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 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 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)
