getimage.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
import roi as r
from matplotlib import pyplot as plt
import h5py
import numpy as np
from skimage import morphology

#importação da imagem

im = '../template/templatePreenchido.png'

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

nomalizacao =  np.ones(dim)
gabaritoInteresse = cv2.addWeighted(gabaritoInteresse, 1.07, np.zeros(gabaritoInteresse.shape, gabaritoInteresse.dtype),0,0)
gabaritoInteresse =  cv2.normalize(gabaritoInteresse,nomalizacao,150,255, cv2.NORM_MINMAX)

gabaritoInteresse = cv2.cvtColor(gabaritoInteresse,cv2.COLOR_BGR2GRAY)

blurred =  cv2.GaussianBlur(gabaritoInteresse, (17,17),1)

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]

questions = []

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]
       
        
        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])

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])

    for o in q:
        boxes.append(o[1])

    q = sorted(q, key=lambda k: k[1][0])
    for o in q:

        questionCnts.append(o[0])


posicaoRespostas = np.empty(0,int)

for (q, i) in enumerate(np.arange(0, len(questionCnts), 30)):


    cnts = contours.sort_contours(questionCnts[i:i+30])[0]
    
    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)
  
    	posicaoRespostas = np.append(posicaoRespostas,(l))

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



cv2.imshow("Bolhas",bolhas)



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

Test