import numpy as np
import pandas as pd
import tensorflow as tf
import matplotlib.pyplot as plt
import seaborn as sb
import splitfolders
import cv2
import random
from keras.models import Sequential
from keras.layers import Conv2D, MaxPooling2D, Dropout, Flatten, Dense, BatchNormalization
from tensorflow.keras.utils import plot_model
from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay, classification_report, accuracy_score
from sklearn.utils.class_weight import compute_class_weight
from imutils import paths
from keras.applications import VGG16
from keras.applications import ResNet50
from keras.applications import DenseNet121
from tensorflow.keras.preprocessing.image import ImageDataGenerator
import os
from tensorflow.keras.callbacks import ModelCheckpoint
import shutil
# ----------------------------
# Global Variables & Constants
# ----------------------------
IMGSIZE = 224
BATCH_SIZE = 64
classes = ['Normal', 'Cyst', 'Tumor', 'Stone']
inputFolder = '/content/CT-KIDNEY-DATASET-Normal-Cyst-Tumor-Stone/CT-KIDNEY-DATASET-Normal-Cyst-Tumor-Stone'
outputFolder = '/content/CT_dataset'
class_to_index = {cls: i for i, cls in enumerate(classes)}
# ----------------------------
# Dataset Split (using splitfolders)
# ----------------------------
splitfolders.ratio(inputFolder, outputFolder, seed=42, ratio=(0.7, 0.15, 0.15))
imgPaths = list(paths.list_images(outputFolder))
random.shuffle(imgPaths)