import numpy as np
from PIL import Image
from scipy.ndimage import zoom

MAX_INFERENCE_SIZE = 1024


def prepare_for_inference(image: Image.Image) -> tuple[Image.Image, float]:
    """Resize for model input. Returns (resized_image, scale_factor)."""
    w, h = image.size
    scale = MAX_INFERENCE_SIZE / max(w, h)
    if scale < 1.0:
        new_size = (int(w * scale), int(h * scale))
        return image.resize(new_size, Image.LANCZOS), scale
    return image, 1.0


def upscale_saliency(saliency: np.ndarray, target_h: int, target_w: int) -> np.ndarray:
    """Upscale saliency map to original image dimensions."""
    if saliency.shape == (target_h, target_w):
        return saliency
    h_scale = target_h / saliency.shape[0]
    w_scale = target_w / saliency.shape[1]
    return zoom(saliency, (h_scale, w_scale), order=1)