application.py

import cv2
import numpy as np
import functions
import json
#fotograf ozellikleri
heightImg = 300*4
widthImg  = 210*4
#pathImage = "denemeler/100luk_numarali.jpg"
questions=25
choices=6

a1 = functions.read_answers("answers/test1-1.txt")
a2 = functions.answers2numbers(a1)


a3 = functions.read_answers("answers/test1-2.txt")
a4 = functions.answers2numbers(a3)


a5 = functions.read_answers("answers/test1-3.txt")
a6 = functions.answers2numbers(a5)


a7 = functions.read_answers("answers/test1-4.txt")
a8 = functions.answers2numbers(a7)

def optic1(ans_txt1,ans_txt2,ans_txt3,ans_txt4,pathImage, save_images= True):
#cevap anahtarini dosyadan okuma ve sayiya cevirme
    
    ans_1 = ans_txt1
    ans_2 = ans_txt2
    ans_3 = ans_txt3
    ans_4 = ans_txt4

#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)
    """cv2.imshow("Image", img)
    cv2.waitKey(0)"""
    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) 
    """rectCon_np = rectCon[0].astype(np.uint8)
    cv2.imshow("Image", rectCon_np)"""
    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_4 = functions.splitBoxes(column_3[3])

        #boxes_1
        myPixelVal_1 = functions.pixelVal(questions,choices,boxes_1)
        for sublist in myPixelVal_1:
            if sublist.any():  # Alt liste boş değilse
                sublist[0] = 0
    

        myIndex_1 = functions.user_answers(questions,myPixelVal_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, 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 ,empty_3 = functions.grading(ans_3,questions,myIndex_3)

        #boxes_3        
        myPixelVal_4 = functions.pixelVal(questions,choices,boxes_4)
        myIndex_4 = functions.user_answers(questions,myPixelVal_4)
        grading_4, wrong_ans_4 ,empty_4 = functions.grading(ans_4,questions,myIndex_4)
        
        resim_listesi = [img,imgGray,imgBlur,imgCanny,imgContours,imgBiggestContour,imgThresh_1,imgThresh_2]
        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])
        
        
        grading = [grading_1,grading_2,grading_3,grading_4]
        wrong_ans = [wrong_ans_1,wrong_ans_2,wrong_ans_3,wrong_ans_4]
        empty = [empty_1,empty_2,empty_3,empty_4]
        myIndexs = [myIndex_1,myIndex_2,myIndex_3,myIndex_4]

        # Convert variables to NumPy arrays if they are not already
        grading_1 = np.array(grading_1)
        grading_2 = np.array(grading_2)
        grading_3 = np.array(grading_3)
        grading_4 = np.array(grading_4)

# Convert tolist() only if variables are NumPy arrays
        grading = [grading_1.tolist(), grading_2.tolist(), grading_3.tolist(), grading_4.tolist()]

        wrong_ans_1 = np.array(wrong_ans_1)
        wrong_ans_2 = np.array(wrong_ans_2)
        wrong_ans_3 = np.array(wrong_ans_3)
        wrong_ans_4 = np.array(wrong_ans_4)

        wrong_ans = [wrong_ans_1.tolist(), wrong_ans_2.tolist(), wrong_ans_3.tolist(), wrong_ans_4.tolist()]

        empty_1 = np.array(empty_1)
        empty_2 = np.array(empty_2)
        empty_3 = np.array(empty_3)
        empty_4 = np.array(empty_4)

        empty = [empty_1.tolist(), empty_2.tolist(), empty_3.tolist(), empty_4.tolist()]

# Similarly, apply the same conversion for other lists like wrong_ans, empty, and myIndexs

# Verileri bir sözlükte topla
        data = {"student_id": student_idFix,"grading": grading,"wrong_ans": wrong_ans, "empty": empty}
        
# JSON formatına dönüştür ve ekrana yazdır
        json_data = json.dumps(data, indent=4)
        json_data = json.loads(json_data)
        print(json_data)
    return json_data

"""json_data = optic1(ans_txt1=a2,
               ans_txt2=a4,
               ans_txt3=a6,
               ans_txt4=a8,
               pathImage="test/test1.1.PNG",
               save_images=True
               )"""
"""json_data = json.loads(json_data)
a = json_data["grading"]
print(a)"""