import torch from torch.utils.data import Dataset,DataLoader from PIL import Image import os import cv2 import tensorflow as tf import numpy as np from tensorflow import keras ############ extractdetection ################ #alpha = "ABCDEFGHIJKLMNOPQRSTUVWXYZ" alpha =['A', 'A1', 'A2' ,'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K', 'L','M', 'N','O', 'P', 'Q', 'R', 'S', 'T', 'U', 'V', 'W', 'X', 'Y', 'Z', 'Z1', 'Z2','Z3','Z4','Z5'] ############ decode_detection ################ # labels from old arabic # characters = [' ', '.', '[', ']', '؟', 'ء', 'آ', 'أ', 'ؤ', 'إ', 'ئ', 'ا', 'ب', 'ة', 'ت', 'ث', 'ج', 'ح', 'خ', 'د', 'ذ', 'ر', 'ز', 'س', 'ش', 'ص', 'ض', 'ط', 'ظ', 'ع', 'غ', 'ـ', 'ف', 'ق', 'ك', 'ل', 'م', 'ن', 'ه', 'و', 'ى', 'ي', 'ً', 'ٌ', 'ٍ', 'َ', 'ُ', 'ِ', 'ّ', 'ْ', 'ٔ', 'ٕ', '١', '٢', '٣', '٤', '٥', '٧', '٨', 'ٮ', 'ٯ', 'ٰ', 'ڡ', 'ک', 'ں', 'ی', '۴', '\u202c', 'ﭐ', 'ﺟ', 'ﺣ', 'ﻛ', '�'] #characters = [' ', '.', '[', ']', '؟', 'ء', 'آ', 'أ', 'ؤ', 'إ', 'ئ', 'ا', 'ب', 'ة', 'ت', 'ث', 'ج', 'ح', 'خ', 'د', 'ذ', 'ر', 'ز', 'س', 'ش', 'ص', 'ض', 'ط', 'ظ', 'ع', 'غ', 'ـ', 'ف', 'ق', 'ك', 'ل', 'م', 'ن', 'ه', 'و', 'ى', 'ي', 'ً', 'ٌ', 'ٍ', 'َ', 'ُ', 'ِ', 'ّ', 'ْ', 'ٔ', 'ٕ', 'ٖ', '٠', '١', '٢', '٣', '٨', 'ٮ', 'ٰ', 'ڡ', 'ک', 'ں', 'ی', 'ݘ', '\u202c', 'ﭐ', 'ﺣ', 'ﻛ'] characters = [' ', '"', '.', ':', '[', ']', '،', '؟', 'ء', 'آ', 'أ', 'ؤ', 'إ', 'ئ', 'ا', 'ب', 'ة', 'ت', 'ث', 'ج', 'ح', 'خ', 'د', 'ذ', 'ر', 'ز', 'س', 'ش', 'ص', 'ض', 'ط', 'ظ', 'ع', 'غ', 'ـ', 'ف', 'ق', 'ك', 'ل', 'م', 'ن', 'ه', 'و', 'ى', 'ي', 'ً', 'ٌ', 'ٍ', 'َ', 'ُ', 'ِ', 'ّ', 'ْ', 'ٔ', 'ٕ', 'ٖ', '٠', '١', '٢', '٣', '٨', 'ٮ', 'ٰ', 'ڡ', 'ک', 'ں', 'ی', 'ݘ', '\u202c', 'ﭐ', 'ﺣ', 'ﻛ'] characters.sort() # characters.sort() max_length = 132 img_height, img_width = 1056,64 def label_to_num(label,max_length=max_length): label_num = [] for ch in label: try: label_num.append(characters.index(ch)) except: pass return keras.utils.pad_sequences( [label_num], maxlen=max_length, dtype='int32', padding='post',truncating='pre',value=len(characters)+2)[0] def num_to_label(num): #if isinstance(num,torch.Tensor): #num=num.to(torch.int8) ret = "" for ch in num: if int(ch)==-1 or ch ==len(characters)+2 :# or ch==-1 : # pad symbole break try: ret+=characters[int(ch)] except: pass return ret def decode_predictions(pred,greedy = True): input_len = np.ones(pred.shape[0]) * pred.shape[1] results = keras.backend.ctc_decode(pred, input_length=input_len, greedy=greedy)[0][0][ :, :max_length ] output_text = [] for res in results: #decoded = tokenizer.sequences_to_texts([res.numpy()]) #decoded = list(map(num_to_label,results)) decoded = num_to_label( res) output_text.append(decoded) return output_text ############ dataloader ################ # def encode_single_sample(path_dir, label=None): # img = tf.io.read_file(path_dir) # img = tf.io.decode_jpeg(img, name=None) # img.set_shape([img.shape[0], img.shape[1], img.shape[-1]]) # img = tf.image.rot90(img, k=1, name=None) # img = tf.image.resize(img, [img_height, img_width]) # # img = tf.image.rgb_to_grayscale(img) # Convert image to grayscale # img = img/255.0 # return img # def encode_single_sample(path_dir): # img = tf.io.read_file(path_dir) # img = tf.io.decode_jpeg( img, name=None) # img.set_shape([img.shape[0], img.shape[1],img.shape[-1]]) # img = tf.image.rot90(img, k=1, name=None) # rand = tf.random.uniform( (), minval=0,maxval=1, dtype=tf.dtypes.float32,seed=None,name=None) # img = tf.image.resize(img, [img_height, img_width]) # img=img/255.0 # return img def encode_single_sample(path_dir): img = tf.io.read_file(path_dir) img = tf.io.decode_jpeg(img, channels=3) # Decode the image in RGB format img = tf.image.rot90(img, k=1, name=None) img = tf.image.resize(img, [img_height, img_width]) img = img / 255.0 # Normalize the pixel values return img batch_size = 16 def Loadlines(path_lines): # path_lines.sort() test_dataset = tf.data.Dataset.from_tensor_slices(path_lines ) test_dataset = ( test_dataset.map( encode_single_sample, num_parallel_calls=tf.data.experimental.AUTOTUNE ) .batch(batch_size,drop_remainder=False ) .prefetch(buffer_size=tf.data.experimental.AUTOTUNE) ) return test_dataset ############ load model ################ """ load model_finetuned """ def load_model(): return keras.models.load_model('/home/user/app/RGB_old_texline_v1.h5')