From 91d2a74ac950e55e75c0c03ece817ae96a4fc377 Mon Sep 17 00:00:00 2001
From: cneud <952378+cneud@users.noreply.github.com>
Date: Wed, 1 Oct 2025 00:38:01 +0200
Subject: [PATCH] remove redundant parentheses

---
 src/eynollah/eynollah.py             |  4 ++--
 src/eynollah/plot.py                 |  2 +-
 src/eynollah/utils/__init__.py       |  8 ++++----
 src/eynollah/utils/counter.py        |  2 +-
 src/eynollah/utils/marginals.py      |  2 +-
 src/eynollah/utils/separate_lines.py | 14 +++++++-------
 src/eynollah/writer.py               |  2 +-
 train/inference.py                   |  8 ++++----
 train/train.py                       | 10 +++++-----
 train/utils.py                       |  6 +++---
 10 files changed, 29 insertions(+), 29 deletions(-)

diff --git a/src/eynollah/eynollah.py b/src/eynollah/eynollah.py
index 20954a0..63f7005 100644
--- a/src/eynollah/eynollah.py
+++ b/src/eynollah/eynollah.py
@@ -4886,9 +4886,9 @@ class Eynollah:
                 textline_mask_tot_ea_org[img_revised_tab==drop_label_in_full_layout] = 0
 
 
-        text_only = ((img_revised_tab[:, :] == 1)) * 1
+        text_only = (img_revised_tab[:, :] == 1) * 1
         if np.abs(slope_deskew) >= SLOPE_THRESHOLD:
-            text_only_d = ((text_regions_p_1_n[:, :] == 1)) * 1
+            text_only_d = (text_regions_p_1_n[:, :] == 1) * 1
 
         #print("text region early 2 in %.1fs", time.time() - t0)
         ###min_con_area = 0.000005
diff --git a/src/eynollah/plot.py b/src/eynollah/plot.py
index 412ae5a..c026e94 100644
--- a/src/eynollah/plot.py
+++ b/src/eynollah/plot.py
@@ -12,7 +12,7 @@ from .utils import crop_image_inside_box
 from .utils.rotate import rotate_image_different
 from .utils.resize import resize_image
 
-class EynollahPlotter():
+class EynollahPlotter:
     """
     Class collecting all the plotting and image writing methods
     """
diff --git a/src/eynollah/utils/__init__.py b/src/eynollah/utils/__init__.py
index 7c06900..de083f5 100644
--- a/src/eynollah/utils/__init__.py
+++ b/src/eynollah/utils/__init__.py
@@ -1267,11 +1267,11 @@ def order_of_regions(textline_mask, contours_main, contours_header, y_ref):
         top = peaks_neg_new[i]
         down = peaks_neg_new[i + 1]
         indexes_in = matrix_of_orders[:, 0][(matrix_of_orders[:, 3] >= top) &
-                                            ((matrix_of_orders[:, 3] < down))]
+                                            (matrix_of_orders[:, 3] < down)]
         cxs_in = matrix_of_orders[:, 2][(matrix_of_orders[:, 3] >= top) &
-                                        ((matrix_of_orders[:, 3] < down))]
+                                        (matrix_of_orders[:, 3] < down)]
         cys_in = matrix_of_orders[:, 3][(matrix_of_orders[:, 3] >= top) &
-                                        ((matrix_of_orders[:, 3] < down))]
+                                        (matrix_of_orders[:, 3] < down)]
         types_of_text = matrix_of_orders[:, 1][(matrix_of_orders[:, 3] >= top) &
                                                (matrix_of_orders[:, 3] < down)]
         index_types_of_text = matrix_of_orders[:, 4][(matrix_of_orders[:, 3] >= top) &
@@ -1408,7 +1408,7 @@ def return_points_with_boundies(peaks_neg_fin, first_point, last_point):
 
 def find_number_of_columns_in_document(region_pre_p, num_col_classifier, tables, pixel_lines, contours_h=None):
     t_ins_c0 = time.time()
-    separators_closeup=( (region_pre_p[:,:,:]==pixel_lines))*1
+    separators_closeup= (region_pre_p[:, :, :] == pixel_lines) * 1
     separators_closeup[0:110,:,:]=0
     separators_closeup[separators_closeup.shape[0]-150:,:,:]=0
 
diff --git a/src/eynollah/utils/counter.py b/src/eynollah/utils/counter.py
index 9a3ed70..e6205c8 100644
--- a/src/eynollah/utils/counter.py
+++ b/src/eynollah/utils/counter.py
@@ -3,7 +3,7 @@ from collections import Counter
 REGION_ID_TEMPLATE = 'region_%04d'
 LINE_ID_TEMPLATE = 'region_%04d_line_%04d'
 
