app.py

import cv2
import re
import pytesseract
import numpy as np
import gradio as gr
import pandas as pd
from pytesseract import Output
from matplotlib import pyplot as plt


# get grayscale image
def get_grayscale(image):
    return cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

# noise removal
def remove_noise(image):
    return cv2.medianBlur(image,5)

#thresholding
def thresholding(image, thresh_hold=0, which='ostu'):
    if which == 'ostu':
        return cv2.threshold(image, thresh_hold, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)[1]
    elif which == 'simple':
        _, img = cv2.threshold(image,thresh_hold,255,cv2.THRESH_BINARY)
        return img
    elif which == 'adaptive':
        return cv2.adaptiveThreshold(image, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY, 11, 2)

#dilation
def dilate(image):
    kernel = np.ones((5,5),np.uint8)
    return cv2.dilate(image, kernel, iterations = 1)

#erosion
def erode(image):
    kernel = np.ones((5,5),np.uint8)
    return cv2.erode(image, kernel, iterations = 1)

#opening - erosion followed by dilation
def opening(image):
    kernel = np.ones((5,5),np.uint8)
    return cv2.morphologyEx(image, cv2.MORPH_OPEN, kernel)

#canny edge detection
def canny(image):
    return cv2.Canny(image, 100, 200)

#skew correction
def deskew(image):
    coords = np.column_stack(np.where(image > 0))
    angle = cv2.minAreaRect(coords)[-1]
    if angle < -45:
        angle = -(90 + angle)
    else:
        angle = -angle
    (h, w) = image.shape[:2]
    center = (w // 2, h // 2)
    M = cv2.getRotationMatrix2D(center, angle, 1.0)
    rotated = cv2.warpAffine(image, M, (w, h), flags=cv2.INTER_CUBIC, borderMode=cv2.BORDER_REPLICATE)
    return rotated

#template matching
def match_template(image, template):
    return cv2.matchTemplate(image, template, cv2.TM_CCOEFF_NORMED)

def show_cvimg(img, figsize=(15, 15)):
    fig, ax = plt.subplots(dpi=80, figsize=figsize)
    img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
    ax.imshow(img)


def extract_purchased_items(txt):
    pat = '.*\s*(FB|FA|NB)'
    p = re.compile("(.*) (\d+[,\.\/\:\']*\d+) (FB|FA|NB)")

    txts = txt.split('\n')
    items = []
    not_parsed = []
    for t in txts:
        if re.match(pat, t):
            result = p.search(t)
            if result is not None:
                items.append({'item': result.group(1),
                              'price': re.sub('[,\.\/\:\']', '.', result.group(2)),
                              'type': result.group(3)
                             })
            else:
                not_parsed.append({'not parsed': t})
    return pd.DataFrame(items), pd.DataFrame(not_parsed)


def parse_receipt(img, **kwargs):
    # preprocessing
    gray = get_grayscale(img)
    thresh = thresholding(gray, **kwargs)

    # ocr
    custom_config = r'--oem 3 --psm 6'
    txt = pytesseract.image_to_string(thresh, config=custom_config)

    return extract_purchased_items(txt)


iface = gr.Interface(fn=parse_receipt, inputs="image", outputs=["dataframe", "dataframe"])
iface.launch()