Synchronize PR #151 and set it to openvino and paddle versions.

python/rapidocr_onnxruntime/main.py

@@ -2,7 +2,6 @@
 # @Author: SWHL
 # @Contact: liekkaskono@163.com
 import copy
-import logging
 from pathlib import Path
 from typing import List, Optional, Tuple, Union
 
@@ -114,8 +113,8 @@ class RapidOCR:
             use_limit_ratio = w / h > self.width_height_ratio
 
         if h <= self.min_height or use_limit_ratio:
-            new_h = w
-            padding_h = int((new_h - h) / 2)
+            new_h = max(int(w / self.width_height_ratio), self.min_height) * 2
+            padding_h = int(abs(new_h - h) / 2)
             block_img = cv2.copyMakeBorder(
                 img, padding_h, padding_h, 0, 0, cv2.BORDER_CONSTANT, value=(0, 0, 0)
             )

python/rapidocr_openvino/main.py

@@ -2,7 +2,6 @@
 # @Author: SWHL
 # @Contact: liekkaskono@163.com
 import copy
-import logging
 from pathlib import Path
 from typing import List, Optional, Tuple, Union
 
@@ -83,6 +82,7 @@ class RapidOCR:
         det_elapse, cls_elapse, rec_elapse = 0.0, 0.0, 0.0
 
         if use_det:
+            img, padding_h = self.maybe_add_letterbox(img)
             dt_boxes, det_elapse = self.auto_text_det(img)
             if dt_boxes is None:
                 return None, None
@@ -95,28 +95,36 @@ class RapidOCR:
         if use_rec:
             rec_res, rec_elapse = self.text_rec(img)
 
+        if dt_boxes is not None and padding_h > 0:
+            for box in dt_boxes:
+                box[:, 1] -= padding_h
+
         ocr_res = self.get_final_res(
             dt_boxes, cls_res, rec_res, det_elapse, cls_elapse, rec_elapse
         )
         return ocr_res
 
-    def auto_text_det(
-        self,
-        img: np.ndarray,
-    ) -> Tuple[Optional[np.ndarray], float, Optional[List[np.ndarray]]]:
+    def maybe_add_letterbox(self, img: np.ndarray) -> Tuple[np.ndarray, int]:
         h, w = img.shape[:2]
+
         if self.width_height_ratio == -1:
             use_limit_ratio = False
         else:
             use_limit_ratio = w / h > self.width_height_ratio
 
         if h <= self.min_height or use_limit_ratio:
-            logging.warning(
-                "Because the aspect ratio of the current image exceeds the limit (min_height or width_height_ratio), the program will skip the detection step."
+            new_h = max(int(w / self.width_height_ratio), self.min_height) * 2
+            padding_h = int(abs(new_h - h) / 2)
+            block_img = cv2.copyMakeBorder(
+                img, padding_h, padding_h, 0, 0, cv2.BORDER_CONSTANT, value=(0, 0, 0)
             )
-            dt_boxes = self.get_boxes_img_without_det(h, w)
-            return dt_boxes, 0.0
+            return block_img, padding_h
+        return img, 0
 
+    def auto_text_det(
+        self,
+        img: np.ndarray,
+    ) -> Tuple[Optional[np.ndarray], float, Optional[List[np.ndarray]]]:
         dt_boxes, det_elapse = self.text_det(img)
         if dt_boxes is None or len(dt_boxes) < 1:
             return None, 0.0
@@ -124,12 +132,6 @@ class RapidOCR:
         dt_boxes = self.sorted_boxes(dt_boxes)
         return dt_boxes, det_elapse
 
-    def get_boxes_img_without_det(self, h, w):
-        x0, y0, x1, y1 = 0, 0, w, h
-        dt_boxes = np.float32([[x0, y0], [x1, y0], [x1, y1], [x0, y1]])
-        dt_boxes = dt_boxes[np.newaxis, ...]
-        return dt_boxes
-
     def get_crop_img_list(self, img, dt_boxes):
         def get_rotate_crop_image(img, points):
             img_crop_width = int(
@@ -241,7 +243,10 @@ def main():
     use_cls = not args.no_cls
     use_rec = not args.no_rec
     result, elapse_list = ocr_engine(
-        args.img_path, use_det=use_det, use_cls=use_cls, use_rec=use_rec
+        args.img_path,
+        use_det=use_det,
+        use_cls=use_cls,
+        use_rec=use_rec,
     )
     print(result)
 

python/rapidocr_paddle/main.py

@@ -2,7 +2,6 @@
 # @Author: SWHL
 # @Contact: liekkaskono@163.com
 import copy
-import logging
 from pathlib import Path
 from typing import List, Optional, Tuple, Union
 
