v2

analysis_func.py

@@ -0,0 +1,8 @@
+import pandas as pd
+import numpy as np 
+
+def read_csv(path):
+    dataFrame = pd.read_csv('temp.csv')
+    return dataFrame
+
+

app.py

@@ -1,7 +1,7 @@
 import streamlit as st
-from screen_add_note import screen_add_main
-from screen_scan import screen_scan_main
-from screen_note import screen_note_main
+from screen.screen_add_note import screen_add_main
+from screen.screen_scan import screen_scan_main
+from screen.screen_note import screen_note_main
 from streamlit_option_menu import option_menu
 
 def local_css(file_name):
@@ -18,8 +18,8 @@ def streamlit_menu(example=3):
         # 2. horizontal menu with custom style
         selected = option_menu(
             menu_title=None,  # required
-            options=["Optik Okuma", "Yeni Not Girisi", "Not Goruntuleme"],  # required
-            icons=["house", "book", "envelope"], 
+            options=["Optik Okuma", "Not Girisi", "Not Duzenleme","Analiz"],  # required
+            icons=["house", "book", "envelope","envelope"], 
             default_index=0,  # optional
             orientation="horizontal",
             styles={
@@ -46,9 +46,11 @@ def main():
     
     if selected == "Optik Okuma":
         screen_scan_main()
-    if selected == "Yeni Not Girisi":
+    if selected == "Not Girisi":
         screen_add_main()
-    if selected == "Not Goruntuleme":
+    if selected == "Not Duzenleme":
+        screen_note_main()
+    if selected == "Analiz":
         screen_note_main()
    
     

cevapanahtari/cevapanahtari_ders1.txt

@@ -0,0 +1,20 @@
+1 a
+2 a
+3 c
+4 d
+5 c
+6 b
+7 e
+8 a
+9 b
+10 c
+11 d
+12 d
+13 c
+14 b
+15 b
+16 e
+17 c
+18 b
+19 a
+20 a

cevapanahtari/cevapanahtari_ders2.txt

@@ -0,0 +1,20 @@
+1 b
+2 c
+3 b
+4 a
+5 a
+6 e
+7 d
+8 a
+9 b
+10 c
+11 b
+12 d
+13 d
+14 e
+15 b
+16 b
+17 a
+18 c
+19 d
+20 b 

cevapanahtari/cevapanahtari_ders3.txt

@@ -0,0 +1,20 @@
+1 a
+2 b
+3 a
+4 a
+5 e
+6 d
+7 c
+8 c
+9 b
+10 b 
+11 e
+12 d
+13 c
+14 b
+15 c
+16 a
+17 b
+18 d
+19 c
+20 b

data

@@ -1 +1 @@
-Subproject commit ac8c215821138cfcb2bad83a69cb3c1fece7b936
+Subproject commit e29ebebe3fcb37c4ac9e61991f29a7924b5890e1

data_func.py

@@ -28,8 +28,8 @@ def make_new_data(sinif_kodu,ogrenci_no,ders_kodu,notu,yanlislar):
     new_data = pd.DataFrame([yeni_satir])
     return new_data 
     
-def update(new_data, repo_df):
-    updated_df = pd.concat([repo_df, new_data])
+def update(new_data, ex_df):
+    updated_df = pd.concat([ex_df, new_data])
     return updated_df
     
 def save_and_push(dataFrame,repo):

functions.py

@@ -84,6 +84,17 @@ def grading(answers,num_questions,myAnswers):
         else:
             grading.append(0)
             wrong_ans.append(x+1)
+            if myAnswers[x]== 1:
+                wrong_ans.append("A")
+            if myAnswers[x]== 2:
+                wrong_ans.append("B")
+            if myAnswers[x]== 3:
+                wrong_ans.append("C")
+            if myAnswers[x]== 4:
+                wrong_ans.append("D")
+            if myAnswers[x]== 5:
+                wrong_ans.append("E")
+        
     score = (sum(grading)/num_questions)*100 
     return score ,wrong_ans
 
