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| import gradio as gr | |
| import requests | |
| import tensorflow as tf | |
| import keras_ocr | |
| import cv2 | |
| import os | |
| import csv | |
| import numpy as np | |
| import pandas as pd | |
| import huggingface_hub | |
| from huggingface_hub import Repository | |
| from datetime import datetime | |
| import scipy.ndimage.interpolation as inter | |
| import easyocr | |
| import datasets | |
| from datasets import load_dataset, Image, Features, Array3D | |
| from PIL import Image | |
| from paddleocr import PaddleOCR | |
| import socket | |
| # from send_email_user import send_user_email | |
| from huggingface_hub import HfApi | |
| import smtplib | |
| HF_TOKEN = os.environ.get("HF_TOKEN") | |
| # mydataset_name = "pragnakalp/OCR-img-to-text" | |
| # print("$$$$$$$$$$$$$$$$$$$$$$$$$$$$",type(mydataset_name)) | |
| # hf_writer = gr.HuggingFaceDatasetSaver(HF_TOKEN,mydataset_name) | |
| DATASET_REPO_URL = "https://huggingface.co/datasets/pragnakalp/OCR-img-to-text" | |
| DATA_FILENAME = "ocr_data.csv" | |
| DATA_FILE = os.path.join("ocr_data", DATA_FILENAME) | |
| # DATA_FILENAME2 = "image" | |
| # DATA_FILE2 = os.path.join("ocr_data",DATA_FILENAME2) | |
| HF_TOKEN = os.environ.get("HF_TOKEN") | |
| DATASET_REPO_ID = "pragnakalp/OCR-img-to-text" | |
| print("is none?", HF_TOKEN is None) | |
| try: | |
| hf_hub_download( | |
| repo_id=DATASET_REPO_ID, | |
| filename=DATA_FILENAME, | |
| cache_dir=DATA_DIRNAME, | |
| force_filename=DATA_FILENAME | |
| ) | |
| except: | |
| print("file not found") | |
| repo = Repository( | |
| local_dir="ocr_data", clone_from=DATASET_REPO_URL, use_auth_token=HF_TOKEN | |
| ) | |
| 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 | |
| # def store_single_disk(image, image_id, label): | |
| # """ Stores a single image as a .png file on disk. | |
| # Parameters: | |
| # --------------- | |
| # image image array, (32, 32, 3) to be stored | |
| # image_id integer unique ID for image | |
| # label image label | |
| # """ | |
| # Image.fromarray(image).save(disk_dir / f"{image_id}.png") | |
| # with open(disk_dir / f"{image_id}.csv", "wt") as csvfile: | |
| # writer = csv.writer( | |
| # csvfile, delimiter=" ", quotechar="|", quoting=csv.QUOTE_MINIMAL | |
| # ) | |
| # writer.writerow([label]) | |
| """ | |
| Generate OCR | |
| """ | |
| def generate_ocr(Method,img): | |
| try: | |
| text_output = '' | |
| add_csv = [] | |
| image_id = 1 | |
| 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) | |
| new_data=img.reshape(img.shape) | |
| imge = Image.fromarray(new_data.astype(np.uint8),'RGB') | |
| feature = datasets.Image(decode=False) | |
| add_csv = [Method,img,text_output] | |
| # new_image = {'image': feature.encode_example(imge)} | |
| # print("newimage: ",new_image) | |
| with open(DATA_FILE, "a") as f: | |
| writer = csv.writer(f) | |
| # write the data | |
| writer.writerow(add_csv) | |
| commit_url = repo.push_to_hub() | |
| print(commit_url) | |
| # print("^^%%",Image.fromarray(img).save(DATA_FILE2 +"/"+ f"{image_id}.png")) | |
| # with open(DATA_FILE2, "wt") as csvfile: | |
| # writer = csv.writer( | |
| # csvfile, delimiter=" ", quotechar="|", quoting=csv.QUOTE_MINIMAL | |
| # ) | |
| # writer.writerow(['0']) | |
| # try: | |
| # dataset = load_dataset("pragnakalp/OCR-img-to-text", streaming=True) | |
| # print(dataset.features) | |
| # except Exception as e: | |
| # print("error in loading data",e) | |
| # with open(DATA_FILE, "a") as csvfile: | |
| # writer = csv.Writer(csvfile) | |
| # writer.writerow(add_csv) | |
| # commit_url = repo.push_to_hub() | |
| # print(commit_url) | |
| # save_details(Method,text_output,img) | |
| # sender="pragnakalp.dev33@gmail.com" | |
| # password="httscgatatbbxxur" | |
| # reciever="pragnakalp.dev35@gmail.com" | |
| # s = smtplib.SMTP('smtp.gmail.com', 587) | |
| # s.starttls() | |
| # s.ehlo() | |
| # s.login(sender,password) | |
| # message = """Subject : Appointment Booking\n\n | |
| # Hello, | |
| # Your OCR generated successfully""" | |
| # s.sendmail(sender, reciever, message) | |
| # s.quit() | |
| # mailsend=1 | |
| # print("Send mail successfully") | |
| return text_output | |
| 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): | |
| # print("//////////") | |
| hostname = get_device_ip_address() | |
| # url = 'https://pragnakalpdev33.pythonanywhere.com/HF_space_image_to_text' | |
| # url = 'http://pragnakalpdev35.pythonanywhere.com/HF_space_image_to_text' | |
| # myobj = {'Method': Method,'text_output':text_output,'img':img.tolist(),'hostname':hostname} | |
| # x = requests.post(url, json = myobj) | |
| # 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("myimage.jpg", img) | |
| # with open('savedata.txt', 'w') as f: | |
| # print("write test") | |
| # f.write("testdata") | |
| # print("write Successfully") | |
| # # img = Image.open(r"/home/user/app/") | |
| # # img.save(modified_picture_path) | |
| # input_img = 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") | |
| return send_user_email() | |
| # return x | |
| """ | |
| Create user interface for OCR demo | |
| """ | |
| image = gr.Image(shape=(224, 224),elem_id="img_div") | |
| method = gr.Radio(["EasyOCR", "KerasOCR", "PaddleOCR"],value="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", | |
| css=".gradio-container {background-color: lightgray} #radio_div {background-color: #FFD8B4; font-size: 40px;}", | |
| allow_flagging = "manual" | |
| # flagging_dir = "flagged", | |
| # flagging_callback=hf_writer | |
| ) | |
| demo.launch(enable_queue = False) | |