push-all-files

Test.py

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+import tkinter as tk
+from tkinter import filedialog
+from PIL import ImageTk, Image
+import os
+import cv2
+import numpy as np
+import matplotlib.pyplot as plt
+from skimage import io
+from tensorflow.keras.applications.resnet_v2 import preprocess_input
+from tensorflow.keras.models import load_model
+
+
+def load_and_prep_image(filename, img_shape_x=200, img_shape_y=200):
+    img = cv2.imread(filename)
+    img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+    img = cv2.resize(img, (img_shape_x, img_shape_y))
+    img = preprocess_input(img)
+    return img
+
+
+def pred_and_plot(filename, model, class_names, img_shape_x=200, img_shape_y=200):
+    img = load_and_prep_image(filename, img_shape_x, img_shape_y)
+    image = io.imread(filename)
+    gray_image = np.mean(image, axis=2)
+
+    fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 5))
+
+    ax1.imshow(gray_image, cmap='hot')
+    ax1.set_title('Heat Map')
+    ax1.set_axis_off()
+
+    pred = model.predict(np.expand_dims(img, axis=0))
+    pred_class = class_names[pred.argmax()]
+    img = cv2.cvtColor(cv2.imread(filename), cv2.COLOR_BGR2RGB)
+    ax2.imshow(img)
+    ax2.set_title(f"Prediction: {pred_class}")
+    ax2.set_axis_off()
+
+    plt.show()
+
+
+
+def select_image():
+    global panel, image_path
+    image_path = filedialog.askopenfilename()
+    image = Image.open(image_path)
+    image = image.resize((300, 300))
+    image = ImageTk.PhotoImage(image)
+    if panel is None:
+        panel = tk.Label(image=image)
+        panel.image = image
+        panel.pack(padx=10, pady=10)
+    else:
+        panel.configure(image=image)
+        panel.image = image
+
+
+def select_class_dir():
+    class_dir = filedialog.askdirectory()
+    class_dir_entry.delete(0, tk.END)
+    class_dir_entry.insert(0, class_dir)
+
+
+def select_model_file():
+    model_file = filedialog.askopenfilename()
+    model_file_entry.delete(0, tk.END)
+    model_file_entry.insert(0, model_file)
+
+
+def predict():
+    global image_path, model, classes, img_shape_x, img_shape_y
+    if image_path is None:
+        return
+    class_dir = class_dir_entry.get()
+    model_file = model_file_entry.get()
+    img_shape_x = int(img_shape_x_entry.get())
+    img_shape_y = int(img_shape_y_entry.get())
+    classes = []
+    for file in os.listdir(class_dir):
+        d = os.path.join(class_dir, file)
+        if os.path.isdir(d):
+            classes.append(file)
+    model = load_model(model_file)
+    pred_and_plot(image_path, model, classes, img_shape_x, img_shape_y)
+
+root = tk.Tk()
+root.title("Image Predictor")
+root.resizable(False, False)
+
+panel = None
+
+image_path = None
+model = None
+classes = []
+img_shape_x = 200
+img_shape_y = 200
+
+class_dir_label = tk.Label(root, text="Class Directory:")
+class_dir_label.grid(row=0, column=0, padx=10, pady=10)
+class_dir_entry = tk.Entry(root)
+class_dir_entry.grid(row=0, column=1, padx=10, pady=10)
+class_dir_button = tk.Button(root, text="Browse", command=select_class_dir)
+class_dir_button.grid(row=0, column=2, padx=10, pady=10)
+
+model_file_label = tk.Label(root, text="Model File Path:")
+model_file_label.grid(row=1, column=0, padx=10, pady=10)
+model_file_entry = tk.Entry(root)
+model_file_entry.grid(row=1, column=1, padx=10, pady=10)
+model_file_button = tk.Button(root, text="Browse", command=select_model_file)
+model_file_button.grid(row=1, column=2, padx=10, pady=10)
+
+img_shape_x_label = tk.Label(root, text="Image Shape X:")
+img_shape_x_label.grid(row=2, column=0, padx=10, pady=10)
+img_shape_x_entry = tk.Entry(root)
+img_shape_x_entry.grid(row=2, column=1, padx=10, pady=10)
+
+img_shape_y_label = tk.Label(root, text="Image Shape Y:")
+img_shape_y_label.grid(row=3, column=0, padx=10, pady=10)
+img_shape_y_entry = tk.Entry(root)
+img_shape_y_entry.grid(row=3, column=1, padx=10, pady=10)
+
+select_button = tk.Button(root, text="Select Image", command=select_image)
