def start_predict_offline():
if file_text.get("1.0", END) == []:
messagebox.showerror("Error", "Please select test file!")
return
else:
test_file = file_text.get("1.0", END)
test_file = test_file.split("\n")[0]
if model_text.get("1.0", END) == []:
messagebox.showerror("Error", "Please select test model!")
return
else:
offline_model = model_text.get("1.0", END)
offline_model = offline_model.split("\n")[0]
test_config = pd.read_csv(offline_model.split(".")[0] + ".txt", sep=" ")
test_config.columns = ['param', 'value']
test_config = pd.DataFrame(data=[test_config['value'].values], columns=test_config['param'].values)
if test_config['filter'].values[0] == '1':
filter_order = int(test_config['order'].values[0])
filter_fc = test_config['fc'].values[0]
filter_type = test_config['type'].values[0]
filter_fs = int(test_config['fs'].values[0])
if filter_type == "bandpass" or filter_type == "bandstop":
fc = filter_fc.split("[")[1].split("]")[0].split(",")
filter_fc = [int(fc[0]), int(fc[1])]
else:
filter_fc = int(filter_fc)
if test_config['smooth'].values[0] == '1':
smooth_wl = int(test_config['windowlength'].values[0])
smooth_po = int(test_config['polyorder'].values[0])
smooth_mode = test_config['mode'].values[0]
if test_config['eliminate'].values[0] == '1':
elim_thre = int(test_config['threshold'].values[0])
elim_ws = int(test_config['windowsize'].values[0])
elim_base = int(test_config['baseon'].values[0])
if test_config['energy'].values[0] == '1':
eng_bs = int(test_config['bandsize'].values[0])
eng_odr = int(test_config['odr'].values[0])
seg_size = int(test_config['segsize'].values[0])
files = pd.read_csv(test_file)
final_features = []
labels = []
for idx, row in files.iterrows():
name = row['file']
dir = row['dir']
label = row['label']
header = row['header']
raw_data = pd.read_csv(dir + '\\' + '\\' + name, header=header, delimiter=';')
ax = raw_data['ax']
ay = raw_data['ay']
az = raw_data['az']
data = np.array([ax, ay, az]).T
if test_config['filter'].values[0] == '1':
data = data_preprocessing.filter(data, filter_order, filter_fc, filter_type, filter_fs)
if test_config['smooth'].values[0] == '1':
smoothed_data = data_preprocessing.smooth(data, smooth_wl, smooth_po, smooth_mode)
if test_config['eliminate'].values[0] == '1':
new_smoothed_data, data = data_preprocessing.eliminate_abnormal_value(smoothed_data, data, elim_ws, elim_thre, elim_base)
else:
data = smoothed_data
num = int(test_config['mean'].values[0]) + int(test_config['std'].values[0]) + int(test_config['min'].values[0]) + int(test_config['max'].values[0]) + int(test_config['rms'].values[0])
for col in range(3):
feature = np.empty([int(len(data[:, col])/seg_size), num])
new_feature = []
for i in range(int(len(data[:, col])/seg_size)):
window = data[i*seg_size:(i+1)*seg_size, col]
k = 0
if int(test_config['mean'].values[0]):
feature[i, k] = feature_extraction.mean(window)
k += 1;
if int(test_config['std'].values[0]):
feature[i, k] = feature_extraction.std(window)
k += 1;
if int(test_config['min'].values[0]):
feature[i, k] = feature_extraction.getmin(window)
k += 1;
if int(test_config['max'].values[0]):
feature[i, k] = feature_extraction.getmax(window)
k += 1;
if int(test_config['rms'].values[0]):
feature[i, k] = feature_extraction.rms(window)
k += 1;
if int(test_config['energy'].values[0]):
energy_vector = feature_extraction.energy_for_each_freq_band(window, eng_odr, eng_bs)
temp = np.append(feature[i, :], energy_vector)
new_feature.append(temp)
else:
new_feature.append(feature[i, :])
new_feature = np.array(new_feature)
#print("new_feature: ", new_feature.shape)
if col == 0:
ex_feature = new_feature
