getPrincipalImage.py

from __future__ import print_function
import cv2
import numpy as np
import align_images as align
import findGabarito as gabarito 
import contornosInternos
import bubble as bubble
import imutils
from imutils.perspective import four_point_transform
from imutils import contours
import bitwiseImage as bt

#importação da imagem

#im = 'ImagensTeste/photo_2019-07-04_13-33-01.jpg'
#im = 'ImagensTeste/photo_2019-07-12_16-43-47.jpg'
#im = 'ImagensTeste/photo_2019-07-12_16-43-47.jpg'
#im = 'template/template.png'
im = 'template/templatePreenchido.png'
#im = 'ImagensTeste/photo_2019-07-12_16-43-47.jpg'



#alinhamento da imagem 
imagemAlinhada =  align.alignImages(im)
cv2.imwrite('processadas/processadaAlinhada.png', imagemAlinhada)

#imagemAlinhada = cv2.addWeighted(imagemAlinhada, 0.9, np.zeros(imagemAlinhada.shape, imagemAlinhada.dtype), 2, 2)

gabarito = gabarito.findGabarito(imagemAlinhada)

dim =  (1595,556)
gabarito =  cv2.resize(gabarito, dim, interpolation=cv2.INTER_CUBIC )

cv2.imwrite('processadas/gabarito.png', gabarito)


gabaritoInteresse = cv2.imread('processadas/gabarito.png')



bolhas =  bubble.bolhas(gabaritoInteresse)
#gabaritoInteresse = cv2.addWeighted(gabaritoInteresse, 1.09, np.zeros(gabaritoInteresse.shape, gabaritoInteresse.dtype),0,0)
gabaritoInteresse = cv2.cvtColor(gabaritoInteresse,cv2.COLOR_BGR2GRAY)

imageBT = bt.bitwise(gabaritoInteresse)

print(imageBT.shape)
cv2.imshow("imageBT",imageBT)


blurred =  cv2.GaussianBlur(gabaritoInteresse, (17,17),0)


#cv2.imshow("dilation",dilation)


thresh = cv2.adaptiveThreshold(gabaritoInteresse, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY_INV, 11, 2)

edged = cv2.Canny(blurred, 100,200)

cnts = cv2.findContours(thresh.copy(), cv2.RETR_CCOMP,cv2.CHAIN_APPROX_NONE)

heirarchy = cnts[1][0]

cnts = cnts[0] if imutils.is_cv4() else cnts[1]

#M =  cv2.moments(cnts)

#print(M)

questions = []


imageBT = bt.bitwise(gabaritoInteresse)
cv2.imshow("imageBT",imageBT)

for c in cnts:
    (x, y, w, h) = cv2.boundingRect(c)
    ar = w / float(h)
    if (w >= 20 and h >= 20) and (w <= 25  and h <= 25) and ar >= 0.7 and ar <= 1.3:
        box = [(x//5)*5, y]
        #box = [x+w/2, y+h/2, w/2]
        
        questions.append([c, box])
        #print(x, y)
        cv2.rectangle(gabarito, (x, y), (x+w, y+h), (255, 0, 0), 1)

questions = sorted(questions, key=lambda q: q[1][1])

print(len(box))

questionCnts = []
''' 
  Agora estamos classificando da esquerda para a direita tomando um lote de 30 contornos 
  que são basicamente uma linha inteira e, em seguida, classificá-los a partir da ordem crescente de x 
''' 

boxes = []
for i in np.arange(0, len(questions), 30):
    # take a row of bubbles
    q = list(questions[i: i+30])
    #print(q)
    for o in q:
        boxes.append(o[1])
    # append each contour sorted from left to right in a row
    # sort them using x
    q = sorted(q, key=lambda k: k[1][0])
    for o in q:
        # append each contour sorted from left to right in a row
        #questionCnts.append(o[0])
        questionCnts.append(o[0])

# each question has 5 possible answers, to loop over the
# question in batches of 5

#matrizRespostas = np.empty((0,4))
#print(matrizRespostas.shape)
respostas = []

questao = []
letra = []
for (q, i) in enumerate(np.arange(0, len(questionCnts), 30)):
    # calculate the old question no
    row = q // 5
    col = q % 5
    old_question_no = col  + row

    #print(q)
    #print(i)
    cnts = contours.sort_contours(questionCnts[i:i+5])[0]


    #cnts = cnts[0:4:5]
    
    for (l ,k )in enumerate(cnts):
    	(x, y, w, h) = cv2.boundingRect(k)
    	if (w >= 20 and h >= 20) and (w <= 25  and h <= 25) and ar >= 0.7 and ar <= 1.3:
    		box = [(x//5)*5, y]
    	#print(x, y)
    	respostas.append(l)

    	#rect = np.array(cv2.boundingRect(k)).reshape(1,4)
    	#print(len(rect.shape))
    	#matrizRespostas = np.append(matrizRespostas,rect,0)

    	cv2.rectangle(bolhas, (x, y), (x+w, y+h), (0, 0, 255), 1)

    	#for o in respostas:
    		#print(o)
    	#	questao.append(o)
    	#	letra.append(o)


#print(questao[15]//5)
#print(letra[15]%5)



#print(type(o))





#print(len(boxes))
#print(len(questionCnts))
#print(len(questions))
#print(len(bolhas))

cv2.imshow("Bolhas",bolhas)
#cv2.imshow("Imagem Alinhada",imagemAlinhada)
#cv2.imshow("Gabarito",gabarito)

cv2.waitKey(0)
cv2.destroyAllWindows()
exit()