feat: added return_bboxes parameter to segment_chemical_structures_from_file

decimer_segmentation/decimer_segmentation.py

@@ -181,17 +181,22 @@ def download_trained_weights(model_url: str, model_path: str, verbose=1):
 
 
 def segment_chemical_structures_from_file(
-    file_path: str, expand: bool = True, **kwargs
-) -> List[np.ndarray]:
+    file_path: str,
+    expand: bool = True,
+    return_bboxes: bool = False,
+    **kwargs,
+) -> Union[List[np.ndarray], Tuple[List[np.ndarray], List[Tuple[int, int, int, int]]]]:
     """
     Segment chemical structures from a PDF or image file.
 
     Args:
         file_path: Path to input file (PDF or image)
         expand: Whether to expand masks to capture complete structures
+        return_bboxes: Whether to return bounding boxes along with segments
 
     Returns:
-        List of segmented chemical structure images as numpy arrays
+        List of segmented chemical structure images as numpy arrays.
+        If return_bboxes is True, returns a tuple of (segments, bboxes).
     """
     if not os.path.exists(file_path):
         raise FileNotFoundError(f"Input file not found: {file_path}")
@@ -203,15 +208,19 @@ def segment_chemical_structures_from_file(
 
     if not images:
         logger.warning(f"No images could be extracted from {file_path}")
-        return []
+        return ([], []) if return_bboxes else []
 
     # Process all images sequentially (model can't parallelize)
     all_segments = []
+    all_bboxes = []
     for image in images:
-        segments = segment_chemical_structures(image, expand)
+        segments, bboxes = segment_chemical_structures(
+            image, expand, return_bboxes=True
+        )
         all_segments.extend(segments)
+        all_bboxes.extend(bboxes)
 
-    return all_segments
+    return (all_segments, all_bboxes) if return_bboxes else all_segments
 
 
 def _load_images_from_file(file_path: str) -> List[np.ndarray]:
@@ -288,7 +297,8 @@ def segment_chemical_structures(
         return_bboxes: Whether to return bounding boxes along with segments
 
     Returns:
-        List of segmented structure images, optionally with bounding boxes
+        List of segmented structure images, optionally with bounding boxes.
+        If return_bboxes is True, returns a tuple of (segments, bboxes).
     """
     if image is None or image.size == 0:
         return ([], []) if return_bboxes else []
@@ -314,11 +324,16 @@ def segment_chemical_structures(
     # Sort in reading order and filter empty
     if segments:
         segments, bboxes = _sort_segments_bboxes(segments, bboxes)
-        segments = [
-            s
-            for s in segments
+        filtered = [
+            (s, b)
+            for s, b in zip(segments, bboxes)
             if s is not None and s.size > 0 and s.shape[0] > 0 and s.shape[1] > 0
         ]
+        if filtered:
+            segments, bboxes = zip(*filtered)
+            segments, bboxes = list(segments), list(bboxes)
+        else:
+            segments, bboxes = [], []
 
     return (segments, bboxes) if return_bboxes else segments
 

tests/test_segmentation.py

@@ -541,6 +541,33 @@ def test_full_pipeline_empty_image(self):
             # Expected if model not available
             pass
 
+    def test_segment_from_file_with_return_bboxes(self):
+        """Test segment_chemical_structures_from_file returns bboxes when return_bboxes=True."""
+        from decimer_segmentation import segment_chemical_structures_from_file
+
+        image = np.ones((500, 500, 3), dtype=np.uint8) * 255
+        cv2.rectangle(image, (50, 50), (150, 150), (0, 0, 0), -1)
+
+        with tempfile.NamedTemporaryFile(suffix=".png", delete=False) as f:
+            cv2.imwrite(f.name, image)
+            try:
+                result = segment_chemical_structures_from_file(
+                    f.name, expand=False, return_bboxes=True
+                )
+                assert isinstance(result, tuple)
+                assert len(result) == 2
+                segments, bboxes = result
+                assert isinstance(segments, list)
+                assert isinstance(bboxes, list)
+                assert len(segments) == len(bboxes)
+                for bbox in bboxes:
+                    assert len(bbox) == 4
+            except Exception:
+                # Expected if model not available
+                pass
+            finally:
+                os.unlink(f.name)
+
 
 # Benchmark tests (optional, for performance monitoring)
 class TestPerformance: