fir

app.py

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+import streamlit as st
+from screen import screen_scan_main
+
+st.markdown("""
+        <style>
+               .block-container {
+                    padding-top: 0rem;
+                    padding-bottom: 0rem;
+                    padding-left: 1rem;
+                    padding-right: 1rem;
+                }
+        </style>
+        """, unsafe_allow_html=True)
+
+
+def main():
+  
+    screen_scan_main()
+
+   
+if __name__ == "__main__":
+    main()
+    
+    
+    

data

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+Subproject commit 4cde9e2675d9a91b0dc66510a8978808d267cfb1

data_func.py

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+import os
+from huggingface_hub import Repository
+import pandas as pd
+
+DATASET_REPO_URL = "https://huggingface.co/datasets/mertbozkurt/school_data"
+DATA_FILENAME = "untitled.csv"
+DATA_FILE = os.path.join("data", DATA_FILENAME)
+HF_TOKEN = "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"
+
+def pull_read():
+    
+    repo = Repository(
+        local_dir="data", clone_from=DATASET_REPO_URL, use_auth_token=HF_TOKEN
+        )
+    
+    with open(DATA_FILE) as csvfile:
+        df = pd.read_csv(csvfile) 
+        df = pd.DataFrame(df)
+    
+    return repo, df
+
+#sinav_kodu,ogrenci_no,notu1,yanlis_sorulari1,notu2,yanlis_sorulari2,notu3,yanlis_sorulari3
+def make_new_data(sinav_kodu,ogrenci_no,notu1,yanlis_sorulari1,notu2,yanlis_sorulari2,notu3,yanlis_sorulari3):
+    yeni_satir = {"sinav_kodu": sinav_kodu, 
+              "ogrenci_no": ogrenci_no, 
+              "notu1": notu1, 
+             "yanlis_sorulari1": yanlis_sorulari1,
+             "notu2": notu2, 
+             "yanlis_sorulari2": yanlis_sorulari2,
+             "notu3": notu3, 
+             "yanlis_sorulari3": yanlis_sorulari3,
+             }
+    new_data = pd.DataFrame([yeni_satir])
+    return 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,fileName):
+    dataFrame.to_csv(fileName,index=False)
+    commit_url = repo.push_to_hub()
+    return commit_url
+
+"""repo, repo_df = pull_read()
+new_data = make_new_data(12,151718,56,80,"2,3,5")
+updated_df = update(new_data,repo_df)
+save_and_push(updated_df,repo)"""

