opencv信用卡数字识别代码

以下是使用OpenCV进行信用卡数字识别的示例代码：

import cv2
import numpy as np

# 读取图片并将其转换为灰度图像
img = cv2.imread('credit_card.jpg')
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

# 对图像进行预处理
gray = cv2.GaussianBlur(gray, (5, 5), 0)
edged = cv2.Canny(gray, 50, 200, 255)

# 查找轮廓
_, contours, _ = cv2.findContours(edged.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)

for i in range(len(contours)):
    # 计算轮廓周长
    perimeter = cv2.arcLength(contours[i], True)
    
    # 进行轮廓近似
    approx = cv2.approxPolyDP(contours[i], 0.02 * perimeter, True)
    
    # 如果轮廓具有4个顶点，则可以认为找到了信用卡
    if len(approx) == 4:
        # 绘制轮廓并进行透视变换
        cv2.drawContours(img, [approx], -1, (0, 255, 0), 2)
        rect = np.float32([approx[0], approx[1], approx[2], approx[3]])
        dst = np.float32([[0, 0], [500, 0], [500, 200], [0, 200]])
        M = cv2.getPerspectiveTransform(rect, dst)
        warp = cv2.warpPerspective(gray, M, (500, 200))
        
        # 对变换后的图像进行二值化处理
        _, thresh = cv2.threshold(warp, 150, 255, cv2.THRESH_BINARY)
        
        # 查找数字轮廓并对其进行识别
        _, digit_contours, _ = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
        for j in range(len(digit_contours)):
            x, y, w, h = cv2.boundingRect(digit_contours[j])
            if w > 10 and h > 10:
                digit = thresh[y:y+h, x:x+w]
                resized_digit = cv2.resize(digit, (20, 20))
                padded_digit = np.pad(resized_digit, ((5,5),(5,5)), "constant", constant_values=0)
                cv2.imshow("Digit", padded_digit)
                cv2.waitKey(0)

上述代码的作用是读取信用卡图片，使用Canny边缘检测算法查找轮廓，并对轮廓进行近似和透视变换，从而得到数字区域的图像。然后对数字区域的图像进行二值化处理和数字识别。