Opencv's practical learning of credit card recognition (4)

Effect display

Process introduction

• First process the picture , Extract the digital part
• Divide the four numbers , And match the template
• Deal with numbers and areas , Marked on the original drawing

step

Do image processing

• The picture is too big , Need to zoom , When marking later, you need to zoom back .
• Perform a capping operation , Expansion characteristics
• use sobel Edge detection , It's used here x Direction detection , Then normalize , Control the value to 255 Inside

sobelX = cv.Sobel(top_hat, cv.CV_32F, 1, 0)

• cv_32F The goal is 32 Bit signed floating point number , because sobel The operator is to subtract the left from the right , It could be a negative number , So keep positive and negative numbers , Then perform the absolute value operation

sobelX = np.absolute(sobelX)
(min, max) = (np.min(top_hat), np.max(top_hat))
sobelX = (255 * ((sobelX - min)/ (max - min)))
sobelX = sobelX.astype('uint8')

• Carry out one-step closing operation , Remove noise
• Binary operation
• Do the closing operation , Connect into blocks
• Edge detection
• Filter selection area
• Carry out digital region segmentation and extraction
• Digital content recognition

def find_num(ori_path):
    card_path = ori_path

    card_img = cv.imread(card_path)
    card_img = cv.resize(card_img, None, fx=0.7, fy=0.7)
    #  Graying 
    card_gray = cv.cvtColor(card_img, cv.COLOR_BGR2GRAY)
    kernel = cv.getStructuringElement(cv.MORPH_RECT, (5, 5))
    rectKernel = cv.getStructuringElement(cv.MORPH_RECT, (13, 3))
    top_hat = cv.morphologyEx(card_gray, cv.MORPH_TOPHAT, kernel)
    #  operator sobel
    sobelX = cv.Sobel(top_hat, cv.CV_32F, 1, 0)
    
    sobelX = np.absolute(sobelX)
    (min, max) = (np.min(top_hat), np.max(top_hat))
    sobelX = (255 * ((sobelX - min)/ (max - min)))
    sobelX = sobelX.astype('uint8')
    #  Two valued 
    card_close = cv.morphologyEx(sobelX, cv.MORPH_CLOSE, rectKernel)
    ret, threshold = cv.threshold(card_close, 0, 255, cv.THRESH_BINARY | cv.THRESH_OTSU)

    card_gray = cv.morphologyEx(threshold, cv.MORPH_CLOSE, kernel, iterations=1)

    contours, hierachy = cv.findContours(card_gray, cv.RETR_TREE, cv.CHAIN_APPROX_SIMPLE)
    # cv.drawContours(card_img, contours, -1, (0, 255, 0), 4)
    list = []
    
    for cont in contours:
        #  Draw a rectangular 
        x, y, w, h = cv.boundingRect(cont)
        rate = w/h
        if 2.7 < rate < 3.5:
            if (50 < w < 95) and (10 < h < 40):
                # rect = cv.rectangle(card_img, (x, y), (x+w, y+h), (0, 255, 0), 1)
                # cv.imshow('card1', card_img[y: y + h, x: x + w])
                list.append(([x, y, w, h], card_img[y: y + h, x: x + w]))
    #  Sort from left to right 
    list = sorted(list, key= lambda a: a[0][0])
    num_list = []
    for cont, img in list:
        four_list = ""
        img1 = cv.cvtColor(img, cv.COLOR_BGR2GRAY)
        ret, img1 = cv.threshold(img1, 0, 255,  cv.THRESH_BINARY | cv.THRESH_OTSU)
        contours1, hierachy = cv.findContours(img1, cv.RETR_EXTERNAL, cv.CHAIN_APPROX_SIMPLE)
        # cv.drawContours(img, contours1, -1, (0, 255, 0), 1)
        temp_list = []
        for cont1 in contours1:
            #  Draw a rectangle 
            x, y, w, h = cv.boundingRect(cont1)
            rect = cv.rectangle(img1, (x, y), (x+w, y+h), (0, 255, 0), 1)
            
            rect1 = img1[y: y + h , x : x + w ]
            temp_list.append(([x, y, w, h], rect1))
        temp_list = sorted(temp_list, key= lambda a: a[0][0])
        for i, img2 in temp_list:
            four_list += (match_img(img2))
        num_list.append(four_list)
    print(num_list)
    return list, num_list

