import os
import cv2
import lpips
import torch 
import numpy as np
import gradio as gr



SPATIAL = True   
ALEXT = lpips.LPIPS(net='alex', spatial=SPATIAL)
VGG   = lpips.LPIPS(net='vgg',  spatial=SPATIAL)

def normalize(img, factor=255./2., cent= 1.):
    r"""
    Image Normalizagion
    [H, W, C], scale : [0 - 1]
    """
    img = (img / factor - cent)
    return img


def resize(img, resize=512):
    r"""
    Image Resize using cv2
    """
    h, w, _ = img.shape
    if h >= w:
        scale = resize/h
    else:
        scale = resize/w
    return cv2.resize(img, (int(w * scale), int(h * scale)))

def to_tensor(img):
    r"""
    make numpy to tensor
    """
    img = torch.Tensor(img[..., np.newaxis].transpose((3, 2, 0, 1)))
    return img



def img_preprocessing(img):
    img = resize(img)
    img = normalize(img)
    img = to_tensor(img)
    return img

def check_img(input_img, gt_img):

    # Preprocessing
    input_img = img_preprocessing(input_img)
    gt_img    = img_preprocessing(gt_img)
   
    # predict similarytiy
    similarity = ALEXT.forward(gt_img, input_img)

    if SPATIAL:    
        return f"{(1 - similarity.mean().item()) *100:.4f}%"
    else:
        return f"{(1 - similarity.item()) *100:.4f}%"