@@ -179,7 +190,7 @@ def image_show(images):
             st.header("2")
             st.image(images[2],width=200)
             
-    col4, col5, col6, col7= st.columns(4)
+    col4, col5= st.columns(2)
     
     with col4:
             st.header("3")
@@ -187,10 +198,12 @@ def image_show(images):
     with col5:
             st.header("4")
             st.image(images[5],width=200)
+    col6, col7= st.columns(2)
     with col6:
             st.header("5")
-            st.image(images[6],width=200)
+            st.image(images[6],width=150)
+    
     with col7:
             st.header("6")
-            st.image(images[7],width=200)
+            st.image(images[7],width=150)
     

optic1.py

@@ -11,10 +11,18 @@ choices=6
 
 
 
-def optic1(ans_txt,pathImage, save_images= True):
+def optic1(ans_txt1,ans_txt2,ans_txt3,pathImage, save_images= True):
 #cevap anahtarini dosyadan okuma ve sayiya cevirme
-    ans_abc = functions.read_answers(ans_txt)
-    ans = functions.answers2numbers(ans_abc)
+    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)
+
+
 
 #perspektif islemleri icin cozunurluk
     wrap_h = 18*20
@@ -42,29 +50,27 @@ def optic1(ans_txt,pathImage, save_images= True):
     rectCon = functions.rectContour(contours) 
     biggestContour = functions.getCornerPoints(rectCon[0])
     secondContour = functions.getCornerPoints(rectCon[1])
-    #thirdContour = functions.getCornerPoints(rectCon[2])
-    #fourthContour = functions.getCornerPoints(rectCon[3])
+    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 
+        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, 170, 255,cv2.THRESH_BINARY_INV )[1] 
-        #imgWarpColored_1 = cv2.convertScaleAbs(imgWarpGray_2)
-        imgThresh_1 = cv2.threshold(imgWarpGray_1,0,255,cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)[1] 
-        
+        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) 
@@ -73,8 +79,7 @@ def optic1(ans_txt,pathImage, save_images= True):
         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] 
-       # imgWarpGray_2 = cv2.convertScaleAbs(imgWarpGray_2)
-        imgThresh_2 = cv2.threshold(imgWarpGray_2,0,255,cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)[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)
@@ -88,9 +93,22 @@ def optic1(ans_txt,pathImage, save_images= True):
         boxes_1 = functions.splitBoxes(column_3[0])
         boxes_2 = functions.splitBoxes(column_3[1])
         boxes_3 = functions.splitBoxes(column_3[2])
-        myPixelVal = functions.pixelVal(questions,choices,boxes_1)
-        myIndex = functions.user_answers(questions,myPixelVal)
-        grading, wrong_ans = functions.grading(ans,questions,myIndex)
+        #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)
+        
+        #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)
+        
+        #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)
+        
+        
         student_idFix = ""
         for number in student_id:
             
@@ -100,7 +118,8 @@ def optic1(ans_txt,pathImage, save_images= True):
                 cv2.imwrite(f"images/{student_idFix}___{i}.jpg",resim_listesi[i])
         
         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
 
 #sonuc = optic1(ans_txt="cevapanahtari.txt",pathImage= "denemeler/100luk_numarali.jpg")

requirements.txt

@@ -6,5 +6,4 @@ huggingface_hub
 opencv-python-headless==4.4.0.42
 pickleshare ==  0.7.5
 requests ==  2.24.0
-scikit-image == 0.19.3
-streamlit_option_menu
+scikit-image == 0.19.3

screen/screen_about.py

@@ -0,0 +1,5 @@
+"""""
+Ornek Pdfler
+Nasıl KUllanılmalı gıbı seyler yaz
+
+"""

screen_add_note.py → screen/screen_add_note.py

@@ -5,10 +5,10 @@ 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")

screen/screen_analysis.py

@@ -0,0 +1,17 @@
+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_analysis_main():
+    #repo, repo_df = data_func.pull_read()
+    #ogrenci_no = st.text_input("Öğrenci No")
+    #filtered_df = repo_df[(repo_df['sinif_no'] == 3) & (repo_df['not'] > 70)]
+    dataFrame = pd.read_csv('temp.csv')
+    
+
+
+if __name__ == "__main__":
+    screen_analysis_main()

screen_note.py → screen/screen_note.py

@@ -4,16 +4,12 @@ import data_func
 from data_func import DATASET_REPO_URL,DATA_FILENAME,DATA_FILE,HF_TOKEN
 
