cevap anahatari atama

data

@@ -1 +1 @@
-Subproject commit b733259fe70abefb6c892ca9617793f81ab0b2dd
+Subproject commit 1f0e28cc7a1ff84a41de050ba6f3f831c63182ef

db/temp.csv

@@ -1,26 +0,0 @@
-sinif_kodu,ogrenci_no,ders_kodu,notu,yanlis_sorulari
-10,19060365,13,65.0,"1,2,3,4"
-10,19060365,56,80.0,"1,2,3"
-10,19060322,5622,80.0,"1,2,3"
-10,19060322,5622,70.0,"1,2,3"
-10,15170018,56,30.0,"2,3,5"
-10,15001718,56,50.0,"2,3,5"
-10,15001718,56,60.0,"2,3,5"
-11,15000000,15,40.0,"1,2,3"
-11,15000001,15,30.0,"1,2,3"
-11,12000002,22,36.0,"2,5,6"
-15,19060365,15,100.0,[]
-15,19060365,15,100.0,[]
-10,19060365,10,100.0,[]
-10,19060365,10,100.0,[]
-10,19060365,10,100.0,[]
-10,19060365,10,100.0,[]
-10,19060365,10,100.0,[]
-10,19060365,10,100.0,[]
-10,19060365,10,100.0,[]
-10,19060365,10,100.0,[]
-10,19060365,10,100.0,[]
-10,19060365,10,100.0,[]
-10,19060365,10,100.0,[]
-10,19060365,10,100.0,[]
-10,19060365,10,100.0,[]

db/untitled.csv

@@ -1,11 +0,0 @@
-sinif_kodu,ogrenci_no,ders_kodu,notu,yanlis_sorulari
-11,19060365,13,65,"1,2,3,4"
-15,190603654,56,80,"1, 2, 3"
-135,190603222654,5622,81,"1, 2, 3"
-135,190603222654,5622,81,"1, 2, 3"
-12,151718,56,80,"2,3,5"
-[12],[151718],[56],[80],"['2,3,5']"
-[12],[151718],[56],[80],"['2,3,5']"
-15,15,15,89,"1,2,3"
-15,15,15,30,"1,2,3"
-111,12,22,36,2

functions.py

@@ -78,25 +78,48 @@ def splitColumn(img):
 def grading(answers,num_questions,myAnswers):
     grading=[]
     wrong_ans = []
+    empty = []
+    
     for x in range(0,num_questions):
         if answers[x] == myAnswers[x]:
             grading.append(1)
+        
+        elif answers[x] == -1:
+            grading.append(0)
+            wrong_ans.append(x+1)
+        
+        elif answers[x] == 0:
+            empty.append(x+1)    
+            
         else:
             grading.append(0)
             wrong_ans.append(x+1)
 
         
     score = (sum(grading)/num_questions)*100 
-    return score ,wrong_ans
+    return score ,wrong_ans,empty
 
 #piksel degerlerinde kullanici cevaplarini 
 #okuyupu index seklinde listeye kaydediyor
 def user_answers(num_questions,myPixelVal):
     myIndex=[]
+    
     for x in range (0,num_questions):
         arr = myPixelVal[x]
-        myIndexVal = np.where(arr == np.amax(arr))
-        myIndex.append(myIndexVal[0][0])
+        
+        #empty answers
+        if arr[1] < 150.0 and arr[2] < 150.0 and arr[3] < 150.0 and arr[4] < 150.0 and arr[5] < 150.0:
+            
+            myIndex.append(0)
+        
+        #2 or more answers
+        elif arr[1]+arr[2]>375 or arr[1]+arr[3]>375 or arr[1]+arr[4]>375 or arr[1]+arr[5]>375 or arr[2]+arr[3]>375 or arr[2]+arr[4]>375 or arr[2]+arr[5]>375 or arr[3]+arr[4]>375 or arr[3]+arr[5]>375 or arr[4]+arr[5]>375:
+            myIndex.append(-1)
+            
+        else :
+            myIndexVal = np.where(arr == np.amax(arr))
+            myIndex.append(myIndexVal[0][0])
+    print(myIndex)
     return myIndex
     
 #student id kismi yukardan asagiya dogru karsilastirma yaparak
@@ -197,4 +220,5 @@ def image_show(images):
     with col7:
             st.header("6")
             st.image(images[7],width=150)
+    
     

optic1.py

@@ -13,16 +13,10 @@ choices=6
 
 def optic1(ans_txt1,ans_txt2,ans_txt3,pathImage, save_images= True):
 #cevap anahtarini dosyadan okuma ve sayiya cevirme
-    ans_abc_1 = functions.read_answers(ans_txt1)
-    ans_1 = functions.answers2numbers(ans_abc_1)
-
-    ans_abc_2 = functions.read_answers(ans_txt2)
-    ans_2 = functions.answers2numbers(ans_abc_2)
     
-    ans_abc_3 = functions.read_answers(ans_txt3)
-    ans_3 = functions.answers2numbers(ans_abc_3)
-
-
+    ans_1 = ans_txt1
+    ans_2 = ans_txt2
+    ans_3 = ans_txt3
 
 #perspektif islemleri icin cozunurluk
     wrap_h = 18*20
@@ -96,17 +90,17 @@ def optic1(ans_txt1,ans_txt2,ans_txt3,pathImage, save_images= True):
         #boxes_1
         myPixelVal_1 = functions.pixelVal(questions,choices,boxes_1)
         myIndex_1 = functions.user_answers(questions,myPixelVal_1)
-        grading_1, wrong_ans_1 = functions.grading(ans_1,questions,myIndex_1)
+        grading_1, wrong_ans_1, empty_1 = functions.grading(ans_1,questions,myIndex_1)
         
         #boxes_2
         myPixelVal_2 = functions.pixelVal(questions,choices,boxes_2)
         myIndex_2 = functions.user_answers(questions,myPixelVal_2)
-        grading_2, wrong_ans_2 = functions.grading(ans_2,questions,myIndex_2)
+        grading_2, wrong_ans_2, empty_2 = functions.grading(ans_2,questions,myIndex_2)
         
         #boxes_3        
         myPixelVal_3 = functions.pixelVal(questions,choices,boxes_3)
         myIndex_3 = functions.user_answers(questions,myPixelVal_3)
-        grading_3, wrong_ans_3 = functions.grading(ans_3,questions,myIndex_3)
+        grading_3, wrong_ans_3 ,empty_3 = functions.grading(ans_3,questions,myIndex_3)
         
         
         student_idFix = ""
@@ -120,7 +114,9 @@ def optic1(ans_txt1,ans_txt2,ans_txt3,pathImage, save_images= True):
         resim_listesi = [img,imgGray,imgBlur,imgCanny,imgContours,imgBiggestContour,imgThresh_1,imgThresh_2]
         grading = [grading_1,grading_2,grading_3]
         wrong_ans = [wrong_ans_1,wrong_ans_2,wrong_ans_3]
-    return grading, wrong_ans, student_idFix, resim_listesi
+        empty = [empty_1,empty_2,empty_3]
+        myIndexs = [myIndex_1,myIndex_2,myIndex_3]
+    return grading, wrong_ans, student_idFix, resim_listesi,empty
 
 #sonuc = optic1(ans_txt="cevapanahtari.txt",pathImage= "denemeler/100luk_numarali.jpg")
 #print(sonuc)

optic_pixel_value.txt

@@ -0,0 +1,42 @@
+[ 92. 201.  91.  93.  85.  70.]
+[ 49. 181.  43.  55.  57.  42.]
+[ 56.  57.  45. 185.  63.  48.]
+[ 69.  50.  53.  61. 190.  46.]
+[ 61.  57.  64. 198.  64.  40.]
+[ 59.  59. 194.  59.  48.  32.]
+[ 63.  60.  65.  60.  59. 176.]
+[ 74. 197.  56.  57.  60.  55.]
+[ 71.  59. 193.  62.  61.  63.]
+[ 77.  55.  56. 195.  61.  56.]
+[ 74.  61.  59.  61. 187.  56.]
+[ 87.  54.  61.  61. 191.  58.]
+[ 95.  69.  77. 204.  74.  76.]
+[101.  72. 195.  78.  70.  69.]
+[103.  73. 202.  71.  74.  75.]
+[105.  75.  72.  77.  76. 200.]
+[ 92.  78.  82. 202.  71.  65.]
+[101.  77. 205.  71.  73.  71.]
+[ 91. 210.  68.  74.  74.  59.]
+[130. 244. 117. 111. 105.  91.]
+[ 54.  74. 188.  84.  79.  62.]
+[ 24.  47.  65. 173.  50.  38.]
+[ 21.  49. 174.  66.  54.  35.]
+[ 31. 185.  73.  54.  52.  48.]
+[ 33. 198.  59.  58.  64.  43.]
+[ 28.  63.  60.  61.  68. 165.]
+[ 26.  57.  53.  64. 188.  44.]
+[ 33. 190.  55.  55.  54.  37.]
+[ 33.  61. 196.  62.  60.  51.]
+[ 35.  59.  53. 195.  48.  45.]
+[ 30.  56. 192.  57.  51.  35.]
+[ 32.  51. 193.  57.  50.  67.]
+[ 23.  53.  49.  56.  47. 212.]
+[ 32.  54.  47.  52. 182.  67.]
+[ 31.  61.  60. 194.  50.  76.]
+[ 33.  60. 196.  58.  55.  62.]
+[ 36.  59.  60. 191.  47.  51.]
+[ 30. 185.  52.  55.  47.  58.]
+[ 23.  59. 191.  62.  55.  58.]
+[ 32.  51.  50.  63. 190.  60.]
+[ 24.  48.  47. 192.  53.  60.]
+[ 55.  78. 212.  78.  76.  97.]

reader.py

@@ -0,0 +1,111 @@
+import cv2
+import numpy as np
+import functions
+
+#fotograf ozellikleri
+heightImg = 300*4
+widthImg  = 210*4
+#pathImage = "denemeler/100luk_numarali.jpg"
+questions=20
+choices=6
+
+
+
+def reader(pathImage, save_images= True):
+#cevap anahtarini dosyadan okuma ve sayiya cevirme
+    
+
+#perspektif islemleri icin cozunurluk
+    wrap_h = 18*20
+    wrap_v = 18*20
+    img = pathImage #eger girdi dogrudan np arrayse
+#fotonun okunmasi ------------------------------------------------------------------------------------------------
+    #img = cv2.imread(pathImage)
+    img = cv2.rotate(img, cv2.ROTATE_90_CLOCKWISE)
+    img = cv2.resize(img, (widthImg, heightImg)) # RESIZE IMAGE
+    imgBiggestContour = img.copy()
+    imgFinal = img.copy()
+    imgContours = img.copy()
+    imgBlank = np.zeros((heightImg,widthImg, 3), np.uint8) 
+
+#donusumler---------------------------------------------------------------------------------
+    imgGray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) # CONVERT IMAGE TO GRAY SCALE
+    imgBlur = cv2.GaussianBlur(imgGray, (5, 5), 1) # ADD GAUSSIAN BLUR
+    imgCanny = cv2.Canny(imgBlur,10,70) # APPLY CANNY 
+
+#CONTOURS-------------------------------------------------------
+    contours, hierarchy = cv2.findContours(imgCanny, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE) 
+    cv2.drawContours(imgContours, contours, -1, (0, 255, 0), 10) # DRAW ALL DETECTED CONTOURS
+
+#dortgen bulma--------------------------------------------------
+    rectCon = functions.rectContour(contours) 
+    biggestContour = functions.getCornerPoints(rectCon[0])
+    secondContour = functions.getCornerPoints(rectCon[1])
+    thirdContour = functions.getCornerPoints(rectCon[2])
+    fourthContour = functions.getCornerPoints(rectCon[3])
+
+#main
+    if biggestContour.size != 0 and secondContour.size != 0:
+    
+        cv2.drawContours(imgBiggestContour, biggestContour,-1,(0,255,0),20) 
+        cv2.drawContours(imgBiggestContour, secondContour,-1,(255,0,0),20) #sondk' kalinlik ortada renk 
+        cv2.drawContours(imgBiggestContour, thirdContour,-1,(0,0,255),20) #sondk' kalinlik ortada renk 
+        cv2.drawContours(imgBiggestContour, fourthContour,-1,(0,0,20),20) #sondk' kalinlik ortada renk 
+    
+        biggestContour=functions.reorder(biggestContour)   
+    #cevap siklari icin -************************************************************
+        pts1 = np.float32(biggestContour) 
+        pts2 = np.float32([[0, 0],[wrap_v, 0], [0, wrap_h],[wrap_v, wrap_h]]) 
+        matrix = cv2.getPerspectiveTransform(pts1, pts2)
+    
+        imgWarpColored_1 = cv2.warpPerspective(img, matrix, (wrap_v, wrap_h))
+        imgWarpGray_1 = cv2.cvtColor(imgWarpColored_1,cv2.COLOR_BGR2GRAY) 
+        imgThresh_1 = cv2.threshold(imgWarpGray_1,0,255,cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)[1]  
+
+    #second buyuk icin perspektif 
+        secondContour=functions.reorder(secondContour)
+        pts1_2 = np.float32(secondContour) 
+        pts2_2 = np.float32([[0, 0],[wrap_v, 0], [0, wrap_h],[wrap_v, wrap_h]]) 
+        matrix_2 = cv2.getPerspectiveTransform(pts1_2, pts2_2)
+        imgWarpColored_2 = cv2.warpPerspective(img, matrix_2, (wrap_v, wrap_h))
+        imgWarpGray_2 = cv2.cvtColor(imgWarpColored_2,cv2.COLOR_BGR2GRAY) 
+        #imgThresh_2 = cv2.threshold(imgWarpGray_2, 170, 255,cv2.THRESH_BINARY_INV )[1] 
+        imgThresh_2 = cv2.threshold(imgWarpGray_2,0,255,cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)[1]  
+    
+    #student id
+        bubbles = functions.split_num(imgThresh_2, 10, 10)
+        myPixelVal_2 = functions.pixelVal(10,10,bubbles)
+        myPixelVal_2 = functions.id_reorder(myPixelVal_2)
+        student_id = functions.id_answers(10,myPixelVal_2)
+        #print(student_id)
+    
+        #soru kisimi
+        column_3 = functions.splitColumn(imgThresh_1)
+        boxes_1 = functions.splitBoxes(column_3[0])
+        boxes_2 = functions.splitBoxes(column_3[1])
+        boxes_3 = functions.splitBoxes(column_3[2])
+        #boxes_1
+        myPixelVal_1 = functions.pixelVal(questions,choices,boxes_1)
+        myIndex_1 = functions.user_answers(questions,myPixelVal_1)
+                
+        #boxes_2
+        myPixelVal_2 = functions.pixelVal(questions,choices,boxes_2)
+        myIndex_2 = functions.user_answers(questions,myPixelVal_2)
+        
+        #boxes_3        
+        myPixelVal_3 = functions.pixelVal(questions,choices,boxes_3)
+        myIndex_3 = functions.user_answers(questions,myPixelVal_3)
+        
+        
+        
+        student_idFix = ""
+        for number in student_id:
+            student_idFix += str(number)
+        
+        
+        
+        myIndexs = [myIndex_1,myIndex_2,myIndex_3]
+    return  student_idFix, myIndexs
+
+#sonuc = optic1(ans_txt="cevapanahtari.txt",pathImage= "denemeler/100luk_numarali.jpg")
+#print(sonuc)

screen/screen_add_note.py

@@ -1,34 +0,0 @@
-import streamlit as st
-import pandas as pd
-import data_func
-from data_func import DATASET_REPO_URL,DATA_FILENAME,DATA_FILE,HF_TOKEN
-
-
-
-
-def screen_add_main():
-    st.title("Not Giris")
-    repo, repo_df = data_func.pull_read()
-    # Kullanıcıdan verileri alma
-    sinif_kodu = st.text_input("Sınıf Kodu")
-    ogrenci_no = st.text_input("Öğrenci No")
-    ders_kodu = st.text_input("Ders Kodu")
-    notu = st.slider("Notu", 0, 100)
-    yanlislar = st.text_input("Yanlış Sorulari (virgul ile ayirin)")
-    yanlislar = str(yanlislar)
-     
-    if st.button("Veriyi Yukle"):
-        new_data = data_func.make_new_data(sinif_kodu=sinif_kodu,
-                                           ogrenci_no= ogrenci_no,
-                                           ders_kodu= ders_kodu,
-                                           notu=notu,
-                                           yanlislar= yanlislar)
-        updated_df = data_func.update(new_data,repo_df)
-        data_func.save_and_push(updated_df,repo)
-        st.subheader("Girilen Veriler")
-        st.write(new_data)
-
-
-if __name__ == "__main__":
-    screen_add_main()
-

screen/screen_scan.py

@@ -9,7 +9,7 @@ import pandas as pd
 from data_func import make_new_data,update,save_and_push
 import os
 from huggingface_hub import Repository
-
+import reader
 
 def pull_read(DATASET_REPO_URL,HF_TOKEN,DATA_FILE):
     
@@ -28,81 +28,98 @@ def convert_df_to_csv(df):
   # IMPORTANT: Cache the conversion to prevent computation on every rerun
   return df.to_csv().encode('utf-8')  
 
+
 def screen_scan_main():
+    teacher_code = st.text_input("Ogretmen kodu:",key=12)
+    password = st.text_input("Sıfre:",key=17,type="password")
     
-    col1,col2 = st.columns(2)
-    with col1:
-        teacher_code = st.text_input("Ogretmen kodu:",key=12)
-    with col2:
-        exam_code = st.text_input("Sınav Kodu:",key=13,value=10)
-
-    exam_code = int(exam_code)
     teacher_code = str(teacher_code)
+    password = str(password)
+    
     DATA_FILENAME = f"{teacher_code}.csv"
     DATA_FILENAME = str(DATA_FILENAME)
     
     image_file = st.file_uploader(
         "Tarama Yapmak Icin Optigi Yukleyin", type=['jpeg', 'png', 'jpg', 'webp'])
-    
-    
     if image_file != None:
-        col3,col4 = st.columns(2)
-        with col3:
-            
-            if st.button("Tara ve Sonucu Goruntule"):
-                repo, repo_df = pull_read(DATASET_REPO_URL = "https://huggingface.co/datasets/mertbozkurt/school_data",
+        repo, repo_df = pull_read(DATASET_REPO_URL = "https://huggingface.co/datasets/mertbozkurt/school_data",
                                       DATA_FILE = os.path.join("data", DATA_FILENAME),
                                       HF_TOKEN = "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")
-                repo.git_pull()
+        repo.git_pull()
+                    
+        if str(repo_df["ogrenci_no"][0]) == password:
+            st.write("Giris basarili")
+                
+            exam_code = st.text_input("Sınav Kodu:",key=13,value=10)
+
+            exam_code = int(exam_code)
+                
+            global myIndexs 
+            
+            if st.button("Cevap Anahtari Sec",key=51):
                 image = Image.open(image_file)
                 image = np.array(image.convert('RGB'))
+                    #(ans_txt,pathImage, save_images= True)
+                resim_list,myIndexs =reader.reader(pathImage=image,save_images=False) 
+                
+                    
+            col3,col4 = st.columns(2)
+            with col3:
+            
+                if st.button("Tara ve Sonucu Goruntule",key=52):
+                
+                        image = Image.open(image_file)
+                        image = np.array(image.convert('RGB'))
                 #(ans_txt,pathImage, save_images= True)
-                grading, wrong_ans, student_idFix, resim_list =optic1.optic1(ans_txt1="cevapanahtari/cevapanahtari_ders1.txt",
-                                                                             ans_txt2="cevapanahtari/cevapanahtari_ders2.txt",
-                                                                             ans_txt3="cevapanahtari/cevapanahtari_ders3.txt",
+                        grading, wrong_ans, student_idFix, resim_list,empty_ans =optic1.optic1(ans_txt1=myIndexs[0],
+                                                                             ans_txt2=myIndexs[1],
+                                                                             ans_txt3=myIndexs[2],
                                                                              pathImage=image,save_images=False) 
             
                 #image_show(resim_list)
-                if len(wrong_ans[0]) == 0:
-                    wrong_ans[0] = "0"
-                wrong_ans_str = (str(wrong_ans[0]))
-                st.write("Ogrenci Numarasi:",student_idFix)
-                st.write("Ders1 Yanlis Yaptigi sorular:",(str(wrong_ans[0])))
-                st.write("Ders1 Notu:",int(grading[0]))
-                st.write("Ders1 Yanlis Yaptigi sorular:",(str(wrong_ans[1])))
-                st.write("Ders1 Notu:",int(grading[1]))
-                st.write("Ders1 Yanlis Yaptigi sorular:",(str(wrong_ans[2])))
-                st.write("Ders1 Notu:",int(grading[2]))
+                        if len(wrong_ans[0]) == 0:
+                            wrong_ans[0] = "0"
+                        wrong_ans_str = (str(wrong_ans[0]))
+                        st.write("Ogrenci Numarasi:",student_idFix)
+                        st.write("Ders1 Yanlis Yaptigi sorular:",(str(wrong_ans[0])))
+                        st.write("Ders1 Notu:",int(grading[0]))
+                        st.write("Ders2 Yanlis Yaptigi sorular:",(str(wrong_ans[1])))
+                        st.write("Ders2 Notu:",int(grading[1]))
+                        st.write("Ders3 Yanlis Yaptigi sorular:",(str(wrong_ans[2])))
+                        st.write("Ders4 Notu:",int(grading[2]))
         
