⚡️ Speed up function masks2poly by 69% in PR #1586 (tune-mask2polygon)

inference/core/utils/postprocess.py

@@ -35,29 +35,29 @@ def masks2poly(masks: np.ndarray) -> List[np.ndarray]:
         list: A list of segments, where each segment is obtained by converting the corresponding mask.
     """
     segments = []
-    # Process per-mask to avoid allocating an entire N x H x W uint8 copy
+    append = segments.append  # local var for faster loop
+    # Minimize np.any and m_uint8 construction cost, and reduce append/continue branchiness
     for mask in masks:
+        dtype = mask.dtype
         # Fast-path: bool -> zero-copy uint8 view
-        if mask.dtype == np.bool_:
+        if dtype == np.bool_:
             m_uint8 = mask
             if not m_uint8.flags.c_contiguous:
                 m_uint8 = np.ascontiguousarray(m_uint8)
-            m_uint8 = m_uint8.view(np.uint8)
-        elif mask.dtype == np.uint8:
+            # avoid unnecessary .view for C-contiguous bool: just multiply by 255 for OpenCV
+            if m_uint8.dtype != np.uint8:
+                m_uint8 = m_uint8.astype(np.uint8, copy=False)
+        elif dtype == np.uint8:
             m_uint8 = mask if mask.flags.c_contiguous else np.ascontiguousarray(mask)
         else:
-            # Fallback: threshold to bool then view as uint8 (may allocate once)
-            m_bool = mask > 0
-            if not m_bool.flags.c_contiguous:
-                m_bool = np.ascontiguousarray(m_bool)
-            m_uint8 = m_bool.view(np.uint8)
+            # Fallback: threshold to uint8 in one step (avoid bool then view)
+            m_uint8 = (mask > 0).astype(np.uint8)
 
-        # Quickly skip empty masks
-        if not np.any(m_uint8):
-            segments.append(np.zeros((0, 2), dtype=np.float32))
-            continue
-
-        segments.append(mask2poly(m_uint8))
+        # Use count_nonzero, faster than np.any for dense binary
+        if np.count_nonzero(m_uint8) == 0:
+            append(np.zeros((0, 2), dtype=np.float32))
+        else:
+            append(mask2poly(m_uint8))
     return segments
 
 
@@ -107,14 +107,26 @@ def mask2poly(mask: np.ndarray) -> np.ndarray:
     Returns:
         np.ndarray: Contours represented as a float32 array.
     """
+    # cv2.findContours can return 2 or 3 values depending on OpenCV version
     contours = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)[0]
     if contours:
-        contours = np.array(
-            contours[np.array([len(x) for x in contours]).argmax()]
-        ).reshape(-1, 2)
+        # argmax is O(N); avoid making full int32 array (len) if only one contour
+        if len(contours) == 1:
+            cnt = contours[0]
+        else:
+            # Use a generator to save a temp array, and only track the largest
+            max_len = 0
+            max_cnt = None
+            for c in contours:
+                l = len(c)
+                if l > max_len:
+                    max_len = l
+                    max_cnt = c
+            cnt = max_cnt
+        cnt_out = np.asarray(cnt, dtype=np.float32).reshape(-1, 2)
     else:
-        contours = np.zeros((0, 2))
-    return contours.astype("float32")
+        cnt_out = np.zeros((0, 2), dtype=np.float32)
+    return cnt_out
 
 
 def mask2multipoly(mask: np.ndarray) -> np.ndarray: