def refresh_dataTrain(request):
# label, directory = get_Dataset('data_train')
lbp, label, directory = get_lbpDataset('data_train', 8, 4)
# local = get_lbpDataset('data_train', 8, 4)
# print(local)
DB.delete_all('tb_fastDataTraining')
for x in range(len(label)):
# # file_name = os.path.join("/home/night/Documents/Python3/TA_Wasis/myWebsite/",directory[x])
string = ""
for z in lbp[x]:
if string == "":
string = str(z)
else:
string += "," + str(z)
data_tabel = {
'lbp': string,
'label': label[x],
'directory': directory[x],
# 'file_name' : file_name,
}
DB.insert('tb_fastDataTraining', data_tabel)
return redirect('fast_train')
def get_eod(request, symbol, start_date=None, end_date=None):
if not request.user.is_authenticated():
msg = { error: 'Not authenticated' }
return HttpResponse(json.dumps(msg), content_type="application/json")
db = DB(settings.YAHOO_DATABASE)
rows = db.get_eod(symbol, start_date, end_date)
return HttpResponse(json.dumps(rows), content_type="application/json")
def get_eod(request, symbol, start_date=None, end_date=None):
if not request.user.is_authenticated():
msg = {error: 'Not authenticated'}
return HttpResponse(json.dumps(msg), content_type="application/json")
db = DB(settings.YAHOO_DATABASE)
rows = db.get_eod(symbol, start_date, end_date)
return HttpResponse(json.dumps(rows), content_type="application/json")
def index(request):
label, directory = get_Dataset('data_train')
# local = get_lbpDataset('data_train', 8, 4)
# print(local)
DB.delete_all('tb_dataTraining')
for x in range(len(label)):
# file_name = os.path.join("/home/night/Documents/Python3/TA_Wasis/myWebsite/",directory[x])
# string = ""
# for z in data[x]:
# if string == "":
# string = str(z)
# else:
# string += ","+str(z)
data_tabel = {
# 'lbp' : string,
'label': label[x],
'directory': directory[x],
# 'file_name' : file_name,
}
DB.insert('tb_dataTraining', data_tabel)
tb_dataTraining = DB.find('tb_dataTraining')
# data_train[0][0]
# for data in data_train:
# for dt in data:
# print(dt)
# print("\n")
# print(data[0])
# print(data['label'])
# print(data['directory'])
# print("\n")
context = {
'Judul': 'Dataset',
'SubJudul':
'Berikut dataset yang akan digunakan sebagai data training k-NN',
'tb_dataTraining': tb_dataTraining
# 'data' : data,
# 'label': label,
# 'directory':directory
}
return render(request, 'Data_Train/index.html', context)
def upload(request):
if request.method == 'POST':
nilai_k = request.POST['nilai_k']
# point = request.POST['point']
# radius = request.POST['radius']
fs = FileSystemStorage()
uploaded_file = request.FILES['image']
name = fs.save(uploaded_file.name, uploaded_file)
print(name)
directory = fs.url(name)
# get directory OS
file_name = os.path.join(MEDIA_ROOT, uploaded_file.name)
# load image
print(file_name)
img = cv2.imread(file_name)
# lbp_value = get_lbpImg(img, int(point), int(radius))
lbp_value = get_lbpImg(img, 8, 4)
print(lbp_value)
# data, label, direc = get_lbpDataset('data_train', int(point), int(radius))
# data, label, direc = get_lbpDataset('data_train', 8, 4)
tb_dataTraining = DB.find('tb_fastDataTraining')
dt_lbp = []
dt_label = []
for data in tb_dataTraining:
lbp = data['lbp'].split(",")
lbp = list(np.float_(lbp))
dt_lbp.append(lbp)
dt_label.append(data['label'])
# result = get_kNN_clasification(int(nilai_k), data, label, lbp_value)
result = get_knn_clasification(int(nilai_k), dt_lbp, dt_label,
lbp_value)
print(result)
final_result = DataTesting.objects.create(image=name,
label=result[0],
directory=directory)
form = DataTestForm()
context = {
'Judul': 'Form Pengujian',
'SubJudul': 'Form Pengujian',
'hasil': result,
'directory': directory,
'form': form
}
return render(request, 'Fast_Testing/upload.html', context)
form = DataTestForm()
context = {'Judul': 'Dataset', 'SubJudul': 'Data Testing', 'form': form}
return render(request, 'Fast_Testing/upload.html', context)
def data_train(request):
tb_dataTraining = DB.find('tb_fastDataTraining')
# data_train[0][0]
# for data in data_train:
# for dt in data:
# print(dt)
# print("\n")
# print(data[0])
# print(data['label'])
# print(data['directory'])
# print("\n")
context = {
'Judul': 'Dataset',
'SubJudul':
'Berikut dataset yang akan digunakan sebagai data training k-NN',
'tb_dataTraining': tb_dataTraining
# 'data' : data,
# 'label': label,
# 'directory':directory
}
return render(request, 'Fast_Testing/data_train.html', context)
def testing(request):
point = 8
radius = 4
nilai_k = 1
fs = FileSystemStorage()
uploaded_file = request.FILES['image']
# get file name
name = fs.save(uploaded_file.name, uploaded_file)
print(name)
# get directori
directory = fs.url(name)
# get directory OS
file_name = os.path.join(MEDIA_ROOT, uploaded_file.name)
print(file_name)
img = cv2.imread(file_name)
lbp_value = get_lbpImg(img, int(point), int(radius))
# result = get_kNN_clasification(int(nilai_k), data, label, lbp_value)
tb_dataTraining = DB.find('tb_fastDataTraining')
dt_lbp = []
dt_label = []
for data in tb_dataTraining:
lbp = data['lbp'].split(",")
lbp = list(np.float_(lbp))
dt_lbp.append(lbp)
dt_label.append(data['label'])
result = get_knn_clasification(int(nilai_k), dt_lbp, dt_label, lbp_value)
final_result = DataTesting.objects.create(image=name,
label=result[0],
directory=directory)
response = {'response': 'sukses post', 'result': result[0]}
return JsonResponse(response)
import pandas as pd
import os
import cv2
from lib.knn import get_knn_clasification
from lib.main_function import get_lbpDataset, get_Dataset, get_lbpImg
import numpy as np
from .forms import DataTestForm
from django.conf import settings
from myWebsite.settings import MEDIA_ROOT
from django.core.files.storage import FileSystemStorage
my_storage = FileSystemStorage(
location=os.path.join(settings.BASE_DIR, 'angga'))
from lib.database import DB
DB.init()
def refresh_dataTrain(request):
# label, directory = get_Dataset('data_train')
lbp, label, directory = get_lbpDataset('data_train', 8, 4)
# local = get_lbpDataset('data_train', 8, 4)
# print(local)
DB.delete_all('tb_fastDataTraining')
for x in range(len(label)):
# # file_name = os.path.join("/home/night/Documents/Python3/TA_Wasis/myWebsite/",directory[x])
if __name__ == "__main__":
SETTINGS_FILE = os.path.dirname(os.path.realpath(__file__)) + '/settings.json'
SETTINGS = json.loads(open(SETTINGS_FILE).read())
transmission = Transmission(SETTINGS['transmission'])
if(transmission.version() == None):
count = 0
showrss = ShowRSS(SETTINGS['showrss']['user_id'])
shows = showrss.get_feed(SETTINGS['showrss']['retries'])
if(shows):
database = DB(SETTINGS['database']['type'], SETTINGS['database'])
for show in shows:
if not database.id_exists(show['_id']):
print 'Found new episode: %s' % (show['title'].encode('utf8'))
if transmission.add_torrent(show['link']):
database.add_torrent(show)
count += 1
else:
print 'Failed to add %s' % (show['title'].encode('utf8'))
if count != 0:
print 'Added %s new torrents' % (count)
def load_db():
db = DB(config['mysql'])
return db.db