-class EynollahIdCounter():
+class EynollahIdCounter:
 
     def __init__(self, region_idx=0, line_idx=0):
         self._counter = Counter()
diff --git a/src/eynollah/utils/marginals.py b/src/eynollah/utils/marginals.py
index ac8dc1d..9ec0737 100644
--- a/src/eynollah/utils/marginals.py
+++ b/src/eynollah/utils/marginals.py
@@ -76,7 +76,7 @@ def get_marginals(text_with_lines, text_regions, num_col, slope_deskew, light_ve
 
         peaks, _ = find_peaks(text_with_lines_y_rev, height=0)
         peaks=np.array(peaks)
-        peaks=peaks[(peaks>first_nonzero) & ((peaks<last_nonzero))]
+        peaks=peaks[(peaks>first_nonzero) & (peaks < last_nonzero)]
         peaks=peaks[region_sum_0[peaks]<min_textline_thickness ]
 
 
diff --git a/src/eynollah/utils/separate_lines.py b/src/eynollah/utils/separate_lines.py
index c87653c..32114aa 100644
--- a/src/eynollah/utils/separate_lines.py
+++ b/src/eynollah/utils/separate_lines.py
@@ -1428,9 +1428,9 @@ def separate_lines_new2(img_path, thetha, num_col, slope_region, logger=None, pl
         img_int = np.zeros((img_xline.shape[0], img_xline.shape[1]))
         img_int[:, :] = img_xline[:, :]  # img_patch_org[:,:,0]
 
-        img_resized = np.zeros((int(img_int.shape[0] * (1.2)), int(img_int.shape[1] * (3))))
-        img_resized[int(img_int.shape[0] * (0.1)) : int(img_int.shape[0] * (0.1)) + img_int.shape[0],
-                    int(img_int.shape[1] * (1.0)) : int(img_int.shape[1] * (1.0)) + img_int.shape[1]] = img_int[:, :]
+        img_resized = np.zeros((int(img_int.shape[0] * 1.2), int(img_int.shape[1] * 3)))
+        img_resized[int(img_int.shape[0] * 0.1): int(img_int.shape[0] * 0.1) + img_int.shape[0],
+        int(img_int.shape[1] * 1.0): int(img_int.shape[1] * 1.0) + img_int.shape[1]] = img_int[:, :]
         # plt.imshow(img_xline)
         # plt.show()
         img_line_rotated = rotate_image(img_resized, slopes_tile_wise[i])
@@ -1442,8 +1442,8 @@ def separate_lines_new2(img_path, thetha, num_col, slope_region, logger=None, pl
         img_patch_separated_returned[:, :][img_patch_separated_returned[:, :] != 0] = 1
 
         img_patch_separated_returned_true_size = img_patch_separated_returned[
-            int(img_int.shape[0] * (0.1)) : int(img_int.shape[0] * (0.1)) + img_int.shape[0],
-            int(img_int.shape[1] * (1.0)) : int(img_int.shape[1] * (1.0)) + img_int.shape[1]]
+            int(img_int.shape[0] * 0.1): int(img_int.shape[0] * 0.1) + img_int.shape[0],
+            int(img_int.shape[1] * 1.0): int(img_int.shape[1] * 1.0) + img_int.shape[1]]
 
         img_patch_separated_returned_true_size = img_patch_separated_returned_true_size[:, margin : length_x - margin]
         img_patch_ineterst_revised[:, index_x_d + margin : index_x_u - margin] = img_patch_separated_returned_true_size
@@ -1471,7 +1471,7 @@ def return_deskew_slop(img_patch_org, sigma_des,n_tot_angles=100,
     img_int[:,:]=img_patch_org[:,:]#img_patch_org[:,:,0]
 