else:
ex_feature = np.append(ex_feature, new_feature, axis=1)
#print("ex_feature: ", ex_feature.shape)
num_features = ex_feature.shape[1] // 3
num_axis = int(test_config['X'].values[0]) +int(test_config['Y'].values[0]) + int(test_config['Z'].values[0])
trn_feature = np.empty([ex_feature.shape[0], num_axis*num_features])
k = 0
if int(test_config['X'].values[0]) == 1:
trn_feature[:, k*num_features:(k+1)*num_features] = ex_feature[:, 0:num_features]
k += 1
if int(test_config['Y'].values[0]) == 1:
trn_feature[:, k*num_features:(k+1)*num_features] = ex_feature[:, num_features:num_features*2]
k += 1
if int(test_config['Z'].values[0]) == 1:
trn_feature[:, k*num_features:(k+1)*num_features] = ex_feature[:, num_features*2:num_features*3]
k += 1;
final_features.extend(trn_feature)
for i in range(len(trn_feature)):
labels.append(label)
final_features = np.array(final_features)
labels = np.array(labels)
test_model = pickle.load(open(offline_model, 'rb'))
pred = test_model.predict(final_features)
acc_tst = accuracy_score(labels, pred)
lb_acc_tst = Label(offline, text=acc_tst, font=("Helvetica", "12", "bold italic"))
lb_acc_tst.place(relx=0.6, rely=0.28)
def startTrain():
model = en_model.get()
if model == "":
messagebox.showerror("Error", "Please select the training model!")
return
trn_ratio = var_ratio.get()
file_idx = trn_files.curselection()
filename = ""
if file_idx == ():
messagebox.showerror("Error", "Please select the training files!")
return
else:
filename = trn_files.get(file_idx)
# Detect if a filter is used and pass all filter parameter to the function
if var_filter.get() == 1:
if en_order.get() != "" and en_fc.get() != "" and en_type.get() != "" and en_fs.get() != "":
filter_order = int(en_order.get())
filter_fc = en_fc.get()
filter_type = en_type.get()
filter_fs = int(en_fs.get())
if filter_type == "bandpass" or filter_type == "bandstop":
if filter_fc.split("[")[0] != "":
messagebox.showerror("Error", "The form of fc should be like \'[fc1, fc2]\'!")
return
fc = filter_fc.split("[")[1].split("]")[0].split(",")
filter_fc = [int(fc[0]), int(fc[1])]
else:
filter_fc = int(filter_fc)
else:
messagebox.showerror("Error", "Please set all the parameters of Filter!")
return
# Detect if smoothing is used and pass all filter parameter to the function
if var_smooth.get() == 1:
if en_wl.get() != "" and en_po.get() != "" and en_mode.get() != "":
smooth_wl = int(en_wl.get())
smooth_po = int(en_po.get())
smooth_mode = en_mode.get()
if smooth_wl % 2 == 0:
messagebox.showerror("Error", "The window length should be odd!")
return
else:
messagebox.showerror("Error", "Please set all the parameters of Smoothing!")
return
if var_elim.get() == 1:
if en_thre.get() != "" and en_win.get() != "":
elim_thre = int(en_thre.get())
elim_ws = int(en_win.get())
else:
messagebox.showerror("Error", "Please set all the parameters of Eliminate Abnormal Data!")
return
if var_energy.get() == 1:
if en_bs.get() != "" and en_odr.get() != "":
eng_bs = int(en_bs.get())
eng_odr = int(en_odr.get())
else:
messagebox.showerror("Error", "Please set all the parameters of Energy!")
return
if en_ws.get() != "":
seg_size = int(en_ws.get())
else:
messagebox.showerror("Error", "Please set the segment size!")
return
if var_accel_x.get() == 0 and var_accel_y.get() == 0 and var_accel_z.get() == 0:
messagebox.showerror("Error", "Please select at least one axis!")
return
if (var_base.get() == 1 and var_accel_x.get() == 0) or (var_base.get() == 2 and var_accel_y.get() == 0) or (var_base.get() == 3 and var_accel_z.get() == 0):
messagebox.showerror("Error", "Please select the axis that is select in Axis Selection!")
return
if var_mean.get() + var_std.get() + var_min.get() + var_max.get() + var_rms.get() + var_energy.get() == 0:
messagebox.showerror("Error", "Please select at least one feature!")
return
save_model = filedialog.asksaveasfilename(initialdir = "/", title = "Save Model", filetypes = (("sav files","*.sav"), ("all files", "*.*")))
if save_model == "":
return
if len(save_model.split(".")) == 1:
save_model = save_model + '.sav'
### write model configuration
parts = save_model.split('/')
directory = '/'.join(parts[0:len(parts)-1])
txtName = parts[len(parts)-1].split('.')[0] + '.txt'
pb['value'] = 10
trn.update_idletasks()
time.sleep(0.5)
f = open(directory + '/' + txtName, "w+")
f.write("param value\n")
if var_filter.get() == 1:
f.write("filter 1\n")
f.write("order %d\n" %filter_order)
f.write("fc %d\n" %filter_fc)
f.write("type " + filter_type + "\n")
f.write("fs %d\n" %filter_fs)
else:
f.write("filter 0\n")
if var_smooth.get() == 1:
f.write("smooth 1\n")
f.write("windowlength %d\n" %smooth_wl)
f.write("polyorder %d\n" %smooth_po)
f.write("mode " + smooth_mode + "\n")
else:
f.write("smooth 0\n")
if var_elim.get() == 1:
f.write("eliminate 1\n")
f.write("threshold %d\n" %elim_thre)
f.write("windowsize %d\n" %elim_ws)
f.write("baseon %d\n" %var_base.get())
else:
f.write("eliminate 0\n")
f.write("X %d\n" %var_accel_x.get())
f.write("Y %d\n" %var_accel_y.get())
f.write("Z %d\n" %var_accel_z.get())
f.write("mean %d\n" %var_mean.get())
f.write("std %d\n" %var_std.get())
f.write("min %d\n" %var_min.get())
f.write("max %d\n" %var_max.get())
f.write("rms %d\n" %var_rms.get())
f.write("energy %d\n" %var_energy.get())
if var_energy.get() == 1:
f.write("bandsize %d\n" %eng_bs)
f.write("odr %d\n" %eng_odr)
f.write("segsize %d\n" %seg_size)
f.close()
files = pd.read_csv(filename)
final_features = []
labels = []
num_rows = files.shape[0]
for idx, row in files.iterrows():
name = row['file']
dir = row['dir']
label = row['label']
header = row['header']
raw_data = pd.read_csv(dir + '\\' + '\\' + name, header=header, delimiter=';')
ax = raw_data['ax']
ay = raw_data['ay']
az = raw_data['az']
data = np.array([ax, ay, az]).T
if var_filter.get() == 1:
data = data_preprocessing.filter(data, filter_order, filter_fc, filter_type, filter_fs)
if var_smooth.get() == 1:
smoothed_data = data_preprocessing.smooth(data, smooth_wl, smooth_po, smooth_mode)
if var_elim.get() == 1:
new_smoothed_data, data = data_preprocessing.eliminate_abnormal_value(smoothed_data, data, elim_ws, elim_thre, var_base.get())
else:
data = smoothed_data
num = var_mean.get() + var_std.get() + var_min.get() + var_max.get() + var_rms.get()
# extract features from x y z axis respectively
for col in range(3):
# number of feature column is determined by selected features
feature = np.empty([int(len(data[:, col])/seg_size), num])
new_feature = []
for i in range(int(len(data[:, col])/seg_size)):
# window is one group of data whose size is seg_size
window = data[i*seg_size:(i+1)*seg_size, col]
# keep track of features
k = 0
if var_mean.get():
feature[i, k] = feature_extraction.mean(window)
k += 1;
if var_std.get():
feature[i, k] = feature_extraction.std(window)
k += 1;
if var_min.get():
feature[i, k] = feature_extraction.getmin(window)
k += 1;
if var_max.get():
feature[i, k] = feature_extraction.getmax(window)
k += 1;
if var_rms.get():
feature[i, k] = feature_extraction.rms(window)
k += 1;
if var_energy.get():
energy_vector = feature_extraction.energy_for_each_freq_band(window, eng_odr, eng_bs)
temp = np.append(feature[i, :], energy_vector)
new_feature.append(temp)
else:
new_feature.append(feature[i, :])
# features on only one axis
new_feature = np.array(new_feature)
#print("new_feature: ", new_feature.shape)
# save features of all axes to ex_feature
if col == 0:
ex_feature = new_feature
else:
ex_feature = np.append(ex_feature, new_feature, axis=1)
# print("ex_feature: ", ex_feature.shape)
num_features = ex_feature.shape[1] // 3
num_axis = var_accel_x.get() + var_accel_y.get() + var_accel_z.get()
trn_feature = np.empty([ex_feature.shape[0], num_axis*num_features])
k = 0
if var_accel_x.get() == 1:
trn_feature[:, k*num_features:(k+1)*num_features] = ex_feature[:, 0:num_features]
k += 1
if var_accel_y.get() == 1:
trn_feature[:, k*num_features:(k+1)*num_features] = ex_feature[:, num_features:num_features*2]
k += 1
if var_accel_z.get() == 1:
trn_feature[:, k*num_features:(k+1)*num_features] = ex_feature[:, num_features*2:num_features*3]
k += 1;
final_features.extend(trn_feature)
for i in range(len(trn_feature)):
labels.append(label)
pb['value'] = 10 + (idx+1)/num_rows * 80
trn.update_idletasks()
time.sleep(0.5)
final_features = np.array(final_features)
labels = np.array(labels)
#print(final_features.shape)
#print(labels.shape)
#np.savetxt('fea', final_features, delimiter=',')
#print(labels)
# f = open(directory + '/' + txtName, "w+")
# f.write("param value\n")
if res_text.get('1.0', END) != []:
res_text.delete('1.0', END)
if model == "Random Forest":
acc_trn, acc_tst, acc_oob = train.RandomForest(final_features, labels, trn_ratio, save_model)
res_text.insert(END, "Training accuracy: " + str(acc_trn) + '\n')
res_text.insert(END, '\n' + "Testing accuracy: " + str(acc_tst) + '\n')
res_text.insert(END, '\n' + "Out of bag accuracy: " + str(acc_oob) + '\n')
if model == "SVM":
acc_trn, acc_tst = train.SVM(final_features, labels, trn_ratio, save_model)
res_text.insert(END, "Training accuracy: " + str(acc_trn) + '\n')
res_text.insert(END, '\n' + "Test accuracy: " + str(acc_tst) + '\n')
pb['value'] = 100
trn.update_idletasks()
time.sleep(0.5)
def plotFigures():
file_idx = trn_files.curselection()
filename = ""
if file_idx == ():
messagebox.showerror("Error", "Please select the training files!")
return
else:
filename = trn_files.get(file_idx)
if var_filter.get() == 1:
if en_order.get() != "" and en_fc.get() != "" and en_type.get() != "" and en_fs.get() != "":
filter_order = int(en_order.get())
filter_fc = en_fc.get()
filter_type = en_type.get()
filter_fs = int(en_fs.get())
if filter_type == "bandpass" or filter_type == "bandstop":
if filter_fc.split("[")[0] != "":
messagebox.showerror("Error", "The form of fc should be like \'[fc1, fc2]\'!")
return
fc = filter_fc.split("[")[1].split("]")[0].split(",")
filter_fc = [int(fc[0]), int(fc[1])]
else:
filter_fc = int(filter_fc)
else:
messagebox.showerror("Error", "Please set all the parameters of Filter!")
return
if var_smooth.get() == 1:
if en_wl.get() != "" and en_po.get() != "" and en_mode.get() != "":
smooth_wl = int(en_wl.get())
smooth_po = int(en_po.get())
smooth_mode = en_mode.get()
if smooth_wl % 2 == 0:
messagebox.showerror("Error", "The window length should be odd!")
return
else:
messagebox.showerror("Error", "Please set all the parameters of Smoothing!")
return
if var_elim.get() == 1:
if en_thre.get() != "" and en_win.get() != "":
elim_thre = int(en_thre.get())
elim_ws = int(en_win.get())
else:
messagebox.showerror("Error", "Please set all the parameters of Eliminate Abnormal Data!")
