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import pandas as pd | |
import PIL | |
from PIL import Image | |
from PIL import ImageDraw | |
import gradio as gr | |
import torch | |
import easyocr | |
import omegaconf | |
import cv2 | |
import json | |
from vietocr.vietocr.tool.predictor import Predictor | |
from vietocr.vietocr.tool.config import Cfg | |
# Configure of VietOCR | |
config = Cfg.load_config_from_name('vgg_transformer') | |
# config = Cfg.load_config_from_file('vietocr/config.yml') | |
# config['weights'] = '/Users/bmd1905/Desktop/pretrain_ocr/vi00_vi01_transformer.pth' | |
config['cnn']['pretrained'] = True | |
config['predictor']['beamsearch'] = True | |
config['device'] = 'cpu' # mps | |
recognitor = Predictor(config) | |
#model_name = "microsoft/xdoc-base-squad2.0" | |
#nlp = pipeline('question-answering', model=model_name) | |
from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline | |
model_name = "timpal0l/mdeberta-v3-base-squad2" | |
model = AutoModelForQuestionAnswering.from_pretrained(model_name) | |
tokenizer = AutoTokenizer.from_pretrained(model_name) | |
nlp = pipeline('question-answering', model=model, tokenizer=tokenizer) | |
def query(doc, labels): | |
questions = labels.split(", ") | |
result={} | |
for question in questions: | |
QA_input = { | |
'question': question+'?', | |
'context': doc | |
} | |
res= nlp(QA_input) | |
print(res) | |
value = res['answer'] | |
result[question]=value | |
return result | |
def draw_boxes(image, bounds, color='yellow', width=2): | |
draw = ImageDraw.Draw(image) | |
for bound in bounds: | |
p0, p1, p2, p3 = bound[0] | |
draw.line([*p0, *p1, *p2, *p3, *p0], fill=color, width=width) | |
return image | |
def inference(filepath, lang, labels): | |
img = cv2.imread(filepath) | |
width, height, _ = img.shape | |
if width>height: | |
height, width, _ = img.shape | |
reader = easyocr.Reader(lang) | |
bounds = reader.readtext(filepath) | |
new_bounds=[] | |
texts='' | |
for (bbox, text, prob) in bounds: | |
(tl, tr, br, bl) = bbox | |
tl = (int(tl[0]), int(tl[1])) | |
tr = (int(tr[0]), int(tr[1])) | |
br = (int(br[0]), int(br[1])) | |
bl = (int(bl[0]), int(bl[1])) | |
min_x = min(tl[0], tr[0], br[0], bl[0]) | |
min_x = max(0, min_x) | |
max_x = max(tl[0], tr[0], br[0], bl[0]) | |
max_x = min(width-1, max_x) | |
min_y = min(tl[1], tr[1], br[1], bl[1]) | |
min_y = max(0, min_y) | |
max_y = max(tl[1], tr[1], br[1], bl[1]) | |
max_y = min(height-1, max_y) | |
# crop the region of interest (ROI) | |
cropped_image = img[min_y:max_y,min_x:max_x] # crop the image | |
cropped_image = Image.fromarray(cropped_image) | |
out = recognitor.predict(cropped_image) | |
texts = texts + '\t' + out | |
result = query(texts, labels) | |
jsonText = json.dumps(result) | |
im = PIL.Image.open(filepath) | |
draw_boxes(im, bounds) | |
im.save('result.jpg') | |
return ['result.jpg', texts, jsonText] | |
title = 'EasyOCR' | |
description = 'Gradio demo for EasyOCR. EasyOCR demo supports 80+ languages.To use it, simply upload your image and choose a language from the dropdown menu, or click one of the examples to load them. Read more at the links below.' | |
article = "<p style='text-align: center'><a href='https://www.jaided.ai/easyocr/'>Ready-to-use OCR with 80+ supported languages and all popular writing scripts including Latin, Chinese, Arabic, Devanagari, Cyrillic and etc.</a> | <a href='https://github.com/JaidedAI/EasyOCR'>Github Repo</a></p>" | |
css = ".output_image, .input_image {height: 40rem !important; width: 100% !important;}" | |
choices = [ | |
"vi" | |
] | |
gr.Interface( | |
inference, | |
[gr.inputs.Image(type='filepath', label='Input'),gr.inputs.CheckboxGroup(choices, type="value", default=['vi'], label='language'), gr.inputs.Textbox(label='Labels',default='NgΖ°α»i nα»p, TiαΊΏp nhαΊn hα» sΖ‘ của')], | |
[gr.outputs.Image(type='pil', label='Output'), gr.outputs.Textbox(label='Text'), gr.outputs.JSON(label='document')], | |
title=title, | |
description=description, | |
article=article, | |
css=css, | |
examples=[['giaytiepnhan.jpg',['vi'],'NgΖ°α»i nα»p, TiαΊΏp nhαΊn hα» sΖ‘ của'],['giaytiepnhan2.jpg',['vi'],'MΓ£ sα» hα» sΖ‘, Δα»a chα»']], | |
enable_queue=True | |
).launch(debug=True) |