#Installing required libraries #pip install datasets transformers[sentencepiece] #pip install accelerate #apt install git-lfs #sudo apt install tesseract-ocr #pip install tesseract-ocr #pip install pytesseract #pip install keras-ocr #pip install gradio #importing required libraries #import os #os.system('apt-get install tesseract-ocr') #os.system('pip install -q pytesseract') #os.system('pip install pytesseract') #sudo apt-get install tesseract-ocr import pytesseract from PIL import Image, ImageFont, ImageDraw from transformers import AutoTokenizer, AutoModelForSeq2SeqLM import matplotlib.pyplot as plt import keras_ocr import cv2 #from cv2 import INPAINT_NS, inpaint, line, cvtColor, COLOR_BGR2RGB, imwrite import math import numpy as np import gradio as gr import numpy as np #Translated in your desired language def choose_language(language): #Support for Hindi, Spanish, French #Support for Arabic, Turish, arabic #Support for German if language == 'hindi': modelnm = "Helsinki-NLP/opus-mt-en-hi" elif language == 'spanish': modelnm = "Helsinki-NLP/opus-mt-en-es" elif language == 'german': modelnm = "Helsinki-NLP/opus-mt-en-de" elif language == 'french': modelnm = "Helsinki-NLP/opus-mt-en-fr" elif language == 'turkish': modelnm = "Helsinki-NLP/opus-mt-en-trk" elif language == 'arabic': modelnm = "Helsinki-NLP/opus-mt-en-ar" else: modelnm = "Helsinki-NLP/opus-mt-en-ga" tokenizer = AutoTokenizer.from_pretrained(modelnm) model = AutoModelForSeq2SeqLM.from_pretrained(modelnm) return tokenizer, model #Function to translate english text to desired language def translator(text, lang): if '\n' in text: text_list = text.splitlines() text = ' '.join(text_list) #Huggingface transformers Magic tokenizer, model = choose_language(lang) input_ids = tokenizer.encode(text, return_tensors="pt", padding=True) #Tokenizer outputs = model.generate(input_ids) #Model #Translated Text decoded_text = tokenizer.decode(outputs[0], skip_special_tokens=True) #Tokenizer return decoded_text #Getting cordinates def midpoint(x1, y1, x2, y2): x_mid = int((x1 + x2)/2) y_mid = int((y1 + y2)/2) return (x_mid, y_mid) pipeline = keras_ocr.pipeline.Pipeline() #Getting cordinates for text insie image #This will help in filling up the space with colors def img_text_cords(im): #, pipeline): #read image img = keras_ocr.tools.read(im) #generate (word, box) tuples prediction_groups = pipeline.recognize([img]) mask = np.zeros(img.shape[:2], dtype="uint8") for box in prediction_groups[0]: x0, y0 = box[1][0] x1, y1 = box[1][1] x2, y2 = box[1][2] x3, y3 = box[1][3] x_mid0, y_mid0 = midpoint(x1, y1, x2, y2) x_mid1, y_mi1 = midpoint(x0, y0, x3, y3) thickness = int(math.sqrt( (x2 - x1)**2 + (y2 - y1)**2 )) cv2.line(mask, (x_mid0, y_mid0), (x_mid1, y_mi1), 255, thickness) img = cv2.inpaint(img, mask, 7, cv2.INPAINT_NS) return img #Extracting text from image def text_extract(im): #Using pytesseract to read text ocr_text = pytesseract.image_to_string(im) return ocr_text #Formatting the text to multi lines structure #This is mainly for translated text to look and fit better on an image def format_text(language,extracted_text): translated_text = translator(extracted_text, language) word_list,i = [],0 for word in translated_text.split(): if i%5 != 0: word_list.append(' '+word) else: word_list.append('\n'+word) i+=1 new_title_text = ''.join(word_list) return new_title_text def translate_image(im, language): #Extract text, translate in your language and format it extracted_text = text_extract(im) #font select -- Getting Unicode Text title_font = ImageFont.truetype('./arial-unicode-ms.ttf') #('/content/gdrive/My Drive/sample_images/arial-unicode-ms.ttf',30) #text to write on image #Example in hindi - Unicode text u"आप जीवन में मिलता हर मौका ले लो, क्योंकि कुछ चीजें केवल एक बार होती हैं. शुभ सुबह" txt = format_text(language,extracted_text) #Editing image img_returned = img_text_cords(im) img_rgb = cv2.cvtColor(img_returned, cv2.COLOR_BGR2RGB) cv2.imwrite("text_free_image.jpg",img_rgb) new_image = Image.open("text_free_image.jpg") #Enable writing on image image_editable = ImageDraw.Draw(new_image) image_editable.multiline_text((10,10), txt,spacing=2, font=title_font, fill= (0, 0, 0)) #(237, 230, 211)) (0, 0, 0)) return new_image title = "Translate English Text to Your Regional Language In Your Forwarded Images" description = "This fun Gradio demo is for translating English quote in an image (usually whatsapp forwards :) ) to your local or preferred language. To use it, simply upload your image, select one of the language choices given (hindi, spanish, german, french, arabic, irish, and turkish) from radio buttons provided. You can alternately click one of the examples to load them and select the language choice along with it." article = "
Image Text Translate by Yuvraj S | Github Repo |
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" pipeline = keras_ocr.pipeline.Pipeline() gr.Interface( translate_image, [gr.inputs.Image(type="filepath", label="Input"), gr.inputs.Radio(choices=['hindi','spanish','french','turkish','german','irish', 'arabic'], type="value", default='hindi', label='Choose A Language')], gr.outputs.Image(type="pil", label="Output"), "text", title=title, description=description, article=article, #examples=[['bill.png','version 0.2'],['keanu.png','version 0.3'],['will.jpeg','version 0.2']], enable_queue=True ).launch(debug=True)