feat: adaptive line kernel based on structure size

decimer_segmentation/complete_structure.py

@@ -366,7 +366,10 @@ def get_neighbour_pixels(
     return neighbour_pixels
 
 
-def detect_horizontal_and_vertical_lines(image: np.ndarray) -> np.ndarray:
+def detect_horizontal_and_vertical_lines(
+    image: np.ndarray,
+    average_depiction_size: Tuple[int, int]
+) -> np.ndarray:
     """
     This function takes an image and returns a binary mask that labels the pixels that
     are part of long horizontal or vertical lines. [Definition of long: 1/5 of the
@@ -382,19 +385,19 @@ def detect_horizontal_and_vertical_lines(image: np.ndarray) -> np.ndarray:
     """
     binarised_im = ~image * 255
     binarised_im = binarised_im.astype("uint8")
+
+    structure_height, structure_width = average_depiction_size
 
-    horizontal_kernel_size = int(binarised_im.shape[1] / 7)
     horizontal_kernel = cv2.getStructuringElement(
-        cv2.MORPH_RECT, (horizontal_kernel_size, 1)
+        cv2.MORPH_RECT, (structure_width, 1)
     )
     horizontal_mask = cv2.morphologyEx(
         binarised_im, cv2.MORPH_OPEN, horizontal_kernel, iterations=2
     )
     horizontal_mask = horizontal_mask == 255
 
-    vertical_kernel_size = int(binarised_im.shape[0] / 7)
     vertical_kernel = cv2.getStructuringElement(
-        cv2.MORPH_RECT, (1, vertical_kernel_size)
+        cv2.MORPH_RECT, (1, structure_height)
     )
     vertical_mask = cv2.morphologyEx(
         binarised_im, cv2.MORPH_OPEN, vertical_kernel, iterations=2
@@ -472,13 +475,30 @@ def expansion_coordination(
 
 
 def complete_structure_mask(
-    image_array: np.array, mask_array: np.array, debug=False
+    image_array: np.array,
+    mask_array: np.array,
+    average_depiction_size: Tuple[int, int],
+    debug=False
 ) -> np.array:
     """
-    This funtion takes an image (array) and an array containing the masks (shape:
+    This funtion takes an image (np.array) and an array containing the masks (shape:
     x,y,n where n is the amount of masks and x and y are the pixel coordinates).
+    Additionally, it takes the average depiction size of the structures in the image
+    which is used to define the kernel size for the vertical and horizontal line
+    detection for the exclusion masks. The exclusion mask is used to exclude pixels
+    from the mask expansion to avoid including whole tables. 
     It detects objects on the contours of the mask and expands it until it frames the
-    complete object in the image. It returns the expanded mask array"""
+    complete object in the image. It returns the expanded mask array
+
+    Args:
+        image_array (np.array): input image
+        mask_array (np.array): shape: y, x, n where n is the amount of masks
+        average_depiction_size (Tuple[int, int]): height, width
+        debug (bool, optional): More verbose if True. Defaults to False.
+
+    Returns:
+        np.array: expanded mask array
+    """
 
     if mask_array.size != 0:
         # Binarization of input image
@@ -498,7 +518,8 @@ def complete_structure_mask(
         split_mask_arrays = np.array(
             [mask_array[:, :, index] for index in range(mask_array.shape[2])]
         )
-        exclusion_mask = detect_horizontal_and_vertical_lines(blurred_image_array)
+        exclusion_mask = detect_horizontal_and_vertical_lines(blurred_image_array,
+                                                              average_depiction_size)
         # Run expansion the expansion
         image_repeat = itertools.repeat(blurred_image_array, mask_array.shape[2])
         exclusion_mask_repeat = itertools.repeat(exclusion_mask, mask_array.shape[2])

decimer_segmentation/decimer_segmentation.py

@@ -98,7 +98,6 @@ def segment_chemical_structures(
     if not expand:
         masks, bboxes, _ = get_mrcnn_results(image)
     else:
-        average_height, average_width = determine_average_depiction_size(bboxes)
         masks = get_expanded_masks(image)
 
     segments, bboxes = apply_masks(image, masks)
@@ -227,9 +226,12 @@ def get_expanded_masks(image: np.array) -> np.array:
         np.array: expanded masks (shape: (h, w, num_masks))
     """
     # Structure detection with MRCNN
-    masks, _, _ = get_mrcnn_results(image)
+    masks, bboxes, _ = get_mrcnn_results(image)
+    size = determine_average_depiction_size(bboxes)
     # Mask expansion
-    expanded_masks = complete_structure_mask(image_array=image, mask_array=masks)
+    expanded_masks = complete_structure_mask(image_array=image,
+                                             mask_array=masks,
+                                             average_depiction_size=size,)
     return expanded_masks