添加检测后处理参数score_mode=slow 增加识别率

python/config.yaml

@@ -37,6 +37,7 @@ Det:
         max_candidates: 1000
         unclip_ratio: 1.6
         use_dilation: true
+        score_mode: "fast"
 
 Cls:
     module_name: ch_ppocr_v2_cls

python/rapidocr_onnxruntime/ch_ppocr_v2_det/config.yaml

@@ -26,3 +26,4 @@ post_process:
     max_candidates: 1000
     unclip_ratio: 1.6
     use_dilation: true
+    score_mode: "fast"

python/rapidocr_onnxruntime/ch_ppocr_v2_det/utils.py

@@ -299,12 +299,14 @@ class DBPostProcess(object):
                  box_thresh=0.7,
                  max_candidates=1000,
                  unclip_ratio=2.0,
+                 score_mode="fast",
                  use_dilation=False):
         self.thresh = thresh
         self.box_thresh = box_thresh
         self.max_candidates = max_candidates
         self.unclip_ratio = unclip_ratio
         self.min_size = 3
+        self.score_mode = score_mode
 
         if use_dilation:
             self.dilation_kernel = np.array([[1, 1], [1, 1]])
@@ -337,7 +339,10 @@ class DBPostProcess(object):
             if sside < self.min_size:
                 continue
             points = np.array(points)
-            score = self.box_score_fast(pred, points.reshape(-1, 2))
+            if self.score_mode == "fast":
+                score = self.box_score_fast(pred, points.reshape(-1, 2))
+            else:
+                score = self.box_score_slow(pred, contour)
             if self.box_thresh > score:
                 continue
 
@@ -400,6 +405,27 @@ class DBPostProcess(object):
         box[:, 1] = box[:, 1] - ymin
         cv2.fillPoly(mask, box.reshape(1, -1, 2).astype(np.int32), 1)
         return cv2.mean(bitmap[ymin:ymax + 1, xmin:xmax + 1], mask)[0]
+    
+    def box_score_slow(self, bitmap, contour):
+        '''
+        box_score_slow: use polyon mean score as the mean score
+        '''
+        h, w = bitmap.shape[:2]
+        contour = contour.copy()
+        contour = np.reshape(contour, (-1, 2))
+
+        xmin = np.clip(np.min(contour[:, 0]), 0, w - 1)
+        xmax = np.clip(np.max(contour[:, 0]), 0, w - 1)
+        ymin = np.clip(np.min(contour[:, 1]), 0, h - 1)
+        ymax = np.clip(np.max(contour[:, 1]), 0, h - 1)
+
+        mask = np.zeros((ymax - ymin + 1, xmax - xmin + 1), dtype=np.uint8)
+
+        contour[:, 0] = contour[:, 0] - xmin
+        contour[:, 1] = contour[:, 1] - ymin
+
+        cv2.fillPoly(mask, contour.reshape(1, -1, 2).astype(np.int32), 1)
+        return cv2.mean(bitmap[ymin:ymax + 1, xmin:xmax + 1], mask)[0]
 
     def __call__(self, pred, shape_list):
         pred = pred[:, 0, :, :]