import os
import cv2
import numpy as np
import joblib
import torch
from torchvision import transforms, models
from PIL import Image
from skimage.feature import hog, local_binary_pattern, graycomatrix, graycoprops
from sklearn.decomposition import PCA
from sklearn.preprocessing import MinMaxScaler
from sklearn.svm import OneClassSVM
from sklearn.model_selection import GridSearchCV, cross_val_score
import mahotas
import albumentations as A
from albumentations.pytorch import ToTensorV2
import matplotlib.pyplot as plt

# Define augmentations
augmentations = A.Compose([
    A.Rotate(limit=2, p=0.5),
    A.RandomBrightnessContrast(p=0.2),
    A.GaussianBlur(blur_limit=3, p=0.2),
    A.ElasticTransform(p=0.2),
    A.CLAHE(clip_limit=2.0, tile_grid_size=(5, 5), p=0.2),
    ToTensorV2()
])

# --- Feature extraction functions ---
def extract_glcm_features(image, distances=[1], angles=[0, np.pi/4, np.pi/2, 3*np.pi/4]):
    ...

def extract_enhanced_edge_features(image):
    ...

def load_and_augment_images(folder):
    ...

def plot_explained_variance(X_scaled):
    ...

# --- Main training function ---
def train_model(CheckItemname, variant, suffix, camname, preset_imag):
    ...
