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import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import cv2
import random
import tensorflow as tf
import tkinter as tk
from tkinter import filedialog
from PIL import ImageTk, Image
from ipywidgets import interact, interactive, fixed, interact_manual
import ipywidgets as widgets
from IPython.display import display, clear_output
from tensorflow.keras.preprocessing import image
from tensorflow.keras.optimizers import Adam, SGD, RMSprop, AdamW, Adadelta, Adagrad, Adamax, Adafactor, Nadam, Ftrl
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tqdm import tqdm
import os
from sklearn.utils import shuffle
from sklearn.preprocessing import LabelEncoder
from sklearn.model_selection import train_test_split
from tensorflow.keras.models import Sequential, Model, load_model
from tensorflow.keras.layers import (
    GlobalAveragePooling2D,
    Dropout,
    Dense,
    Conv2D,
    MaxPooling2D,
    Flatten,
    Dropout,
    BatchNormalization,
    Activation,
    concatenate,
    Conv2DTranspose,
    Input,
    Reshape,
    UpSampling2D,
)
from tensorflow.keras.applications import (
    EfficientNetV2B0,
    EfficientNetV2B1,
    EfficientNetV2B2,
    EfficientNetV2B3,
    EfficientNetV2L,
    EfficientNetV2M,
    EfficientNetV2S,
)
from tensorflow.keras.applications import Xception
from tensorflow.keras.applications import VGG16, VGG19
from tensorflow.keras.applications import ResNet50, ResNet101, ResNet152, ResNetRS50, ResNetRS101
from tensorflow.keras.applications import InceptionResNetV2, ConvNeXtXLarge, ConvNeXtBase, DenseNet121, MobileNetV2, NASNetLarge, NASNetMobile
from tensorflow.keras.utils import to_categorical
from tensorflow.keras.callbacks import EarlyStopping, ReduceLROnPlateau, TensorBoard, ModelCheckpoint
from sklearn.metrics import classification_report, confusion_matrix
import ipywidgets as widgets
import io
from PIL import Image
from IPython.display import display, clear_output
from warnings import filterwarnings
from google.colab import drive
drive.mount("/content/gdrive")

def load_data(data_folders):
    X_data = []  # Combined data
    y_class_labels = []  # Combined classification labels
    y_seg_labels = []  # Combined segmentation labels
    for folderPath in data_folders:
        for label in labels:
            label_folder_path = os.path.join(folderPath, label)
            for filename in tqdm(os.listdir(label_folder_path)):
                if filename.endswith(".jpg"):
                    img = cv2.imread(os.path.join(label_folder_path, filename))
                    img = cv2.resize(img, (image_size, image_size))
                    X_data.append(img)
                    y_class_labels.append(label)
                    seg_filename = filename.split(".")[0] + ".png"
                    seg_img = cv2.imread(os.path.join(label_folder_path, seg_filename), 0)
                    seg_img = cv2.resize(seg_img, (image_size, image_size))
                    seg_img = np.where(seg_img > 0, 1, 0)  # Convert segmentation mask to binary
                    y_seg_labels.append(seg_img)
    X_data = np.array(X_data)
    y_class_labels = np.array(y_class_labels)
    y_seg_labels = np.array(y_seg_labels)
    X_data, y_class_labels, y_seg_labels = shuffle(X_data, y_class_labels, y_seg_labels, random_state=101)
    return X_data, y_class_labels, y_seg_labels

def split_data(X_data, y_class_labels, y_seg_labels, class_data_counts):
    X_train = []
    y_train_class = []
    y_train_seg = []
    X_val = []
    y_val_class = []
    y_val_seg = []
    X_test = []
    y_test_class = []
    y_test_seg = []

    for label, count in class_data_counts.items():
        label_indices = np.where(y_class_labels == label)[0]
        class_X_data = X_data[label_indices]
        class_y_class_labels = y_class_labels[label_indices]
        class_y_seg_labels = y_seg_labels[label_indices]

        train_count = count[0]
        val_count = count[1]
        test_count = count[2]

        class_X_train = class_X_data[:train_count]
        class_y_train_class = class_y_class_labels[:train_count]
        class_y_train_seg = class_y_seg_labels[:train_count]
        class_X_val = class_X_data[train_count: train_count + val_count]
        class_y_val_class = class_y_class_labels[train_count: train_count + val_count]
        class_y_val_seg = class_y_seg_labels[train_count: train_count + val_count]
        class_X_test = class_X_data[train_count + val_count: train_count + val_count + test_count]
        class_y_test_class = class_y_class_labels[train_count + val_count: train_count + val_count + test_count]
        class_y_test_seg = class_y_seg_labels[train_count + val_count: train_count + val_count + test_count]

        X_train.extend(class_X_train)
        y_train_class.extend(class_y_train_class)
        y_train_seg.extend(class_y_train_seg)
        X_val.extend(class_X_val)
        y_val_class.extend(class_y_val_class)
        y_val_seg.extend(class_y_val_seg)
        X_test.extend(class_X_test)
        y_test_class.extend(class_y_test_class)
        y_test_seg.extend(class_y_test_seg)

    # Convert class labels to categorical
    label_encoder = LabelEncoder()
    y_train_class_encoded = label_encoder.fit_transform(y_train_class)
    y_train_class_categorical = to_categorical(y_train_class_encoded)
    y_val_class_encoded = label_encoder.transform(y_val_class)
    y_val_class_categorical = to_categorical(y_val_class_encoded)
    y_test_class_encoded = label_encoder.transform(y_test_class)
    y_test_class_categorical = to_categorical(y_test_class_encoded)

    return (
        np.array(X_train),
        np.array(y_train_class_categorical),
        np.array(y_train_seg),
        np.array(X_val),
        np.array(y_val_class_categorical),
        np.array(y_val_seg),
        np.array(X_test),
        np.array(y_test_class_categorical),
        np.array(y_test_seg),
    )

def count_labels(y_class_categorical, label_encoder):
    # Convert one-hot encoded labels back to label encoded
    y_class_labels = np.argmax(y_class_categorical, axis=1)
    # Convert label encoded labels back to original class names
    y_class_names = label_encoder.inverse_transform(y_class_labels)
    unique, counts = np.unique(y_class_names, return_counts=True)
    return dict(zip(unique, counts))

def build_model(input_shape, num_classes):
    num_filter = 32  # 16/32 best, 8: best classification but no segment
    # Encoder (Done)
    inputs = Input(input_shape)
    conv1 = Conv2D(num_filter * 1, 3, activation="linear", padding="same", strides=1)(inputs)
    bn1 = BatchNormalization()(conv1)
    relu1 = Activation("relu")(bn1)
    conv2 = Conv2D(num_filter * 1, 3, activation="linear", padding="same", strides=1)(relu1)
    bn2 = BatchNormalization()(conv2)
    relu2 = Activation("relu")(bn2)
    down1 = MaxPooling2D(pool_size=(2, 2), strides=2)(relu2)
    conv3 = Conv2D(num_filter * 2, 3, activation="linear", padding="same", strides=1)(down1)
    bn3 = BatchNormalization()(conv3)
    relu3 = Activation("relu")(bn3)
    conv4 = Conv2D(num_filter * 2, 3, activation="linear", padding="same", strides=1)(relu3)
    bn4 = BatchNormalization()(conv4)
    relu4 = Activation("relu")(bn4)
    down2 = MaxPooling2D(pool_size=(2, 2), strides=2)(relu4)
    conv5 = Conv2D(num_filter * 4, 3, activation="linear", padding="same", strides=1)(down2)
    bn5 = BatchNormalization()(conv5)
    relu5 = Activation("relu")(bn5)
    conv6 = Conv2D(num_filter * 4, 3, activation="linear", padding="same", strides=1)(relu5)
    bn6 = BatchNormalization()(conv6)
    relu6 = Activation("relu")(bn6)
    down3 = MaxPooling2D(pool_size=(2, 2), strides=2)(relu6)
    conv7 = Conv2D(num_filter * 8, 3, activation="linear", padding="same", strides=1)(down3)
    bn7 = BatchNormalization()(conv7)
    relu7 = Activation("relu")(bn7)
    conv8 = Conv2D(num_filter * 8, 3, activation="linear", padding="same", strides=1)(relu7)
    bn8 = BatchNormalization()(conv8)
    relu8 = Activation("relu")(bn8)
    # Middle
    down4 = MaxPooling2D(pool_size=(2, 2), strides=2)(relu8)
    conv9 = Conv2D(num_filter * 16, 3, activation="linear", padding="same", strides=1)(down4)
    bn9 = BatchNormalization()(conv9)
    relu9 = Activation("relu")(bn9)
    conv10 = Conv2D(num_filter * 16, 3, activation="linear", padding="same", strides=1)(relu9)
    bn10 = BatchNormalization()(conv10)
    relu10 = Activation("relu")(bn10)
    up1 = UpSampling2D(size=(2, 2), interpolation="bilinear")(relu10)
    # Decoder (Done)
    concat1 = concatenate([up1, relu8], axis=-1)  # , axis=3
    conv11 = Conv2D(num_filter * 8, 3, activation="linear", padding="same", strides=1)(concat1)
    bn11 = BatchNormalization()(conv11)
    relu11 = Activation("relu")(bn11)
    conv12 = Conv2D(num_filter * 8, 3, activation="linear", padding="same", strides=1)(relu11)
    bn12 = BatchNormalization()(conv12)
    relu12 = Activation("relu")(bn12)
    up2 = UpSampling2D(size=(2, 2), interpolation="bilinear")(relu12)
    concat2 = concatenate([up2, relu6], axis=-1)  # , axis=3
    conv13 = Conv2D(num_filter * 4, 3, activation="linear", padding="same", strides=1)(concat2)
    bn13 = BatchNormalization()(conv13)
    relu13 = Activation("relu")(bn13)
    conv14 = Conv2D(num_filter * 4, 3, activation="linear", padding="same", strides=1)(relu13)
    bn14 = BatchNormalization()(conv14)
    relu14 = Activation("relu")(bn14)
    up3 = UpSampling2D(size=(2, 2), interpolation="bilinear")(relu14)
    concat3 = concatenate([up3, relu4], axis=-1)  # , axis=3
    conv15 = Conv2D(num_filter * 2, 3, activation="linear", padding="same", strides=1)(concat3)
    bn15 = BatchNormalization()(conv15)
    relu15 = Activation("relu")(bn15)
    conv16 = Conv2D(num_filter * 2, 3, activation="linear", padding="same", strides=1)(relu15)
    bn16 = BatchNormalization()(conv16)
    relu16 = Activation("relu")(bn16)
    up4 = UpSampling2D(size=(2, 2), interpolation="bilinear")(relu16)
    concat4 = concatenate([up4, relu2], axis=-1)  # , axis=3
    conv17 = Conv2D(num_filter * 1, 3, activation="linear", padding="same", strides=1)(concat4)
    bn17 = BatchNormalization()(conv17)
    relu17 = Activation("relu")(bn17)
    conv18 = Conv2D(num_filter * 1, 3, activation="linear", padding="same", strides=1)(relu17)
    bn18 = BatchNormalization()(conv18)
    relu18 = Activation("relu")(bn18)
    # Segmentation branch
    segmentation_output = Conv2D(1, 1, activation="sigmoid", name="segmentation_output")(relu18)  # original
    # Classification branch (Not done)
    gap1 = GlobalAveragePooling2D()(relu8)
    gap2 = GlobalAveragePooling2D()(relu10)
    gap3 = GlobalAveragePooling2D()(relu12)
    conv20 = Conv2D(num_filter * 1, 3, activation="linear", padding="same", strides=1)(segmentation_output)
    bn20 = BatchNormalization()(conv20)
    relu20 = Activation("relu")(bn20)
    down5 = MaxPooling2D(pool_size=(4, 4), strides=4)(relu20)
    conv21 = Conv2D(num_filter * 2, 3, activation="linear", padding="same", strides=1)(down5)
    bn21 = BatchNormalization()(conv21)
    relu21 = Activation("relu")(bn21)
    down6 = MaxPooling2D(pool_size=(4, 4), strides=4)(relu21)
    conv22 = Conv2D(num_filter * 4, 3, activation="linear", padding="same", strides=1)(down6)
    bn22 = BatchNormalization()(conv22)
    relu22 = Activation("relu")(bn22)
    down7 = MaxPooling2D(pool_size=(4, 4), strides=4)(relu22)
    flatten1 = Flatten()(down7)
    concat5 = concatenate([gap1, gap2, gap3, flatten1], axis=-1)
    # FC layers
    fc1 = Dense(1024, activation="relu")(concat5)
    dropout1 = Dropout(0.5)(fc1)
    fc2 = Dense(1024, activation="relu")(dropout1)
    dropout2 = Dropout(0.5)(fc2)
    classification_output = Dense(num_classes, activation="softmax", name="classification_output")(dropout2)
    # Define the model
    model = Model(inputs=inputs, outputs=[classification_output, segmentation_output])
    return model