+select_button.grid(row=4, column=0, padx=10, pady=10)
+
+predict_button = tk.Button(root, text="Predict", command=predict)
+predict_button.grid(row=4, column=1, padx=10, pady=10)
+
+root.mainloop()

Train.py

@@ -0,0 +1,212 @@
+import tkinter as tk
+from tkinter import filedialog
+from tkinter import messagebox
+import matplotlib.pyplot as plt
+plt.style.use('default')
+import os
+import tensorflow as tf
+from tensorflow.keras.preprocessing.image import ImageDataGenerator
+from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint
+from tensorflow.keras.models import Sequential
+from tensorflow.keras.layers import *
+
+root = tk.Tk()
+root.title("Skin Cancer Classification")
+root.resizable(False, False)
+
+train_dir_var = tk.StringVar()
+val_dir_var = tk.StringVar()
+img_shape_x_var = tk.IntVar()
+img_shape_y_var = tk.IntVar()
+batch_size_var = tk.IntVar()
+save_model_path_var = tk.StringVar()
+save_model_name_var = tk.StringVar()
+num_epoch_var = tk.IntVar()
+
+def browse_folder(var):
+    folder_selected = filedialog.askdirectory()
+    var.set(folder_selected)
+
+train_dir_label = tk.Label(root, text="Training Directory: ")
+train_dir_label.grid(row=0, column=0, padx=5, pady=5, sticky="w")
+
+train_dir_entry = tk.Entry(root, textvariable=train_dir_var)
+train_dir_entry.grid(row=0, column=1, padx=5, pady=5)
+
+train_dir_button = tk.Button(root, text="Browse", command=lambda: browse_folder(train_dir_var))
+train_dir_button.grid(row=0, column=2, padx=5, pady=5)
+
+val_dir_label = tk.Label(root, text="Validation Directory: ")
+val_dir_label.grid(row=1, column=0, padx=5, pady=5, sticky="w")
+
+val_dir_entry = tk.Entry(root, textvariable=val_dir_var)
+val_dir_entry.grid(row=1, column=1, padx=5, pady=5)
+
+val_dir_button = tk.Button(root, text="Browse", command=lambda: browse_folder(val_dir_var))
+val_dir_button.grid(row=1, column=2, padx=5, pady=5)
+
+img_shape_x_label = tk.Label(root, text="Image Shape X: ")
+img_shape_x_label.grid(row=2, column=0, padx=5, pady=5, sticky="w")
+
+img_shape_x_entry = tk.Entry(root, textvariable=img_shape_x_var)
+img_shape_x_entry.grid(row=2, column=1, padx=5, pady=5)
+
+img_shape_y_label = tk.Label(root, text="Image Shape Y: ")
+img_shape_y_label.grid(row=3, column=0, padx=5, pady=5, sticky="w")
+
+img_shape_y_entry = tk.Entry(root, textvariable=img_shape_y_var)
+img_shape_y_entry.grid(row=3, column=1, padx=5, pady=5)
+
+batch_size_label = tk.Label(root, text="Batch Size: ")
+batch_size_label.grid(row=4, column=0, padx=5, pady=5, sticky="w")
+
+batch_size_entry = tk.Entry(root, textvariable=batch_size_var)
+batch_size_entry.grid(row=4, column=1, padx=5, pady=5)
+
+save_model_path_label = tk.Label(root, text="Save Model Path: ")
+save_model_path_label.grid(row=5, column=0, padx=5, pady=5, sticky="w")
+
+save_model_path_entry = tk.Entry(root, textvariable=save_model_path_var)
+save_model_path_entry.grid(row=5, column=1, padx=5, pady=5)
+
+save_model_path_button = tk.Button(root, text="Browse", command=lambda: browse_folder(save_model_path_var))
+save_model_path_button.grid(row=5, column=2, padx=5, pady=5)
+
+save_model_name_label = tk.Label(root, text="Save Model Name: ")
+save_model_name_label.grid(row=6, column=0, padx=5, pady=5, sticky="w")
+
+save_model_name_entry = tk.Entry(root, textvariable=save_model_name_var)
+save_model_name_entry.grid(row=6, column=1, padx=5, pady=5)
+
+num_epoch_label = tk.Label(root, text="Number of Epochs: ")
+num_epoch_label.grid(row=7, column=0, padx=5, pady=5, sticky="w")
+
+num_epoch_entry = tk.Entry(root, textvariable=num_epoch_var)
+num_epoch_entry.grid(row=7, column=1, padx=5, pady=5)
+
+def Create_ResNet50V2_Model(num_classes, img_shape_x, img_shape_y):
+    ResNet50V2 = tf.keras.applications.ResNet50V2(input_shape=(img_shape_x, img_shape_y, 3),
+    include_top= False,
+    weights='imagenet'
+    )
+    ResNet50V2.trainable = True
+
+    for layer in ResNet50V2.layers[:-50]:
+        layer.trainable = False
+
+
+    model = Sequential([
+                    ResNet50V2,
