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
try:
## FIND ALL COUNTOURS
imgContours = img.copy() # COPY IMAGE FOR DISPLAY PURPOSES
imgBigContour = img.copy() # COPY IMAGE FOR DISPLAY PURPOSES
contours, hierarchy = cv2.findContours(
imgCanny, cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_NONE) # FIND ALL CONTOURS
cv2.drawContours(imgContours, contours, -1, (0, 255, 0),
10) # DRAW ALL DETECTED CONTOURS
rectCon = utlis.rectContour(contours) # FILTER FOR RECTANGLE CONTOURS
biggestPoints = utlis.getCornerPoints(
rectCon[0]) # GET CORNER POINTS OF THE BIGGEST RECTANGLE
gradePoints = utlis.getCornerPoints(
rectCon[1]) # GET CORNER POINTS OF THE SECOND BIGGEST RECTANGLE
if biggestPoints.size != 0 and gradePoints.size != 0:
# BIGGEST RECTANGLE WARPING
biggestPoints = utlis.reorder(biggestPoints) # REORDER FOR WARPING
cv2.drawContours(imgBigContour, biggestPoints, -1, (0, 255, 0),
20) # DRAW THE BIGGEST CONTOUR
pts1 = np.float32(biggestPoints) # PREPARE POINTS FOR WARP
pts2 = np.float32([[0, 0], [widthImg, 0], [0, heightImg],
[widthImg,
heightImg]]) # PREPARE POINTS FOR WARP
matrix = cv2.getPerspectiveTransform(
pts1, pts2) # GET TRANSFORMATION MATRIX
img=cv2.resize(img,(widthImg,heightImg))
imgContours=img.copy()
imgFinal=img.copy()
imgBiggestContours=img.copy()
imgGray=cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
imgBlur=cv2.GaussianBlur(imgGray,(5,5),1)
imgCanny=cv2.Canny(imgBlur,10,50)
try:
########## FINDING ALL CONTOURS
contours,hierarchy=cv2.findContours(imgCanny,cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_NONE)
cv2.drawContours(imgContours,contours,-1,(0,255,0),10)
######## FIND RECTANGLES
rectCon=utlis.rectContour(contours)
biggestContours=utlis.getCornerPoints(rectCon[0])
gradePoints=utlis.getCornerPoints(rectCon[1])
# print(biggestContours,gradePoint)
# print(len(biggestContours))
if biggestContours.size !=0 and gradePoints.size !=0:
cv2.drawContours(imgBiggestContours,biggestContours,-1,(0,255,0),20)
cv2.drawContours(imgBiggestContours, gradePoints, -1, (255, 0, 0), 20)
# reorder the cordinate points of req rect for finding out origin point and to be used without confusion in future
biggestContours=utlis.reorder(biggestContours)
gradePoints=utlis.reorder(gradePoints)
pt1=np.float32(biggestContours)
pt2=np.float32([[0,0],[widthImg,0],[0,heightImg],[widthImg,heightImg]])
matrix=cv2.getPerspectiveTransform(pt1,pt2)
imgWrapColored=cv2.warpPerspective(img,matrix,(widthImg,heightImg))
def omr(imgpath,field,answer):
pathImage = imgpath
heightImg = 700
widthImg = 700
questions=5
choices=5
ans= answer
img = cv2.imread(pathImage)
img = cv2.resize(img, (widthImg, heightImg)) # RESIZE IMAGE
imgFinal = img.copy()
imgBlank = np.zeros((heightImg,widthImg, 3), np.uint8) # CREATE A BLANK IMAGE FOR TESTING DEBUGGING IF REQUIRED