return
files = pd.read_csv(filename)
num_rows = files.shape[0]
for idx, row in files.iterrows():
name = row['file']
dir = row['dir']
label = row['label']
header = row['header']
raw_data = pd.read_csv(dir + '\\' + '\\' + name, header=header, delimiter=';')
ax = raw_data['ax']
ay = raw_data['ay']
az = raw_data['az']
data = np.array([ax, ay, az]).T
top = Toplevel(root)
top.title("Figure " + str(idx+1) + " / " + str(num_rows))
f = Figure(figsize=(14, 8), dpi=100)
sub = var_filter.get() + var_smooth.get() + var_elim.get() + 1
if var_elim.get() == 1:
sub += 1
axes = f.subplots(sub, 1)
if sub == 1:
axes.plot(data[:, 0], label='raw ax - ' + label, alpha=0.8)
axes.plot(data[:, 1], label='raw ay - ' + label, alpha=0.8)
axes.plot(data[:, 2], label='raw az - ' + label, alpha=0.8)
axes.legend()
axes.grid()
else:
axes[0].plot(data[:, 0], label='raw ax - ' + label, alpha=0.8)
axes[0].plot(data[:, 1], label='raw ay - ' + label, alpha=0.8)
axes[0].plot(data[:, 2], label='raw az - ' + label, alpha=0.8)
axes[0].legend()
axes[0].grid()
index = 1
if var_filter.get() == 1:
data = data_preprocessing.filter(data, filter_order, filter_fc, filter_type, filter_fs)
axes[index].plot(data[:, 0], label='filtered ax - ' + label, alpha=0.8)
axes[index].plot(data[:, 1], label='filtered ay - ' + label, alpha=0.8)
axes[index].plot(data[:, 2], label='filtered az - ' + label, alpha=0.8)
axes[index].legend()
axes[index].grid()
index += 1
if var_smooth.get() == 1:
smoothed_data = data_preprocessing.smooth(data, smooth_wl, smooth_po, smooth_mode)
axes[index].plot(smoothed_data[:, 0], label='smoothed ax - ' + label, alpha=0.8)
axes[index].plot(smoothed_data[:, 1], label='smoothed ay - ' + label, alpha=0.8)
axes[index].plot(smoothed_data[:, 2], label='smoothed az - ' + label, alpha=0.8)
axes[index].legend()
axes[index].grid()
axes[index].set_ylim((-6000, 6000))
index += 1
if var_elim.get() == 1:
new_smoothed_data, data = data_preprocessing.eliminate_abnormal_value(smoothed_data, data, elim_ws, elim_thre, var_base.get())
axes[index].plot(data[:, 0], label='after eliminate ax - ' + label, alpha=0.8)
axes[index].plot(data[:, 1], label='after eliminate ay - ' + label, alpha=0.8)
axes[index].plot(data[:, 2], label='after eliminate az - ' + label, alpha=0.8)
axes[index].legend()
axes[index].grid()
index += 1
axes[index].plot(new_smoothed_data[:, 0], label='after eliminate smoothed ax - ' + label, alpha=0.8)
axes[index].plot(new_smoothed_data[:, 1], label='after eliminate smoothed ay - ' + label, alpha=0.8)
axes[index].plot(new_smoothed_data[:, 2], label='after eliminate smoothed az - ' + label, alpha=0.8)
axes[index].legend()
axes[index].grid()
index += 1
canvas_fig = FigureCanvasTkAgg(f, top)
canvas_fig.draw()
canvas_fig.get_tk_widget().pack(side=BOTTOM, fill=BOTH, expand=True)
toolbar = NavigationToolbar2Tk(canvas_fig, top)
toolbar.update()
canvas_fig._tkcanvas.pack(side=TOP, fill=BOTH, expand=True)
def extractAndSave():
file_idx = trn_files.curselection()
filename = ""
if file_idx == ():
messagebox.showerror("Error", "Please select the training files!")
