First Demo

.gitattributes

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app.py

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+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 streamlit_option_menu import option_menu
+
+def local_css(file_name):
+    with open(file_name) as f:
+        st.markdown(f"<style>{f.read()}</style>", unsafe_allow_html=True)
+
+local_css("style.css")
+
+
+
+def streamlit_menu(example=3):
+    
+    if 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"], 
+            default_index=0,  # optional
+            orientation="horizontal",
+            styles={
+                "container": {"padding": "0!important", "background-color": "#b2d8d8"},
+                "icon": {"color": "orange", "font-size": "0px"},
+                "nav-link": {
+                    "font-size": "15px",
+                    "text-align": "left",
+                    "margin": "0px",
+                    "--hover-color": "#eee",
+                },
+                "nav-link-selected": {"background-color": "#008080"},
+            },
+        )
+        return selected
+
+
+
+
+def main():
+    
+    
+    selected = streamlit_menu(example=3)
+    
+    if selected == "Optik Okuma":
+        screen_scan_main()
+    if selected == "Yeni Not Girisi":
+        screen_add_main()
+    if selected == "Not Goruntuleme":
+        screen_note_main()
+   
+    
+if __name__ == "__main__":
+    main()
+    
+    
+    

cevapanahtari.txt

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

data

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+Subproject commit ac8c215821138cfcb2bad83a69cb3c1fece7b936

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
+
+def make_new_data(sinif_kodu,ogrenci_no,ders_kodu,notu,yanlislar):
+    yeni_satir = {"sinif_kodu": sinif_kodu, 
+              "ogrenci_no": ogrenci_no, 
+              "ders_kodu": ders_kodu,
+              "notu": notu, 
+             "yanlis_sorulari": yanlislar}
+    new_data = pd.DataFrame([yeni_satir])
+    return new_data 
+    
+def update(new_data, repo_df):
+    updated_df = pd.concat([repo_df, new_data])
+    return updated_df
+    
+def save_and_push(dataFrame,repo):
+    dataFrame.to_csv("data/untitled.csv",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 = []
+    for x in range(0,num_questions):
+        if answers[x] == myAnswers[x]:
+            grading.append(1)
+        else:
+            grading.append(0)
+            wrong_ans.append(x+1)
+    score = (sum(grading)/num_questions)*100 
+    return score ,wrong_ans
+
+#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])
+    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, col6 = st.columns(3)
+    
+    with col4:
+            st.header("3")
+            st.image(images[4],width=200)
+    with col5:
+            st.header("4")
+            st.image(images[5],width=200)
+    with col6:
+            st.header("5")
+            st.image(images[6],width=200)
+    

optic1.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 optic1(ans_txt,pathImage, save_images= True):
+#cevap anahtarini dosyadan okuma ve sayiya cevirme
+    ans_abc = functions.read_answers(ans_txt)
+    ans = functions.answers2numbers(ans_abc)
+
+#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.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, 170, 255,cv2.THRESH_BINARY_INV )[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] 
+    
+    
+    #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])
+        myPixelVal = functions.pixelVal(questions,choices,boxes_1)
+        myIndex = functions.user_answers(questions,myPixelVal)
+        grading, wrong_ans = functions.grading(ans,questions,myIndex)
+        student_idFix = ""
+        for number in student_id:
+            
+            student_idFix += str(number)
+        if save_images:
+            for i in range(0,len(resim_listesi)):
+                cv2.imwrite(f"images/{student_idFix}___{i}.jpg",resim_listesi[i])
+        
+        resim_listesi = [img,imgGray,imgBlur,imgCanny,imgContours,imgBiggestContour,imgThresh_1,imgThresh_2]
+         
+    return grading, wrong_ans, student_idFix, resim_listesi
+
+#sonuc = optic1(ans_txt="cevapanahtari.txt",pathImage= "denemeler/100luk_numarali.jpg")
+#print(sonuc)

requirements.txt

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+streamlit ==  1.11.0
+pandas == 1.4.3
+numpy ==  1.23.1
+Pillow== 9.2.0
+huggingface_hub 
+opencv-python-headless==4.4.0.42
+pickleshare ==  0.7.5
+requests ==  2.24.0
+scikit-image == 0.19.3

screen_add_note.py

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+import streamlit as st
+import pandas as pd
+import data_func
+from data_func import DATASET_REPO_URL,DATA_FILENAME,DATA_FILE,HF_TOKEN
+
+repo, repo_df = data_func.pull_read()
+
+
+def screen_add_main():
+    st.title("Not Giris")
+
+    # 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_note.py

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+import streamlit as st
+import pandas as pd
+import data_func
+from data_func import DATASET_REPO_URL,DATA_FILENAME,DATA_FILE,HF_TOKEN
+
+
+
+
+repo, repo_df = data_func.pull_read()
+
+def screen_note_main():
+    
+
+    st.subheader("Girilen Veriler")
+    st.write(repo_df)
+        
+        
+if __name__ == "__main__":
+    screen_note_main()

screen_scan.py

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+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)
+
+                st.subheader("Notu:",grading)
+                st.subheader("Yanlis Yaptigi sorular:",wrong_ans)
+                st.subheader("Ogrenci Numarasi:",student_idFix)
+  
+#python -m streamlit run app.py
+if __name__ == '__main__':
+    screen_scan_main()	   

style.css

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+section[data-testid="stSidebar"] div.stButton button {
+    background-color: rgb(95, 197, 228);
+    width: 200px;
+}