def segmentation_loss(y_true, y_pred):
    y_true = tf.cast(y_true, tf.float32)
    y_pred = tf.cast(y_pred, tf.float32)
    bce_loss = tf.keras.losses.binary_crossentropy(y_true, y_pred)
    smooth = 1e-5
    intersection = tf.reduce_sum(y_true * y_pred)
    union = tf.reduce_sum(y_true) + tf.reduce_sum(y_pred)
    dice_loss = 1.0 - 2.0 * (intersection + smooth) / (union + smooth)
    segmentation_loss = bce_loss + 1 * dice_loss
    return segmentation_loss

def train_model(model, X_train, y_train_class, y_train_seg, X_val, y_val_class, y_val_seg, batch_size, epochs):
    checkpoint = ModelCheckpoint(
        "multitask_best_weights.h5",
        monitor="val_classification_output_accuracy",
        save_best_only=True,
        mode="max",
        verbose=1,)
    reduce_lr = ReduceLROnPlateau(
        monitor="val_classification_output_accuracy",
        factor=0.3,
        patience=2,
        min_delta=0.001,
        mode="auto",
        verbose=1,)
    tensorboard = TensorBoard(log_dir="logs")
    model.compile(
        optimizer=Adam(learning_rate=0.001),
        loss={"classification_output": "categorical_crossentropy", "segmentation_output": segmentation_loss},
        metrics={"classification_output": "accuracy", "segmentation_output": "accuracy"},
        loss_weights={"classification_output": 1, "segmentation_output": 1},)
    history = model.fit(
        X_train,
        {"classification_output": y_train_class, "segmentation_output": y_train_seg},
        validation_data=(X_val, {"classification_output": y_val_class, "segmentation_output": y_val_seg}),
        epochs=epochs,
        verbose=1,
        batch_size=batch_size,
        callbacks=[checkpoint, reduce_lr, tensorboard],)
    return history

def evaluate_model(model, X_test, y_test_class, y_test_seg):
    with tf.keras.utils.custom_object_scope({"segmentation_loss": segmentation_loss}):
        # Load the best model weights
        best_model = load_model("multitask_best_weights.h5")
        # Evaluate the model on test data
        test_loss, test_class_loss, test_seg_loss, test_class_acc, test_seg_acc = best_model.evaluate(
            X_test, {"classification_output": y_test_class, "segmentation_output": y_test_seg})
        print("Test Classification Loss:", test_class_loss)
        print("Test Segmentation Loss:", test_seg_loss)
        print("Test Classification Accuracy:", test_class_acc)
        print("Test Segmentation Accuracy:", test_seg_acc)
        # Evaluate the model on validation data
        val_loss, val_class_loss, val_seg_loss, val_class_acc, val_seg_acc = best_model.evaluate(
            X_val, {'classification_output': y_val_class, 'segmentation_output': y_val_seg})
        print("Validation Classification Loss:", val_class_loss)
        print("Validation Segmentation Loss:", val_seg_loss)
        print("Validation Classification Accuracy:", val_class_acc)
        print("Validation Segmentation Accuracy:", val_seg_acc)
        # Evaluate the model on training data
        train_loss, train_class_loss, train_seg_loss, train_class_acc, train_seg_acc = best_model.evaluate(X_train, {'classification_output': y_train_class, 'segmentation_output': y_train_seg})
        print("Train Classification Loss:", train_class_loss)
        print("Train Segmentation Loss:", train_seg_loss)
        print("Train Classification Accuracy:", train_class_acc)
        print("Train Segmentation Accuracy:", train_seg_acc)
        # Return test classification accuracy
        return test_class_acc

