skin_cancer_classification_project_with_Tkinter.py

# libraries
import tkinter as tk
from tkinter import ttk
from tkinter import filedialog
from tkinter import messagebox

from PIL import ImageTk, Image
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import seaborn as sns

from keras.models import Sequential
from keras.layers import Conv2D, MaxPool2D, Dropout, Flatten, Dense
from keras.optimizers import Adam

from keras.utils.np_utils import to_categorical 
from keras.models import load_model

#%%
skin_df = pd.read_csv("HAM10000_metadata.csv")

skin_df.head()
skin_df.info()
sns.countplot(x = "dx", data = skin_df)

# %% preprocess
data_folder_name = "HAM10000_images_part_1/"
ext = ".jpg"
#"HAM10000_images_part_1\ISIC_0027419.jpg"
#data_folder_name + image_id[i] + ext
skin_df["path"] = [ data_folder_name + i + ext for i in skin_df["image_id"]]
skin_df["image"] = skin_df["path"].map( lambda x: np.asarray(Image.open(x).resize((100,75))))
plt.imshow(skin_df["image"][0])
skin_df["dx_idx"] = pd.Categorical(skin_df["dx"]).codes
skin_df.to_pickle("skin_df.pkl")

# %% load pkl
skin_df = pd.read_pickle("skin_df.pkl")

# %% stardardization
x_train = np.asarray(skin_df["image"].tolist())
x_train_mean = np.mean(x_train)
x_train_std = np.std(x_train)
x_train = (x_train - x_train_mean)/x_train_std

# one hot encoding
y_train = to_categorical(skin_df["dx_idx"], num_classes = 7)

#%% CNN

input_shape = (75,100,3)
num_classes = 7

model = Sequential()
model.add(Conv2D(32, kernel_size = (3,3), activation = "relu", padding = "Same", input_shape = input_shape))
model.add(Conv2D(32, kernel_size = (3,3), activation = "relu", padding = "Same"))
model.add(MaxPool2D(pool_size = (2,2)))
model.add(Dropout(0.25))

model.add(Conv2D(64, kernel_size = (3,3), activation = "relu", padding = "Same"))
model.add(Conv2D(64, kernel_size = (3,3), activation = "relu", padding = "Same"))
model.add(MaxPool2D(pool_size = (2,2)))
model.add(Dropout(0.5))

model.add(Flatten())
model.add(Dense(128,activation="relu"))
model.add(Dropout(0.5))
model.add(Dense(num_classes,activation="softmax"))
model.summary()

optimizer = Adam(lr = 0.001)
model.compile(optimizer = optimizer, loss = "categorical_crossentropy", metrics = ["accuracy"])

epochs = 5
batch_size = 25

history = model.fit(x = x_train, y = y_train, batch_size = batch_size, epochs = epochs, verbose = 1, shuffle = True)

model.save("my_model1.h5")

# %% load
model1 = load_model("my_model1.h5")
model2 = load_model("my_model2.h5")

# %% prediction
index = 5
y_pred = model1.predict(x_train[index].reshape(1,75,100,3))
y_pred_class = np.argmax(y_pred, axis = 1)

# %% Skin Cancer Classification GUI

window = tk.Tk()
window.geometry("1080x640")
window.wm_title("Skin Cancer Classification")

## global variables
img_name = ""
count = 0
img_jpg = ""

## frames
frame_left = tk.Frame(window, width = 540, height = 640, bd = "2")
frame_left.grid(row = 0, column = 0)

frame_right = tk.Frame(window, width = 540, height = 640, bd = "2")
frame_right.grid(row = 0, column = 1)

frame1 = tk.LabelFrame(frame_left, text = "Image", width = 540, height = 500)
frame1.grid(row = 0, column = 0)

frame2 = tk.LabelFrame(frame_left, text = "Model and Save", width = 540, height = 140)
frame2.grid(row = 1, column = 0)

frame3 = tk.LabelFrame(frame_right, text = "Features", width = 270, height = 640)
frame3.grid(row = 0, column = 0)

frame4 = tk.LabelFrame(frame_right, text = "Result", width = 270, height = 640)
frame4.grid(row = 0, column = 1, padx = 10)