return
else:
filename = trn_files.get(file_idx)
if var_filter.get() == 1:
if en_order.get() != "" and en_fc.get() != "" and en_type.get() != "" and en_fs.get() != "":
filter_order = int(en_order.get())
filter_fc = en_fc.get()
filter_type = en_type.get()
filter_fs = int(en_fs.get())
if filter_type == "bandpass" or filter_type == "bandstop":
if filter_fc.split("[")[0] != "":
messagebox.showerror("Error", "The form of fc should be like \'[fc1, fc2]\'!")
return
fc = filter_fc.split("[")[1].split("]")[0].split(",")
filter_fc = [int(fc[0]), int(fc[1])]
else:
filter_fc = int(filter_fc)
else:
messagebox.showerror("Error", "Please set all the parameters of Filter!")
return
if var_smooth.get() == 1:
if en_wl.get() != "" and en_po.get() != "" and en_mode.get() != "":
smooth_wl = int(en_wl.get())
smooth_po = int(en_po.get())
smooth_mode = en_mode.get()
if smooth_wl % 2 == 0:
messagebox.showerror("Error", "The window length should be odd!")
return
else:
messagebox.showerror("Error", "Please set all the parameters of Smoothing!")
return
if var_elim.get() == 1:
if en_thre.get() != "" and en_win.get() != "":
elim_thre = int(en_thre.get())
elim_ws = int(en_win.get())
else:
messagebox.showerror("Error", "Please set all the parameters of Eliminate Abnormal Data!")
return
if var_energy.get() == 1:
if en_bs.get() != "" and en_odr.get() != "":
eng_bs = int(en_bs.get())
eng_odr = int(en_odr.get())
else:
messagebox.showerror("Error", "Please set all the parameters of Energy!")
return
if en_ws.get() != "":
seg_size = int(en_ws.get())
else:
messagebox.showerror("Error", "Please set the segment size!")
return
if var_accel_x.get() == 0 and var_accel_y.get() == 0 and var_accel_z.get() == 0:
messagebox.showerror("Error", "Please select at least one axis!")
return
if (var_base.get() == 1 and var_accel_x.get() == 0) or (var_base.get() == 2 and var_accel_y.get() == 0) or (var_base.get() == 3 and var_accel_z.get() == 0):
messagebox.showerror("Error", "Please select the axis that is select in Axis Selection!")
return
if var_mean.get() + var_std.get() + var_min.get() + var_max.get() + var_rms.get() + var_energy.get() == 0:
messagebox.showerror("Error", "Please select at least one feature!")
return
save_features = filedialog.asksaveasfilename(initialdir = "/", title = "Save file", filetypes = (("numpy files","*.npy"), ("all files", "*.*")))
if save_features == "":
return
files = pd.read_csv(filename)
final_features = []
labels = []
for idx, row in files.iterrows():
name = row['file']
dir = row['dir']
label = row['label']
header = row['header']
raw_data = pd.read_csv(dir + '\\' + '\\' + name, header=header, delimiter=';')
ax = raw_data['ax']
ay = raw_data['ay']
az = raw_data['az']
data = np.array([ax, ay, az]).T
if var_filter.get() == 1:
data = data_preprocessing.filter(data, filter_order, filter_fc, filter_type, filter_fs)
if var_smooth.get() == 1:
smoothed_data = data_preprocessing.smooth(data, smooth_wl, smooth_po, smooth_mode)
if var_elim.get() == 1:
new_smoothed_data, data = data_preprocessing.eliminate_abnormal_value(smoothed_data, data, elim_ws, elim_thre, var_base.get())
else:
data = smoothed_data
num = var_mean.get() + var_std.get() + var_min.get() + var_max.get() + var_rms.get()
for col in range(3):
feature = np.empty([int(len(data[:, col])/seg_size), num])
new_feature = []
for i in range(int(len(data[:, col])/seg_size)):
window = data[i*seg_size:(i+1)*seg_size, col]
k = 0
if var_mean.get():
feature[i, k] = feature_extraction.mean(window)
k += 1;
if var_std.get():
feature[i, k] = feature_extraction.std(window)
k += 1;
if var_min.get():
feature[i, k] = feature_extraction.getmin(window)
k += 1;
if var_max.get():