Template processing

• Divide directly with normal operation
• Write local data

def split_template():
    reference_path = './data/reference.png'
    img_base = cv.imread(reference_path)
    img_gray = cv.cvtColor(img_base, cv.COLOR_BGR2GRAY)
    ret, threshold = cv.threshold(img_gray, 0, 255, cv.THRESH_BINARY_INV)

    contours, hierachy = cv.findContours(threshold, cv.RETR_EXTERNAL, cv.CHAIN_APPROX_SIMPLE)
    for i, cont in enumerate(contours):
        x, y, w, h = cv.boundingRect(cont)
        cv.imwrite('template/%s.png'%i, threshold[y- 5: y + h + 5, x - 5: x + w + 5])

Match template

• Because the image exported above is named
  
• So the number should be 9- Subscript , You should pay attention to .

import os
def match_img(img):
    max = 0
    max_index = 0
    for i,name in enumerate(os.listdir("./template")):
        img_bg = cv.imread('./template/%s'%name, 0)
        img = cv.resize(img, (img_bg.shape[1], img_bg.shape[0]))
        result = cv.matchTemplate(img_bg, img, cv.TM_CCOEFF_NORMED)
        if result > max:
            max = result
            max_index = 9 - i
    return str(max_index)

Show the matching effect

• Here you need to pass in the original image , Picture list , A list of numbers
• Traverse the annotation

def draw_card(img, list, num_list):
    #  Input coordinates list,  Numbers list (list, numList)
    for i, cont in enumerate(list):
        x, y, w, h = cont[0]
        x = int(x/0.7); y = int(y/0.7); w = int(w/0.7); h = int(h/0.7)
        print(x, y, w, h)
        cv.rectangle(img, (x, y), (x + w, y + h), (0, 255, 0), 2)
        num_str = num_list[i]
        cv.putText(img, num_str, (x, y - 10), cv.FONT_HERSHEY_COMPLEX, 1, (0, 255, 0), 2, cv.LINE_AA)
    cv.imshow('card', img)
    cv.waitKey(0)
    cv.destroyAllWindows()

Call the main function

def main():
    ori_path = './data/card3.png'
    split_template()
    list, num_list = find_num(ori_path)
    draw_card(cv.imread(ori_path), list, num_list)
main()

effect

defects

• Here is a silver one with reflective effect , It's not handled very well , And the following English is also very similar to the ratio of numbers .
• There are students who have found a solution , Leave a comment

The resource acquisition

Click to download

Complete code

import numpy as np
import cv2 as cv
from PIL import Image

import os
def match_img(img):
    max = 0
    max_index = 0
    for i,name in enumerate(os.listdir("./template")):
        img_bg = cv.imread('./template/%s'%name, 0)
        img = cv.resize(img, (img_bg.shape[1], img_bg.shape[0]))
        result = cv.matchTemplate(img_bg, img, cv.TM_CCOEFF_NORMED)
        if result > max:
            max = result
            max_index = 9 - i
    
    return str(max_index)
def draw_card(img, list, num_list):
    #  Input coordinates list,  Numbers list (list, numList)
    for i, cont in enumerate(list):
        x, y, w, h = cont[0]
        x = int(x/0.7); y = int(y/0.7); w = int(w/0.7); h = int(h/0.7)
        print(x, y, w, h)
        cv.rectangle(img, (x, y), (x + w, y + h), (0, 255, 0), 2)
        num_str = num_list[i]
        cv.putText(img, num_str, (x, y - 10), cv.FONT_HERSHEY_COMPLEX, 1, (0, 255, 0), 2, cv.LINE_AA)
    cv.imshow('card', img)
    cv.waitKey(0)
    cv.destroyAllWindows()