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
## FIND ALL COUNTOURS
imgContours = img.copy() # COPY IMAGE FOR DISPLAY PURPOSES
imgBigContour = img.copy() # COPY IMAGE FOR DISPLAY PURPOSES
contours, hierarchy = cv2.findContours(imgCanny, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE) # FIND ALL CONTOURS
cv2.drawContours(imgContours, contours, -1, (0, 255, 0), 10) # DRAW ALL DETECTED CONTOURS
rectCon = utlis.rectContour(contours) # FILTER FOR RECTANGLE CONTOURS
biggestPoints= utlis.getCornerPoints(rectCon[0]) # GET CORNER POINTS OF THE BIGGEST RECTANGLE
# gradePoints = utlis.getCornerPoints(rectCon[1]) # GET CORNER POINTS OF THE SECOND BIGGEST RECTANGLE
if biggestPoints.size != 0:
# BIGGEST RECTANGLE WARPING
biggestPoints=utlis.reorder(biggestPoints) # REORDER FOR WARPING
cv2.drawContours(imgBigContour, biggestPoints, -1, (0, 255, 0), 20) # DRAW THE BIGGEST CONTOUR
pts1 = np.float32(biggestPoints) # PREPARE POINTS FOR WARP
pts2 = np.float32([[0, 0],[widthImg, 0], [0, heightImg],[widthImg, heightImg]]) # PREPARE POINTS FOR WARP
matrix = cv2.getPerspectiveTransform(pts1, pts2) # GET TRANSFORMATION MATRIX
imgWarpColored = cv2.warpPerspective(img, matrix, (widthImg, heightImg)) # APPLY WARP PERSPECTIVE
# APPLY THRESHOLD
imgWarpGray = cv2.cvtColor(imgWarpColored,cv2.COLOR_BGR2GRAY) # CONVERT TO GRAYSCALE
imgThresh = cv2.threshold(imgWarpGray, 170, 255,cv2.THRESH_BINARY_INV )[1] # APPLY THRESHOLD AND INVERSE
boxes = utlis.splitBoxes(imgThresh) # GET INDIVIDUAL BOXES
# cv2.imshow("Split Test ", boxes[3])
countR=0
countC=0
myPixelVal = np.zeros((questions,choices)) # TO STORE THE NON ZERO VALUES OF EACH BOX
nonPixelVal = np.zeros((1,1))
nonPixelVal[0][0] = 5
for image in boxes:
#cv2.imshow(str(countR)+str(countC),image)
totalPixels = cv2.countNonZero(image)
myPixelVal[countR][countC]= totalPixels
countC += 1
if (countC==choices):countC=0;countR +=1
# FIND THE USER ANSWERS AND PUT THEM IN A LIST
myIndex=[]
for x in range (0,questions):
arr = myPixelVal[x]
print("arrrr",arr)
max1 = np.amax(arr)
myIndexVal = np.where(arr == np.amax(arr))
print(max1)
temp =np.delete(arr,myIndexVal)
print("arrrr", temp)
max2= np.amax(temp)
myIndexVal1 = np.where(arr == max2)
if(max1.tolist()/1000 - max2.tolist()/1000 < 2):
print("masbcjasfjhds",max1,max2)
myIndexVal = nonPixelVal
myIndex.append(myIndexVal[0][0])
print("USER ANSWERS",myIndex)
# COMPARE THE VALUES TO FIND THE CORRECT ANSWERS
if field == 1:
grading=[]
for x in range(0,questions):
if ans[x] == myIndex[x]:
grading.append(1)
else:grading.append(0)
#print("GRADING",grading)
score = (sum(grading)/questions)*50 # FINAL GRADE
print("SCORE",score)
imageArray = ([img,imgGray,imgBlur, imgCanny],
[imgContours,imgBigContour,imgThresh,imgBlank])
lables = [["Original","Gray","Blur","Edges"],
["Contours","Biggest Contour","Threshold"," "]]
