import pandas as pd import PIL from PIL import Image from PIL import ImageDraw import gradio as gr import torch import easyocr import cv2 as cv import math from numpy import asarray import numpy as np torch.hub.download_url_to_file('https://i.pinimg.com/736x/93/d3/54/93d354497ea26d5dc181055b9356cf79.jpg', 'korean.jpg') torch.hub.download_url_to_file('https://64.media.tumblr.com/1a5796817934a179664508693cae82d3/67c2ea7948c385cc-1b/s1280x1920/a70d4fcc0a5528de415b024db4ee6036da9c4c35.jpg', 'chinese.jpg') torch.hub.download_url_to_file('https://jtalkonline.com/wp-content/uploads/2014/06/ichigo-bleach-manga-japanese.jpg', 'japanese.jpg') torch.hub.download_url_to_file('https://img.mghubcdn.com/file/imghub/moriarty-the-patriot/68/10.jpg', 'english.jpg') def midpoint(x1, y1, x2, y2): x_mid = int((x1 + x2)/2) y_mid = int((y1 + y2)/2) return (x_mid, y_mid) def draw_mask(img, bounds): mask = np.zeros(img.shape[:2], dtype="uint8") for bound in bounds: box0, box1, box2, box3 = bound[0] x0, y0 = box0 x1, y1 = box1 x2, y2 = box2 x3, y3 = box3 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)) cv.line(mask, (x_mid0, y_mid0), (x_mid1, y_mi1), 255, thickness) img = cv.inpaint(img, mask, 3, cv.INPAINT_TELEA) return(img) def inference(img, lang): if lang == "english": lang = ['en'] elif lang == "chinese": lang = ['ch_sim'] elif lang == "korean": lang = ['ko'] else: lang = ['ja'] reader = easyocr.Reader(lang) bounds = reader.readtext(img.name) im = PIL.Image.open(img.name) img_array = np.array(im) im = draw_mask(img_array, bounds) im = Image.fromarray(im, 'RGB') lang = "" im.save('result.jpg') return ['result.jpg', pd.DataFrame(bounds). iloc[: , 1:2]] title = 'Manga Image Cleaner' description = 'Image inpainting and text detection demo with the use of EasyOCR and CV2. To use it, simply upload your image and choose a language from the dropdown menu, or click one of the examples to test the program.' article = "
Ready-to-use image inpainting with supported languages such as: Chinese, English, Japanese, and Korean | Github OCR Repo | CV2 Reference
" css = ".output_image, .input_image {height: 40rem !important; width: 100% !important;}" examples = [['chinese.jpg',"chinese"], ['english.jpg',"english"], ['japanese.jpg',"japanese"], ['korean.jpg',"korean"]] choices = [ "chinese", "english", "japanese", "korean" ] gr.Interface( inference, [gr.inputs.Image(type='file', label='Input'),gr.inputs.Dropdown(choices, type="value", default="korean", label='language')], [gr.outputs.Image(type='file', label='Output'), #gr.outputs.Image(type='file', label='Output'), gr.outputs.Dataframe()], title=title, description=description, article=article, examples=examples, css=css, enable_queue=True ).launch(debug=True)