def find_num(ori_path):
    card_path = ori_path

    card_img = cv.imread(card_path)
    card_img = cv.resize(card_img, None, fx=0.7, fy=0.7)
    #  Graying 
    card_gray = cv.cvtColor(card_img, cv.COLOR_BGR2GRAY)
    kernel = cv.getStructuringElement(cv.MORPH_RECT, (5, 5))
    rectKernel = cv.getStructuringElement(cv.MORPH_RECT, (13, 3))
    top_hat = cv.morphologyEx(card_gray, cv.MORPH_TOPHAT, kernel)
    #  operator sobel
    sobelX = cv.Sobel(top_hat, cv.CV_32F, 1, 0)
    sobelX = np.absolute(sobelX)
    (min, max) = (np.min(top_hat), np.max(top_hat))
    sobelX = (255 * ((sobelX - min)/ (max - min)))
    sobelX = sobelX.astype('uint8')
    #  Two valued 
    card_close = cv.morphologyEx(sobelX, cv.MORPH_CLOSE, rectKernel)
    ret, threshold = cv.threshold(card_close, 0, 255, cv.THRESH_BINARY | cv.THRESH_OTSU)

    card_gray = cv.morphologyEx(threshold, cv.MORPH_CLOSE, kernel, iterations=1)

    contours, hierachy = cv.findContours(card_gray, cv.RETR_TREE, cv.CHAIN_APPROX_SIMPLE)
    # cv.drawContours(card_img, contours, -1, (0, 255, 0), 4)
    list = []
    
    for cont in contours:
        #  Draw a rectangular 
        x, y, w, h = cv.boundingRect(cont)
        rate = w/h
        if 2.7 < rate < 3.5:
            if (50 < w < 95) and (10 < h < 40):
                # rect = cv.rectangle(card_img, (x, y), (x+w, y+h), (0, 255, 0), 1)
                # cv.imshow('card1', card_img[y: y + h, x: x + w])
                list.append(([x, y, w, h], card_img[y: y + h, x: x + w]))
    #  Sort from left to right 
    list = sorted(list, key= lambda a: a[0][0])
    num_list = []
    for cont, img in list:
        four_list = ""
        img1 = cv.cvtColor(img, cv.COLOR_BGR2GRAY)
        ret, img1 = cv.threshold(img1, 0, 255,  cv.THRESH_BINARY | cv.THRESH_OTSU)
        contours1, hierachy = cv.findContours(img1, cv.RETR_EXTERNAL, cv.CHAIN_APPROX_SIMPLE)
        # cv.drawContours(img, contours1, -1, (0, 255, 0), 1)
        temp_list = []
        for cont1 in contours1:
            #  Draw a rectangle 
            x, y, w, h = cv.boundingRect(cont1)
            rect = cv.rectangle(img1, (x, y), (x+w, y+h), (0, 255, 0), 1)
            
            rect1 = img1[y: y + h , x : x + w ]
            temp_list.append(([x, y, w, h], rect1))
        temp_list = sorted(temp_list, key= lambda a: a[0][0])
        for i, img2 in temp_list:
            four_list += (match_img(img2))
        num_list.append(four_list)
    print(num_list)
    return list, num_list

def split_template():
    reference_path = './data/reference.png'
    img_base = cv.imread(reference_path)
    img_gray = cv.cvtColor(img_base, cv.COLOR_BGR2GRAY)
    ret, threshold = cv.threshold(img_gray, 0, 255, cv.THRESH_BINARY_INV)

    contours, hierachy = cv.findContours(threshold, cv.RETR_EXTERNAL, cv.CHAIN_APPROX_SIMPLE)
    for i, cont in enumerate(contours):
        x, y, w, h = cv.boundingRect(cont)
        cv.imwrite('template/%s.png'%i, threshold[y- 5: y + h + 5, x - 5: x + w + 5])
def main():
    ori_path = './data/card4.png'
    split_template()
    list, num_list = find_num(ori_path)
    draw_card(cv.imread(ori_path), list, num_list)
main()