stackedImage = utlis.stackImages(imageArray,0.5,lables)
cv2.imshow('Result', stackedImage)
# cv2.imwrite('r.jpg', stackedImage)
cv2.waitKey()
return(str(score))
return(myIndex)
def GoruntuIsle(self, img):
try:
global widthImg, heightImg, ans, x, y, xsag, xsol, w, h, esikdeger, ox, oy, ow, oh, secimSayisi, sorusayisi, sayac, sonbulunanNumara, bulunanNumara, sonbulunanpuan, bulunanpuan
img2 = cv2.resize(img, (widthImg, heightImg))
if cameraNo == -1:
pass
else:
img2 = cv2.rotate(img2, cv2.ROTATE_180)
imgCountours = img2.copy()
imageFinal = img2.copy()
imgBiggestCountours = img2.copy()
imgGray = cv2.cvtColor(img2, cv2.COLOR_BGR2GRAY)
imgBlur = cv2.GaussianBlur(imgGray, (5, 5), 1)
imgCanny = cv2.Canny(imgBlur, 10, 50)
#cv2.imshow("test",imgCanny)
try:
#FIND ALL COUNTERS
countours, hierarchy = cv2.findContours(
imgCanny, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
cv2.drawContours(imgCountours, countours, -1, (0, 255, 0), 10)
#FIND RECTANGLES
rectCon = utlis.rectContour(countours)
biggestContour = utlis.getCornerPoints(rectCon[0])
#print(biggestContour)
if biggestContour.size != 0:
cv2.drawContours(imgBiggestCountours, biggestContour, -1,
(0, 255, 0), 20)
biggestContour = utlis.reorder(biggestContour)
pts1 = np.float32(
biggestContour) # PREPARE POINTS FOR WARP
pts2 = np.float32([[0, 0], [widthImg, 0], [0, heightImg],
[widthImg,
heightImg]]) # PREPARE POINTS FOR WARP
matrix = cv2.getPerspectiveTransform(
pts1, pts2) # GET TRANSFORMATION MATRIX
imgWarpColored = cv2.warpPerspective(
img2, matrix,
(widthImg, heightImg)) # APPLY WARP PERSPECTIVE
#cv2.imshow("bulunan",imgWarpColored)
#APPLY TRESHOLD
imgWarpGray = cv2.cvtColor(imgWarpColored,
cv2.COLOR_BGR2GRAY)
imgThresh = cv2.threshold(imgWarpGray, tresh, 255,
cv2.THRESH_BINARY_INV)[1]
#boxes=utlis.splitBoxes(imgThresh)
crop_imgSol = imgThresh[y:y + h, xsol:(xsol + w)]
crop_imgSag = imgThresh[y:y + h, xsag:(xsag + w)]
crop_imgOgrenciNu = imgThresh[oy:oy + oh, ox:(ox + ow)]
#cv2.imshow("cropped", crop_imgSol)
#cv2.imwrite("croppedsol.jpg",crop_imgSol)
#cv2.imwrite("croppedsag.jpg",crop_imgSag)
boxesSol = utlis.splitBoxes(crop_imgSol)
boxesSag = utlis.splitBoxes(crop_imgSag)
boxesOgrenciNu = utlis.splitBoxesOgrenciNu(
crop_imgOgrenciNu)
sorusayisi = 20
#GETTING NOPIXEL VALUES OF EACH
myPixelVal = np.zeros((sorusayisi, secimSayisi))
myPixelValOgrenciNu = np.zeros((4, 10))
countC = 0
countR = 0
for image in boxesOgrenciNu:
totalPixels = cv2.countNonZero(image)
myPixelValOgrenciNu[countR][countC] = totalPixels
countC += 1
if (countC == 10):
countR += 1
countC = 0
#print(myPixelValOgrenciNu)
countC = 0
countR = 0
for image in boxesSol:
totalPixels = cv2.countNonZero(image)
myPixelVal[countR][countC] = totalPixels
countC += 1
if (countC == secimSayisi):
countR += 1
countC = 0
#print(myPixelVal)
for image in boxesSag:
