def getAccuracy(top, accuracy):
classifier = Retina_cnn.retrieveModel()
paccuracy = Retina_cnn.getAccuracy(classifier)
accuracy.set(paccuracy * 100)
accuracyLabel = tk.Label(top,
textvariable=accuracy,
height=2,
width=20,
fg='blue',
bg='orange').place(relx=0.423, rely=0.12)
def predictImg(top, image_name, predicted_output):
classifier = Retina_cnn.retrieveModel()
predicted_output.set(Retina_cnn.predictSingle(classifier,
image_name.get()))
tk.Label(top,
textvariable=predicted_output,
height=2,
width=20,
fg='blue',
bg='red').place(relx=0.423, rely=0.8)
def predictRetinaImg(top, image_name, predicted_output, retina_presence_str):
classifier = Retina_cnn.retrieveModel()
if (Retina_cnn.predictSingle(classifier, image_name.get())):
predictImg(top, image_name, predicted_output)
retina_presence_str.set("Retina Present")
tk.Label(top, textvariable=retina_presence_str, height=2, width=20).place(relx=0.5, rely=0.2)
else:
retina_presence_str.set("Retina not Present")
tk.Label(top, textvariable=retina_presence_str, height=2, width=20).place(relx=0.5, rely=0.2)
def plotGraph(top):
history = Retina_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):
Retina_cnn.savingModel()