# frame1
def imageResize(img):
    
    basewidth = 500
    wpercent = (basewidth/float(img.size[0]))   # 1000 *1200
    hsize = int((float(img.size[1])*float(wpercent)))
    img = img.resize((basewidth,hsize),Image.ANTIALIAS)
    return img
    
def openImage():
    
    global img_name
    global count
    global img_jpg
    
    count += 1
    if count != 1:
        messagebox.showinfo(title = "Warning", message = "Only one image can be opened")
    else:
        img_name = filedialog.askopenfilename(initialdir = "D:\codes",title = "Select an image file")
        
        img_jpg = img_name.split("/")[-1].split(".")[0]
        # image label
        tk.Label(frame1, text =img_jpg, bd = 3 ).pack(pady = 10)
    
        # open and show image
        img = Image.open(img_name)
        img = imageResize(img)
        img = ImageTk.PhotoImage(img)
        panel = tk.Label(frame1, image = img)
        panel.image = img
        panel.pack(padx = 15, pady = 10)
        
        # image feature
        data = pd.read_csv("HAM10000_metadata.csv")
        cancer = data[data.image_id == img_jpg]
        
        for i in range(cancer.size):
            x = 0.4
            y = (i/10)/2
            tk.Label(frame3, font = ("Times",12), text = str(cancer.iloc[0,i])).place(relx = x, rely = y)
                   
menubar = tk.Menu(window)
window.config(menu = menubar)
file = tk.Menu(menubar)
menubar.add_cascade(label = "File",menu = file)
file.add_command(label = "Open", command = openImage)

# frame3
def classification():
    
    if img_name != "" and models.get() != "":
        
        # model selection
        if models.get() == "Model1":
            classification_model = model1
        else:
            classification_model = model2
        
        z = skin_df[skin_df.image_id == img_jpg]
        z = z.image.values[0].reshape(1,75,100,3)
        
        # 
        z = (z - x_train_mean)/x_train_std
        h = classification_model.predict(z)[0]
        h_index = np.argmax(h)
        predicted_cancer = list(skin_df.dx.unique())[h_index]
        
        for i in range(len(h)):
            x = 0.5
            y = (i/10)/2
            
            if i != h_index:
                tk.Label(frame4,text = str(h[i])).place(relx = x, rely = y)
            else:
                tk.Label(frame4,bg = "green",text = str(h[i])).place(relx = x, rely = y)
        
        if chvar.get() == 1:
            
            val = entry.get()
            entry.config(state = "disabled")
            path_name = val + ".txt" # result1.txt
            
            save_txt = img_name + "--" + str(predicted_cancer)
            
            text_file = open(path_name,"w")
            text_file.write(save_txt)
            text_file.close()
        else:
            print("Save is not selected")
    else:
        messagebox.showinfo(title = "Warning", message = "Choose image and Model First!")
        tk.Label(frame3, text = "Choose image and Model First!" ).place(relx = 0.1, rely = 0.6)
                          
columns = ["lesion_id","image_id","dx","dx_type","age","sex","localization"]
for i in range(len(columns)):
    x = 0.1
    y = (i/10)/2
    tk.Label(frame3, font = ("Times",12), text = str(columns[i]) + ": ").place(relx = x, rely = y)

classify_button = tk.Button(frame3, bg = "red", bd = 4, font = ("Times",13),activebackground = "orange",text = "Classify",command = classification)
classify_button.place(relx = 0.1, rely = 0.5)
# frame 4
labels = skin_df.dx.unique()

for i in range(len(columns)):
    x = 0.1
    y = (i/10)/2
    tk.Label(frame4, font = ("Times",12), text = str(labels[i]) + ": ").place(relx = x, rely = y)
# frame 2 
# combo box
model_selection_label = tk.Label(frame2, text = "Choose classification model: ")
model_selection_label.grid(row = 0, column = 0, padx = 5)

models = tk.StringVar()
model_selection = ttk.Combobox(frame2,textvariable = models, values = ("Model1","Model2"), state = "readonly")
model_selection.grid(row = 0, column = 1, padx = 5)

# check box
chvar = tk.IntVar()
chvar.set(0)
xbox = tk.Checkbutton(frame2, text = "Save Classification Result", variable = chvar)
xbox.grid(row = 1, column =0 , pady = 5)

# entry
entry = tk.Entry(frame2, width = 23)
entry.insert(string = "Saving name...",index = 0)
entry.grid(row = 1, column =1 )

window.mainloop()