imagePreprocess.py

import os
from pathlib import Path
from PIL import Image, ImageOps
import cv2
import numpy as np
import tensorflow as tf
from tensorflow.keras.applications.resnet50 import preprocess_input
import torch
import clip

BASE_DIR = "MAS-AI-0000/Authentica"
MODELS_DIR = os.path.join(BASE_DIR, "Lib/Models/Image")

# Load models and preprocessing once at module level
clip_mod, clip_pre = clip.load("ViT-B/32", jit=False)
clip_mod.eval()
for p in clip_mod.parameters():
    p.requires_grad = False
mlp_model= tf.keras.models.load_model(os.path.join(MODELS_DIR, "clip_model.keras"))
cnn_model = tf.keras.models.load_model(os.path.join(MODELS_DIR, "cnn_model.keras"))
resnet_model = tf.keras.models.load_model(os.path.join(MODELS_DIR, "resnet_model.keras"))


def center_crop(image: Image.Image, crop_size=512) -> Image.Image | str:
    try:
            image = ImageOps.exif_transpose(image)
            w, h = image.size
            if w < crop_size or h < crop_size:
                # skip small images
                return f"skipped image (too small) ({w}x{h})"
            left = (w - crop_size) // 2
            top = (h - crop_size) // 2
            right = left + crop_size
            bottom = top + crop_size
            cropped = image.crop((left, top, right, bottom))
            return cropped
    except Exception as e:
        return f"Error when cropping center: {e}"


def denoise(src_image: Image) -> np.ndarray | str:
    """Read image, denoise (GPU if available) and return denoised image."""
    img = np.array(src_image)   # BGR uint8 numpy array
    img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)
    if src_image is None:
        print(f"WARNING: No source image, skipping.")
        return False
    # Denoising parameters
    H = 10           # filter strength for luminance component (recommended 3-15)
    H_COLOR = 10     # same for color components
    TEMPLATE_WINDOW_SIZE = 7
    SEARCH_WINDOW_SIZE = 21
    # Use CUDA if available, otherwise CPU fallback
    use_cuda = False
    try:
        use_cuda = hasattr(cv2, 'cuda') and cv2.cuda.getCudaEnabledDeviceCount() > 0
    except Exception:
        use_cuda = False
    if use_cuda:
        # Create a GpuMat and upload the numpy image to GPU
        gpu_img = cv2.cuda_GpuMat()
        gpu_img.upload(img)   # <-- this converts numpy -> GpuMat on device
        den_gpu = cv2.cuda.fastNlMeansDenoisingColored(
           gpu_img,H,H_COLOR,None,SEARCH_WINDOW_SIZE,TEMPLATE_WINDOW_SIZE
        )

        # Download result back to CPU
        den = den_gpu.download()
    else:
        # Fallback to CPU implementation
        print("NOTICE: CUDA not available — using CPU denoiser.")
        den = cv2.fastNlMeansDenoisingColored(
            img, None,
            H, H_COLOR,
            TEMPLATE_WINDOW_SIZE,
            SEARCH_WINDOW_SIZE
        )
    #cv2.imwrite("denoised.png", den)  # for debugging
    den = cv2.cvtColor(den, cv2.COLOR_BGR2RGB)
    den = Image.fromarray(den)
    return den
    
def compute_profile(raw_image: Image, den_image: Image, normalize=False ,verbose= True) -> np.ndarray | str:
    # read images
    if raw_image is None:
        return print(f"WARNING: couldn't read raw image")
    if den_image is None:
        return print(f"WARNING: couldn't read denoised image")
        
    raw = np.array(raw_image)  # RGB uint8 numpy array
    raw = cv2.cvtColor(raw, cv2.COLOR_RGB2BGR)
    den = np.array(den_image)  # RGB uint8 numpy array
    den = cv2.cvtColor(den, cv2.COLOR_RGB2BGR)
    # if shapes differ, resize den to raw's size (keeps alignment); warn
    if den.shape != raw.shape:
        if verbose:
            print(f"NOTE: shape mismatch, resizing denoised from {den.shape[:2]} to {raw.shape[:2]}")
        den = cv2.resize(den, (raw.shape[1], raw.shape[0]), interpolation=cv2.INTER_LINEAR)

    # absolute difference per-channel
    diff = cv2.absdiff(raw, den)               # BGR, uint8
    gray = cv2.cvtColor(diff, cv2.COLOR_BGR2GRAY)  # single-channel uint8

    # optionally normalize to full 0-255 (per-image)
    if normalize:
        # cv2.normalize will map min->0 and max->255
        # but if the image is flat (min==max) normalize will set to 0; handle that
        minv = int(gray.min())
        maxv = int(gray.max())
        if maxv > minv:
            norm = cv2.normalize(gray, None, alpha=0, beta=255, norm_type=cv2.NORM_MINMAX)
            out = norm
        else:
            # nothing to normalize (flat), keep as-is (all zeros)
            out = gray
    else:
        # keep raw diff values but ensure dtype uint8 (already uint8) and values are 0..255
        out = gray
    #cv2.imwrite("profile.png", out)  # for debugging
    return out

def profile_image_for_cnn_predict(pil_img: Image, crop_size=512):
    """Preprocess the input image and return a numpy array ready for model prediction."""
    # Step 1: Center crop the image
    cropped_img = center_crop(pil_img, crop_size=crop_size)
    if isinstance(cropped_img, str):
        return cropped_img  # return error message if cropping failed
    # Step 2: Denoise the cropped image
    denoised_img = denoise(cropped_img)
    if isinstance(denoised_img, str):
        return denoised_img  # return error message if denoising failed
    # Step 3: Compute the profile image
    profile_img = compute_profile(cropped_img, denoised_img, normalize=False)
    if isinstance(profile_img, str):
        return profile_img  # return error message if profile computation failed
    return profile_img


def prepare_cv2_image_for_resnet(cv2_gray_img, target_size=(512,512)):
    img_rgb = cv2.cvtColor(cv2_gray_img, cv2.COLOR_GRAY2RGB)
    img_rgb = cv2.resize(img_rgb, (target_size[1], target_size[0]), interpolation=cv2.INTER_AREA)
    img_rgb = img_rgb.astype('float32')
    # 5) add batch dim
    x = np.expand_dims(img_rgb, axis=0)   # shape (1, H, W, 3)
    x = preprocess_input(x)              
    return x

def predict_image_prob_clip(image: Image.Image, threshold=0.5,
                       clip_model=None, clip_preprocess=None,
                       keras_mlp=None):
    """
    Predicts probability that image is AI-generated (AI=1) using CLIP + Keras MLP.

    Args:
      path_or_image: str (file path) or PIL.Image.Image or numpy array (H,W,3)
      threshold: float threshold for binary label
      clip_model, clip_preprocess: optionally pass existing CLIP objects
      keras_mlp: optionally pass existing loaded Keras model
    Returns:
      dict: {'prob': float_prob_AI, 'label': 'AI' or 'Real'}
    """


    # --- try to reuse provided CLIP objects, otherwise load ---
    if clip_model is None or clip_preprocess is None:
        print("Loading Default CLIP model...")
        # pick a model name: prefer provided arg, else try global, else ViT-B/32
        cmn = "ViT-B/32"
        clip_model, clip_preprocess = clip.load(cmn, device="cpu", jit=False)
        clip_model.eval()
        for p in clip_model.parameters():
            p.requires_grad = False

    # --- try to reuse provided keras model, otherwise load from disk ---
    if keras_mlp is None:
        print("No keras model provided...")
        return None
    # --- load/normalize image ---
    # assume PIL image
    img = image.convert('RGB')
   
    # --- preprocess for CLIP and get embedding ---
    input_tensor = clip_preprocess(img).unsqueeze(0).to("cpu")  # shape (1,C,H,W)
    with torch.no_grad():
        emb = clip_model.encode_image(input_tensor)   # (1, D)
        emb = emb / emb.norm(dim=-1, keepdim=True)    # L2 normalize

    emb_np = emb.cpu().numpy().astype('float32')     # shape (1, D)

    # --- predict with Keras MLP ---
    probs = keras_mlp.predict(emb_np, verbose=0).reshape(-1,)
    prob = float(probs[0])
    return prob

def clip_predict(pil_img: Image, crop_size=512):
    # pass model objects explicitly (faster if you call this repeatedly)
    pil_img = center_crop(pil_img, crop_size=crop_size)
    
    if isinstance(pil_img, str):
        return pil_img  # return error message
    
    return predict_image_prob_clip(pil_img,
                         clip_model=clip_mod,
                         clip_preprocess=clip_pre,
                         keras_mlp=mlp_model)
    

def CNNPredict(predict_img: np.ndarray):
    #1 Real 0 AI
     #normalize image
    # expand dims to add channel axis
     predict_img = predict_img.astype('float32') / 255.0  # shape (H, W)
     predict_img = np.expand_dims(predict_img, axis=-1)  # shape (H, W, 1)
    # expand dims to add batch axis
     predict_img = np.expand_dims(predict_img, axis=0)   # shape (1, H, W, 1)
     prediction = cnn_model.predict(predict_img)
     return prediction[0][0]
 
def ResnetPredict(predict_img):
    #1 Real 0 AI
    predict_img = prepare_cv2_image_for_resnet(predict_img)
    prediction = resnet_model.predict(predict_img)
    return prediction[0][0]