     max_shape=np.max(img_int.shape)
-    img_resized=np.zeros((int( max_shape*(1.1) ) , int( max_shape*(1.1) ) ))
+    img_resized=np.zeros((int(max_shape * 1.1) , int(max_shape * 1.1)))
 
     onset_x=int((img_resized.shape[1]-img_int.shape[1])/2.)
     onset_y=int((img_resized.shape[0]-img_int.shape[0])/2.)
@@ -1536,7 +1536,7 @@ def return_deskew_slop_old_mp(img_patch_org, sigma_des,n_tot_angles=100,
     img_int[:,:]=img_patch_org[:,:]#img_patch_org[:,:,0]
 
     max_shape=np.max(img_int.shape)
-    img_resized=np.zeros((int( max_shape*(1.1) ) , int( max_shape*(1.1) ) ))
+    img_resized=np.zeros((int(max_shape * 1.1) , int(max_shape * 1.1)))
 
     onset_x=int((img_resized.shape[1]-img_int.shape[1])/2.)
     onset_y=int((img_resized.shape[0]-img_int.shape[0])/2.)
diff --git a/src/eynollah/writer.py b/src/eynollah/writer.py
index 2f9caf3..4d97153 100644
--- a/src/eynollah/writer.py
+++ b/src/eynollah/writer.py
@@ -21,7 +21,7 @@ from ocrd_models.ocrd_page import (
         )
 import numpy as np
 
-class EynollahXmlWriter():
+class EynollahXmlWriter:
 
     def __init__(self, *, dir_out, image_filename, curved_line,textline_light, pcgts=None):
         self.logger = getLogger('eynollah.writer')
diff --git a/train/inference.py b/train/inference.py
index 0e55aa8..595cfe7 100644
--- a/train/inference.py
+++ b/train/inference.py
@@ -173,7 +173,7 @@ class sbb_predict:
         ##if self.weights_dir!=None:
             ##self.model.load_weights(self.weights_dir)
             
-        if (self.task != 'classification' and self.task != 'reading_order'):
+        if self.task != 'classification' and self.task != 'reading_order':
             self.img_height=self.model.layers[len(self.model.layers)-1].output_shape[1]
             self.img_width=self.model.layers[len(self.model.layers)-1].output_shape[2]
             self.n_classes=self.model.layers[len(self.model.layers)-1].output_shape[3]
@@ -560,7 +560,7 @@ class sbb_predict:
         if self.image:
             res=self.predict(image_dir = self.image)
             
-            if (self.task == 'classification' or self.task == 'reading_order'):
+            if self.task == 'classification' or self.task == 'reading_order':
                 pass
             elif self.task == 'enhancement':
                 if self.save:
@@ -583,7 +583,7 @@ class sbb_predict:
                 image_dir = os.path.join(self.dir_in, ind_image)
                 res=self.predict(image_dir)
                 
-                if (self.task == 'classification' or self.task == 'reading_order'):
+                if self.task == 'classification' or self.task == 'reading_order':
                     pass
                 elif self.task == 'enhancement':
                     self.save = os.path.join(self.out, f_name+'.png')
@@ -664,7 +664,7 @@ def main(image, dir_in, model, patches, save, save_layout, ground_truth, xml_fil
     with open(os.path.join(model,'config.json')) as f:
         config_params_model = json.load(f)
     task = config_params_model['task']
-    if (task != 'classification' and task != 'reading_order'):
+    if task != 'classification' and task != 'reading_order':
         if image and not save:
             print("Error: You used one of segmentation or binarization task with image input but not set -s, you need a filename to save visualized output with -s")
             sys.exit(1)
diff --git a/train/train.py b/train/train.py
index 795009a..add878a 100644
--- a/train/train.py
+++ b/train/train.py
@@ -269,10 +269,10 @@ def run(_config, n_classes, n_epochs, input_height,
                 num_patches = num_patches_x * num_patches_y
                 
