minor fixes to avoid frequent warnings

src/eynollah/eynollah.py

@@ -32,7 +32,7 @@ from scipy.ndimage import gaussian_filter1d
 from numba import cuda
 
 from ocrd import OcrdPage
-from ocrd_utils import getLogger
+from ocrd_utils import getLogger, tf_disable_interactive_logs
 
 try:
     import torch
@@ -47,14 +47,11 @@ try:
 except ImportError:
     TrOCRProcessor = VisionEncoderDecoderModel = None
 
-os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3"
 #os.environ['CUDA_VISIBLE_DEVICES'] = '-1'
-stderr = sys.stderr
+tf_disable_interactive_logs()
-sys.stderr = open(os.devnull, "w")
 import tensorflow as tf
 from tensorflow.python.keras import backend as K
 from tensorflow.keras.models import load_model
-sys.stderr = stderr
 tf.get_logger().setLevel("ERROR")
 warnings.filterwarnings("ignore")
 # use tf1 compatibility for keras backend
@@ -3614,7 +3611,7 @@ class Eynollah:
             for ij in range(len(all_found_textline_polygons[j])):
                 con_ind = all_found_textline_polygons[j][ij]
                 area = cv2.contourArea(con_ind)
-                con_ind = con_ind.astype(np.float)
+                con_ind = con_ind.astype(float)
                 
                 x_differential = np.diff( con_ind[:,0,0])
                 y_differential = np.diff( con_ind[:,0,1])
@@ -3718,7 +3715,7 @@ class Eynollah:
             con_ind = all_found_textline_polygons[j]
             #print(len(con_ind[:,0,0]),'con_ind[:,0,0]')
             area = cv2.contourArea(con_ind)
-            con_ind = con_ind.astype(np.float)
+            con_ind = con_ind.astype(float)
             
             x_differential = np.diff( con_ind[:,0,0])
             y_differential = np.diff( con_ind[:,0,1])
@@ -3821,7 +3818,7 @@ class Eynollah:
                 con_ind = all_found_textline_polygons[j][ij]
                 area = cv2.contourArea(con_ind)
                 
-                con_ind = con_ind.astype(np.float)
+                con_ind = con_ind.astype(float)
                 
                 x_differential = np.diff( con_ind[:,0,0])
                 y_differential = np.diff( con_ind[:,0,1])
@@ -4053,7 +4050,7 @@ class Eynollah:
         for j in range(len(all_found_textline_polygons)):
             for i in range(len(all_found_textline_polygons[j])):
                 con_ind = all_found_textline_polygons[j][i]
-                con_ind = con_ind.astype(np.float)
+                con_ind = con_ind.astype(float)
                 
                 x_differential = np.diff( con_ind[:,0,0])
                 y_differential = np.diff( con_ind[:,0,1])

src/eynollah/sbb_binarize.py

@@ -4,24 +4,18 @@ Tool to load model and binarize a given image.
 
 import sys
 from glob import glob
-from os import environ, devnull
-from os.path import join
-from warnings import catch_warnings, simplefilter
 import os
+import logging
 
 import numpy as np
 from PIL import Image
 import cv2
-environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
+from ocrd_utils import tf_disable_interactive_logs
-stderr = sys.stderr
+tf_disable_interactive_logs()
-sys.stderr = open(devnull, 'w')
 import tensorflow as tf
 from tensorflow.keras.models import load_model
 from tensorflow.python.keras import backend as tensorflow_backend
-sys.stderr = stderr
-
 
-import logging
 
 def resize_image(img_in, input_height, input_width):
     return cv2.resize(img_in, (input_width, input_height), interpolation=cv2.INTER_NEAREST)
@@ -53,7 +47,7 @@ class SbbBinarizer:
         del self.session
 
     def load_model(self, model_name):
-        model = load_model(join(self.model_dir, model_name), compile=False)
+        model = load_model(os.path.join(self.model_dir, model_name), compile=False)
         model_height = model.layers[len(model.layers)-1].output_shape[1]
         model_width = model.layers[len(model.layers)-1].output_shape[2]
         n_classes = model.layers[len(model.layers)-1].output_shape[3]

src/eynollah/utils/contour.py

@@ -247,7 +247,7 @@ def get_textregion_contours_in_org_image_light(cnts, img, slope_first, map=map):
     img = cv2.resize(img, (int(img.shape[1]/6), int(img.shape[0]/6)), interpolation=cv2.INTER_NEAREST)
     ##cnts = list( (np.array(cnts)/2).astype(np.int16) )
     #cnts = cnts/2
-    cnts = [(i/6).astype(np.int) for i in cnts]
+    cnts = [(i/6).astype(int) for i in cnts]
     results = map(partial(do_back_rotation_and_get_cnt_back,
                           img=img,
                           slope_first=slope_first,

src/eynollah/utils/pil_cv2.py

@@ -1,3 +1,4 @@
+from contextlib import nullcontext
 from PIL import Image
 import numpy as np
 from ocrd_models import OcrdExif
@@ -17,12 +18,13 @@ def pil2cv(img):
 def check_dpi(img):
     try:
         if isinstance(img, Image.Image):
-            pil_image = img
+            pil_image = nullcontext(img)
         elif isinstance(img, str):
             pil_image = Image.open(img)
         else:
-            pil_image = cv2pil(img)
+            pil_image = nullcontext(cv2pil(img))
-        exif = OcrdExif(pil_image)
+        with pil_image:
+            exif = OcrdExif(pil_image)
         resolution = exif.resolution
         if resolution == 1:
             raise Exception()

src/eynollah/utils/separate_lines.py

@@ -1616,7 +1616,7 @@ def do_work_of_slopes_new(
             textline_con_fil = filter_contours_area_of_image(img_int_p, textline_con,
                                                              hierarchy,
                                                              max_area=1, min_area=0.00008)
-            y_diff_mean = find_contours_mean_y_diff(textline_con_fil)
+            y_diff_mean = find_contours_mean_y_diff(textline_con_fil) if len(textline_con_fil) > 1 else np.NaN
             if np.isnan(y_diff_mean):
                 slope_for_all = MAX_SLOPE
             else: