[Python+OpenCV项目实战]——答题卡识别判卷

╰半橙微兮° 2022-11-26 07:52 229阅读 0赞

### 文章目录 ###

import numpy as np
    import cv2
    
    # 正确答案
    ANSWER_KEY = {
        0: 1, 1: 4, 2: 0, 3: 3, 4: 1}
    
    def order_points(pts):
    	# 一共4个坐标点
    	rect = np.zeros((4, 2), dtype = "float32")
    
    	# 按顺序找到对应坐标0123分别是 左上，右上，右下，左下
    	# 计算左上，右下
    	s = pts.sum(axis = 1)
    	rect[0] = pts[np.argmin(s)]
    	rect[2] = pts[np.argmax(s)]
    
    	# 计算右上和左下
    	diff = np.diff(pts, axis = 1)
    	rect[1] = pts[np.argmin(diff)]
    	rect[3] = pts[np.argmax(diff)]
    
    	return rect
    
    def four_point_transform(image, pts):
    	# 获取输入坐标点
    	rect = order_points(pts)
    	(tl, tr, br, bl) = rect
    
    	# 计算输入的w和h值
    	widthA = np.sqrt(((br[0] - bl[0]) ** 2) + ((br[1] - bl[1]) ** 2))
    	widthB = np.sqrt(((tr[0] - tl[0]) ** 2) + ((tr[1] - tl[1]) ** 2))
    	maxWidth = max(int(widthA), int(widthB))
    
    	heightA = np.sqrt(((tr[0] - br[0]) ** 2) + ((tr[1] - br[1]) ** 2))
    	heightB = np.sqrt(((tl[0] - bl[0]) ** 2) + ((tl[1] - bl[1]) ** 2))
    	maxHeight = max(int(heightA), int(heightB))
    
    	# 变换后对应坐标位置
    	dst = np.array([
    		[0, 0],
    		[maxWidth - 1, 0],
    		[maxWidth - 1, maxHeight - 1],
    		[0, maxHeight - 1]], dtype = "float32")
    
    	# 计算变换矩阵
    	M = cv2.getPerspectiveTransform(rect, dst)
    	warped = cv2.warpPerspective(image, M, (maxWidth, maxHeight))
    
    	# 返回变换后结果
    	return warped
    def sort_contours(cnts, method="left-to-right"):
    	reverse = False
    	i = 0
    	if method == "right-to-left" or method == "bottom-to-top":
    		reverse = True
    	if method == "top-to-bottom" or method == "bottom-to-top":
    		i = 1
    	boundingBoxes = [cv2.boundingRect(c) for c in cnts]
    	(cnts, boundingBoxes) = zip(*sorted(zip(cnts, boundingBoxes),
                                            key=lambda b: b[1][i], reverse=reverse))
    	return cnts, boundingBoxes
    def cv_show(name,img):
    	cv2.imshow(name, img)
    	cv2.waitKey(0)
    	cv2.destroyAllWindows()
    
    # 预处理
    image = cv2.imread("images/test_02.png")
    contours_img = image.copy()
    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
    blurred = cv2.GaussianBlur(gray, (5, 5), 0)
    cv_show('blurred',blurred)
    edged = cv2.Canny(blurred, 75, 200)
    cv_show('edged',edged)
    
    # 轮廓检测
    cnts,_ = cv2.findContours(edged.copy(), cv2.RETR_EXTERNAL,
    	cv2.CHAIN_APPROX_SIMPLE)
    
    cv2.drawContours(contours_img,cnts,-1,(0,0,255),3)
    cv_show('contours_img',contours_img)
    docCnt = None
    
    # 确保检测到了
    
    if len(cnts) > 0:
    	# 根据轮廓大小进行排序
    	cnts = sorted(cnts, key=cv2.contourArea, reverse=True)
    
    	# 遍历每一个轮廓
    	for c in cnts:
    		# 近似
    		peri = cv2.arcLength(c, True)
    		approx = cv2.approxPolyDP(c, 0.02 * peri, True)
    