 
-
-
-
-
 def screen_note_main():
-    
     repo, repo_df = data_func.pull_read()
+
     st.subheader("Girilen Veriler")
-    st.write(repo_df)
-        
+    st.dataframe(repo_df,use_container_width=True)
+    #filtered_df = repo_df[(repo_df['sinif_no'] == 3) & (repo_df['not'] > 70)]
         
 if __name__ == "__main__":
     screen_note_main()

screen/screen_scan.py

@@ -0,0 +1,55 @@
+import csv
+import streamlit as st
+import numpy as np
+import cv2
+from PIL import Image
+import optic1
+from functions import image_show
+import pandas as pd 
+from data_func import make_new_data,update
+
+@st.cache
+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():
+    st.title("Optik Okuma")
+    dataFrame = pd.read_csv('data/temp.csv')
+    sinif_kodu = int(st.text_input("Sınıf Kodu",value=10))
+    ders_kodu = int(st.text_input("Ders Kodu",value=10))
+    image_file = st.file_uploader(
+        "Upload image for testing", type=['jpeg', 'png', 'jpg', 'webp'])
+    st.dataframe(dataFrame)
+    
+    if image_file != None:
+        image = Image.open(image_file)
+        image = np.array(image.convert('RGB'))
+        
+        if st.button("Process"):
+            #(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",
+                                                                             pathImage=image,save_images=False) 
+
+            image_show(resim_list)
+
+            st.write("Notu:",grading[0])
+            st.write("Yanlis Yaptigi sorular:",wrong_ans[0])
+            st.write("Ogrenci Numarasi:",student_idFix)
+            new_data = make_new_data(sinif_kodu=sinif_kodu,ders_kodu=ders_kodu, ogrenci_no=int(student_idFix),
+                                     notu=grading[0],yanlislar=wrong_ans[0])
+            
+            st.dataframe(new_data)
+            updated = update(new_data=new_data,ex_df=dataFrame)
+            st.dataframe(updated,use_container_width=True)
+            updated.to_csv("data/temp.csv",index=False)
+            
+            st.download_button(label="Download data as CSV",data=convert_df_to_csv(updated),
+                               file_name='large_df.csv',mime='text/csv',)        
+            
+
+#python -m streamlit run app.py
+if __name__ == '__main__':
+    screen_scan_main()	   

screen_scan.py

@@ -1,33 +0,0 @@
-import streamlit as st
-import numpy as np
-import cv2
-from PIL import Image
-import optic1
-from functions import image_show
-
-
-
-
-def screen_scan_main():
-    st.title("Optik Okuma")
-
-    image_file = st.file_uploader(
-        "Upload image for testing", type=['jpeg', 'png', 'jpg', 'webp'])
-    if image_file != None:
-        image = Image.open(image_file)
-        image = np.array(image.convert('RGB'))
-        if st.button("Process"):
-                #(ans_txt,pathImage, save_images= True)
-                grading, wrong_ans, student_idFix, resim_list =optic1.optic1(ans_txt="cevapanahtari.txt",pathImage=image,save_images=False) 
-
-                image_show(resim_list)
-                grading = str(grading)
-                wrong_ans = str(wrong_ans)
-                #student_idFix = str(student_idFix)
-                st.write("Notu:",grading)
-                st.write("Yanlis Yaptigi sorular:",wrong_ans)
-                st.write("Ogrenci Numarasi:",student_idFix)
-  
-#python -m streamlit run app.py
-if __name__ == '__main__':
-    screen_scan_main()	   

toDo.txt

@@ -0,0 +1,11 @@
+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