@@ -83,6 +82,7 @@ class RapidOCR:
         det_elapse, cls_elapse, rec_elapse = 0.0, 0.0, 0.0
 
         if use_det:
+            img, padding_h = self.maybe_add_letterbox(img)
             dt_boxes, det_elapse = self.auto_text_det(img)
             if dt_boxes is None:
                 return None, None
@@ -95,28 +95,36 @@ class RapidOCR:
         if use_rec:
             rec_res, rec_elapse = self.text_rec(img)
 
+        if dt_boxes is not None and padding_h > 0:
+            for box in dt_boxes:
+                box[:, 1] -= padding_h
+
         ocr_res = self.get_final_res(
             dt_boxes, cls_res, rec_res, det_elapse, cls_elapse, rec_elapse
         )
         return ocr_res
 
-    def auto_text_det(
-        self,
-        img: np.ndarray,
-    ) -> Tuple[Optional[np.ndarray], float, Optional[List[np.ndarray]]]:
+    def maybe_add_letterbox(self, img: np.ndarray) -> Tuple[np.ndarray, int]:
         h, w = img.shape[:2]
+
         if self.width_height_ratio == -1:
             use_limit_ratio = False
         else:
             use_limit_ratio = w / h > self.width_height_ratio
 
         if h <= self.min_height or use_limit_ratio:
-            logging.warning(
-                "Because the aspect ratio of the current image exceeds the limit (min_height or width_height_ratio), the program will skip the detection step."
+            new_h = max(int(w / self.width_height_ratio), self.min_height) * 2
+            padding_h = int(abs(new_h - h) / 2)
+            block_img = cv2.copyMakeBorder(
+                img, padding_h, padding_h, 0, 0, cv2.BORDER_CONSTANT, value=(0, 0, 0)
             )
-            dt_boxes = self.get_boxes_img_without_det(h, w)
-            return dt_boxes, 0.0
+            return block_img, padding_h
+        return img, 0
 
+    def auto_text_det(
+        self,
+        img: np.ndarray,
+    ) -> Tuple[Optional[np.ndarray], float, Optional[List[np.ndarray]]]:
         dt_boxes, det_elapse = self.text_det(img)
         if dt_boxes is None or len(dt_boxes) < 1:
             return None, 0.0
@@ -124,12 +132,6 @@ class RapidOCR:
         dt_boxes = self.sorted_boxes(dt_boxes)
         return dt_boxes, det_elapse
 
-    def get_boxes_img_without_det(self, h, w):
-        x0, y0, x1, y1 = 0, 0, w, h
-        dt_boxes = np.float32([[x0, y0], [x1, y0], [x1, y1], [x0, y1]])
-        dt_boxes = dt_boxes[np.newaxis, ...]
-        return dt_boxes
-
     def get_crop_img_list(self, img, dt_boxes):
         def get_rotate_crop_image(img, points):
             img_crop_width = int(
@@ -241,7 +243,10 @@ def main():
     use_cls = not args.no_cls
     use_rec = not args.no_rec
     result, elapse_list = ocr_engine(
-        args.img_path, use_det=use_det, use_cls=use_cls, use_rec=use_rec
+        args.img_path,
+        use_det=use_det,
+        use_cls=use_cls,
+        use_rec=use_rec,
     )
     print(result)
 

python/tests/test_ort.py

@@ -26,7 +26,7 @@ package_name = "rapidocr_onnxruntime"
         (
             "test_letterbox_like.jpg",
             2,
-            "A:：取决于所使用的执行提供者，它可能没有完全支持模型中的所有操作。回落到CPU操作可能会导致性能速度的下降。此外，即使一个操作是由CUDAexecution",
+            "A：：取决于所使用的执行提供者，它可能没有完全支持模型中的所有操作。回落到CPU操作可能会导致性能速度的下降。此外，即使一个操作是由CUDAeXecution",
         ),
         ("test_without_det.jpg", 1, "在中国作家协会第三届儿童文学"),
     ],

python/tests/test_paddle.py

@@ -20,11 +20,23 @@ img_path = tests_dir / "ch_en_num.jpg"
 package_name = "rapidocr_paddle"
 
