Upload combine_demo.py

combine_demo.py

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+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
+
+
+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 = []
+    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;}"
+)
+# .gradio-container.gap-2 {gap: 10rem;row-gap: 10rem;column-gap: 10rem;}
+# .gradio-container {background-color: lightgray}
+demo.launch()
+
+# with gr.Blocks(css=".gradio-container {background-color: red}") as demo:
+#     input = [gr.Image(shape=(224, 224)), gr.Radio(["EasyOCR", "KerasOCR", "PaddleOCR"],text_color="blue")]
+#     sub_btn = gr.Button("Submit")
+#     output = gr.Textbox(label="Output")
+#     event = sub_btn.click(generate_ocr, inputs=input, outputs=output)
+
+# demo.launch()