基于opencv的银行卡识别

基于OpenCV的银行卡识别可以通过以下步骤实现：

1.读取图像并将其转换为灰度图像和二进制图像。

import cv2
import matplotlib.pyplot as plt

def read(img, thresh=127, inv=False):
    origin = cv2.imread(img)
    gray = cv2.imread(img, 0)
    binary = cv2.threshold(gray, thresh, 255, cv2.THRESH_BINARY_INV if inv else cv2.THRESH_BINARY)[1]
    return origin, gray, binary

2.将图像中的银行卡区域提取出来。

def extract_card(img):
    _, contours, _ = cv2.findContours(img, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
    contours = sorted(contours, key=cv2.contourArea, reverse=True)
    for contour in contours:
        perimeter = cv2.arcLength(contour, True)
        approx = cv2.approxPolyDP(contour, 0.02 * perimeter, True)
        if len(approx) == 4:
            x, y, w, h = cv2.boundingRect(contour)
            return x, y, w, h

3.将银行卡区域与模板进行匹配，找到银行卡号码的位置。

def match_template(img, template):
    res = cv2.matchTemplate(img, template, cv2.TM_CCOEFF_NORMED)
    _, max_val, _, max_loc = cv2.minMaxLoc(res)
    return max_val, max_loc

4.将银行卡号码数字割出来，并与模板比较，选出相似度最高的答案。

def extract_number(img, x, y, w, h):
    number_img = img[y:y+h, x:x+w]
    number_img = cv2.resize(number_img, (w*5, h*5))
    _, number_img = cv2.threshold(number_img, 0, 255, cv2.THRESH_BINARY+cv2.THRESH_OTSU)
    number_img = cv2.resize(number_img, (w, h))
    return number_img

def match_number(number_img, templates):
    max_val = 0
    max_index = 0
    for i, template in enumerate(templates):
        val, _ = match_template(number_img, template)
        if val > max_val:
            max_val = val
            max_index = i
    return max_index