from accuracyScore2 import accuracy
#from imutils import paths
#import paths #imutils module
#import argparse
import cv2
import os
import glob
import time
import numpy as np
start = time.time()
trainingPath = "/Users/fegvilela/Documents/Unb/TCC/baseDados/training2/"
testingPath = "/Users/fegvilela/Documents/Unb/TCC/baseDados/testing2/"
pngFiles1 = convertImage(trainingPath)
# initialize the local binary patterns descriptor along with
# the data and label lists
desc = LocalBinaryPatterns(30, 9) # LBP 30 pontos e raio 9 = melhor acurácia
data = []
labels = []
trueClass = []
predClass = []
accuracyList = []
# loop over the training images
for file in pngFiles1:
# load the image, convert it to grayscale, and describe it
image = cv2.imread(file)
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
from sklearn.preprocessing import LabelEncoder
from imageResize import imResize
#import numpy as np
import cv2
import os
import glob
import time
# Guarda o horário em que o código começou a rodar
start = time.time()
# path para as imagens
imagesPath = "/Users/fegvilela/Desktop/backupImagensTCC/baseImagensMelhorada8/"
#converte todas imagens para png
pngFiles = convertImage(imagesPath)
print("total imagens: %d" % int(len(pngFiles)))
#salva resultado em arquivo
with open("Resultado.txt", "w") as file1:
# initialize the local binary patterns descriptor along with
# the data and label lists
resultsList = []
data = []
labels = []
desc = LocalBinaryPatterns(21,
9) # LBP 21 pontos e raio 9 = melhor acurácia
#PREPROCESSING AND FEATURE EXTRACTION
# loop over all images