# COMPARE THE VALUES TO FIND THE CORRECT ANSWERS
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) * 100 # FINAL GRADE
#print("SCORE",score)
# DISPLAYING ANSWERS
utlis.showAnswers(imgWarpColored, myIndex, grading,
ans) # DRAW DETECTED ANSWERS
utlis.drawGrid(imgWarpColored) # DRAW GRID
imgRawDrawings = np.zeros_like(
imgWarpColored) # NEW BLANK IMAGE WITH WARP IMAGE SIZE
utlis.showAnswers(imgRawDrawings, myIndex, grading,
ans) # DRAW ON NEW IMAGE
invMatrix = cv2.getPerspectiveTransform(
pts2, pts1) # INVERSE TRANSFORMATION MATRIX
imgInvWarp = cv2.warpPerspective(
imgRawDrawings, invMatrix,
(widthImg, heightImg)) # INV IMAGE WARP
# DISPLAY GRADE
imgRawGrade = np.zeros_like(
imgGradeDisplay,
np.uint8) # NEW BLANK IMAGE WITH GRADE AREA SIZE
cv2.putText(imgRawGrade,
myIndex.append(myIndexVal)
# Compare the value to find the correct ones
grading = []
for x in range(0, questions):
if ans[x] == myIndex[x]:
grading.append(1)
else:
grading.append(0)
score = (sum(grading) / questions) * 100
# Dispaly answers
utlis.showAnswers(imgWarpColored, myIndex, grading, ans)
utlis.drawGrid(imgWarpColored)
# New blank image with the size of the warpped image
imgRawDrawings = np.zeros_like(imgWarpColored)
utlis.showAnswers(imgRawDrawings, myIndex, grading, ans)
# Inverse the transformation matrix
invMatrix = cv2.getPerspectiveTransform(pts2, pts1)
imgInvWarp = cv2.warpPerspective(imgRawDrawings, invMatrix,
(widthImg, heightImg))
# Display grade
imgRawGrade = np.zeros_like(imgGradeDisplay, np.uint8)
cv2.putText(imgRawGrade,
str(int(score)) + "%", (70, 100),
cv2.FONT_HERSHEY_COMPLEX, 3, (0, 255, 255), 3)
invGradeMatrix = cv2.getPerspectiveTransform(ptsG2, ptsG1)
imgInvGradeDisplay = cv2.warpPerspective(imgRawGrade,