OCR-Translate / app.py
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# OCR Translate v0.2
# 创建人:曾逸夫 (Founder: Zeng Yifu)
# Creation time:2022-07-19
import os
#os.system("apt-get install xclip")
import gradio as gr
import nltk
import pyclip
import pytesseract
import nltk
from nltk.tokenize import sent_tokenize
from transformers import MarianMTModel, MarianTokenizer
from transformers import T5Tokenizer, T5ForConditionalGeneration
nltk.download('punkt')
nltk.download('punkt_tab')
OCR_TR_DESCRIPTION = '''# OCR Translate v0.2
<div id="content_align">OCR translation system based on Tesseract</div>'''
# Image Path
img_dir = "./data"
# Get tesseract language list
choices = os.popen('tesseract --list-langs').read().split('\n')[1:-1]
# Translation model selection
def model_choice(src, trg):
# https://huggingface.co/Helsinki-NLP/opus-mt-tc-big-pt-en
# https://huggingface.co/Helsinki-NLP/opus-mt-tc-big-en-pt
# https://huggingface.co/unicamp-dl/translation-pt-en-t5
# https://huggingface.co/unicamp-dl/translation-en-pt-t5
model_name = f"unicamp-dl/translation-{src}-{trg}-t5" # Model Name
#tokenizer = MarianTokenizer.from_pretrained(model_name) # Tokenizer
#model = MarianMTModel.from_pretrained(model_name) # Model
tokenizer = T5Tokenizer.from_pretrained(model_name)
model = T5ForConditionalGeneration.from_pretrained(model_name)
return tokenizer, model
# Convert tesseract language list to pytesseract language
def ocr_lang(lang_list):
lang_str = ""
lang_len = len(lang_list)
if lang_len == 1:
return lang_list[0]
else:
for i in range(lang_len):
lang_list.insert(lang_len - i, "+")
lang_str = "".join(lang_list[:-1])
return lang_str
# ocr tesseract
def ocr_tesseract(img, languages):
ocr_str = pytesseract.image_to_string(img, lang=ocr_lang(languages))
return ocr_str
# Clear
def clear_content():
return None
# Copy to Clipboard
def cp_text(input_text):
# sudo apt-get install xclip
try:
pyclip.copy(input_text)
except Exception as e:
print("sudo apt-get install xclip")
print(e)
# Clear Clipboard
def cp_clear():
pyclip.clear()
# Translate
def translate(input_text, inputs_transStyle):
# Reference:https://huggingface.co/docs/transformers/model_doc/marian
if input_text is None or input_text == "":
return "System prompt: There is no content to translate!"
# Selecting a translation model
trans_src, trans_trg = inputs_transStyle.split("-")[0], inputs_transStyle.split("-")[1]
tokenizer, model = model_choice(trans_src, trans_trg)
translate_text = ""
input_text_list = input_text.split("\n\n")
translate_text_list_tmp = []
for i in range(len(input_text_list)):
if input_text_list[i] != "":
translate_text_list_tmp.append(input_text_list[i])
for i in range(len(translate_text_list_tmp)):
translated_sub = model.generate(
**tokenizer(sent_tokenize(translate_text_list_tmp[i]), return_tensors="pt", truncation=True, padding=True))
tgt_text_sub = [tokenizer.decode(t, skip_special_tokens=True) for t in translated_sub]
translate_text_sub = "".join(tgt_text_sub)
translate_text = translate_text + "\n\n" + translate_text_sub
return translate_text[2:]
def main():
with gr.Blocks(css='style.css') as ocr_tr:
gr.Markdown(OCR_TR_DESCRIPTION)
# -------------- OCR Text Extraction --------------
with gr.Blocks():
with gr.Row():
gr.Markdown("### Step 01: Text Extraction")
with gr.Row():
with gr.Column():
with gr.Row():
inputs_img = gr.Image(image_mode="RGB", sources="upload", type="pil", label="image")
with gr.Row():
inputs_lang = gr.CheckboxGroup(choices=["por", "eng"],
type="value",
value=['eng'],
label='language')
with gr.Row():
clear_img_btn = gr.Button('Clear')
ocr_btn = gr.Button(value='OCR Extraction', variant="primary")
with gr.Column():
with gr.Row():
outputs_text = gr.Textbox(label="Extract content", lines=20)
with gr.Row():
inputs_transStyle = gr.Radio(choices=["pt-en", "en-pt"],
type="value",
value="pt-en",
label='translation mode')
with gr.Row():
clear_text_btn = gr.Button('Clear')
translate_btn = gr.Button(value='Translate', variant="primary")
with gr.Row():
example_list = [["./data/test.png", ["eng"]], ["./data/test02.png", ["eng"]],
["./data/test03.png", ["por"]]]
gr.Examples(example_list, [inputs_img, inputs_lang], outputs_text, ocr_tesseract, cache_examples=False)
# -------------- Translate --------------
with gr.Blocks():
with gr.Row():
gr.Markdown("### Step 02: Translation")
with gr.Row():
outputs_tr_text = gr.Textbox(label="Translate Content", lines=20)
with gr.Row():
cp_clear_btn = gr.Button(value='Clear Clipboard')
cp_btn = gr.Button(value='Copy to clipboard', variant="primary")
# ---------------------- OCR Tesseract ----------------------
ocr_btn.click(fn=ocr_tesseract, inputs=[inputs_img, inputs_lang], outputs=[
outputs_text,])
clear_img_btn.click(fn=clear_content, inputs=[], outputs=[inputs_img])
# ---------------------- Translate ----------------------
translate_btn.click(fn=translate, inputs=[outputs_text, inputs_transStyle], outputs=[outputs_tr_text])
clear_text_btn.click(fn=clear_content, inputs=[], outputs=[outputs_text])
# ---------------------- Copy to Clipboard ----------------------
cp_btn.click(fn=cp_text, inputs=[outputs_tr_text], outputs=[])
cp_clear_btn.click(fn=cp_clear, inputs=[], outputs=[])
ocr_tr.launch(inbrowser=True)
if __name__ == '__main__':
main()