added suggested changes

paddleocr.py

@@ -693,6 +693,7 @@ def ocr(
             bin: binarize image to black and white. Default is False.
             inv: invert image colors. Default is False.
             alpha_color: set RGB color Tuple for transparent parts replacement. Default is pure white.
+            slice: use sliding window inference for large images, det and rec must be True. Requires int values for slice["horizontal_stride"], slice["vertical_stride"], slice["merge_x_thres"], slice["merge_y_thres] (See doc/doc_en/slice_en.md). Default is {}.
         """
         assert isinstance(img, (np.ndarray, list, str, bytes))
         if isinstance(img, list) and det == True:
@@ -721,18 +722,7 @@ def preprocess_image(_image):
                 _image = binarize_img(_image)
             return _image
 
-        if det and rec and not slice:
-            ocr_res = []
-            for idx, img in enumerate(imgs):
-                img = preprocess_image(img)
-                dt_boxes, rec_res, _ = self.__call__(img, cls)
-                if not dt_boxes and not rec_res:
-                    ocr_res.append(None)
-                    continue
-                tmp_res = [[box.tolist(), res] for box, res in zip(dt_boxes, rec_res)]
-                ocr_res.append(tmp_res)
-            return ocr_res
-        elif det and rec and slice:
+        if det and rec:
             ocr_res = []
             for idx, img in enumerate(imgs):
                 img = preprocess_image(img)

tools/infer/predict_system.py

@@ -93,11 +93,12 @@ def __call__(self, img, cls=True, slice={}):
             for slice_crop, v_start, h_start in slice_gen:
                 dt_boxes, elapse = self.text_detector(slice_crop)
                 if dt_boxes.size:
-                    dt_boxes[:, :, 0] = dt_boxes[:, :, 0] + h_start
-                    dt_boxes[:, :, 1] = dt_boxes[:, :, 1] + v_start
+                    dt_boxes[:, :, 0] += h_start
+                    dt_boxes[:, :, 1] += v_start
                     dt_slice_boxes.append(dt_boxes)
                     elapsed.append(elapse)
             dt_boxes = np.concatenate(dt_slice_boxes)
+
             dt_boxes = merge_fragmented(
                 boxes=dt_boxes,
                 x_threshold=slice["merge_x_thres"],

tools/infer/utility.py

@@ -732,34 +732,36 @@ def slice_generator(image, horizontal_stride, vertical_stride, maximum_slices=50
             yield (horizontal_slice, v_start, h_start)
 
 
+def calculate_box_extents(box):
+    min_x = box[0][0]
+    max_x = box[1][0]
+    min_y = box[0][1]
+    max_y = box[2][1]
+    return min_x, max_x, min_y, max_y
+
+
 def merge_fragmented(boxes, x_threshold=10, y_threshold=10):
     merged_boxes = []
     visited = set()
-    merged_counter = 0
     for i in range(len(boxes)):
         if i in visited:
             continue
 
         current_box = boxes[i]
         merged_box = [current_box[0], current_box[1], current_box[2], current_box[3]]
-        min_x, max_x, min_y, max_y = (
-            current_box[0][0],
-            current_box[1][0],
-            current_box[0][1],
-            current_box[2][1],
-        )
+        min_x, max_x, min_y, max_y = calculate_box_extents(current_box)
 
         for j in range(len(boxes)):
             if i == j:
                 continue
 
             compare_box = boxes[j]
-            compare_min_x, compare_max_x, compare_min_y, compare_max_y = (
-                compare_box[0][0],
-                compare_box[1][0],
-                compare_box[0][1],
-                compare_box[2][1],
-            )
+            (
+                compare_min_x,
+                compare_max_x,
+                compare_min_y,
+                compare_max_y,
+            ) = calculate_box_extents(compare_box)
             if (
                 abs(min_y - compare_min_y) <= y_threshold
                 and abs(max_y - compare_max_y) <= y_threshold
@@ -786,7 +788,6 @@ def merge_fragmented(boxes, x_threshold=10, y_threshold=10):
 
                     merged_box[3][0] = new_xmin
                     merged_box[3][1] = new_ymax
-                    merged_counter += 1
                     visited.add(j)
 
         merged_boxes.append(merged_box)