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Upload combine_demo.py
Browse files- combine_demo.py +171 -0
combine_demo.py
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import gradio as gr
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import requests
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import tensorflow as tf
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import keras_ocr
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import cv2
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import os
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import numpy as np
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import pandas as pd
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from datetime import datetime
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import scipy.ndimage.interpolation as inter
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import easyocr
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from PIL import Image
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from paddleocr import PaddleOCR
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import socket
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from send_email_user import send_user_email
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def get_device_ip_address():
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if os.name == "nt":
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result = "Running on Windows"
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hostname = socket.gethostname()
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result += "\nHostname: " + hostname
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host = socket.gethostbyname(hostname)
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result += "\nHost-IP-Address:" + host
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return result
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elif os.name == "posix":
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gw = os.popen("ip -4 route show default").read().split()
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s = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)
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s.connect((gw[2], 0))
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ipaddr = s.getsockname()[0]
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gateway = gw[2]
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host = socket.gethostname()
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result = "\nIP address:\t\t" + ipaddr + "\r\nHost:\t\t" + host
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return result
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else:
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result = os.name + " not supported yet."
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return result
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"""
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Paddle OCR
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"""
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def ocr_with_paddle(img):
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finaltext = ''
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ocr = PaddleOCR(lang='en', use_angle_cls=True)
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# img_path = 'exp.jpeg'
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result = ocr.ocr(img)
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for i in range(len(result[0])):
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text = result[0][i][1][0]
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finaltext += ' '+ text
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return finaltext
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"""
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Keras OCR
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"""
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def ocr_with_keras(img):
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output_text = ''
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pipeline=keras_ocr.pipeline.Pipeline()
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images=[keras_ocr.tools.read(img)]
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predictions=pipeline.recognize(images)
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first=predictions[0]
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for text,box in first:
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output_text += ' '+ text
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return output_text
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"""
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easy OCR
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"""
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# gray scale image
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def get_grayscale(image):
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return cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
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# Thresholding or Binarization
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def thresholding(src):
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return cv2.threshold(src,127,255, cv2.THRESH_TOZERO)[1]
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def ocr_with_easy(img):
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gray_scale_image=get_grayscale(img)
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thresholding(gray_scale_image)
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cv2.imwrite('image.png',gray_scale_image)
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reader = easyocr.Reader(['th','en'])
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bounds = reader.readtext('image.png',paragraph="False",detail = 0)
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bounds = ''.join(bounds)
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return bounds
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"""
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Generate OCR
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"""
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def generate_ocr(Method,img):
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try:
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text_output = ''
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print("Method___________________",Method)
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if Method == 'EasyOCR':
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text_output = ocr_with_easy(img)
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if Method == 'KerasOCR':
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text_output = ocr_with_keras(img)
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if Method == 'PaddleOCR':
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text_output = ocr_with_paddle(img)
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save_details(Method,text_output,img)
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return text_output
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# hostname = socket.gethostname()
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# IPAddr = socket.gethostbyname(hostname)
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# print(hostname)
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# print("\nHost-IP-Address:" + IPAddr)
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except Exception as e:
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print("Error in ocr generation ==>",e)
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text_output = "Something went wrong"
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return text_output
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"""
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Save generated details
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"""
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def save_details(Method,text_output,img):
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method = []
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img_path = []
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text = []
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picture_path = "image.jpg"
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curr_datetime = datetime.now().strftime('%Y-%m-%d %H-%M-%S')
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if text_output:
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splitted_path = os.path.splitext(picture_path)
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modified_picture_path = splitted_path[0] + curr_datetime + splitted_path[1]
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cv2.imwrite('images/'+ modified_picture_path, img)
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input_img = 'images/'+ modified_picture_path
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try:
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df = pd.read_csv("AllDetails.csv")
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df2 = {'method': Method, 'input_img': input_img, 'generated_text': text_output}
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df = df.append(df2, ignore_index = True)
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df.to_csv("AllDetails.csv", index=False)
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except:
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method.append(Method)
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img_path.append(input_img)
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text.append(text_output)
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dict = {'method': method, 'input_img': img_path, 'generated_text': text}
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df = pd.DataFrame(dict,index=None)
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df.to_csv("AllDetails.csv")
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hostname = get_device_ip_address()
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return send_user_email(input_img,hostname,text_output,Method)
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# return hostname
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"""
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Create user interface for OCR demo
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"""
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image = gr.Image(shape=(224, 224),elem_id="img_div")
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method = gr.Radio(["EasyOCR", "KerasOCR", "PaddleOCR"],elem_id="radio_div")
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output = gr.Textbox(label="Output")
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demo = gr.Interface(
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generate_ocr,
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[method,image],
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output,
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title="Optical Character Recognition",
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description="Try OCR with different methods",
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theme="darkpeach",
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css=".gradio-container {background-color: lightgray} #radio_div {background-color: #FFD8B4; font-size: 40px;}"
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)
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161 |
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# .gradio-container.gap-2 {gap: 10rem;row-gap: 10rem;column-gap: 10rem;}
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162 |
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# .gradio-container {background-color: lightgray}
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demo.launch()
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+
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# with gr.Blocks(css=".gradio-container {background-color: red}") as demo:
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# input = [gr.Image(shape=(224, 224)), gr.Radio(["EasyOCR", "KerasOCR", "PaddleOCR"],text_color="blue")]
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167 |
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# sub_btn = gr.Button("Submit")
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168 |
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# output = gr.Textbox(label="Output")
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169 |
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# event = sub_btn.click(generate_ocr, inputs=input, outputs=output)
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170 |
+
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171 |
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# demo.launch()
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