+                    Dropout(.25),
+                    BatchNormalization(),
+                    Flatten(),
+                    Dense(64, activation='relu'),
+                    BatchNormalization(),
+                    Dropout(.5),
+                    Dense(num_classes,activation='softmax')
+                    ])
+    return model
+
+def train_model():
+    train_data_path = train_dir_var.get()
+    val_data_path = val_dir_var.get()
+    img_shape_x = img_shape_x_var.get()
+    img_shape_y = img_shape_y_var.get()
+    batch_size = batch_size_var.get()
+    save_model_path = save_model_path_var.get()
+    save_model_name = save_model_name_var.get()
+    num_epoch = num_epoch_var.get()
+
+    train_preprocessor = ImageDataGenerator(
+        rescale = 1 / 255.,
+        rotation_range=10,
+        zoom_range=0.2,
+        width_shift_range=0.1,
+        height_shift_range=0.1,
+        horizontal_flip=True,                                        
+        fill_mode='nearest',
+    )
+
+    val_preprocessor = ImageDataGenerator(
+        rescale = 1 / 255.,
+    )
+
+    train_data = train_preprocessor.flow_from_directory(
+        train_data_path,
+        class_mode="categorical",
+        target_size=(img_shape_x,img_shape_y),
+        color_mode='rgb', 
+        shuffle=True,
+        batch_size=batch_size,
+        subset='training', 
+    )
+
+    val_data = val_preprocessor.flow_from_directory(
+        val_data_path,
+        class_mode="categorical",
+        target_size=(img_shape_x,img_shape_y),
+        color_mode="rgb",
+        shuffle=False,
+        batch_size=batch_size,
+    )
+
+    num_classes = train_data.num_classes
+
+    ResNet50V2_Model = Create_ResNet50V2_Model(num_classes, img_shape_x, img_shape_y)
+    ResNet50V2_Model.summary()
+
+    ResNet50V2_Model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
+
+    checkpoint_path = os.path.join(save_model_path, save_model_name)
+    ModelCheckpoint(checkpoint_path, monitor="val_accuracy", save_best_only=True)
+
+    Early_Stopping = EarlyStopping(monitor = 'val_accuracy', patience = 7, restore_best_weights = True, verbose=1)
+
+    Reducing_LR = tf.keras.callbacks.ReduceLROnPlateau(monitor='val_loss',
+                                                factor=0.2,
+                                                patience=2,
+                                                verbose=1)
+
+    callbacks = [Early_Stopping, Reducing_LR]
+
+    steps_per_epoch = train_data.n // train_data.batch_size
+    validation_steps = val_data.n // val_data.batch_size
+
+    ResNet50V2_history = ResNet50V2_Model.fit(train_data ,validation_data = val_data , epochs=num_epoch, batch_size=batch_size,
+                                        callbacks = callbacks, steps_per_epoch=steps_per_epoch, validation_steps=validation_steps)
+    ResNet50V2_Model.save(os.path.join(save_model_path, save_model_name + '.h5'))
+    def plot_curves(history):
+
+        loss = history.history["loss"]
+        val_loss = history.history["val_loss"]
+
+        accuracy = history.history["accuracy"]
+        val_accuracy = history.history["val_accuracy"]
+
+        epochs = range(len(history.history["loss"]))
+
+        plt.figure(figsize=(15,5))
+
+        plt.subplot(1, 2, 1)
+        plt.plot(epochs, loss, label = "training_loss")
+        plt.plot(epochs, val_loss, label = "val_loss")
+        plt.title("Loss")
+        plt.xlabel("epochs")
+        plt.legend()
+
+        plt.subplot(1, 2, 2)
+        plt.plot(epochs, accuracy, label = "training_accuracy")
+        plt.plot(epochs, val_accuracy, label = "val_accuracy")
+        plt.title("Accuracy")
+        plt.xlabel("epochs")
+        plt.legend()
+
+        plt.show()
+
+    plot_curves(ResNet50V2_history)
+    messagebox.showinfo(title="Training Completed", message="The model has been trained successfully!")
+
+train_button = tk.Button(root, text="Train", command=train_model)
+train_button.grid(row=8, column=1, padx=5, pady=5)
+
+root.mainloop()