def plot_performance(history):
    # Plot classification accuracy
    classification_train_accuracy = history.history["classification_output_accuracy"]
    classification_val_accuracy = history.history["val_classification_output_accuracy"]
    plt.figure(figsize=(7, 3))
    plt.plot(classification_train_accuracy, label="Training Accuracy")
    plt.plot(classification_val_accuracy, label="Validation Accuracy")
    plt.title("Classification Accuracy")
    plt.xlabel("Epochs")
    plt.ylabel("Accuracy")
    plt.legend()
    plt.show()
    # Plot classification loss
    classification_train_loss = history.history["classification_output_loss"]
    classification_val_loss = history.history["val_classification_output_loss"]
    plt.figure(figsize=(7, 3))
    plt.plot(classification_train_loss, "b", label="Training Loss")
    plt.plot(classification_val_loss, "r", label="Validation Loss")
    plt.title("Classification Loss")
    plt.xlabel("Epochs")
    plt.ylabel("Loss")
    plt.legend()
    plt.show()
    # Plot segmentation accuracy
    segmentation_train_accuracy = history.history["segmentation_output_accuracy"]
    segmentation_val_accuracy = history.history["val_segmentation_output_accuracy"]
    plt.figure(figsize=(7, 3))
    plt.plot(segmentation_train_accuracy, label="Training Accuracy")
    plt.plot(segmentation_val_accuracy, label="Validation Accuracy")
    plt.title("Segmentation Accuracy")
    plt.xlabel("Epochs")
    plt.ylabel("Accuracy")
    plt.legend()
    plt.show()
    # Plot segmentation loss
    segmentation_train_loss = history.history["segmentation_output_loss"]
    segmentation_val_loss = history.history["val_segmentation_output_loss"]
    plt.figure(figsize=(7, 3))
    plt.plot(segmentation_train_loss, "b", label="Training Loss")
    plt.plot(segmentation_val_loss, "r", label="Validation Loss")
    plt.title("Segmentation Loss")
    plt.xlabel("Epochs")
    plt.ylabel("Loss")
    plt.legend()
    plt.show()

# Set image size
image_size = 224
# Define labels
labels = ["bridge", "excess", "good", "insuff", "no"]
# Set data folders
data_folders = [
    "/content/gdrive/MyDrive/Deep learning/FYP_3/4 Dataset Ratio 60 20 20/jit0/b_dip/train",
    "/content/gdrive/MyDrive/Deep learning/FYP_3/4 Dataset Ratio 60 20 20/jit0/b_dip/val",
    "/content/gdrive/MyDrive/Deep learning/FYP_3/4 Dataset Ratio 60 20 20/jit0/b_dip/test",]

# Load data
X_data, y_class_labels, y_seg_labels = load_data(data_folders)
# Define train:val:test ratio for each class (ratio x4 = exact)
class_data_counts = {
    "bridge": [40, 80, 80],
    "excess": [40, 80, 80],
    "good": [40, 80, 80],
    "insuff": [40, 80, 80],
    "no": [40, 80, 80]
    }

# Split data
X_train, y_train_class, y_train_seg, X_val, y_val_class, y_val_seg, X_test, y_test_class, y_test_seg = split_data(
    X_data, y_class_labels, y_seg_labels, class_data_counts)
'''
print("Number of train images:", len(X_train))
print("Number of train binary masks:", len(y_train_seg))
print("Number of validation images:", len(X_val))
print("Number of validation binary masks:", len(y_val_seg))
print("Number of test images:", len(X_test))
print("Number of test binary masks:", len(y_test_seg))
'''
# Initialize the label encoder
label_encoder = LabelEncoder()
label_encoder.fit(y_class_labels)
# Count the number of images of each class in the train, validation, and test sets
train_counts = count_labels(y_train_class, label_encoder)
val_counts = count_labels(y_val_class, label_encoder)
test_counts = count_labels(y_test_class, label_encoder)
print("Train counts:     ", train_counts,"  Total in train set:", sum(train_counts.values()))
print("Validation counts:", val_counts, "  Total in validation set:", sum(val_counts.values()))
print("Test counts:      ", test_counts,"  Total in test set:", sum(test_counts.values()))
# Build model
input_shape = (image_size, image_size, 3)
num_classes = len(labels)
model = build_model(input_shape, num_classes)
model.summary()

# Train model n times
test_class_acc_list = []
for i in range(5):
    print(f"\nTrain {i+1}:\n")
    model = build_model(input_shape, num_classes)
    batch_size = 16
    epochs = 50
    history = train_model(model, X_train, y_train_class, y_train_seg, X_val, y_val_class, y_val_seg, batch_size, epochs)
    # Evaluate model on test data
    test_class_acc = evaluate_model(model, X_test, y_test_class, y_test_seg)
    plot_performance(history)
    test_class_acc_list.append(test_class_acc)

# Calculate average test classification accuracy
average_test_class_acc = sum(test_class_acc_list) / len(test_class_acc_list)
print("Test Classification Accuracy List:", test_class_acc_list)
print("Average Test Classification Accuracy:", average_test_class_acc)

"

The above is the Python code with Keras to do multi-task learning with binary segmentation and classification using one mult-task learning model. Specifically for the small dataset. But the Test Classification Loss: 1.4311378002166748,
Test Segmentation Loss: 1.0658704042434692, Test Classification Accuracy: 0.3474999964237213, Test Segmentation Accuracy: 0.7845422625541687. How to improve the classification accuracy?