totalPixels = cv2.countNonZero(image)
myPixelVal[countR][countC] = totalPixels
countC += 1
if (countC == secimSayisi):
countR += 1
countC = 0
#FINDING INDEX VALUES OF THE MARKINGS
myIndexOgrenciNu = []
for x in range(0, 4):
arr = myPixelValOgrenciNu[x]
#print("arr",arr)
myIndexVal = np.where(arr == np.amax(arr))
#print(myIndexVal[0])
myIndexOgrenciNu.append(myIndexVal[0][0])
ogrenciNumarasi = str(myIndexOgrenciNu[0]) + str(
myIndexOgrenciNu[1]) + str(myIndexOgrenciNu[2]) + str(
myIndexOgrenciNu[3])
#print('Öğrenci numarası {}'.format(ogrenciNumarasi))
#cv2.imshow('mum',utlis.showNumber2(imgWarpColored,myIndexOgrenciNu,4,10,ox,oy,ow,oh))
myIndex = []
for x in range(0, sorusayisi):
isaretsayisi = 0
arr = myPixelVal[x]
#print("arr-"+str(x),arr)
#print('max',np.amax(arr))
#print('sayı',np.count_nonzero(arr>esikdeger))
isaretsayisi = np.count_nonzero(arr > esikdeger)
enfazla = np.amax(arr)
if isaretsayisi > 1:
myIndexVal[0][
0] = 5 #iki ve dahafazla işaretlenmiş
elif esikdeger < enfazla:
myIndexVal = np.where(arr == np.amax(arr))
#print(np.where(arr==np.amax(arr))[0])
else:
#pass
myIndexVal[0][0] = 4
#print(myIndexVal[0])
myIndex.append(myIndexVal[0][0])
#print(myIndex)
#GRADING
grading = []
for x in range(0, sorusayisi):
if myIndex[x] == 4:
grading.append(4)
elif myIndex[x] == 5:
grading.append(5)
elif ans[x] == myIndex[x]:
grading.append(1)
else:
grading.append(0)
#print(grading)
#SCORE
DogrularSay = grading.count(1)
YanlislariSay = grading.count(0) + grading.count(5)
BoslariSay = grading.count(4)
score = (DogrularSay / sorusayisi) * 100
mesaj = 'No:' + ogrenciNumarasi + ' Puan:' + str(
score) + ' Doğru:' + str(
DogrularSay) + ' Yanlış:' + str(
YanlislariSay) + ' Boş:' + str(BoslariSay)
#print(score)
#DISPLAY ANSWERS
#imgResult=imgWarpColored.copy()
imgResultSol = imgWarpColored.copy()
#imgResultSag=imgWarpColored.copy()
#imgResult= imgResult[y:y+h, x:x+w]
imgResultNu = utlis.showNumber2(imgResultSol,
myIndexOgrenciNu, 4, 10,
ox, oy, ow, oh)
imgResultSol = utlis.showAnswers2(imgResultSol,
myIndex[0:10],
grading[0:10], ans[0:10],
10, 4, xsol, y, w, h)
imgResultSag = utlis.showAnswers2(imgResultSol,
myIndex[10:20],
grading[10:20],
ans[10:20], 10, 4, xsag,
y, w, h)
#cv2.imshow("imgResultSag",imgResultSag)
imRawDrawingSol = np.zeros_like(imgResultSol)
imgResultNu = utlis.showNumber2(imRawDrawingSol,
myIndexOgrenciNu, 4, 10,
ox, oy, ow, oh)
imRawDrawingSol = utlis.showAnswers2(
imRawDrawingSol, myIndex[0:10], grading[0:10],
ans[0:10], 10, 4, xsol, y, w, h)
imRawDrawingSag = utlis.showAnswers2(
imRawDrawingSol, myIndex[10:20], grading[10:20],
ans[10:20], 10, 4, xsag, y, w, h)
#cv2.imshow("imgResult1",imRawDrawing)
#pts2 = np.float32([[0, 0],[widthImg, 0], [0, heightImg],[widthImg, heightImg]]) # PREPARE POINTS FOR WARP
#pts2s = np.float32([[0, 0],[w, 0], [0, h],[w, h]]) # PREPARE POINTS FOR WARP
