table

table.py

def normalize_image(input_image):
  input_image = tf.cast(input_image, tf.float32) / 255.0
  return input_image

def decode_image(img):  
  img = tf.image.decode_jpeg(img)  
  return tf.image.resize(img, [img_height, img_width])

def decode_mask_image(img):  
  img = tf.image.decode_jpeg(img, channels=1)  
  return tf.image.resize(img, [img_height, img_width])

def p1(file_path): 
  file_path_new = file_path
  col_path = tf.strings.regex_replace(file_path_new, 'cs2_table', 'cs2_col')
  file_path_f = tf.strings.regex_replace(col_path, 'cs2_col', 'data_cs2')
  file_path_q = tf.strings.regex_replace(file_path_f, '.jpeg', '.jpg')
  file_actual = normalize_image(decode_image(tf.io.read_file(file_path_q)))
  table_mask = normalize_image(decode_mask_image(tf.io.read_file(file_path_new)))
  column_mask = normalize_image(decode_mask_image(tf.io.read_file(col_path)))
  #tf.print(col_path)  
  return file_actual, {"table_output" : table_mask, "column_output" : column_mask }