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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)""" | |