functions.py

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+import cv2
+import numpy as np
+import streamlit as st 
+
+def rectContour(contours):
+    rectCon = []
+    max_area = 0
+    for i in contours:
+        area = cv2.contourArea(i) #alan hesabi
+        #piksel alani 50den byukse gecerlidir
+        if area > 30:
+            peri = cv2.arcLength(i, True)
+            approx = cv2.approxPolyDP(i, 0.02 * peri, True) #kac tane koseye sahip oldugu
+            if len(approx) == 4: #4 ise dortgendir
+                rectCon.append(i)
+    rectCon = sorted(rectCon, key=cv2.contourArea,reverse=True) #alanlari hesaplicak ve siralicak ki ona gore alanlari belirleyelim
+    #print(len(rectCon))
+    return rectCon
+
+def getCornerPoints(cont):
+    peri = cv2.arcLength(cont, True)
+    approx = cv2.approxPolyDP(cont, 0.02 * peri, True) #kose degerleri
+    return approx
+
+
+def reorder(myPoints):
+
+    myPoints = myPoints.reshape((4, 2)) #fazla  koseliyi kaldiralim
+    #print(myPoints)
+    myPointsNew = np.zeros((4, 1, 2), np.int32) 
+    add = myPoints.sum(1)
+    #print(add)
+    #print(np.argmax(add))
+    myPointsNew[0] = myPoints[np.argmin(add)]  #[0,0]
+    myPointsNew[3] =myPoints[np.argmax(add)]   #[w,h]
+    diff = np.diff(myPoints, axis=1)
+    myPointsNew[1] =myPoints[np.argmin(diff)]  #[w,0]
+    myPointsNew[2] = myPoints[np.argmax(diff)] #[h,0]
+
+    return myPointsNew
+
+#siklari bolmek icin 20 tane soru vertical/ 5 tane isaret alani +1 tane soru sayisi yazan yer
+#6 horizatanl bolmek
+def splitBoxes(img):
+    rows = np.vsplit(img,20) #vertical
+    boxes=[]
+    for r in rows:
+        cols= np.hsplit(r,6) #horizantal
+        for box in cols:
+            boxes.append(box)
+    return boxes
+
+#ogrenci numarasi alani icin ayni fonksiyonu kullandik 
+#yuakrdakisini silip sadece bu da kullanilabilir dogru degerler ile
+#ogrenci numarasi alani 0-9 arasi sayilardan 10 tane isaretleme yeri iceriyor
+#10x10seklinde boleriz
+def split_num(img,vertical, horizantal):
+    rows = np.vsplit(img,vertical) #vertical
+    boxes=[]
+    for r in rows:
+        cols= np.hsplit(r,horizantal) #horizantal
+        for box in cols:
+            boxes.append(box)
+    return boxes
+
+#yan yana 3 tane birlesik ders alani oldugu icin onlari 3 ayri 
+#sekle getiriyor
+def splitColumn(img):
+    column = np.hsplit(img,3)
+    
+    return column
+
+
+#puan hesaplama alani
+#soru sayisi dogru cevaplari ve ogrenci cevaplarini aliyor
+#bunlari karsilastirip yeni bir listeye 1/0 seklinde kodluyor
+#1ler toplanip puan hesaplanmis oluyor
+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 myAnswers[x] == -1:
+            grading.append(0)
+            wrong_ans.append(x+1)
+        
+        elif myAnswers[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,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]
+        t =150
+        #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]>t and arr[2]>t) or ( arr[1]>t and arr[3]>t) or (arr[1]>t and arr[4]>t) or (arr[1]>t and arr[5]> t) or (arr[2]>t and arr[3]> t) or (arr[2]>t and arr[4]> t) or (arr[2]>t and arr[5]> t) or (arr[3]>t and arr[4]> t) or (arr[3]>t and arr[5]> t) or (arr[4]>t and arr[5]>t) :
+            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
+#isretli alan tespit edilecegi icin satir ve sutunlari tekrar duzenlemiz gerekti
+#[[1,2,3],[4,5,6],[7,8,9]] ----> [[1,4,7],[2,5,8],[3,6,9]]
+def id_reorder(myPixelVal):
+    duz_liste = []
+    for sutun in range(len(myPixelVal[0])):
+        for satir in range(len(myPixelVal)):
+            duz_liste.append(myPixelVal[satir][sutun])
+    yeni_liste = []
+    satir = []
+    for eleman in duz_liste:
+        satir.append(eleman)
+        if len(satir) == len(myPixelVal):
+            yeni_liste.append(satir)
+            satir = []
+    return yeni_liste
+                
+#ogrenci numarasi kisminin piksel degerine gore hangisinin iseretli
+#oldugunun tespiti    
+def id_answers(vertical_num,myPixelVal):
+    myIndex=[]
+    for x in range (0,vertical_num):
+        arr = myPixelVal[x]
+        myIndexVal = np.where(arr == np.amax(arr))
+        myIndex.append(myIndexVal[0][0])
+    return myIndex
+
+def pixelVal(num_questions,choices,box):
+    countR=0 #rows
+    countC=0 #column
+    myPixelVal = np.zeros((num_questions,choices))
+    for image in box:
+        totalPixels = cv2.countNonZero(image)
+        myPixelVal[countR][countC]= totalPixels
+        countC += 1
+        if (countC==choices):countC=0;countR +=1
+    return myPixelVal
+
+#dosyadan cevap anahtarinin okunmasi
+def read_answers(dosya_adi):
+    with open(dosya_adi, 'r') as f:
+        satirlar = f.readlines()
+
+    okunan_veriler = []
+    for satir in satirlar:
+        sutunlar = satir.split()
+        okunan_veriler.append(sutunlar[1])
+        
+    return okunan_veriler
+
+
+#dosyadan okunan cevaplarin numerik hale getirilmesi
+def answers2numbers(answers):
+    num_answers = []
+    for i in answers:
+        if i == "a":
+            num_answers.append(1)
+        elif i == "b":
+            num_answers.append(2)
+        elif i == "c":
+            num_answers.append(3)
+        elif i == "d":
+            num_answers.append(4)
+        elif i == "e":
+            num_answers.append(5)
+        else:
+            print("Oppss Check Txt file")
+    return num_answers
+
+   
+def image_show(images):
+    col1, col2, col3 = st.columns(3)
+    with col1:
+            st.header("0")
+            st.image(images[0],width=200)
+    with col2:
+            st.header("1")
+            st.image(images[1],width=200)
+    with col3:
+            st.header("2")
+            st.image(images[2],width=200)
+            
+    col4, col5= st.columns(2)
+    
+    with col4:
+            st.header("3")
+            st.image(images[4],width=200)
+    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=150)
+    
+    with col7:
+            st.header("6")
+            st.image(images[7],width=150)
+    
+    

reader.py

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+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.py