    		# 准备做透视变换
    		if len(approx) == 4:
    			docCnt = approx
    			break
    
    # 执行透视变换
    
    warped = four_point_transform(gray, docCnt.reshape(4, 2))
    cv_show('warped',warped)
    # Otsu's 阈值处理
    thresh = cv2.threshold(warped, 0, 255,
    	cv2.THRESH_BINARY_INV | cv2.THRESH_OTSU)[1]
    cv_show('thresh',thresh)
    thresh_Contours = thresh.copy()
    # 找到每一个圆圈轮廓
    cnts,_ = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL,
    	cv2.CHAIN_APPROX_SIMPLE)
    cv2.drawContours(thresh_Contours,cnts,-1,(0,0,255),3)
    cv_show('thresh_Contours',thresh_Contours)
    questionCnts = []
    
    # 遍历
    for c in cnts:
    	# 计算比例和大小
    	(x, y, w, h) = cv2.boundingRect(c)
    	ar = w / float(h)
    
    	# 根据实际情况指定标准
    	if w >= 20 and h >= 20 and ar >= 0.9 and ar <= 1.1:
    		questionCnts.append(c)
    
    # 按照从上到下进行排序
    questionCnts = sort_contours(questionCnts,
    	method="top-to-bottom")[0]
    correct = 0
    
    # 每排有5个选项
    for (q, i) in enumerate(np.arange(0, len(questionCnts), 5)):
    	# 排序
    	cnts = sort_contours(questionCnts[i:i + 5])[0]
    	bubbled = None
    
    	# 遍历每一个结果
    	for (j, c) in enumerate(cnts):
    		# 使用mask来判断结果
    		mask = np.zeros(thresh.shape, dtype="uint8")
    		cv2.drawContours(mask, [c], -1, 255, -1) #-1表示填充
    		cv_show('mask',mask)
    		# 通过计算非零点数量来算是否选择这个答案
    		mask = cv2.bitwise_and(thresh, thresh, mask=mask)
    		total = cv2.countNonZero(mask)
    
    		# 通过阈值判断
    		if bubbled is None or total > bubbled[0]:
    			bubbled = (total, j)
    
    	# 对比正确答案
    	color = (0, 0, 255)
    	k = ANSWER_KEY[q]
    
    	# 判断正确
    	if k == bubbled[1]:
    		color = (0, 255, 0)
    		correct += 1
    
    	# 绘图
    	cv2.drawContours(warped, [cnts[k]], -1, color, 3)
    
    
    score = (correct / 5.0) * 100
    print("[INFO] score: {:.2f}%".format(score))
    cv2.putText(warped, "{:.2f}%".format(score), (10, 30),
    	cv2.FONT_HERSHEY_SIMPLEX, 0.9, (0, 0, 255), 2)
    cv2.imshow("Original", image)
    cv2.imshow("Exam", warped)
    cv2.waitKey(0)

out：

[INFO] score: 80.00%

结果：  
![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][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][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dhMXR6eQ_size_16_color_FFFFFF_t_70 6]  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dhMXR6eQ_size_16_color_FFFFFF_t_70 7]

[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dhMXR6eQ_size_16_color_FFFFFF_t_70]: /images/20221124/7aa7d4b9536b4f64aba34b3b04ff5ebe.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dhMXR6eQ_size_16_color_FFFFFF_t_70 1]: /images/20221124/6e7584b2cd024a8a908c44be5f1a1e63.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dhMXR6eQ_size_16_color_FFFFFF_t_70 2]: /images/20221124/0108f6a32d1e4c6185f902ac110b7668.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dhMXR6eQ_size_16_color_FFFFFF_t_70 3]: /images/20221124/517aa59ae7ce497e9121263c6df5a14e.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dhMXR6eQ_size_16_color_FFFFFF_t_70 4]: /images/20221124/790b21274b6b4c6f8c8cd23fa9699267.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dhMXR6eQ_size_16_color_FFFFFF_t_70 5]: /images/20221124/176e458c0e564b2f84a61798cdadf0f9.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dhMXR6eQ_size_16_color_FFFFFF_t_70 6]: /images/20221124/51dbfbe24b84464f97f1412ca7c424f6.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dhMXR6eQ_size_16_color_FFFFFF_t_70 7]: /images/20221124/f64fc234832f4204823c11eab292a7ae.png