 
-def test_without_det():
-    img_path = tests_dir / "test_without_det.png"
+@pytest.mark.parametrize(
+    "img_name,gt_len,gt_first_len",
+    [
+        (
+            "test_letterbox_like.jpg",
+            2,
+            "A：：取决于所使用的执行提供者，它可能没有完全支持模型中的所有操作。回落到CPU操作可能会导致性能速度的下降。此外，即使一个操作是由CUDAeXecution",
+        ),
+        ("test_without_det.jpg", 1, "在中国作家协会第三届儿童文学"),
+    ],
+)
+def test_letterbox_like(img_name, gt_len, gt_first_len):
+    img_path = tests_dir / img_name
     result, _ = engine(img_path)
-    assert result[0][1] == "の持、持场所无。"
-    assert len(result) == 1
+
+    assert len(result) == gt_len
+    assert result[0][1] == gt_first_len
 
 
 def test_only_det():
@@ -114,7 +126,7 @@ def test_input_parameters():
 
 def test_input_det_parameters():
     with pytest.raises(FileNotFoundError) as exc_info:
-        engine = RapidOCR(det_model_path="1")
+        engine = RapidOCR(det_model_path="1.onnx")
         result, _ = engine(img_path)
         raise FileNotFoundError()
     assert exc_info.type is FileNotFoundError
@@ -122,7 +134,7 @@ def test_input_det_parameters():
 
 def test_input_cls_parameters():
     with pytest.raises(FileNotFoundError) as exc_info:
-        engine = RapidOCR(cls_model_path="1")
+        engine = RapidOCR(cls_model_path="1.onnx")
         result, _ = engine(img_path)
         raise FileNotFoundError()
     assert exc_info.type is FileNotFoundError
@@ -130,7 +142,7 @@ def test_input_cls_parameters():
 
 def test_input_rec_parameters():
     with pytest.raises(FileNotFoundError) as exc_info:
-        engine = RapidOCR(rec_model_path="1")
+        engine = RapidOCR(rec_model_path="1.onnx")
         result, _ = engine(img_path)
         raise FileNotFoundError()
     assert exc_info.type is FileNotFoundError
@@ -142,7 +154,7 @@ def test_input_three_ndim_two_channel():
     result, _ = engine(image_array)
 
     assert len(result) == 1
-    assert result[0][1] == " TREND PLOT REPORT"
+    assert result[0][1] == "TREND PLOT REPORT"
 
 
 def test_input_three_ndim_one_channel():

python/tests/test_vino.py

@@ -20,11 +20,23 @@ img_path = tests_dir / "ch_en_num.jpg"
 package_name = "rapidocr_openvino"
 
 
-def test_without_det():
-    img_path = tests_dir / "test_without_det.png"
+@pytest.mark.parametrize(
+    "img_name,gt_len,gt_first_len",
+    [
+        (
+            "test_letterbox_like.jpg",
+            2,
+            "A：：取决于所使用的执行提供者，它可能没有完全支持模型中的所有操作。回落到CPU操作可能会导致性能速度的下降。此外，即使一个操作是由CUDAeXecution",
+        ),
+        ("test_without_det.jpg", 1, "在中国作家协会第三届儿童文学"),
+    ],
+)
+def test_letterbox_like(img_name, gt_len, gt_first_len):
+    img_path = tests_dir / img_name
     result, _ = engine(img_path)
-    assert result[0][1] == "の持、持场所无。"
-    assert len(result) == 1
+
+    assert len(result) == gt_len
+    assert result[0][1] == gt_first_len
 
 
 def test_only_det():
@@ -142,7 +154,7 @@ def test_input_three_ndim_two_channel():
     result, _ = engine(image_array)
 
     assert len(result) == 1
-    assert result[0][1] == " TREND PLOT REPORT"
+    assert result[0][1] == "TREND PLOT REPORT"
 
 
 def test_input_three_ndim_one_channel():