[Python+OpenCV项目实战]——信用卡数字识别

朱雀 2022-11-26 07:55 96阅读 0赞

### 文章目录 ###

*  一、代码
 *  二、效果

# 一、代码 #

# 导入工具包
    from imutils import contours
    import numpy as np
    import argparse
    import cv2
    import myutils
    
    # 指定信用卡类型
    FIRST_NUMBER = {
        
    	"3": "American Express",
    	"4": "Visa",
    	"5": "MasterCard",
    	"6": "Discover Card"
    }
    # 绘图展示
    def cv_show(name,img):
    	cv2.imshow(name, img)
    	cv2.waitKey(0)
    	cv2.destroyAllWindows()
    # 读取一个模板图像
    img = cv2.imread("images/ocr_a_reference.png")
    cv_show('img',img)
    # 灰度图
    ref = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    cv_show('ref',ref)
    # 二值图像
    ref = cv2.threshold(ref, 10, 255, cv2.THRESH_BINARY_INV)[1]
    cv_show('ref',ref)
    
    # 计算轮廓
    #cv2.findContours()函数接受的参数为二值图，即黑白的（不是灰度图）,cv2.RETR_EXTERNAL只检测外轮廓，cv2.CHAIN_APPROX_SIMPLE只保留终点坐标
    #返回的list中每个元素都是图像中的一个轮廓
    
    refCnts, hierarchy = cv2.findContours(ref.copy(), cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)
    
    cv2.drawContours(img,refCnts,-1,(0,0,255),3) 
    cv_show('img',img)
    # print(np.array(refCnts).shape)
    refCnts = myutils.sort_contours(refCnts, method="left-to-right")[0] #排序，从左到右，从上到下
    digits = {
        }
    
    # 遍历每一个轮廓
    for (i, c) in enumerate(refCnts):
    	# 计算外接矩形并且resize成合适大小
    	(x, y, w, h) = cv2.boundingRect(c)
    	roi = ref[y:y + h, x:x + w]
    	roi = cv2.resize(roi, (57, 88))
    
    	# 每一个数字对应每一个模板
    	digits[i] = roi
    
    # 初始化卷积核
    rectKernel = cv2.getStructuringElement(cv2.MORPH_RECT, (9, 3))
    sqKernel = cv2.getStructuringElement(cv2.MORPH_RECT, (5, 5))
    
    #读取输入图像，预处理
    image = cv2.imread("images/credit_card_01.png")
    cv_show('image',image)
    image = myutils.resize(image, width=300)
    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
    cv_show('gray',gray)
    
    #礼帽操作，突出更明亮的区域
    tophat = cv2.morphologyEx(gray, cv2.MORPH_TOPHAT, rectKernel) 
    cv_show('tophat',tophat) 
    # 
    gradX = cv2.Sobel(tophat, ddepth=cv2.CV_32F, dx=1, dy=0, #ksize=-1相当于用3*3的
    	ksize=-1)
    
    
    gradX = np.absolute(gradX)
    (minVal, maxVal) = (np.min(gradX), np.max(gradX))
    gradX = (255 * ((gradX - minVal) / (maxVal - minVal)))
    gradX = gradX.astype("uint8")
    
    print (np.array(gradX).shape)
    cv_show('gradX',gradX)
    
    #通过闭操作（先膨胀，再腐蚀）将数字连在一起
    gradX = cv2.morphologyEx(gradX, cv2.MORPH_CLOSE, rectKernel) 
    cv_show('gradX',gradX)
    #THRESH_OTSU会自动寻找合适的阈值，适合双峰，需把阈值参数设置为0
    thresh = cv2.threshold(gradX, 0, 255,
    	cv2.THRESH_BINARY | cv2.THRESH_OTSU)[1] 
    cv_show('thresh',thresh)
    
    #再来一个闭操作
    
    thresh = cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, sqKernel) #再来一个闭操作
    cv_show('thresh',thresh)
    
    # 计算轮廓
    
    threshCnts, hierarchy = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL,
    	cv2.CHAIN_APPROX_SIMPLE)
    
    cnts = threshCnts
    cur_img = image.copy()
    cv2.drawContours(cur_img,cnts,-1,(0,0,255),3) 
    cv_show('img',cur_img)
    locs = []
    
    # 遍历轮廓
    for (i, c) in enumerate(cnts):
    	# 计算矩形
    	(x, y, w, h) = cv2.boundingRect(c)
    	ar = w / float(h)
    
    	# 选择合适的区域，根据实际任务来，这里的基本都是四个数字一组
    	if ar > 2.5 and ar < 4.0:
    
    		if (w > 40 and w < 55) and (h > 10 and h < 20):
    			#符合的留下来
    			locs.append((x, y, w, h))
    
    # 将符合的轮廓从左到右排序
    locs = sorted(locs, key=lambda x:x[0])
    output = []
    
    # 遍历每一个轮廓中的数字
    for (i, (gX, gY, gW, gH)) in enumerate(locs):
    	# initialize the list of group digits
    	groupOutput = []
    
