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| 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.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 | |
| 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/finetune_low_resolution_v4.h5') | |