app.py

import re
import cv2
import numpy as np
from paddleocr import PaddleOCR
from PIL import Image
import matplotlib.pyplot as plt
import pandas as pd
import matplotlib.pyplot as plt
import onnxruntime
import gradio as gr

# initialize the OCR
ocr = PaddleOCR(lang='sl',
                enable_mkldnn=True,
                cls=False,
                show_log= False)

# initialize the models 
model_deskew = onnxruntime.InferenceSession("./models/CNN_deskew_v0.0.2.onnx")
model_denoise = onnxruntime.InferenceSession("./models/autoencoder_denoise_v0.0.2.onnx")

##### All Functions #####

def preprocess_image(image):
    '''
    Function: preprocess image to make it lighter to work on 
    Input: resized image
    Output: image
    '''
    image = np.array(image)   
    scale = 1.494
    width = int(image.shape[1] / scale)
    height = int(image.shape[0] / scale)
    dim = (width, height)
    image = cv2.resize(image, dim, interpolation = cv2.INTER_AREA)  
    return image


def deskew(image, model):
    '''
    Function: deskew an image
    Input: takes an image as an array
    Output: deskewed image
    '''
    # map the model classes to the actual degree of skew
    map = { 0: '-1', 1: '-10', 2: '-11', 3: '-12', 4: '-13',
            5: '-14',6: '-15', 7: '-2',  8: '-3',  9: '-4',
            10: '-5',11: '-6',12: '-7', 13: '-8', 14: '-9',
            15: '0', 16: '1', 17: '10', 18: '11', 19: '12',
            20: '13',21: '14',22: '15', 23: '180',24: '2',
            25: '270',26: '3',27: '4',  28: '5',  29: '6',
            30: '7',  31: '8',32: '9',  33: '90'}

    image_d = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
    width = int(image_d.shape[1] * 0.2)
    height = int(image_d.shape[0] * 0.2)
    dim = (width, height)
    # resize image
    res = cv2.resize(image_d, dim, interpolation = cv2.INTER_AREA)  
    resized = cv2.resize(res, (200, 200))
    # add two dimensions to feed to the model 
    resized = resized.astype('float32').reshape(1, 200, 200 ,1)
    # normalize
    resized = resized/255  
    # predictions
    predictions = model.run(None, {'conv2d_input': resized})
    # best prediction
    pred = predictions[0].argmax()
    # angle of skew
    angle = int(map[pred])
    skew_confidence = predictions[0][0][pred] * 100
    # deskew original image
    if angle == 90:
        deskewed_image = cv2.rotate(image, cv2.ROTATE_90_COUNTERCLOCKWISE)
        return deskewed_image, angle, skew_confidence
    if angle == 270:
        deskewed_image = cv2.rotate(image, cv2.ROTATE_90_CLOCKWISE)
        return deskewed_image, angle, skew_confidence
    
    (h, w) = image.shape[:2]
    center = (w // 2, h // 2)
    M = cv2.getRotationMatrix2D(center, -angle, 1.0)
    deskewed_image = cv2.warpAffine(image, M, (w, h), flags=cv2.INTER_CUBIC,
                            borderMode=cv2.BORDER_REPLICATE)
    return deskewed_image, angle, skew_confidence


def prepare_image_to_autoencoder(image):
    '''
    Function: prepare the image to be passed to the autoencoder.
    Input: image (_type_): deskewed image
    Output: resized image to be passed to the autoencoder
    '''
    height, width = image.shape[:2]
    target_height = 600 
    target_width = 600
    image = image[int(height/3.6): int(height/1.87), int(width/3.67): int(width/1.575)]
    # reshape image to fixed size 
    image = cv2.resize(image, (target_width, target_height))
    image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
     # normalize images
    image = image / 255.0 
    # reshape to pass image to autoencoder
    image = image.reshape(target_height, target_width, 1) 
    return image


def autoencode_ONNX(image, model):
    '''
    Function: remove noise from image
    Input: image and autoencoder model
    Output: image
    '''
    image = image.astype(np.float32).reshape(1, 600, 600, 1)
    image = model.run(None, {'input_2': image})
    image = image[0]
    image = image.squeeze()
    image = image * 255
    image = image.astype('uint8')
    return image

def extract_detected_entries_pdl(image):
    """
    Extracts text, scores, and boundary boxes from an image using OCR and returns a DataFrame.

    This function takes an input image, applies OCR to detect text in the image, and then extracts
    the detected text, confidence scores, and boundary boxes for each text entry. The extracted
    information is returned in a DataFrame with columns "Text", "Score", and "Boundary Box".

    Parameters
    ----------
    image : numpy.ndarray
        The input image to be processed.