    	# 根据坐标提取每一个组
    	group = gray[gY - 5:gY + gH + 5, gX - 5:gX + gW + 5]
    	cv_show('group',group)
    	# 预处理
    	group = cv2.threshold(group, 0, 255,
    		cv2.THRESH_BINARY | cv2.THRESH_OTSU)[1]
    	cv_show('group',group)
    	# 计算每一组的轮廓
    	digitCnts,hierarchy = cv2.findContours(group.copy(), cv2.RETR_EXTERNAL,
    		cv2.CHAIN_APPROX_SIMPLE)
    	digitCnts = contours.sort_contours(digitCnts,
    		method="left-to-right")[0]
    
    	# 计算每一组中的每一个数值
    	for c in digitCnts:
    		# 找到当前数值的轮廓，resize成合适的的大小
    		(x, y, w, h) = cv2.boundingRect(c)
    		roi = group[y:y + h, x:x + w]
    		roi = cv2.resize(roi, (57, 88))
    		cv_show('roi',roi)
    
    		# 计算匹配得分
    		scores = []
    
    		# 在模板中计算每一个得分
    		for (digit, digitROI) in digits.items():
    			# 模板匹配
    			result = cv2.matchTemplate(roi, digitROI,
    				cv2.TM_CCOEFF)
    			(_, score, _, _) = cv2.minMaxLoc(result)
    			scores.append(score)
    
    		# 得到最合适的数字
    		groupOutput.append(str(np.argmax(scores)))
    
    	# 画出来
    	cv2.rectangle(image, (gX - 5, gY - 5),
    		(gX + gW + 5, gY + gH + 5), (0, 0, 255), 1)
    	cv2.putText(image, "".join(groupOutput), (gX, gY - 15),
    		cv2.FONT_HERSHEY_SIMPLEX, 0.65, (0, 0, 255), 2)
    
    	# 得到结果
    	output.extend(groupOutput)
    
    # 打印结果
    print("Credit Card Type: {}".format(FIRST_NUMBER[output[0]]))
    print("Credit Card #: {}".format("".join(output)))
    cv2.imshow("Image", image)
    cv2.waitKey(0)

out：

(189, 300)
    Credit Card Type: Visa
    Credit Card #: 4000123456789010

# 二、效果 #

![在这里插入图片描述][20200811104014928.png]  
![在这里插入图片描述][20200811104033561.png]  
![在这里插入图片描述][2020081110404626.png]  
![在这里插入图片描述][20200811104056718.png]  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dhMXR6eQ_size_16_color_FFFFFF_t_70]  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dhMXR6eQ_size_16_color_FFFFFF_t_70 1]  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dhMXR6eQ_size_16_color_FFFFFF_t_70 2]  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dhMXR6eQ_size_16_color_FFFFFF_t_70 3]  
![在这里插入图片描述][20200811104153434.png]  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dhMXR6eQ_size_16_color_FFFFFF_t_70 4]  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dhMXR6eQ_size_16_color_FFFFFF_t_70 5]  
![在这里插入图片描述][20200811104235350.png]  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dhMXR6eQ_size_16_color_FFFFFF_t_70 6]

[20200811104014928.png]: /images/20221124/f90db5f8a4d54a3db8a40ad3fc0315e2.png
[20200811104033561.png]: /images/20221124/93ca2a09d80a41b9a45d5ccb44ff7c03.png
[2020081110404626.png]: /images/20221124/a964d307363444a5a68ea0d61a2c86b2.png
[20200811104056718.png]: /images/20221124/4418cab5313b458eadf8a5dfd6427175.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dhMXR6eQ_size_16_color_FFFFFF_t_70]: /images/20221124/1584c27900894387bbdff1e23c566169.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dhMXR6eQ_size_16_color_FFFFFF_t_70 1]: /images/20221124/73d9ec02a44045a7882b45ad7cb6ee87.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dhMXR6eQ_size_16_color_FFFFFF_t_70 2]: /images/20221124/436ce973ef4b48778a40b6e39f33641f.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dhMXR6eQ_size_16_color_FFFFFF_t_70 3]: /images/20221124/43bcc83d72134cde92d463ac105715a2.png
[20200811104153434.png]: /images/20221124/0c7ffe6053b64bfbafcc73e942d2c660.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dhMXR6eQ_size_16_color_FFFFFF_t_70 4]: /images/20221124/f43a6a7328014c14a26b5f747a742344.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dhMXR6eQ_size_16_color_FFFFFF_t_70 5]: /images/20221124/e57adad8630941089411c2ee1b6fcb95.png
[20200811104235350.png]: /images/20221124/b724c743c30d45d098fb204c94fb5c3b.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dhMXR6eQ_size_16_color_FFFFFF_t_70 6]: /images/20221124/3efa70de461548348199b0489875681a.png