added parameter row_tol for box sorting

.gitignore

@@ -11,4 +11,3 @@ orisys.egg-info/
 /dist_nuitka/
 /build_nuitka
 /data/
-/config/

config/prototype_of1.json

@@ -15,7 +15,8 @@
     "index": [1,2,3,6,7,8,11,12,13],
     "margin": 10,
     "nominal_ov_pos": 100,
-    "n": [3,3]
+    "n": [3,3],
+    "row_tol": 0.4
   },
   "tracker": {
     "adaptive": true,

config/prototype_of2.json

@@ -15,7 +15,8 @@
     "index": [1,2,3,6,7,8,11,12,13],
     "margin": 10,
     "nominal_ov_pos": 100,
-    "n": [3,3]
+    "n": [3,3],
+    "row_tol": 0.4
   },
   "tracker": {
     "adaptive": true,

config/prototype_of3.json

@@ -15,7 +15,8 @@
     "index": [1,2,3,6,7,8,11,12,13],
     "margin": 10,
     "nominal_ov_pos": 100,
-    "n": [3,3]
+    "n": [3,3],
+    "row_tol": 0.4
   },
   "tracker": {
     "adaptive": true,

src/orisys/Stitcher.py

@@ -27,6 +27,7 @@ class Stitcher:
         self.nominal_ov_pos=config_stitcher["nominal_ov_pos"] 
         self.margin=config_stitcher["margin"]
         self.bound=self.nominal_ov_pos - self.margin
+        self.row_tol = config_stitcher["row_tol"]
 
         if self.verbose:
             print(f"Stitcher initialized with config: {self.config_stitcher}")
@@ -110,7 +111,7 @@ class Stitcher:
     def find_stitch_boxes(self,img_src,show = False):
 
         img_corr = self.__correct_edge_bloom(img_src)
-        self.__detect_nonblack_regions(img_corr,show = show)
+        self.__detect_nonblack_regions(img_corr,show = show, row_tol = self.row_tol)
         return len(self.boxes), self.boxes
         
         # return self.boxes
@@ -431,7 +432,7 @@ class Stitcher:
         return pos_list
 
         # Computation Functions  
-    def __detect_nonblack_regions(self, img, morph_k=3, n_boxes_target=15, bk_thresh_step=10, bk_thresh_max=100,show = False):
+    def __detect_nonblack_regions(self, img, morph_k=3, n_boxes_target=15, bk_thresh_step=10, bk_thresh_max=100,show = False, row_tol = 0.4):
         """
         Return binary mask of non-black regions and list of bounding boxes (x,y,w,h).
         Uses adaptive thresholding on grayscale + small morphology to be robust to illumination.
@@ -509,7 +510,7 @@ class Stitcher:
             heights = [b[3] - b[1] for b in cropped_boxes]
             median_height = np.median(heights) if heights else target_h
             # Use a fraction of the box height as tolerance (e.g., 20% of box height)
-            row_tolerance = max(1, int(median_height * 0.4))
+            row_tolerance = max(1, int(median_height * row_tol))
             
             # Quantize y-coordinates to group boxes into rows
             def get_row_key(box):

src/orisys/configs/prototype.json

@@ -15,7 +15,8 @@
     "index": [1,2,3,6,7,8,11,12,13],
     "margin": 10,
     "nominal_ov_pos": 100,
-    "n": [3,3]
+    "n": [3,3],
+    "row_tol": 0.4
   },
   "tracker": {
     "adaptive": true,