invMatrix = cv2.getPerspectiveTransform(
pts2, pts1) # GET TRANSFORMATION MATRIX
# font
font = cv2.FONT_HERSHEY_SIMPLEX
# org
org = (50), (heightImg - 20)
# fontScale
fontScale = 0.8
# Blue color in BGR
#b,g,r,a
color = (0, 0, 0, 0)
# Line thickness of 2 px
thickness = 2
# Using cv2.putText() method
#cv2.putText(imRawDrawingSol, 'No:'+ogrenciNumarasi+' Puan:'+str(score) +' D:'+str(DogrularSay)+' Y:'+str(YanlislariSay)+' B:'+str(BoslariSay), org, font,fontScale, color, thickness, cv2.LINE_AA,)
imgInvWarp = cv2.warpPerspective(
imRawDrawingSol, invMatrix,
((widthImg), heightImg)) # APPLY WARP PERSPECTIVE
#cv2.putText(imageFinal,str(score),((widthImg-150),(heightImg-100)),cv2.FONT_HERSHEY_COMPLEX,3,(0,255,255),3)
imageFinal = cv2.addWeighted(imageFinal, 1, imgInvWarp, 1,
0)
imageFinal = cv2.rectangle(imageFinal,
(50, (heightImg - 50)),
(widthImg - 50,
(heightImg - 10)),
(255, 255, 255), -1)
imageFinal = utlis.print_utf8_text(
imageFinal, mesaj, color,
(widthImg / 2 - (len(mesaj) * 6)), (heightImg - 50))
imageFinal = cv2.rectangle(imageFinal, (150, 25),
(600, 450), (0, 255, 255), 3,
cv2.LINE_AA)
#cv2.putText(imageFinal,'Deneme',(50,125),cv2.FONT_HERSHEY_COMPLEX,3,(0,255,255),3)
#cv2.imshow("Camera",imageFinal)
if asamalariGoster == True:
imgBlank = np.zeros_like(img2)
imageArray = ([img2, imgGray, imgBlur, imgCanny], [
imgCountours, imgBiggestCountours, imgWarpColored,
imgThresh
], [imgResultSag, imageFinal, imgBlank, imgBlank])
imgStacked = utlis.stackImages(imageArray, .5)
cv2.imshow("imgStacked", imgStacked)
genislik = int(widthImg / 1.5)
yukseklik = int(heightImg / 1.5)
imageFinal = cv2.resize(imageFinal, (genislik, yukseklik))
# print(f'yeni gen-yuk {genislik} {yukseklik}')
height, width, channel = imageFinal.shape
step = channel * width
# create QImage from image
qImg = QImage(imageFinal.data, width, height, step,
QImage.Format_BGR888)
# show image in img_label
self.ui.imgCamera_2.setPixmap(QPixmap.fromImage(qImg))
ogrenciCevapSikleri = []
ogrenciDogruYanlis = []
cevaplar = []
for i in myIndex:
if i == 0: ogrenciCevapSikleri.append('A')
elif i == 1: ogrenciCevapSikleri.append('B')
elif i == 2: ogrenciCevapSikleri.append('C')
elif i == 3: ogrenciCevapSikleri.append('D')
elif i == 4: ogrenciCevapSikleri.append('N')
elif i == 5: ogrenciCevapSikleri.append('M')
for x in range(0, sorusayisi):
if myIndex[x] == ans[x]: ogrenciDogruYanlis.append('D')
else: ogrenciDogruYanlis.append('Y')
for i in myIndex:
cevaplar.append(i)
#print(ogrenciCevapSikleri)
#print(ogrenciDogruYanlis)
# self.ui.imgBulunan.setVisible(False)
if otomatikDurdur == True:
print(f'otomatik durdurma açık')
bulunanNumara = ogrenciNumarasi
bulunanpuan = score
if sayac < 5:
print(
f'sayac {sayac} bulunanpuan {bulunanpuan} sonbulunan {sonbulunanpuan}'
)
if (sonbulunanpuan == bulunanpuan) and (
