Merge branch 'PaddlePaddle:main' into setup2toml

Author: Liyulingyue

configs/det/ch_PP-OCRv4/ch_PP-OCRv4_det_cml.yml

@@ -9,7 +9,7 @@ Global:
   eval_batch_step:
   - 0
   - 1000
-  cal_metric_during_train: true
+  cal_metric_during_train: false
   checkpoints: null
   pretrained_model: null
   save_inference_dir: null

ppocr/postprocess/db_postprocess.py

@@ -89,7 +89,9 @@ def polygons_from_bitmap(self, pred, _bitmap, dest_width, dest_height):
                     continue
             else:
                 continue
-            box = box.reshape(-1, 2)
+            box = np.array(box).reshape(-1, 2)
+            if len(box) == 0:
+                continue
 
             _, sside = self.get_mini_boxes(box.reshape((-1, 1, 2)))
             if sside < self.min_size + 2:
@@ -138,7 +140,10 @@ def boxes_from_bitmap(self, pred, _bitmap, dest_width, dest_height):
             if self.box_thresh > score:
                 continue
 
-            box = self.unclip(points, self.unclip_ratio).reshape(-1, 1, 2)
+            box = self.unclip(points, self.unclip_ratio)
+            if len(box) > 1:
+                continue
+            box = np.array(box).reshape(-1, 1, 2)
             box, sside = self.get_mini_boxes(box)
             if sside < self.min_size + 2:
                 continue
@@ -157,7 +162,7 @@ def unclip(self, box, unclip_ratio):
         distance = poly.area * unclip_ratio / poly.length
         offset = pyclipper.PyclipperOffset()
         offset.AddPath(box, pyclipper.JT_ROUND, pyclipper.ET_CLOSEDPOLYGON)
-        expanded = np.array(offset.Execute(distance))
+        expanded = offset.Execute(distance)
         return expanded
 
     def get_mini_boxes(self, contour):

ppocr/utils/network.py

@@ -25,6 +25,9 @@
 
 def download_with_progressbar(url, save_path):
     logger = get_logger()
+    if save_path and os.path.exists(save_path):
+        logger.info(f"Path {save_path} already exists. Skipping...")
+        return
     response = requests.get(url, stream=True)
     if response.status_code == 200:
         total_size_in_bytes = int(response.headers.get("content-length", 1))

tools/infer/predict_det.py

@@ -179,6 +179,14 @@ def order_points_clockwise(self, pts):
         rect[3] = tmp[np.argmax(diff)]
         return rect
 
+    def pad_polygons(self, polygon, max_points):
+        padding_size = max_points - len(polygon)
+        if padding_size == 0:
+            return polygon
+        last_point = polygon[-1]
+        padding = np.repeat([last_point], padding_size, axis=0)
+        return np.vstack([polygon, padding])
+
     def clip_det_res(self, points, img_height, img_width):
         for pno in range(points.shape[0]):
             points[pno, 0] = int(min(max(points[pno, 0], 0), img_width - 1))
@@ -209,6 +217,13 @@ def filter_tag_det_res_only_clip(self, dt_boxes, image_shape):
                 box = np.array(box)
             box = self.clip_det_res(box, img_height, img_width)
             dt_boxes_new.append(box)
+
+        if len(dt_boxes_new) > 0:
+            max_points = max(len(polygon) for polygon in dt_boxes_new)
+            dt_boxes_new = [
+                self.pad_polygons(polygon, max_points) for polygon in dt_boxes_new
+            ]
+
         dt_boxes = np.array(dt_boxes_new)
         return dt_boxes