import pandas as pd import PIL from PIL import Image from PIL import ImageDraw import gradio as gr import torch import easyocr import omegaconf from vietocr.model.transformerocr import VietOCR from vietocr.model.vocab import Vocab from vietocr.translate import translate, process_input config = omegaconf.OmegaConf.load("vgg-seq2seq.yaml") config = omegaconf.OmegaConf.to_container(config, resolve=True) vocab = Vocab(config['vocab']) model = VietOCR(len(vocab), config['backbone'], config['cnn'], config['transformer'], config['seq_modeling']) model.load_state_dict(torch.load('train_old.pth', map_location=torch.device('cpu'))) torch.hub.download_url_to_file('https://github.com/JaidedAI/EasyOCR/raw/master/examples/english.png', 'english.png') torch.hub.download_url_to_file('https://github.com/JaidedAI/EasyOCR/raw/master/examples/thai.jpg', 'thai.jpg') torch.hub.download_url_to_file('https://github.com/JaidedAI/EasyOCR/raw/master/examples/french.jpg', 'french.jpg') torch.hub.download_url_to_file('https://github.com/JaidedAI/EasyOCR/raw/master/examples/chinese.jpg', 'chinese.jpg') torch.hub.download_url_to_file('https://github.com/JaidedAI/EasyOCR/raw/master/examples/japanese.jpg', 'japanese.jpg') torch.hub.download_url_to_file('https://github.com/JaidedAI/EasyOCR/raw/master/examples/korean.png', 'korean.png') torch.hub.download_url_to_file('https://i.imgur.com/mwQFd7G.jpeg', 'Hindi.jpeg') 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): reader = easyocr.Reader(lang) bounds = reader.readtext(filepath) new_bounds=[] for (bbox, text, prob) in bounds: x1,y1 = bbox[0] x2,y2 = bbox[1] x3,y3 = bbox[2] x4,y4 = bbox[3] # crop the region of interest (ROI) img = Image.open(filepath) #img = img[y0:y1, x0:x1] img = img.crop((x1, y1, x3, y3)) # crop the image img = process_input(img, config['dataset']['image_height'], config['dataset']['image_min_width'], config['dataset']['image_max_width']) out = translate(img, model)[0].tolist() out = vocab.decode(out) new_bounds.append((bbox, out, prob)) im = PIL.Image.open(filepath) draw_boxes(im, bounds) im.save('result.jpg') return ['result.jpg', pd.DataFrame(bounds).iloc[: , 1:]] 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 = "
Ready-to-use OCR with 80+ supported languages and all popular writing scripts including Latin, Chinese, Arabic, Devanagari, Cyrillic and etc. | Github Repo
" examples = [['english.png',['en']],['thai.jpg',['th']],['french.jpg',['fr', 'en']],['chinese.jpg',['ch_sim', 'en']],['japanese.jpg',['ja', 'en']],['korean.png',['ko', 'en']],['Hindi.jpeg',['hi', 'en']]] css = ".output_image, .input_image {height: 40rem !important; width: 100% !important;}" choices = [ "abq", "ady", "af", "ang", "ar", "as", "ava", "az", "be", "bg", "bh", "bho", "bn", "bs", "ch_sim", "ch_tra", "che", "cs", "cy", "da", "dar", "de", "en", "es", "et", "fa", "fr", "ga", "gom", "hi", "hr", "hu", "id", "inh", "is", "it", "ja", "kbd", "kn", "ko", "ku", "la", "lbe", "lez", "lt", "lv", "mah", "mai", "mi", "mn", "mr", "ms", "mt", "ne", "new", "nl", "no", "oc", "pi", "pl", "pt", "ro", "ru", "rs_cyrillic", "rs_latin", "sck", "sk", "sl", "sq", "sv", "sw", "ta", "tab", "te", "th", "tjk", "tl", "tr", "ug", "uk", "ur", "uz", "vi" ] gr.Interface( inference, [gr.inputs.Image(type='filepath', label='Input'),gr.inputs.CheckboxGroup(choices, type="value", default=['en'], label='language')], [gr.outputs.Image(type='pil', label='Output'), gr.outputs.Dataframe(type='pandas', headers=['text', 'confidence'])], title=title, description=description, article=article, examples=examples, css=css, enable_queue=True ).launch(debug=True)