                 if transformer_cnn_first:
-                    if (input_height != (num_patches_y * transformer_patchsize_y * 32) ):
+                    if input_height != (num_patches_y * transformer_patchsize_y * 32):
                         print("Error: transformer_patchsize_y or transformer_num_patches_xy height value error . input_height should be equal to ( transformer_num_patches_xy height value * transformer_patchsize_y * 32)")
                         sys.exit(1)
-                    if (input_width != (num_patches_x * transformer_patchsize_x * 32) ):
+                    if input_width != (num_patches_x * transformer_patchsize_x * 32):
                         print("Error: transformer_patchsize_x or transformer_num_patches_xy width value error . input_width should be equal to ( transformer_num_patches_xy width value * transformer_patchsize_x * 32)")
                         sys.exit(1)
                     if (transformer_projection_dim % (transformer_patchsize_y * transformer_patchsize_x)) != 0:
@@ -282,10 +282,10 @@ def run(_config, n_classes, n_epochs, input_height,
                     
                     model = vit_resnet50_unet(n_classes, transformer_patchsize_x, transformer_patchsize_y, num_patches, transformer_mlp_head_units, transformer_layers, transformer_num_heads, transformer_projection_dim, input_height, input_width, task, weight_decay, pretraining)
                 else:
-                    if (input_height != (num_patches_y * transformer_patchsize_y) ):
+                    if input_height != (num_patches_y * transformer_patchsize_y):
                         print("Error: transformer_patchsize_y or transformer_num_patches_xy height value error . input_height should be equal to ( transformer_num_patches_xy height value * transformer_patchsize_y)")
                         sys.exit(1)
-                    if (input_width != (num_patches_x * transformer_patchsize_x) ):
+                    if input_width != (num_patches_x * transformer_patchsize_x):
                         print("Error: transformer_patchsize_x or transformer_num_patches_xy width value error . input_width should be equal to ( transformer_num_patches_xy width value * transformer_patchsize_x)")
                         sys.exit(1)
                     if (transformer_projection_dim % (transformer_patchsize_y * transformer_patchsize_x)) != 0:
@@ -297,7 +297,7 @@ def run(_config, n_classes, n_epochs, input_height,
         model.summary()
 
         
-        if (task == "segmentation" or task == "binarization"):
+        if task == "segmentation" or task == "binarization":
             if not is_loss_soft_dice and not weighted_loss:
                 model.compile(loss='categorical_crossentropy',
                               optimizer=Adam(learning_rate=learning_rate), metrics=['accuracy'])
diff --git a/train/utils.py b/train/utils.py
index 2bb7261..ead4887 100644
--- a/train/utils.py
+++ b/train/utils.py
@@ -260,7 +260,7 @@ def generate_data_from_folder_training(path_classes, batchsize, height, width, n
             
             if batchcount>=batchsize:
                 ret_x = ret_x/255.
-                yield (ret_x, ret_y)
+                yield ret_x, ret_y
                 ret_x= np.zeros((batchsize, height,width, 3)).astype(np.int16)
                 ret_y= np.zeros((batchsize, n_classes)).astype(np.int16)
                 batchcount = 0
@@ -446,7 +446,7 @@ def generate_arrays_from_folder_reading_order(classes_file_dir, modal_dir, batch
             ret_y[batchcount, :] =  label_class
             batchcount+=1
             if batchcount>=batchsize:
-                yield (ret_x, ret_y)
+                yield ret_x, ret_y
                 ret_x= np.zeros((batchsize, height, width, 3))#.astype(np.int16)
                 ret_y= np.zeros((batchsize, n_classes)).astype(np.int16)
                 batchcount = 0
@@ -464,7 +464,7 @@ def generate_arrays_from_folder_reading_order(classes_file_dir, modal_dir, batch
                     ret_y[batchcount, :] =  label_class
                     batchcount+=1
                     if batchcount>=batchsize:
-                        yield (ret_x, ret_y)
+                        yield ret_x, ret_y
                         ret_x= np.zeros((batchsize, height, width, 3))#.astype(np.int16)
                         ret_y= np.zeros((batchsize, n_classes)).astype(np.int16)
                         batchcount = 0