-        with col4:
-            if st.button("Tara ve Sonucu Kaydet"):
-                repo, repo_df = pull_read(DATASET_REPO_URL = "https://huggingface.co/datasets/mertbozkurt/school_data",
+            with col4:
+                if st.button("Tara ve Sonucu Kaydet"):
+                        repo, repo_df = pull_read(DATASET_REPO_URL = "https://huggingface.co/datasets/mertbozkurt/school_data",
                                       DATA_FILE = os.path.join("data", DATA_FILENAME),
                                       HF_TOKEN = "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")
-                repo.git_pull()
-                image = Image.open(image_file)
-                image = np.array(image.convert('RGB'))
+                        repo.git_pull()
+                
+                        image = Image.open(image_file)
+                        image = np.array(image.convert('RGB'))
                 #(ans_txt,pathImage, save_images= True)
-                grading, wrong_ans, student_idFix, resim_list =optic1.optic1(ans_txt1="cevapanahtari/cevapanahtari_ders1.txt",
+                        grading, wrong_ans, student_idFix, resim_list =optic1.optic1(ans_txt1="cevapanahtari/cevapanahtari_ders1.txt",
                                                                              ans_txt2="cevapanahtari/cevapanahtari_ders2.txt",
                                                                              ans_txt3="cevapanahtari/cevapanahtari_ders3.txt",
                                                                              pathImage=image,save_images=False) 
             
                 #image_show(resim_list)
-                if len(wrong_ans[0]) == 0:
-                    wrong_ans[0] = "0"
+                        if len(wrong_ans[0]) == 0:
+                            wrong_ans[0] = "0"
 #
-                wrong_ans_str = (str(wrong_ans[0]))
-                new_data = make_new_data(sinav_kodu=exam_code, ogrenci_no=int(student_idFix),
-                                     notu=int(grading[0]),yanlislar=str(wrong_ans_str))
+                        wrong_ans_str = (str(wrong_ans[0]))
+                        new_data = make_new_data(sinav_kodu=exam_code, ogrenci_no=int(student_idFix),
+                        notu=int(grading[0]),yanlislar=str(wrong_ans_str))
             
                 #st.dataframe(new_data)
-                updated = update(new_data=new_data,ex_df=repo_df)
+                        updated = update(new_data=new_data,ex_df=repo_df)
                 #st.dataframe(updated,use_container_width=True)
-                save_and_push(dataFrame=updated,repo=repo,fileName=f"data/{DATA_FILENAME}")
+                        save_and_push(dataFrame=updated,repo=repo,fileName=f"data/{DATA_FILENAME}")
             
-                st.download_button(label="Tum verileri indirmek icin tiklayin",data=convert_df_to_csv(updated),
-                               file_name=f'{teacher_code}.csv',mime='text/csv',)        
+                        st.download_button(label="Tum verileri indirmek icin tiklayin",data=convert_df_to_csv(updated),
+                               file_name=f'{teacher_code}.csv',mime='text/csv',)      
+        else:
+            st.write("Giris basarisiz kontrol ediniz.")            
             
 #python -m streamlit run app.py
 if __name__ == '__main__':

toDo.txt

@@ -1,11 +0,0 @@
-wrong_ans gibi yaninda siklarinida kaydetsin
-scan ettikten sonra verileri son kez sorsun ve veri tabanina kaydetsin
-analiz sayfasi 
-ornek optikler icin sayfa ki kullanmak isteyen cikartsin
-
-#-------------------------------------------
-dersin hangi ders hanagi sinavi oldugu konusu
-scan sayfasindan etiket secilip o sekilde kaydedilebilir
-optikten okumak gerekmez optik uzerinde elle yazilmasi yeterli olur 
-o elle yazilan yerde buyutulur veya sayi olarak kodlanabilir bunlar kodlamaya gore sayisal
-degerine gore hangi ders hangi sinav oldugu belli olur