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Fix filename stem extraction using binarization. Restore the CNN-RNN model to its previous version, as setting channels_last alone was insufficient for running on both CPU and GPU. Prevent errors caused by null values in image shape elements.

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vahidrezanezhad 2026-01-26 15:03:11 +01:00
parent 30f39e7383
commit 6ae244bf9b
3 changed files with 9 additions and 5 deletions
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4
src/eynollah/sbb_binarize.py
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@ -19,7 +19,7 @@ from eynollah.model_zoo import EynollahModelZoo
tf_disable_interactive_logs() tf_disable_interactive_logs()
import tensorflow as tf import tensorflow as tf
from tensorflow.python.keras import backend as tensorflow_backend from tensorflow.python.keras import backend as tensorflow_backend
from pathlib import Path
from .utils import is_image_filename from .utils import is_image_filename
def resize_image(img_in, input_height, input_width): 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) self.logger.info("Found %d image files to binarize in %s", len(ls_imgs), dir_in)
for i, image_path in enumerate(ls_imgs): for i, image_path in enumerate(ls_imgs):
self.logger.info('Binarizing [%3d/%d] %s', i + 1, len(ls_imgs), image_path) 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) ) image = cv2.imread(os.path.join(dir_in,image_path) )
img_last = 0 img_last = 0
model_file, model = self.models model_file, model = self.models

2
src/eynollah/training/models.py
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@ -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) 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.BatchNormalization(name="bn9")(out)
out = tf.keras.layers.Activation("relu", name="relu9")(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) #out = tf.keras.layers.Conv1D(n_classes, 1, activation='relu', data_format="channels_last")(out)

8
src/eynollah/training/utils.py
View file

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