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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) | |
| jsonText='{"result":"ok"' | |
| 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.Image(type='filepath', label='Input'),gr.CheckboxGroup(choices, type="value", default=['vi'], label='language'), gr.Textbox(label='Labels',default='NgΖ°α»i nα»p, TiαΊΏp nhαΊn hα» sΖ‘ của')], | |
| [gr.Image(type='pil', label='Output'), gr.Textbox(label='Text'), gr.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) |