pbrain.py

from imports import *


class LCD_CNN:
    def __init__(self, root):
        self.root = root
        self.root.geometry("1006x500+0+30")
        self.root.resizable(False, False)
        self.root.title("Brain Tumor Detection")

        img4 = Image.open(r"./xray/train/Tumor/Y55.jpg")
        img4 = img4.resize((1006, 500), Image.ANTIALIAS)
        self.photoimg4 = ImageTk.PhotoImage(img4)
        bg_img = Label(self.root, image=self.photoimg4)
        bg_img.place(x=0, y=50, width=1006, height=500)

        title_lbl = Label(text="Brain Tumor Detection", font=(
            "Bradley Hand ITC", 30, "bold"), bg="black", fg="white",)
        title_lbl.place(x=0, y=0, width=1006, height=50)

        self.b1 = Button(text="Import Data", cursor="hand2", command=self.import_data, font=(
            "Times New Roman", 15, "bold"), bg="white", fg="black")
        self.b1.place(x=80, y=130, width=180, height=30)

        self.b3 = Button(text="Train Data", cursor="hand2", command=self.train_data, font=(
            "Times New Roman", 15, "bold"), bg="white", fg="black")
        self.b3.place(x=80, y=180, width=180, height=30)
        self.b3["state"] = "disabled"
        self.b3.config(cursor="arrow")

        self.b4 = Button(text="Test Data", cursor="hand2", command=self.test_data, font=(
            "Times New Roman", 15, "bold"), bg="white", fg="black")
        self.b4.place(x=80, y=230, width=180, height=30)
        self.b4["state"] = "disabled"
        self.b4.config(cursor="arrow")

        

    def import_data(self):
        self.dataDirectory = 'xray/train/'
        self.TumorPatients = os.listdir(self.dataDirectory)
        self.size = 10
        self.NoSlices = 5
        messagebox.showinfo("Import Data", "Data Imported Successfully!")

        self.b1["state"] = "disabled"
        self.b1.config(cursor="arrow")
        self.b3["state"] = "normal"
        self.b3.config(cursor="hand2")

    def train_data(self):
        cnn.add(Conv2D(32, (3, 3), activation="relu", input_shape=(64, 64, 3)))

        cnn.add(MaxPooling2D(pool_size=(2, 2)))

        cnn.add(Conv2D(32, (3, 3), activation="relu"))

        cnn.add(MaxPooling2D(pool_size=(2, 2)))

        cnn.add(Conv2D(32, (3, 3), activation="relu"))

        cnn.add(MaxPooling2D(pool_size=(2, 2)))

        cnn.add(Flatten())

        cnn.add(Dense(activation='relu', units=128))
        cnn.add(Dense(activation='sigmoid', units=1))

        cnn.compile(optimizer='adam', loss='binary_crossentropy',
                    metrics=['accuracy'])
        num_of_test_samples = 200
        batch_size = 32

        train_datagen = ImageDataGenerator(rescale=1./255,
                                           shear_range=0.2,
                                           zoom_range=0.2,
                                           horizontal_flip=True)

        test_datagen = ImageDataGenerator(rescale=1./255)

        training_set = train_datagen.flow_from_directory('xray/train',
                                                         target_size=(64, 64),
                                                         batch_size=32,
                                                         class_mode='binary')

        validation_generator = test_datagen.flow_from_directory('xray/val/',
                                                                target_size=(
                                                                    64, 64),
                                                                batch_size=32,
                                                                class_mode='binary')

        test_set = test_datagen.flow_from_directory('xray/test',
                                                    target_size=(64, 64),
                                                    batch_size=32,
                                                    class_mode='binary')
        cnn_model = cnn.fit_generator(training_set,
                                      steps_per_epoch=8,
                                      epochs=10,
                                      validation_data=validation_generator,
                                      validation_steps=20)
        test_accu = cnn.evaluate_generator(test_set, steps=20)
        print('The testing accuracy is :', test_accu[1]*100, '%')
        messagebox.showinfo("ACCURACY", test_accu[1]*100)

        messagebox.showinfo("Train Data", "Model Trained Successfully!")

        self.b3["state"] = "disabled"
        self.b3.config(cursor="arrow")
        self.b4["state"] = "normal"
        self.b4.config(cursor="hand2")
        

    def test_data(self):
        f_types = [('Jpg Files', '*.jpg')]
        filename = filedialog.askopenfilename(filetypes=f_types)
        img = ImageTk.PhotoImage(file=filename)
        img = mpimg.imread(filename)
        plt.imshow(img)
        plt.show()
        img = image.load_img(filename, target_size=(64, 64))
        x = image.img_to_array(img)
        x = np.expand_dims(x, axis=0)
        classes = cnn.predict(x)
        def ans():
            if classes > 0.5:
                return ("Tumor")
            else:
                return ("Normal") 
        print('The test found match of : ', classes[0][0]*100, '%')
        messagebox.showinfo("Test Data", ans())
    

if __name__ == "__main__":
    root = Tk()
    obj = LCD_CNN(root)
    root.mainloop()