| 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): |
|
|
| |
| input_img = img_preprocessing(input_img) |
| gt_img = img_preprocessing(gt_img) |
| |
| |
| 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}%" |