sonbulunanNumara == bulunanNumara) and (
int(sonbulunanNumara) > 0):
sayac += 1
else:
sayac = 0
sonbulunanNumara = bulunanNumara
sonbulunanpuan = bulunanpuan
else:
global toogledurum
toogledurum = False
# self.StartStop()
sayac = 0
print(f'bulundu ')
# print(ogrenciCevapSikleri)
# print(ogrenciDogruYanlis)
b = ''
for i in range(len(ogrenciCevapSikleri)):
b += (ogrenciCevapSikleri[i])
self.ui.lblogrencicevaplar.setPlainText(b)
imgResultSag = cv2.resize(imgResultSag,
(genislik, yukseklik))
# print(f'yeni gen-yuk {genislik} {yukseklik}')
height, width, channel = imgResultSag.shape
step = channel * width
# create QImage from image
qImg = QImage(imgResultSag.data, width, height,
step, QImage.Format_BGR888)
# show image in img_label
# self.ui.imgBulunan.setVisible(True)
self.ui.imgBulunan.setPixmap(
QPixmap.fromImage(qImg))
except Exception as Hata:
print('Bulma hatası oluştu :', Hata)
except Exception as Hata:
print('Hata oluştu :', Hata)
def readfile(path):
#===============================
#path = "scantron-100.jpg"
widthImg = 1245
heightImg=3000
question =50
choices = 5
#===============================
img = cv2.imread(path)
#PREPROCESSING
img = cv2.resize(img,(widthImg,heightImg))
imgContours = img.copy()
imgBiggestContours = img.copy()
imgGray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
imgBlur = cv2.GaussianBlur(imgGray,(5,5),1)
imgCanny = cv2.Canny(imgBlur,10,50)
# FINDING ALL CONTOURS
countours, hierarchy = cv2.findContours(imgCanny,cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_NONE)
cv2.drawContours(imgContours,countours,-1,(0,255,0),10)
#FIND RECTANGLES
rectCon = utlis.rectContour(countours)
biggestContour = utlis.getCornerPoints(rectCon[0])
gradePoints = utlis.getCornerPoints(rectCon[1])
test = biggestContour.copy()
test[0][0]=[333,2617]
test[1][0]=[331,437]
test[2][0]=[775,437]
test[3][0]=[778,2617]
#print("ttt:",test)
#print("\n for contour\n",biggestContour )
#print("\n for grade\n",gradePoints)
biggestContour=test
if biggestContour.size != 0 and gradePoints.size != 0:
cv2.drawContours(imgBiggestContours,biggestContour,-1,(0,255,0),20)
cv2.drawContours(imgBiggestContours,gradePoints,-1,(255,0,0),20)
biggestContour= utlis.reorder(biggestContour)
gradePoints = utlis.reorder(gradePoints)
pt1 = np.float32(biggestContour)
pt2= np.float32([[0,0],[widthImg,0],[0,heightImg],[widthImg,heightImg]])
matrix = cv2.getPerspectiveTransform(pt1,pt2)
imgWarpColored = cv2.warpPerspective(img,matrix,(widthImg,heightImg))
ptG1 = np.float32(gradePoints)
ptG2 = np.float32([[0,0],[325,0],[0,150],[325,150]])
matrixG = cv2.getPerspectiveTransform(ptG1, ptG2)
imgGradeDisplay = cv2.warpPerspective(img, matrixG,(325, 150))
#cv2.imshow("Grade", imgGradeDisplay)
imgWarpGray = cv2.cvtColor(imgWarpColored,cv2.COLOR_BGR2GRAY)
imgThresh = cv2.threshold(imgWarpGray,150,255,cv2.THRESH_BINARY_INV)[1]