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+import csv
+import streamlit as st
+import numpy as np
+import cv2
+from PIL import Image
+from functions import image_show
+import pandas as pd 
+from data_func import make_new_data,update,save_and_push
+import os
+from huggingface_hub import Repository
+import reader
+
+st.markdown("""
+        <style>
+               .block-container {
+                    padding-top: 1rem;
+                    padding-bottom: 0rem;
+                    padding-left: 1rem;
+                    padding-right: 1rem;
+                }
+        </style>
+        """, unsafe_allow_html=True)
+
+
+
+def pull_read(DATASET_REPO_URL,HF_TOKEN,DATA_FILE):
+    
+    repo = Repository(
+        local_dir="data", clone_from=DATASET_REPO_URL, use_auth_token=HF_TOKEN
+        )
+    
+    with open(DATA_FILE) as csvfile:
+        df = pd.read_csv(csvfile) 
+        df = pd.DataFrame(df)
+    
+    return repo, df
+
+@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():
+    teacher_code = st.text_input("Ogretmen kodu:",key=12)
+    password = st.text_input("Sıfre:",key=17,type="password")
+    
+    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:
+        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()
+                    
+        if str(repo_df["ogrenci_no"][0]) == password:
+            st.write("Giriş başarılı")       
+            answer_code = st.radio(
+            "Cevap Anahtari Kodu",
+            ('1', '2', '3',"4","5"))
+            answer_code = int(answer_code)      
+            global myIndexs 
+                        
+            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) 
+              
+            #myIndex1_str =", ".join(map(str, myIndexs[0]))
+            st.write("Ders 1 icin cevap anahtari")
+            df = pd.DataFrame(
+    [
+       {"Sorular": "Soru1 ", "Cevap": myIndexs[0][0],"Sorular2": "Soru11 ", "Cevap6": myIndexs[0][10]},
+       {"Sorular": "Soru2", "Cevap": myIndexs[0][1],"Sorular2": "Soru12 ", "Cevap6": myIndexs[0][11]},
+       {"Sorular": "Soru3", "Cevap": myIndexs[0][2],"Sorular2": "Soru13 ", "Cevap6": myIndexs[0][12]},
+       {"Sorular": "Soru4 ", "Cevap": myIndexs[0][3],"Sorular2": "Soru14 ", "Cevap6": myIndexs[0][13]},
+       {"Sorular": "Soru5", "Cevap": myIndexs[0][4],"Sorular2": "Soru15 ", "Cevap6": myIndexs[0][14]},
+       {"Sorular": "Soru6 ", "Cevap": myIndexs[0][5],"Sorular2": "Soru16 ", "Cevap6": myIndexs[0][15]},
+       {"Sorular": "Soru7", "Cevap": myIndexs[0][6],"Sorular2": "Soru17 ", "Cevap6": myIndexs[0][16]},
+       {"Sorular": "Soru8", "Cevap": myIndexs[0][7],"Sorular2": "Soru18 ", "Cevap6": myIndexs[0][17]},
+       {"Sorular": "Soru9 ", "Cevap": myIndexs[0][8],"Sorular2": "Soru19 ", "Cevap6": myIndexs[0][18]},
+       {"Sorular": "Soru10", "Cevap": myIndexs[0][9],"Sorular2": "Soru20 ", "Cevap6": myIndexs[0][19]}
+   ]
+)
+            edited_df = st.experimental_data_editor(df,use_container_width=True,height=400)
+            
+ 
+      
+            if st.button("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'))
+                #(ans_txt,pathImage, save_images= True)
+                        
+                        myIndex1_str =", ".join(map(str, edited_df["Cevap"]))
+                        myIndex2_str =", ".join(map(str, myIndexs[1]))
+                        myIndex3_str =", ".join(map(str, myIndexs[2]))
+                        #new_data1 = pd.DataFrame(data, index, columns)
+                        data = [[1,answer_code,80,myIndex1_str,90,myIndex2_str,10,myIndex3_str]]
+  
+# Create the pandas DataFrame
+                        new_data = pd.DataFrame(data, columns=['sinav_kodu', 'ogrenci_no',
+                                                         "notu1","yanlis_sorulari1",
+                                                         "notu2","yanlis_sorulari2",
+                                                         "notu3","yanlis_sorulari3"])
+                        
+                        repo_df = repo_df[repo_df['ogrenci_no'] != answer_code]
+                    
+
+                        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}")
+            
+                        #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__':
+    screen_scan_main()