app.py

import asyncio
import string
from collections import Counter
from itertools import count, tee
import cv2
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import streamlit as st
import torch
from PIL import Image
from transformers import (DetrImageProcessor, TableTransformerForObjectDetection)
from vietocr.tool.config import Cfg
from vietocr.tool.predictor import Predictor

st.set_option('deprecation.showPyplotGlobalUse', False)
st.set_page_config(layout='wide')
st.title("Table Detection and Table Structure Recognition")
st.write(
    "Implemented by MSFT team: https://github.com/microsoft/table-transformer")

# Config (optional, comment out if not using)
# config = Cfg.load_config_from_name('vgg_transformer')
# config = Cfg.load_config_from_name('vgg_seq2seq')
# config['cnn']['pretrained'] = False
# config['device'] = 'cpu'
# config['predictor']['beamsearch'] = False
# detector = Predictor(config)

table_detection_model = TableTransformerForObjectDetection.from_pretrained(
    "microsoft/table-transformer-detection")
table_recognition_model = TableTransformerForObjectDetection.from_pretrained(
    "microsoft/table-transformer-structure-recognition")

def PIL_to_cv(pil_img):
    return cv2.cvtColor(np.array(pil_img), cv2.COLOR_RGB2BGR)

def cv_to_PIL(cv_img):
    return Image.fromarray(cv2.cvtColor(cv_img, cv2.COLOR_BGR2RGB))

async def pytess(cell_pil_img, threshold: float = 0.5):
    text, prob = detector.predict(cell_pil_img, return_prob=True)  # Assuming detector is defined
    if prob < threshold:
        return ""
    return text.strip()

def sharpen_image(pil_img):
    img = PIL_to_cv(pil_img)
    sharpen_kernel = np.array([[-1, -1, -1], [-1, 9, -1], [-1, -1, -1]])
    sharpen = cv2.filter2D(img, -1, sharpen_kernel)
    pil_img = cv_to_PIL(sharpen)
    return pil_img

def uniquify(seq, suffs=count(1)):
    """Make all the items unique by adding a suffix (1, 2, etc).

    Credit: https://stackoverflow.com/questions/30650474/python-rename-duplicates-in-list-with-progressive-numbers-without-sorting-list
    """
    not_unique = [k for k, v in Counter(seq).items() if v > 1]
    suff_gens = dict(zip(not_unique, tee(suffs, len(not_unique))))
    for idx, s in enumerate(seq):
        try:
            suffix = str(next(suff_gens[s]))
        except KeyError:
            continue
        else:
            seq[idx] += suffix
    return seq

def binarizeBlur_image(pil_img):
    image = PIL_to_cv(pil_img)
    thresh = cv2.threshold(image, 150, 255, cv2.THRESH_BINARY_INV)[1]
    result = cv2.GaussianBlur(thresh, (5, 5), 0)
    result = 255 - result
    return cv_to_PIL(result)

def td_postprocess(pil_img):
    '''
    Removes gray background from tables
    '''
    img = PIL_to_cv(pil_img)
    hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
    mask = cv2.inRange(hsv, (0, 0, 100), (255, 5, 255))  # (0, 0, 100), (255, 5, 255)
    nzmask = cv2.inRange(hsv, (0, 0, 5), (255, 255, 255))  # (0, 0,