#cv2.imshow("Grade", imgThresh)
boxes = utlis.splitBoxes(imgThresh)
#cv2.imshow("test", boxes[4])
#print(cv2.countNonZero(boxes[2]),cv2.countNonZero(boxes[0]))
#GETTING NO ZERO PIXEL VALUES OF EACH BOX
myPixelVal = np.zeros((question,choices))
countC = 0
countR = 0
for image in boxes:
totalPixels = cv2.countNonZero(image)
myPixelVal[countR][countC] = totalPixels
countC +=1
if (countC == choices):countR+=1; countC=0
#print(myPixelVal)
global myIndex
localmyIndex = []
for x in range(0, question):
arrline = myPixelVal[x]
arrmed= np.median(arrline)
localmyIndex.append(-1)
for y in range(0,choices):
if(myPixelVal[x][y]/arrmed > 2):
localmyIndex[x]=y
myIndex = localmyIndex
imgBlank = np.zeros_like(img)
imageArray = ([img,imgGray,imgBlur,imgCanny],
[imgContours,imgBiggestContours,imgWarpColored,imgThresh])
imgStacked = utlis.stackImages(imageArray,0.5)
#cv2.imshow("stacked images",imgStacked)
cv2.waitKey(0)
def process (path):
file_path=path
file=open(file_path,'r')
lines=file.readlines()
img_path=lines[0]
img_path=img_path.strip()
questions = int(lines[1])
choices = 5
if(questions==15):
heightImg=690
elif(questions==20):
heightImg=700
elif(questions==25):
heightImg=700
else:
fileName = path.split('\\')[-1]
fileName = fileName.split('.')[0]
f = open('E:\\FinalProject\\Outputs\\' + fileName + '.txt', "w+")
f.write("-1")
print(img_path, questions)
img = cv2.imread(img_path)
img = cv2.resize(img, (widthImg, heightImg)) # RESIZE IMAGE
imgFinal = img.copy()
imgBlank = np.zeros((heightImg, widthImg, 3), np.uint8) # CREATE A BLANK IMAGE FOR TESTING DEBUGGING IF REQUIRED
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
try:
## FIND ALL COUNTOURS
imgContours = img.copy() # COPY IMAGE FOR DISPLAY PURPOSES
imgBigContour = img.copy() # COPY IMAGE FOR DISPLAY PURPOSES
contours, hierarchy = cv2.findContours(imgCanny, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE) # FIND ALL CONTOURS
cv2.drawContours(imgContours, contours, -1, (0, 255, 0), 10) # DRAW ALL DETECTED CONTOURS
rectCon = utlis.rectContour(contours) # FILTER FOR RECTANGLE CONTOURS
biggestPoints = utlis.getCornerPoints(rectCon[0]) # GET CORNER POINTS OF THE BIGGEST RECTANGLE
#gradePoints = utlis.getCornerPoints(rectCon[1]) # GET CORNER POINTS OF THE SECOND BIGGEST RECTANGLE
if biggestPoints.size != 0 :
# BIGGEST RECTANGLE WARPING
biggestPoints = utlis.reorder(biggestPoints) # REORDER FOR WARPING
cv2.drawContours(imgBigContour, biggestPoints, -1, (0, 255, 0), 20) # DRAW THE BIGGEST CONTOUR
pts1 = np.float32(biggestPoints) # PREPARE POINTS FOR WARP
pts2 = np.float32([[0, 0], [widthImg, 0], [0, heightImg], [widthImg, heightImg]]) # PREPARE POINTS FOR WARP
matrix = cv2.getPerspectiveTransform(pts1, pts2) # GET TRANSFORMATION MATRIX
imgWarpColored = cv2.warpPerspective(img, matrix, (widthImg, heightImg)) # APPLY WARP PERSPECTIVE