feature[i, k] = feature_extraction.getmax(window)
k += 1;
if var_rms.get():
feature[i, k] = feature_extraction.rms(window)
k += 1;
if var_energy.get():
energy_vector = feature_extraction.energy_for_each_freq_band(window, eng_odr, eng_bs)
temp = np.append(feature[i, :], energy_vector)
new_feature.append(temp)
else:
new_feature.append(feature[i, :])
new_feature = np.array(new_feature)
#print("new_feature: ", new_feature.shape)
if col == 0:
ex_feature = new_feature
else:
ex_feature = np.append(ex_feature, new_feature, axis=1)
#print("ex_feature: ", ex_feature.shape)
num_features = ex_feature.shape[1] // 3
num_axis = var_accel_x.get() + var_accel_y.get() + var_accel_z.get()
trn_feature = np.empty([ex_feature.shape[0], num_axis*num_features])
k = 0
if var_accel_x.get() == 1:
trn_feature[:, k*num_features:(k+1)*num_features] = ex_feature[:, 0:num_features]
k += 1
if var_accel_y.get() == 1:
trn_feature[:, k*num_features:(k+1)*num_features] = ex_feature[:, num_features:num_features*2]
k += 1
if var_accel_z.get() == 1:
trn_feature[:, k*num_features:(k+1)*num_features] = ex_feature[:, num_features*2:num_features*3]
k += 1;
final_features.extend(trn_feature)
for i in range(len(trn_feature)):
labels.append(label)
final_features = np.array(final_features)
labels = np.array(labels)
save_labels = save_features
save_features = save_features.split(".")[0] + "_features.npy"
save_labels = save_labels.split(".")[0] + "_labels.npy"
np.save(save_features, final_features)
np.save(save_labels, labels)
messagebox.showinfo("Congratulations", "Features and labels are successfully saved!")
def start_predict_online(data_online):
global online_window
global online_window_size
global flag
if model_text2.get("1.0", END) == []:
messagebox.showerror("Error", "Please select test model!")
return
else:
online_model = model_text2.get("1.0", END)
online_model = online_model.split("\n")[0]
if res_text2.get("1.0", END) == []:
res_text2.delete("1.0", END)
test_config = pd.read_csv(online_model.split(".")[0] + ".txt", sep=" ")
test_config.columns = ['param', 'value']
test_config = pd.DataFrame(data=[test_config['value'].values], columns=test_config['param'].values)
if test_config['filter'].values[0] == '1':
filter_order = int(test_config['order'].values[0])
filter_fc = test_config['fc'].values[0]
filter_type = test_config['type'].values[0]
filter_fs = int(test_config['fs'].values[0])
if filter_type == "bandpass" or filter_type == "bandstop":
fc = filter_fc.split("[")[1].split("]")[0].split(",")
filter_fc = [int(fc[0]), int(fc[1])]
else:
filter_fc = int(filter_fc)
if test_config['smooth'].values[0] == '1':
smooth_wl = int(test_config['windowlength'].values[0])
smooth_po = int(test_config['polyorder'].values[0])
smooth_mode = test_config['mode'].values[0]
if test_config['eliminate'].values[0] == '1':
elim_thre = int(test_config['threshold'].values[0])
elim_ws = int(test_config['windowsize'].values[0])
elim_base = int(test_config['baseon'].values[0])
if test_config['energy'].values[0] == '1':
eng_bs = int(test_config['bandsize'].values[0])
eng_odr = int(test_config['odr'].values[0])
seg_size = int(test_config['segsize'].values[0])
#print(flag)
if flag == False:
online_window = []
online_window_size = seg_size * 1.5
flag = True
if online_window_size == 0:
