import gradio as gr import requests import tensorflow as tf import keras_ocr import cv2 import os import numpy as np import pandas as pd from datetime import datetime import scipy.ndimage.interpolation as inter import easyocr from PIL import Image from paddleocr import PaddleOCR import socket from send_email_user import send_user_email # if not os.path.isdir('images'): # os.mkdir('images') def get_device_ip_address(): if os.name == "nt": result = "Running on Windows" hostname = socket.gethostname() result += "\nHostname: " + hostname host = socket.gethostbyname(hostname) result += "\nHost-IP-Address:" + host return result elif os.name == "posix": gw = os.popen("ip -4 route show default").read().split() s = socket.socket(socket.AF_INET, socket.SOCK_DGRAM) s.connect((gw[2], 0)) ipaddr = s.getsockname()[0] gateway = gw[2] host = socket.gethostname() result = "\nIP address:\t\t" + ipaddr + "\r\nHost:\t\t" + host return result else: result = os.name + " not supported yet." return result """ Paddle OCR """ def ocr_with_paddle(img): finaltext = '' ocr = PaddleOCR(lang='en', use_angle_cls=True) # img_path = 'exp.jpeg' result = ocr.ocr(img) for i in range(len(result[0])): text = result[0][i][1][0] finaltext += ' '+ text return finaltext """ Keras OCR """ def ocr_with_keras(img): output_text = '' pipeline=keras_ocr.pipeline.Pipeline() images=[keras_ocr.tools.read(img)] predictions=pipeline.recognize(images) first=predictions[0] for text,box in first: output_text += ' '+ text return output_text """ easy OCR """ # gray scale image def get_grayscale(image): return cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) # Thresholding or Binarization def thresholding(src): return cv2.threshold(src,127,255, cv2.THRESH_TOZERO)[1] def ocr_with_easy(img): gray_scale_image=get_grayscale(img) thresholding(gray_scale_image) cv2.imwrite('image.png',gray_scale_image) reader = easyocr.Reader(['th','en']) bounds = reader.readtext('image.png',paragraph="False",detail = 0) bounds = ''.join(bounds) return bounds """ Generate OCR """ def generate_ocr(Method,img): try: text_output = '' print("Method___________________",Method) if Method == 'EasyOCR': text_output = ocr_with_easy(img) if Method == 'KerasOCR': text_output = ocr_with_keras(img) if Method == 'PaddleOCR': text_output = ocr_with_paddle(img) # save_details(Method,text_output,img) return text_output # hostname = socket.gethostname() # IPAddr = socket.gethostbyname(hostname) # print(hostname) # print("\nHost-IP-Address:" + IPAddr) except Exception as e: print("Error in ocr generation ==>",e) text_output = "Something went wrong" return text_output """ Save generated details """ # def save_details(Method,text_output,img): # method = [] # img_path = [] # text = [] # input_img = '' # hostname = '' # picture_path = "image.jpg" # curr_datetime = datetime.now().strftime('%Y-%m-%d %H-%M-%S') # if text_output: # splitted_path = os.path.splitext(picture_path) # modified_picture_path = splitted_path[0] + curr_datetime + splitted_path[1] # cv2.imwrite('images/'+ modified_picture_path, img) # input_img = 'images/'+ modified_picture_path # try: # df = pd.read_csv("AllDetails.csv") # df2 = {'method': Method, 'input_img': input_img, 'generated_text': text_output} # df = df.append(df2, ignore_index = True) # df.to_csv("AllDetails.csv", index=False) # except: # method.append(Method) # img_path.append(input_img) # text.append(text_output) # dict = {'method': method, 'input_img': img_path, 'generated_text': text} # df = pd.DataFrame(dict,index=None) # df.to_csv("AllDetails.csv") # hostname = get_device_ip_address() # return send_user_email(input_img,hostname,text_output,Method) # return hostname """ Create user interface for OCR demo """ image = gr.Image(shape=(224, 224),elem_id="img_div") method = gr.Radio(["EasyOCR", "KerasOCR", "PaddleOCR"],elem_id="radio_div") output = gr.Textbox(label="Output") demo = gr.Interface( generate_ocr, [method,image], output, title="Optical Character Recognition", description="Try OCR with different methods", theme="darkpeach", css=".gradio-container {background-color: lightgray} #radio_div {background-color: #FFD8B4; font-size: 40px;}" ) demo.launch(enable_queue = False)