    Returns
    -------
    pandas.DataFrame
        A DataFrame containing the extracted text, confidence scores, and boundary boxes
        for each detected text entry. The DataFrame has the following columns:
        - "Text": The detected text.
        - "Score": The confidence score for the detected text.
        - "Boundary Box": The coordinates of the boundary box for the detected text.
    """
    # run the OCR
    result = ocr.ocr(image)
    # creates Pandas Dataframe
    txt = []
    scores = []
    boxes = []
    for r in result[0]:
        txt.append(cleanString_basic(r[-1][0]))
        scores.append(r[-1][1])
        boxes.append(tuple(map(tuple, r[0])))

    return pd.DataFrame({"Text": txt, "Score": scores, "Boundary Box": boxes})

def cleanString_basic(word):
  word = word.replace("$", "s")
  return word

def clean_string_start(string: 'str'):

    names_flags = "√"
    chars_to_remove = ['!', "'", '[', ']', '*', '|', '.', ':', '\\', '/']
    if string.startswith(tuple(chars_to_remove)):
        names_flags = string[0]
        string = string[1:]
    return string, names_flags

def clean_string_end(string: 'str'):

    names_flags = "√"
    chars_to_remove = ['!', "'", '[', ']', '*', '|', '.', ':', '\\', '/']
    if string.endswith(tuple(chars_to_remove)):
        names_flags = string[-1]
        string = string[:-1]
    return string, names_flags

def clean_dates(date: 'str'):
    '''
    Function: cleans the fields "datum smrti" and returns the char removed.
    Input: date (string format)
    Output: cleaned frame
    '''

    date_flags = "Y"
    # finds special characters in the string
    special_char = re.findall(r'[a-zA-Z!\[\|]', date)
    if len(special_char) > 0:
        date_flags = special_char
    # remove special characters in the string
    string = re.sub(r'[a-zA-Z!\[\|]', '', date)
    return string, date_flags


##### Main Function #####

def pdf_extract_gr(image):
  extractimg = preprocess_image(image)
  #extractimg = np.array(image)
  # deskew the image 
  deskewed_image, angle, skew_confidence = deskew(extractimg, model_deskew)
  # prepare the image for the autoencoder
  cleanimg = prepare_image_to_autoencoder(deskewed_image)
  # clean the image
  img = autoencode_ONNX(cleanimg, model_denoise)
  # extract the entries from the image
  df = extract_detected_entries_pdl(img)
  # first name 
  firstnamerow = df.iloc[0]
  firstname = firstnamerow[0]
  firstnameconfidence = round(float(firstnamerow[1]) * 100,3)
  firstnameconfidence = f"{firstnameconfidence}%"
  # surname 
  surnamerow = df.iloc[1]
  surname = surnamerow[0]
  surnameconfidence = round(float(surnamerow[1]) * 100,3)
  surnameconfidence = f"{surnameconfidence}%"
  # death date condifence
  dodrow = df.iloc[2]
  dodname = dodrow[0]
  dodconfidence = round(float(dodrow[1]) * 100,3)
  dodconfidence = f"{dodconfidence}%"
  # return all the results 
  return df, deskewed_image, angle, skew_confidence, img, firstname, firstnameconfidence, surname, surnameconfidence, dodname, dodconfidence


##### Gradio Style #####

css = """
.run_container {
  display: flex;
  flex-direction: column;
  align-items: center;
  gap: 10px;
}
.run_btn {
  margin: auto;
  width: 50%;
  display: flex;
}
.upload_cell {
  margin: auto;
  display: flex;
}
.results_container {
  display: flex;
  justify-content: space-evenly;
}
.results_cell {
}
"""

##### Gradio Blocks #####

with gr.Blocks(css = css) as demo:
  gr.Markdown("""
      # Death Certificate Extraction
      """, elem_classes = "h1")
  gr.Markdown("Upload a PDF, extract data")
  with gr.Box(elem_classes = "run_container"):
    # ExtractInput = gr.File(label = "Death Certificate", elem_classes="upload_cell")
    ExtractButton = gr.Button(label = "Extract", elem_classes="run_btn")
  with gr.Row(elem_id = "hide"):
    with gr.Column():
      ExtractInput = gr.Image()
    with gr.Column():
      # ExtractResult = gr.Image(label = "result")
      with gr.Row(elem_classes = "results_container"):
        FirstNameBox = gr.Textbox(label = "First Name", elem_classes = "results_cell")
        FirstNameConfidenceBox = gr.Textbox(label = "First Name Confidence", elem_classes = "results_cell")
      with gr.Row(elem_classes = "results_container"):
        SurnameNameBox = gr.Textbox(label = "Surname", elem_classes = "results_cell")
        SurnameNameConfidenceBox = gr.Textbox(label = "Surname Confidence", elem_classes = "results_cell")
      with gr.Row(elem_classes = "results_container"):
        DODBox = gr.Textbox(label = "Date of Death", elem_classes = "results_cell")
        DODConfidenceBox = gr.Textbox(label = "Date of Death Confidence", elem_classes = "results_cell")

      with gr.Accordion("Full Results", open = False):
        ExtractDF = gr.Dataframe(label = "Results")

      with gr.Accordion("Clean Image", open = False):
        CleanOutput = gr.Image()

      with gr.Accordion("Deskew", open = False):
        DeskewOutput = gr.Image()
        with gr.Column():
          DeskewAngle = gr.Number(label = "Angle")
        with gr.Column():
          DeskewConfidence = gr.Number(label = "Confidence")

  ExtractButton.click(fn=pdf_extract_gr, 
                      inputs = ExtractInput, 
                      outputs = [ExtractDF, DeskewOutput, DeskewAngle, 
                                 DeskewConfidence, CleanOutput, FirstNameBox, 
                                 FirstNameConfidenceBox, SurnameNameBox, 
                                 SurnameNameConfidenceBox, DODBox, DODConfidenceBox])

demo.launch(show_api=True, share=False, debug=True)