fix(python): preserve resize ratios across both size limits (#761)

python/rapidocr/utils/process_img.py

@@ -107,17 +107,17 @@ def get_rotate_crop_image(img: np.ndarray, points: np.ndarray) -> np.ndarray:
 def resize_image_within_bounds(
     img: np.ndarray, min_side_len: float, max_side_len: float
 ) -> Tuple[np.ndarray, float, float]:
-    h, w = img.shape[:2]
+    original_h, original_w = img.shape[:2]
+    h, w = original_h, original_w
     max_value = max(h, w)
-    ratio_h = ratio_w = 1.0
     if max_value > max_side_len:
-        img, ratio_h, ratio_w = reduce_max_side(img, max_side_len)
+        img, _, _ = reduce_max_side(img, max_side_len)
 
     h, w = img.shape[:2]
     min_value = min(h, w)
     if min_value < min_side_len:
-        img, ratio_h, ratio_w = increase_min_side(img, min_side_len)
-    return img, ratio_h, ratio_w
+        img, _, _ = increase_min_side(img, min_side_len)
+    return img, original_h / img.shape[0], original_w / img.shape[1]
 
 
 def reduce_max_side(

python/tests/test_image_preprocessing.py

@@ -4,10 +4,10 @@
 from types import SimpleNamespace
 
 import numpy as np
-
 import rapidocr.main as main_module
 from rapidocr import RapidOCR
 from rapidocr.ch_ppocr_det import TextDetOutput
+from rapidocr.utils.process_img import map_boxes_to_original, resize_image_within_bounds
 
 
 def test_preprocess_img_returns_original_when_disabled(monkeypatch):
@@ -51,3 +51,25 @@ def test_detect_and_crop_skips_vertical_padding_when_disabled(monkeypatch):
     assert cropped_imgs[0] is img
     assert det_res.boxes is boxes
     assert op_record["padding_1"] == {"top": 0, "left": 0}
+
+
+def test_resize_ratios_map_boxes_after_both_size_limits():
+    original = np.zeros((100, 1000, 3), dtype=np.uint8)
+    resized, ratio_h, ratio_w = resize_image_within_bounds(
+        original, min_side_len=128, max_side_len=512
+    )
+
+    assert resized.shape[:2] == (128, 1024)
+    assert ratio_h == original.shape[0] / resized.shape[0]
+    assert ratio_w == original.shape[1] / resized.shape[1]
+
+    boxes = np.array([[[128, 32], [256, 32], [256, 64], [128, 64]]], dtype=np.float32)
+    mapped = map_boxes_to_original(
+        boxes,
+        {"preprocess": {"ratio_h": ratio_h, "ratio_w": ratio_w}},
+        *original.shape[:2],
+    )
+    np.testing.assert_allclose(
+        mapped,
+        [[[125, 25], [250, 25], [250, 50], [125, 50]]],
+    )