Merge branch 'adding-cnn-rnn-training-script' of https://github.com/qurator-spk/eynollah into adding-cnn-rnn-training-script

src/eynollah/sbb_binarize.py

@@ -19,7 +19,7 @@ from eynollah.model_zoo import EynollahModelZoo
 tf_disable_interactive_logs()
 import tensorflow as tf
 from tensorflow.python.keras import backend as tensorflow_backend
-
+from pathlib import Path 
 from .utils import is_image_filename
 
 def resize_image(img_in, input_height, input_width):
@@ -347,7 +347,7 @@ class SbbBinarizer:
             self.logger.info("Found %d image files to binarize in %s", len(ls_imgs), dir_in)
             for i, image_path in enumerate(ls_imgs):
                 self.logger.info('Binarizing [%3d/%d] %s', i + 1, len(ls_imgs), image_path)
-                image_stem = image_path.split('.')[0]
+                image_stem = Path(image_path).stem
                 image = cv2.imread(os.path.join(dir_in,image_path) )
                 img_last = 0
                 model_file, model = self.models

src/eynollah/training/gt_gen_utils.py

@@ -737,9 +737,12 @@ def get_images_of_ground_truth(gt_list, dir_in, output_dir, output_type, config_
             
             cnt_size = np.array([cv2.contourArea(contours[j]) for j in range(len(contours))])
             
+            try:
                 cnt = contours[np.argmax(cnt_size)]
-
                 x, y, w, h = cv2.boundingRect(cnt)
+            except:
+                x, y , w, h = 0, 0, x_len, y_len
+            
             bb_xywh = [x, y, w, h]
             
             

src/eynollah/training/models.py

@@ -843,7 +843,7 @@ def cnn_rnn_ocr_model(image_height=None, image_width=None, n_classes=None, max_s
     
     addition_rnn = tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(image_width, return_sequences=True, dropout=0.25))(addition)
     
-    out = tf.keras.layers.Conv1D(max_seq, 1, data_format="channels_last")(addition_rnn)
+    out = tf.keras.layers.Conv1D(max_seq, 1, data_format="channels_first")(addition_rnn)
     out = tf.keras.layers.BatchNormalization(name="bn9")(out)
     out = tf.keras.layers.Activation("relu", name="relu9")(out)
     #out = tf.keras.layers.Conv1D(n_classes, 1, activation='relu', data_format="channels_last")(out)

src/eynollah/training/utils.py

@@ -1,7 +1,7 @@
 import os
 import math
 import random
-
+from pathlib import Path
 import cv2
 import numpy as np
 import seaborn as sns
@@ -32,6 +32,9 @@ def scale_padd_image_for_ocr(img, height, width):
     else:
         width_new = width
 
+    if width_new <= 0:
+        width_new = width
+
     img_res= resize_image (img, height, width_new)
     img_fin = np.ones((height, width, 3))*255
 
@@ -1304,7 +1307,8 @@ def data_gen_ocr(padding_token, n_batch, input_height, input_width, max_len, dir
     batchcount = 0
     while True:
         for i in ls_files_images:
-            f_name = i.split('.')[0]
+            print(i, 'i')
+            f_name = Path(i).stem#.split('.')[0]
 
             txt_inp  = open(os.path.join(dir_train, "labels/"+f_name+'.txt'),'r').read().split('\n')[0]