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)