python智能方案系统_利用Python开发智能阅卷系统

1 importnumpy as np2 importargparse3 importimutils4 importcv25 #设置参数

6 ap =argparse.ArgumentParser()7 ap.add_argument("-i", "--image", default="test_01.png")8 args =vars(ap.parse_args())9 #正确答案

10 ANSWER_KEY = {0: 1, 1: 4, 2: 0, 3: 3, 4: 1} #11 deforder_points(pts):12 #一共4个坐标点

13 rect = np.zeros((4, 2), dtype = "float32")14 ​15 #按顺序找到对应坐标0,1,2,3分别是 左上，右上，右下，左下

16 #计算左上，右下

17 s = pts.sum(axis = 1)18 rect[0] =pts[np.argmin(s)]19 rect[2] =pts[np.argmax(s)]20 #计算右上和左下

21 diff = np.diff(pts, axis = 1)22 rect[1] =pts[np.argmin(diff)]23 rect[3] =pts[np.argmax(diff)]24 returnrect25 ​26 deffour_point_transform(image, pts):27 #获取输入坐标点

28 rect =order_points(pts)29 (tl, tr, br, bl) =rect30 #计算输入的w和h值

31 widthA = np.sqrt(((br[0]-bl[0])** 2) + ((br[1]-bl[1])**2))32 widthB = np.sqrt(((tr[0] -tl[0]) ** 2) + ((tr[1] - tl[1]) ** 2))33 maxWidth =max(int(widthA), int(widthB))34 heightA = np.sqrt(((tr[0]-br[0])**2)+((tr[1]-br[1])**2))35 heightB = np.sqrt(((tl[0]-bl[0])**2)+((tl[1]-bl[1])**2))36 maxHeight =max(int(heightA), int(heightB))37 #变换后对应坐标位置

38 dst =np.array([39 [0, 0],40 [maxWidth - 1, 0],41 [maxWidth - 1, maxHeight - 1],42 [0, maxHeight - 1]], dtype = "float32")43 #计算变换矩阵

44 M =cv2.getPerspectiveTransform(rect, dst)45 warped =cv2.warpPerspective(image, M, (maxWidth, maxHeight))46 return warped #返回变换后结果

47 ​48 def sort_contours(cnts, method="left-to-right"):49 reverse =False50 i =051 if method == "right-to-left" or method == "bottom-to-top":52 reverse =True53 if method == "top-to-bottom" or method == "bottom-to-top":54 i = 1

55 boundingBoxes = [cv2.boundingRect(c) for c incnts]56 (cnts, boundingBoxes) = zip(*sorted(zip(cnts, boundingBoxes),57 key=lambda b: b[1][i], reverse=reverse))58 returncnts, boundingBoxes59 defcv_show(name,img):60 cv2.imshow(name, img)61 cv2.waitKey(0)62 cv2.destroyAllWindows()63 ​64 ​65 image = cv2.imread(args["image"])66 contours_img =image.copy()67 gray =cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)68 blurred = cv2.GaussianBlur(gray, (5, 5), 0)69 edged = cv2.Canny(blurred, 75, 200)70 #轮廓检测

71 cnts =cv2.findContours(edged.copy(), cv2.RETR_EXTERNAL,72 cv2.CHAIN_APPROX_SIMPLE)[1]73 cv2.drawContours(contours_img,cnts,-1,(0,0,255),3)74 docCnt =None75 ​76 #确保检测到了

77 if len(cnts) >0:78 #根据轮廓大小进行排序

79 cnts = sorted(cnts, key=cv2.contourArea, reverse=True)80 for c in cnts: #遍历每一个轮廓

81 #近似

82 peri =cv2.arcLength(c, True)83 approx = cv2.approxPolyDP(c, 0.02 *peri, True)84 #准备做透视变换

85 if len(approx) == 4:86 docCnt =approx87 break

88 #执行透视变换

89 warped = four_point_transform(gray, docCnt.reshape(4, 2))90 ​91 thresh = cv2.threshold(warped, 0, 255,92 cv2.THRESH_BINARY_INV | cv2.THRESH_OTSU)[1]93 thresh_Contours =thresh.copy()94 #找到每一个圆圈轮廓

95 cnts =cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL,96 cv2.CHAIN_APPROX_SIMPLE)[1]97 cv2.drawContours(thresh_Contours,cnts,-1,(0,0,255),3)98 questionCnts =[]99 for c in cnts:#遍历

100 #计算比例和大小

101 (x, y, w, h) =cv2.boundingRect(c)102 ar = w /float(h)103 #根据实际情况指定标准

104 if w >= 20 and h >= 20 and ar >= 0.9 and ar <= 1.1:105 questionCnts.append(c)106 #按照从上到下进行排序

107 questionCnts =sort_contours(questionCnts,108 method="top-to-bottom")[0]109 correct =0110 #每排有5个选项

111 for (q, i) in enumerate(np.arange(0, len(questionCnts), 5)):112 cnts = sort_contours(questionCnts[i:i + 5])[0]113 bubbled =None114 for (j, c) in enumerate(cnts): #遍历每一个结果

115 #使用mask来判断结果

116 mask = np.zeros(thresh.shape, dtype="uint8")117 cv2.drawContours(mask, [c], -1, 255, -1) #-1表示填充

118 #通过计算非零点数量来算是否选择这个答案

119 mask = cv2.bitwise_and(thresh, thresh, mask=mask)120 total =cv2.countNonZero(mask)121 #通过阈值判断

122 if bubbled is None or total >bubbled[0]:123 bubbled =(total, j)124 #第二步，与正确答案进行对比

125 color = (0, 0, 255)126 k =ANSWER_KEY[q]127 #判断正确

128 if k == bubbled[1]:129 color = (0, 255, 0)130 correct += 1

131 cv2.drawContours(warped, [cnts[k]], -1, color, 3) #绘图

132 ​133 #正确率的文本显示

134 score = (correct / 5.0) * 100

135 print("[INFO] score: {:.2f}%".format(score))136 cv2.putText(warped, "{:.2f}%".format(score), (10, 30),137 cv2.FONT_HERSHEY_SIMPLEX, 0.9, (0, 0, 255), 2)138 cv2.imshow("Input", image)139 cv2.imshow("Output", warped)140 cv2.waitKey(0)