# test_config = pd.read_csv(online_model.split(".")[0] + ".txt", sep=" ")
# test_config.columns = ['param', 'value']
# test_config = pd.DataFrame(data=[test_config['value'].values], columns=test_config['param'].values)
# if test_config['filter'].values[0] == '1':
# filter_order = int(test_config['order'].values[0])
# filter_fc = test_config['fc'].values[0]
# filter_type = test_config['type'].values[0]
# filter_fs = int(test_config['fs'].values[0])
# if filter_type == "bandpass" or filter_type == "bandstop":
# fc = filter_fc.split("[")[1].split("]")[0].split(",")
# filter_fc = [int(fc[0]), int(fc[1])]
# else:
# filter_fc = int(filter_fc)
# if test_config['smooth'].values[0] == '1':
# smooth_wl = int(test_config['windowlength'].values[0])
# smooth_po = int(test_config['polyorder'].values[0])
# smooth_mode = test_config['mode'].values[0]
# if test_config['eliminate'].values[0] == '1':
# elim_thre = int(test_config['threshold'].values[0])
# elim_ws = int(test_config['windowsize'].values[0])
# elim_base = int(test_config['baseon'].values[0])
# if test_config['energy'].values[0] == '1':
# eng_bs = int(test_config['bandsize'].values[0])
# eng_odr = int(test_config['odr'].values[0])
# seg_size = int(test_config['segsize'].values[0])
data = np.array(online_window)
#print(data)
#final_features = []
if test_config['filter'].values[0] == '1':
data = data_preprocessing.filter(data, filter_order, filter_fc, filter_type, filter_fs)
if test_config['smooth'].values[0] == '1':
smoothed_data = data_preprocessing.smooth(data, smooth_wl, smooth_po, smooth_mode)
if test_config['eliminate'].values[0] == '1':
new_smoothed_data, data = data_preprocessing.eliminate_abnormal_value(smoothed_data, data, elim_ws, elim_thre, elim_base)
if data.shape[0] >= seg_size:
num = int(test_config['mean'].values[0]) + int(test_config['std'].values[0]) + int(test_config['min'].values[0]) + int(test_config['max'].values[0]) + int(test_config['rms'].values[0])
for col in range(3):
feature = np.empty([int(len(data[:, col])/seg_size), num])
new_feature = []
for i in range(int(len(data[:, col])/seg_size)):
window = data[i*seg_size:(i+1)*seg_size, col]
k = 0
if int(test_config['mean'].values[0]):
feature[i, k] = feature_extraction.mean(window)
k += 1;
if int(test_config['std'].values[0]):
feature[i, k] = feature_extraction.std(window)
k += 1;
if int(test_config['min'].values[0]):
feature[i, k] = feature_extraction.getmin(window)
k += 1;
if int(test_config['max'].values[0]):
feature[i, k] = feature_extraction.getmax(window)
k += 1;
if int(test_config['rms'].values[0]):
feature[i, k] = feature_extraction.rms(window)
k += 1;
if int(test_config['energy'].values[0]):
energy_vector = feature_extraction.energy_for_each_freq_band(window, eng_odr, eng_bs)
temp = np.append(feature[i, :], energy_vector)
new_feature.append(temp)
else:
new_feature.append(feature[i, :])
new_feature = np.array(new_feature)
#print("new_feature: ", new_feature.shape)
if col == 0:
ex_feature = new_feature
else:
ex_feature = np.append(ex_feature, new_feature, axis=1)
#print("ex_feature: ", ex_feature.shape)
num_features = ex_feature.shape[1] // 3
num_axis = int(test_config['X'].values[0]) +int(test_config['Y'].values[0]) + int(test_config['Z'].values[0])
trn_feature = np.empty([ex_feature.shape[0], num_axis*num_features])
k = 0
if int(test_config['X'].values[0]) == 1:
trn_feature[:, k*num_features:(k+1)*num_features] = ex_feature[:, 0:num_features]
k += 1
if int(test_config['Y'].values[0]) == 1:
trn_feature[:, k*num_features:(k+1)*num_features] = ex_feature[:, num_features:num_features*2]
k += 1
if int(test_config['Z'].values[0]) == 1:
trn_feature[:, k*num_features:(k+1)*num_features] = ex_feature[:, num_features*2:num_features*3]
k += 1;
test_model = pickle.load(open(online_model, 'rb'))
predict_output = test_model.predict(trn_feature)
print(predict_output[0])
#res_text2.insert(END, predict_output[0])
#online_window = []
#online_window_size = 200
flag = False
else:
online_window.append(data_online)
online_window_size -= 1