Error with pyclipper inhomogeneous expanded array (#12108)

* pyclipper inhomogeneous expanded array solved

For some images, `np.array(offset.Execute(distance))` can result in inhomogeneous part of the detection box list, which cannot be casted into numpy array directly.

* corrected box reshape position

- box reshape was mistakenly done at line 145 which is now correctly done at line 92 of `db_postprocess.py`
- if box is empty then continue

* reverted mistakenly changed line 147

- reverted mistakenly changed `box.array(box)` to `np.array(box)`

* expanded array fix for `det_box_type=quad`

* polygons padding

For `--det_box_type = poly`, pad the detected polygon arrays if they have different shapes to ensure even shapes of polygon arrays

* fix codestyle

---------

Co-authored-by: Wang Xin <xinwang614@gmail.com>

Author: zovelsanj

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):

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