Fix type bug

README.md

@@ -2,7 +2,7 @@
 
 End-to-end Optical Music Recognition system build on deep learning models and machine learning techniques.
 Default to use **Onnxruntime** for model inference. If you want to use **tensorflow** for the inference,
-run `export INFERENCE_WITH_TF=True` and make sure there is TF installed.
+run `export INFERENCE_WITH_TF=true` and make sure there is TF installed.
 
 ![](figures/tabi_mix.jpg)
 

oemer/bbox.py

@@ -15,8 +15,8 @@ def get_bbox(data):
 
 
 def get_center(bbox):
-    cen_y = round((bbox[1] + bbox[3]) / 2)
-    cen_x = round((bbox[0] + bbox[2]) / 2)
+    cen_y = int(round((bbox[1] + bbox[3]) / 2))
+    cen_x = int(round((bbox[0] + bbox[2]) / 2))
     return cen_x, cen_y
 
 

oemer/build_system.py

@@ -825,6 +825,7 @@ def get_rest(duration):
 
 def get_chroma_pitch(pos, clef_type):
     order = G_CLEF_POS_TO_PITCH if clef_type == ClefType.G_CLEF else F_CLEF_POS_TO_PITCH
+    pos = int(pos)
     return order[pos%7] if pos >= 0 else order[pos%-7]
 
 
@@ -895,7 +896,7 @@ def decode_note(note, clef_type, is_chord=False, voice=1) -> Element:
     alter = SubElement(pitch, 'alter')
     octave = SubElement(pitch, 'octave')
     alter.text = '0'
-    pos = note.staff_line_pos
+    pos = int(note.staff_line_pos)
     if clef_type == ClefType.G_CLEF:
         order = G_CLEF_POS_TO_PITCH
         oct_offset = 4

oemer/dewarp.py

@@ -144,7 +144,7 @@ def connect_nearby_grid_group(gg_map, grid_groups, grid_map, grids, ref_count=8,
         for i in range(max_step):
             tar_x = (-i - 1) * step_size
             cen_y = model.predict([[tar_x]])[0]  # Interpolate y center
-            y = round(cen_y - h / 2)
+            y = int(round(cen_y - h / 2))
             region = new_gg_map[y:y+h, end_x-step_size:end_x]  # Area to check
             unique, counts = np.unique(region, return_counts=True)
             labels = set(unique)  # Overlapped grid group IDs
@@ -202,6 +202,8 @@ def connect_nearby_grid_group(gg_map, grid_groups, grid_map, grids, ref_count=8,
                         max(gg.bbox[2], box[2]),
                         max(gg.bbox[3], box[3])
                     )
+                    gg.bbox = [int(bb) for bb in gg.bbox]
+                    box = [int(bb) for bb in box]
                     grids.append(grid)
                     new_gg_map[box[1]:box[3], box[0]:box[2]] = gg.id
 
@@ -236,7 +238,7 @@ def build_mapping(gg_map, min_width_ratio=0.4):
                 meta_idx = np.where(x==ux)[0]
                 sub_y = y[meta_idx]
                 cen_y = round(np.mean(sub_y))
-                coords_y[target_y, ux] = cen_y
+                coords_y[int(target_y), int(ux)] = cen_y
                 points.append((target_y, ux))
 
     # Add corner case
@@ -267,7 +269,7 @@ def estimate_coords(staff_pred):
     coords_y, points = build_mapping(new_gg_map)
 
     logger.debug("Dewarping")
-    vals = coords_y[points[:, 0], points[:, 1]]
+    vals = coords_y[points[:, 0].astype(int), points[:, 1].astype(int)]
     grid_x, grid_y = np.mgrid[0:gg_map.shape[0]:1, 0:gg_map.shape[1]:1]
     coords_y = griddata(points, vals, (grid_x, grid_y), method='linear')
 

oemer/note_group_extraction.py

@@ -133,7 +133,7 @@ def search(cur_y, y_bound, step):
                 break
             elif step < 0 and cur_y < y_bound:
                 break
-            pxs = group_map[cur_y, start_x:end_x]
+            pxs = group_map[int(cur_y), int(start_x):int(end_x)]
             gids = set(np.unique(pxs))
             if 0 in gids:
                 gids.remove(0)

oemer/notehead_extraction.py

@@ -92,12 +92,12 @@ def __repr__(self):
 
