Python script which scan ooredoo topup cards and extract the topup code using opencv+tesseract

ooredoo-topupcard-reader.py

#! /usr/bin/env python2

import cv2
import numpy as np
from collections import deque

orig_image = None
gray_image = None
threshold_image = None
edge_image = None
def gray_step():
    """ Convert image to gray image """
    global orig_image, gray_image
    gray_image = cv2.cvtColor(orig_image,cv2.COLOR_BGR2GRAY)
    return gray_image

def threshold_step():
    """ extract white regions """
    global threshold_image
    _, threshold_image = cv2.threshold(gray_image, 127, 255, cv2.THRESH_BINARY)
    return threshold_image

def edge_step ():
    """ Add edges to white regions """
    global edge_image
    #edge_image =  cv2.Canny(gray_image, 50, 150,apertureSize = 3)
    edge_image = cv2.adaptiveThreshold(gray_image,255,cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY,11,2)
    edge_image = cv2.bitwise_and(edge_image, edge_image, mask=threshold_image)

    kernel = np.ones((5,5),np.uint8)
    edge_image = cv2.erode(edge_image, kernel)
    return edge_image

def mask_image():
    """ Find the biggest white blob in the image """
    global mask_image
    contours, _ = cv2.findContours(edge_image,cv2.RETR_LIST,cv2.CHAIN_APPROX_SIMPLE)
    height, width = edge_image.shape[:2]
    mask_image = np.zeros(edge_image.shape,np.uint8)
    area = height * width

    for cnt in contours:
        if (area/5) <cv2.contourArea(cnt):
            x,y,w,h = cv2.boundingRect(cnt)
            cv2.rectangle(mask_image,(x,y),(x+w,y+h),255, -1)

    return mask_image

def find_topup_region ():
    """ Find the gray topup region """
    topup_region = cv2.inRange(gray_image, 60, 105)
    topup_region = cv2.bitwise_and(topup_region, topup_region, mask=mask_image)

    contours, _ = cv2.findContours(topup_region, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
    maxcnt = None
    maxarea = 0

    for cnt in contours:
        if cv2.contourArea(cnt) > maxarea:
            maxcnt = cnt
            maxarea = cv2.contourArea(cnt)

    x, y, w, h = cv2.boundingRect(maxcnt)
    result = gray_image[y:y+h, x:x+w]
    kernel = np.ones((5,5),np.uint8)

    result = cv2.erode(result, kernel)
    return result

def main():
    global orig_image

    orig_image = cv2.imread('1.jpg')
    cv2.namedWindow('Image', cv2.WINDOW_NORMAL)
    cv2.imshow('Image',orig_image)

    steps = deque([gray_step, threshold_step, edge_step, mask_image, find_topup_region])
    current_image = orig_image

    while True:
        key = cv2.waitKey(1)
        if key & 0xFF == ord('q'):
            break

        if key & 0xFF == ord('n'):
            try:
                next_step = steps.popleft()
                current_image = next_step()
                cv2.imshow('Image',current_image)
            except IndexError:
                height, width = current_image.shape[:2]
                ratio = (float(300)/width)
                print width, height, ratio, ratio*height
                result = cv2.resize(current_image, (300, int(ratio*height)))
                cv2.imshow('Image', result)
                cv2.imwrite('roi.png', result)

                import pytesseract, Image, string
                img = Image.open('roi.png')
                ocr_result = pytesseract.image_to_string(img)
                ocr_result = "".join(c for c in ocr_result if c in string.digits) # only numbers
                ocr_result = " ".join(ocr_result[i*4:i*4+4] for i, _ in enumerate(ocr_result[::4]))

                cv2.putText(orig_image, ocr_result, (50, 100), cv2.FONT_HERSHEY_SIMPLEX, 3, (0, 0, 255), 5)
                cv2.imshow('Image', orig_image)

main()