def predictPneumoniaImg(top, predicted_output, pneumonia_presence_str,
image_name):
classifier = Pneumonia_cnn.retrieveModel()
if (Pneumonia_cnn.predictSingle(classifier, image_name.get())):
# predictImg(predicted_ouput)
pneumonia_presence_str.set("Pneumonia Present")
ag = tk.Label(top,
textvariable=pneumonia_presence_str,
height=2,
width=20,
bg='red',
fg='blue',
font=("Helvetica", 20)).place(relx=0.33, rely=0.8)
# ag['font'] = myfont
else:
pneumonia_presence_str.set("Pneumonia not Present")
predicted_output.set("")
# tk.Label(top, textvariable=predicted_output).place(relx=0.42, rely=0.16)
fg = tk.Label(top,
textvariable=pneumonia_presence_str,
height=2,
width=20,
bg='red',
fg='blue',
font=("Helvetica", 20)).place(relx=0.33, rely=0.8)
def getAccuracy(top, accuracy):
classifier = Pneumonia_cnn.retrieveModel()
paccuracy = Pneumonia_cnn.getAccuracy(classifier)
accuracy.set(paccuracy * 100)
accuracyLabel = tk.Label(top,
textvariable=accuracy,
height=1,
width=15,
bg='yellow',
font=("Helvetica", 20)).place(relx=0.359,
rely=0.122)
def plotGraph(top):
history = Pneumonia_cnn.loadHistory()
acc = history['accuracy']
# print(acc)
val_acc = history['val_accuracy']
loss = history['loss']
val_loss = history['val_loss']
Gui_graphplot.createWindowforGraph(top, acc, val_acc, loss, val_loss)
def generateModel(top):
Pneumonia_cnn.savingModel()