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))
print(lbp_value)
data, label, direc = get_lbpDataset('data_train', int(point), int(radius))
print(data)
# result = get_kNN_clasification(int(nilai_k), data, label, lbp_value)
result = get_knn_clasification(int(nilai_k), data, label, lbp_value)
print(result)
response={
'response' :'sukses post',
'result' : result[0]
}
return JsonResponse(response)
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 upload(request):
if request.method == 'POST':
fs = FileSystemStorage()
uploaded_file = request.FILES['image']
label = request.POST['label']
# get file name
name = fs.save(uploaded_file.name, uploaded_file)
# get directori
directory = fs.url(name)
# get directory OS
file_name = os.path.join(MEDIA_ROOT,uploaded_file.name)
# load image
img = cv2.imread(file_name)
# get LBP
lbp_value = get_lbpImg(img, 8, 4)
data = ""
for x in lbp_value:
if data == "":
data = str(x)
else:
data = data + ", "+ str(x)
dataTraining = Dataset.objects.create(
lbp_hist = data,
image = name,
label = label,
directory = directory
)
return redirect('dataset')
form = DatasetForm()
context = {
'Judul' : 'Tambah Dataset',
'SubJudul' : 'Tambah Dataset',
'form' : form
}
return render(request, 'dataset/upload.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)
def upload(request):
if request.method == 'POST':
x_train = []
y_train = []
tb_dataTraining = Dataset.objects.all()
for data in tb_dataTraining:
y_train.append(data.label)
fiture = []
data = data.lbp_hist.split(", ")
for z in data:
z = float(z)
fiture.append(z)
x_train.append(fiture)
fs = FileSystemStorage()
uploaded_file = request.FILES['image']
nilai_k = int(request.POST['nilai_k'])
# get file name
name = fs.save(uploaded_file.name, uploaded_file)
# get directori
directory = fs.url(name)
# get directory OS
file_name = os.path.join(MEDIA_ROOT,uploaded_file.name)
# load image
img = cv2.imread(file_name)
# get LBP
lbp_value = get_lbpImg(img, 8, 4)
hasil = "not yet"
hasil = get_kNN_clasification(nilai_k, x_train, y_train, lbp_value)
dataTraining = DataTesting.objects.create(
image = name,
label = hasil,
directory = directory
)
form = DataTestForm()
context = {
'Judul' : 'Form Pengujian',
'SubJudul' : 'Form Pengujian',
'hasil' : hasil,
'directory' : directory,
'form' : form
}
return render(request, 'testing/upload.html', context)
# return redirect('testing/upload')
form = DataTestForm()
context = {
'Judul' : 'Tambah Data Testing',
'SubJudul' : 'Tambah Data Testing',
'hasil' : 'notyet',
'directory' : '/media_dataset/profil.jpg' ,
'form' : form
}
return render(request, 'testing/upload.html', context)