# APPLY THRESHOLD
imgWarpGray = cv2.cvtColor(imgWarpColored, cv2.COLOR_BGR2GRAY) # CONVERT TO GRAYSCALE
imgAdaptiveTh = cv2.adaptiveThreshold(imgWarpGray, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY, 11, 2)
imgThresh = cv2.threshold(imgAdaptiveTh, 170, 255, cv2.THRESH_BINARY_INV)[1] # APPLY THRESHOLD AND INVERSE
boxes = utlis.splitBoxes(imgThresh,questions) # GET INDIVIDUAL BOXES
countR = 0
countC = 0
myPixelVal = np.zeros((questions, choices)) # TO STORE THE NON ZERO VALUES OF EACH BOX
for image in boxes:
#cv2.imshow(str(countR)+str(countC),image)
totalPixels = cv2.countNonZero(image)
myPixelVal[countR][countC] = totalPixels
#cv2.imshow("Split Test ", image)
#print("hi",myPixelVal[countR][countC])
#cv2.waitKey(0)
countC += 1
if (countC == choices): countC = 0;countR += 1
# FIND THE USER ANSWERS AND PUT THEM IN A LIST
myIndex = []
for x in range(0, questions):
arr = myPixelVal[x]
myIndexVal = np.where(arr == np.amax(arr))
myIndex.append(myIndexVal[0][0])
# COMPARE THE VALUES TO FIND THE CORRECT ANSWERS
answers=''
#grading = []
#print("hi")
for x in range(0, questions):
answers+=str(myIndex[x])
#if ans[x] == myIndex[x]:
#grading.append(1)
#else:
#grading.append(0)
# print("GRADING",grading)
#score = (sum(grading) / questions) * 100 # FINAL GRADE
#print("SCORE",score)
#result =str(myIndex)
print("USER ANSWERS", answers)
fileName = path.split('\\')[-1]
fileName = fileName.split('.')[0]
f = open('E:\\FinalProject\\Outputs\\' + fileName + '.txt', "w+")
f.write(answers)
except:
imageArray = ([img, imgGray, imgCanny, imgContours],
[imgBlank, imgBlank, imgBlank, imgBlank])
print("Exception")
fileName = path.split('\\')[-1]
fileName = fileName.split('.')[0]
f = open('E:\\FinalProject\\Outputs\\' + fileName + '.txt', "w+")
f.write("-1")
imageFinal = img.copy()
imgBiggestCountours = img.copy()
imgGray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
imgBlur = cv2.GaussianBlur(imgGray, (5, 5), 1)
imgCanny = cv2.Canny(imgBlur, 10, 50)
try:
#FIND ALL COUNTERS
countours, hierarchy = cv2.findContours(imgCanny, cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_NONE)
cv2.drawContours(imgCountours, countours, -1, (0, 255, 0), 10)
#FIND RECTANGLES
rectCon = utlis.rectContour(countours)
biggestContour = utlis.getCornerPoints(rectCon[0])
#print(biggestContour)
if biggestContour.size != 0:
cv2.drawContours(imgBiggestCountours, biggestContour, -1,
(0, 255, 0), 20)
biggestContour = utlis.reorder(biggestContour)
pts1 = np.float32(biggestContour) # PREPARE POINTS FOR WARP
pts2 = np.float32([[0, 0], [widthImg, 0], [0, heightImg],
[widthImg,
heightImg]]) # PREPARE POINTS FOR WARP
matrix = cv2.getPerspectiveTransform(
pts1, pts2) # GET TRANSFORMATION MATRIX
imgWarpColored = cv2.warpPerspective(
img, matrix, (widthImg, heightImg)) # APPLY WARP PERSPECTIVE