def getTrainSample(self):
"""
:param attributes: List of attributes we want to obtain. Use constants defined in the class Fingerprint
:return: trainSet, a list of Fingerprint objects
"""
attributes = set(self.attributes).intersection(
Fingerprint.MYSQL_ATTRIBUTES)
attributes = ",".join(attributes)
if len(self.train) == 0:
self.splitData()
counterString = "("
for counter in self.train:
counterString += str(counter) + ","
counterString = counterString[0:len(counterString) - 1]
counterString += ")"
self.cur.execute('SELECT ' + attributes +
' FROM fpData WHERE counter in ' + counterString)
res = self.cur.fetchall()
trainSet = list()
for v in res:
trainSet.append(Fingerprint(self.attributes, v))
return trainSet
def setCompoundEncoder(self, compFilename):
"""
compFilename: csv path for a file containing smiles
calc descriptors and save those info as self.CompDat
"""
#calc FPs
FP = Fingerprint(CF.FINGERPRINT_DIM, 4)
FP.processCompFile(compFilename)
self.FP = FP
#set comp encoder
CompDat = CompDatabase()
CompDat.initialize(compFilename, FP.descDf, dimensionNum=CF.VALUE_DIM)
self.CompDat = CompDat
def getDataTest(self):
attributes = set(self.attributes).intersection(
Fingerprint.MYSQL_ATTRIBUTES)
attributes = ",".join(attributes)
if len(self.train) == 0:
self.splitData()
counterString = "("
for counter in self.train:
counterString += str(counter) + ","
counterString = counterString[0:len(counterString) - 1]
counterString += ")"
self.cur.execute('SELECT ' + attributes +
' FROM fpData WHERE counter in (8242,8239)')
res = self.cur.fetchall()
fps = list()
for v in res:
fps.append(Fingerprint(self.attributes, v))
return fps
def __init__(self, audio_file=None, fingerprint=None):
if fingerprint is not None:
self.fingerprint = fingerprint
if audio_file is not None:
self.fingerprint = Fingerprint(audio_file, amp_min=-90, plot=True)
def test_get_movements(self):
fingerprint = Fingerprint([0xb53f208a, 0x2f95a90a, 0xfb9bd053, 0x6ff99733], 'RSA 2048', 'MD5')
me = open('natmchugh.txt', 'r').read()
print str(fingerprint)
self.assertEqual(str(fingerprint), me)
def test_hash_to_moves(self):
fingerprint = Fingerprint([0xfc94b0c1], 'RSA 2048', 'MD5')
expected = [0,3,3,3, 0,1,1,2, 0,0,3,2, 1,0,0,3]
actual = fingerprint.hash_to_moves([0xfc94b0c1])
self.assertEqual(actual, expected)
import numpy as np
from Holt import Holt
from CrossingNumber import CrossingNumber
from Fingerprint import Fingerprint
###################
images = []
target = None
# 1. Aquisição da imagem
for file in glob.glob("../dataset_test/*.png"
): # ALTERAR AQUI O CAMINNO DA PASTA COM AS IMAGENS
filename = os.path.basename(file)
if filename == 'target.png':
target = Fingerprint(cv2.imread(file, 0))
images.append(target)
else:
img = Fingerprint(cv2.imread(file, 0))
images.append(img)
print(len(images))
control_index = 0
for image in images:
# 2. Filtro da mediana
mediana = cv2.medianBlur(image.img, 5)
# 3. Filtro de aguçamento
filtro = np.array([[-1, -1, -1, -1, -1], [-1, 2, 2, 2, -1],
[-1, 2, 3, 2, -1], [-1, 2, 2, 2, -1],
