data_analysis.py

import os
import cv2
import numpy as np
from sklearn.model_selection import train_test_split

REAL_FOLDER_NAME = 'real'   
FAKE_FOLDER_NAME = 'fake'   
IMAGE_SIZE = 224             
SAMPLE_LIMIT = 20000        

X_data = [] 
y_labels = [] 

def load_images_from_folder(folder_name, label_value):
    print(f"Loading images from folder: '{folder_name}'...")
    if not os.path.exists(folder_name):
        print(f"Error: Folder '{folder_name}' not found!")
        return
        
    all_files = os.listdir(folder_name)
    sampled_files = all_files[:SAMPLE_LIMIT]
    
    count = 0
    for filename in sampled_files:
        if filename.lower().endswith(('.jpg', '.jpeg', '.png', '.jfif', '.webp')):
            img_path = os.path.join(folder_name, filename)
            img = cv2.imread(img_path)
            
            if img is not None:
                img_resized = cv2.resize(img, (IMAGE_SIZE, IMAGE_SIZE))
                img_normalized = img_resized / 255.0
                X_data.append(img_normalized)
                y_labels.append(label_value)
                count += 1
            
    print(f"Successfully loaded {count} images from '{folder_name}'")

load_images_from_folder(REAL_FOLDER_NAME, label_value=0)
load_images_from_folder(FAKE_FOLDER_NAME, label_value=1)

if len(X_data) == 0:
    print("Process failed: No valid images found. Check folder contents.")
    exit()

X_data = np.array(X_data)
y_labels = np.array(y_labels)

print("\n--- Final Data Analysis Summary ---")
print(f"Total processed images: {len(X_data)}")
print(f"Images array shape: {X_data.shape}")
print(f"Labels array shape: {y_labels.shape}")

print("Splitting data into 80% Training and 20% Testing...")
X_train, X_test, y_train, y_test = train_test_split(X_data, y_labels, test_size=0.2, random_state=42)
print(f"Success! Training set size: {len(X_train)} | Testing set size: {len(X_test)}")