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import cv2 | |
import os | |
import sys | |
import numpy as np | |
import math | |
import glob | |
import pyspng | |
import PIL.Image | |
import torch | |
import lpips | |
def read_image(image_path): | |
with open(image_path, 'rb') as f: | |
if pyspng is not None and image_path.endswith('.png'): | |
image = pyspng.load(f.read()) | |
else: | |
image = np.array(PIL.Image.open(f)) | |
if image.ndim == 2: | |
image = image[:, :, np.newaxis] # HW => HWC | |
if image.shape[2] == 1: | |
image = np.repeat(image, 3, axis=2) | |
image = image.transpose(2, 0, 1) # HWC => CHW | |
image = torch.from_numpy(image).float().unsqueeze(0) | |
image = image / 127.5 - 1 | |
return image | |
def calculate_metrics(folder1, folder2): | |
l1 = sorted(glob.glob(folder1 + '/*.png') + glob.glob(folder1 + '/*.jpg')) | |
l2 = sorted(glob.glob(folder2 + '/*.png') + glob.glob(folder2 + '/*.jpg')) | |
assert(len(l1) == len(l2)) | |
print('length:', len(l1)) | |
# l1 = l1[:3]; l2 = l2[:3]; | |
device = torch.device('cuda:0') | |
loss_fn = lpips.LPIPS(net='alex').to(device) | |
loss_fn.eval() | |
# loss_fn = lpips.LPIPS(net='vgg').to(device) | |
lpips_l = [] | |
with torch.no_grad(): | |
for i, (fpath1, fpath2) in enumerate(zip(l1, l2)): | |
print(i) | |
_, name1 = os.path.split(fpath1) | |
_, name2 = os.path.split(fpath2) | |
name1 = name1.split('.')[0] | |
name2 = name2.split('.')[0] | |
assert name1 == name2, 'Illegal mapping: %s, %s' % (name1, name2) | |
img1 = read_image(fpath1).to(device) | |
img2 = read_image(fpath2).to(device) | |
assert img1.shape == img2.shape, 'Illegal shape' | |
lpips_l.append(loss_fn(img1, img2).mean().cpu().numpy()) | |
res = sum(lpips_l) / len(lpips_l) | |
return res | |
if __name__ == '__main__': | |
folder1 = 'path to the inpainted result' | |
folder2 = 'path to the gt' | |
res = calculate_metrics(folder1, folder2) | |
print('lpips: %.4f' % res) | |
with open('lpips.txt', 'w') as f: | |
f.write('lpips: %.4f' % res) | |