|
import os |
|
import sys |
|
sys.path.append('.') |
|
import cv2 |
|
import math |
|
import torch |
|
import argparse |
|
import numpy as np |
|
from torch.nn import functional as F |
|
from model.pytorch_msssim import ssim_matlab |
|
from model.RIFE import Model |
|
device = torch.device("cuda" if torch.cuda.is_available() else "cpu") |
|
|
|
model = Model() |
|
model.load_model('train_log') |
|
model.eval() |
|
model.device() |
|
|
|
path = 'vimeo_interp_test/' |
|
f = open(path + 'tri_testlist.txt', 'r') |
|
psnr_list = [] |
|
ssim_list = [] |
|
for i in f: |
|
name = str(i).strip() |
|
if(len(name) <= 1): |
|
continue |
|
print(path + 'target/' + name + '/im1.png') |
|
I0 = cv2.imread(path + 'target/' + name + '/im1.png') |
|
I1 = cv2.imread(path + 'target/' + name + '/im2.png') |
|
I2 = cv2.imread(path + 'target/' + name + '/im3.png') |
|
I0 = (torch.tensor(I0.transpose(2, 0, 1)).to(device) / 255.).unsqueeze(0) |
|
I2 = (torch.tensor(I2.transpose(2, 0, 1)).to(device) / 255.).unsqueeze(0) |
|
mid = model.inference(I0, I2)[0] |
|
ssim = ssim_matlab(torch.tensor(I1.transpose(2, 0, 1)).to(device).unsqueeze(0) / 255., torch.round(mid * 255).unsqueeze(0) / 255.).detach().cpu().numpy() |
|
mid = np.round((mid * 255).detach().cpu().numpy()).astype('uint8').transpose(1, 2, 0) / 255. |
|
I1 = I1 / 255. |
|
psnr = -10 * math.log10(((I1 - mid) * (I1 - mid)).mean()) |
|
psnr_list.append(psnr) |
|
ssim_list.append(ssim) |
|
print("Avg PSNR: {} SSIM: {}".format(np.mean(psnr_list), np.mean(ssim_list))) |
|
|