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)