Python+OpenCV实现信用卡数字识别的方法详解

template = cv2.imread('C:/Users/bwy/Desktop/number.png')
template_gray = cv2.cvtColor(template, cv2.COLOR_BGR2GRAY)
cv_show('template_gray', template_gray)
 
# 形成二值图像，因为要做轮廓检测
ret, template_thresh = cv2.threshold(template_gray, 127, 255, cv2.THRESH_BINARY_INV)
cv_show('template_thresh', template_thresh)

template_contours, hierarchy = cv2.findContours(template_thresh,
                                                cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
cv2.drawContours(template,template_contours,-1,(0,0,255),2)
cv_show('template',template)

print(np.array(refCnts,-1,(0,0,255),3)

def contours_sort(contours, method=0):
    if method == 0:
        contours = sorted(contours, key=lambda x: cv2.boundingRect(x)[0])
    else:
        contours = sorted(contours, key=lambda x: cv2.boundingRect(x)[0], reverse=True)
    return contours

dict_template = {}
for i, contour in enumerate(template_contours):
    # 画出其外接矩阵，获得其位置信息
    x, y, w, h = cv2.boundingRect(contour)
    template_img = template_thresh[y:y + h, x:x + w]
    # 使用cv2.resize变化模板的大小
    template_img = cv2.resize(template_img, dsize)
    cv_show('template_img{}'.format(i), template_img)
    dict_template[i] = template_img 

card_gray = cv2.cvtColor(card, cv2.COLOR_BGR2GRAY)
cv_show('card_gray',card_gray)

card_thresh =cv2.threshold(card_gray,0,255,cv2.THRESH_BINARY|cv2.THRESH_OTSU)[1]
cv_show('card_thresh',card_thresh)

kernel=np.ones((9,3),np.uint8)
card_tophat=cv2.morphologyEx(card_gray,cv2.MORPH_TOPHAT,kernel)
cv_show('card_tophat',card_tophat)

bankcard_contours, hierarchy = cv2.findContours(card_thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
banck_card_cnts = []
draw_img = card.copy() 
for i, contour in enumerate(bankcard_contours):
    x, y, w, h = cv2.boundingRect(contour)
    # 数字的x 坐标在 一定的位置范围
    if 0.5 * card_h < y < 0.6 * card_h: 
        banck_card_cnts.append((x, y, w, h))
        draw_img = cv2.rectangle(draw_img, pt1=(x, y), pt2=(x + w, y + h), color=(0, 0, 255),
                                 thickness=2)  # 画出这个矩形，会在原图上画
cv_show_image('rectangle_contours_img', draw_img)

for i, locs in enumerate(banck_card_cnts):
 
    x, y, w, h = locs[:]  # 保留了在原始图像的位置信息
    dst_img = card_thresh[y:y + h, x:x + w]  # 获得当前图像的位置和区域
    dst_img = cv2.resize(dst_img, dsize)
    cv_show('rectangle_contours_img', dst_img)
    tm_vals = {}
    for number, temp_img in dict_template.items():
        # 模板匹配，采用计算相关性系数，值越大越相关
        res = cv2.matchTemplate(dst_img, temp_img, cv2.TM_CCOEFF_NORMED)
        min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res)
        tm_vals[number] = max_val
 
    number_tm = max(tm_vals, key=tm_vals.get)
 
    # 在图像上画出结果来
    draw_img = cv2.rectangle(draw_img, pt1=(x, y), pt2=(x + w, y + h), color=(0, 0, 255),
                             thickness=2)  
    cv2.putText(draw_img, str(number_tm), (x, y - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.65,
                color=(0, 0, 255), thickness=2)
 
cv_show_image('final_result', draw_img)