 
 def morph_notehead(pred, unit_size):
-    small_size = round(unit_size / 3)
+    small_size = int(round(unit_size / 3))
     small_ker = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (small_size, small_size))
     pred = cv2.erode(cv2.dilate(pred.astype(np.uint8), small_ker), small_ker)
     size = (
-        round(unit_size*nhc.NOTEHEAD_MORPH_WIDTH_FACTOR),
-        round(unit_size*nhc.NOTEHEAD_MORPH_HEIGHT_FACTOR)
+        int(round(unit_size*nhc.NOTEHEAD_MORPH_WIDTH_FACTOR)),
+        int(round(unit_size*nhc.NOTEHEAD_MORPH_HEIGHT_FACTOR))
     )
     kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, size)
     img = cv2.erode(pred.astype(np.uint8), kernel)
@@ -146,7 +146,7 @@ def check_bbox_size(bbox, noteheads, unit_size):
             tmp_new.extend(check_bbox_size(box, noteheads, unit_size))
         new_bbox = tmp_new
     else:
-        num_notes = round(h / note_h)
+        num_notes = int(round(h / note_h))
         if num_notes > 0:
             sub_h = h // num_notes
             for i in range(num_notes):

oemer/rhythm_extraction.py

@@ -23,7 +23,7 @@ def scan_dot(symbols, note_id_map, bbox, unit_size, min_count, max_count):
         # Find the right most bound for scan the dot.
         # Should have width less than unit_size, and can't
         # touch the nearby note.
-        cur_scan_line = note_id_map[start_y:bbox[3], right_bound]
+        cur_scan_line = note_id_map[int(start_y):int(bbox[3]), int(right_bound)]
         ids = set(np.unique(cur_scan_line))
         if -1 in ids:
             ids.remove(-1)
@@ -34,13 +34,13 @@ def scan_dot(symbols, note_id_map, bbox, unit_size, min_count, max_count):
             break
 
     left_bound = bbox[2] + round(unit_size*0.4)
-    dot_region = symbols[start_y:bbox[3], left_bound:right_bound]
+    dot_region = symbols[int(start_y):int(bbox[3]), int(left_bound):int(right_bound)]
     pixels = np.sum(dot_region)
     if min_count <= pixels <= max_count:
         color = (255, random.randint(0, 255), random.randint(0, 255))
-        cv2.rectangle(dot_img, (left_bound, start_y), (right_bound, bbox[3]), color, 1)
+        cv2.rectangle(dot_img, (int(left_bound), int(start_y)), (int(right_bound), int(bbox[3])), color, 1)
         msg = f"{min_count:.2f}/{pixels:.2f}/{max_count:.2f}"
-        cv2.putText(dot_img, msg, (bbox[0], bbox[3]+30), cv2.FONT_HERSHEY_SIMPLEX, 0.8, color, 1)
+        cv2.putText(dot_img, msg, (int(bbox[0]), int(bbox[3])+30), cv2.FONT_HERSHEY_SIMPLEX, 0.8, color, 1)
         return True
 
     # color = (255, random.randint(0, 255), random.randint(0, 255))
@@ -326,6 +326,11 @@ def scan_beam_flag(
     min_width_ratio=0.25,
     max_width_ratio=0.9):
 
+    start_x = int(start_x)
+    start_y = int(start_y)
+    end_x = int(end_x)
+    end_y = int(end_y)
+
     cv2.line(beam_img, (start_x, start_y), (end_x, start_y), (66, 245, 212), 1, cv2.LINE_8)
     cv2.line(beam_img, (start_x, end_y), (end_x, end_y), (66, 245, 212), 1, cv2.LINE_8)
 
@@ -558,7 +563,7 @@ def parse_rhythm(beam_map, map_info, agree_th=0.15):
         )
 
         #cv2.rectangle(beam_img, (gbox[0], gbox[1]), (gbox[2], gbox[3]), (255, 0, 255), 1)
-        cv2.putText(beam_img, str(count), (cen_x, gbox[3]+2), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (255, 0, 0), 1)
+        cv2.putText(beam_img, str(count), (int(cen_x), int(gbox[3])+2), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (255, 0, 0), 1)
 
         # Assign note label
         for nid in group.note_ids:

oemer/symbol_extraction.py

@@ -352,7 +352,7 @@ def gen_clefs(bboxes, labels):
 
 def get_nearby_note_id(box, note_id_map):
     cen_x, cen_y = get_center(box)
-    unit_size = round(get_unit_size(cen_x, cen_y))
+    unit_size = int(round(get_unit_size(cen_x, cen_y)))
     nid = None
     for x in range(box[2], box[2]+unit_size):
         if note_id_map[cen_y, x] != -1:
@@ -413,7 +413,7 @@ def gen_rests(bboxes, labels):
         rr.track = st1.track
         rr.group = st1.group
 
-        unit_size = round(get_unit_size(*get_center(box)))
+        unit_size = int(round(get_unit_size(*get_center(box))))
         dot_range = range(box[2]+1, box[2]+unit_size)
         dot_region = symbols[box[1]:box[3], dot_range]
         if 0 < np.sum(dot_region) < unit_size**2 / 7: