt, eps, True)
     out: List[List[float]] = []
     for p in approx: