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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
from skimage.color import rgb2yuv, yuv2rgb
from yuv_frame_io import YUV_Read,YUV_Write
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = Model(arbitrary=True)
model.load_model('RIFE_m_train_log')
model.eval()
model.device()
name_list = [
('HD_dataset/HD720p_GT/parkrun_1280x720_50.yuv', 720, 1280),
('HD_dataset/HD720p_GT/shields_1280x720_60.yuv', 720, 1280),
('HD_dataset/HD720p_GT/stockholm_1280x720_60.yuv', 720, 1280),
('HD_dataset/HD1080p_GT/BlueSky.yuv', 1080, 1920),
('HD_dataset/HD1080p_GT/Kimono1_1920x1080_24.yuv', 1080, 1920),
('HD_dataset/HD1080p_GT/ParkScene_1920x1080_24.yuv', 1080, 1920),
('HD_dataset/HD1080p_GT/sunflower_1080p25.yuv', 1080, 1920),
('HD_dataset/HD544p_GT/Sintel_Alley2_1280x544.yuv', 544, 1280),
('HD_dataset/HD544p_GT/Sintel_Market5_1280x544.yuv', 544, 1280),
('HD_dataset/HD544p_GT/Sintel_Temple1_1280x544.yuv', 544, 1280),
('HD_dataset/HD544p_GT/Sintel_Temple2_1280x544.yuv', 544, 1280),
]
def inference(I0, I1, pad, multi=2, arbitrary=True):
img = [I0, I1]
if not arbitrary:
for i in range(multi):
res = [I0]
for j in range(len(img) - 1):
res.append(model.inference(img[j], img[j + 1]))
res.append(img[j + 1])
img = res
else:
img = [I0]
p = 2**multi
for i in range(p-1):
img.append(model.inference(I0, I1, timestep=(i+1)*(1./p)))
img.append(I1)
for i in range(len(img)):
img[i] = img[i][0][:, pad: -pad]
return img[1: -1]
tot = []
for data in name_list:
psnr_list = []
name = data[0]
h = data[1]
w = data[2]
if 'yuv' in name:
Reader = YUV_Read(name, h, w, toRGB=True)
else:
Reader = cv2.VideoCapture(name)
_, lastframe = Reader.read()
# fourcc = cv2.VideoWriter_fourcc('m', 'p', '4', 'v')
# video = cv2.VideoWriter(name + '.mp4', fourcc, 30, (w, h))
for index in range(0, 100, 4):
gt = []
if 'yuv' in name:
IMAGE1, success1 = Reader.read(index)
IMAGE2, success2 = Reader.read(index + 4)
if not success2:
break
for i in range(1, 4):
tmp, _ = Reader.read(index + i)
gt.append(tmp)
else:
print('Not Implement')
I0 = torch.from_numpy(np.transpose(IMAGE1, (2,0,1)).astype("float32") / 255.).cuda().unsqueeze(0)
I1 = torch.from_numpy(np.transpose(IMAGE2, (2,0,1)).astype("float32") / 255.).cuda().unsqueeze(0)
if h == 720:
pad = 24
elif h == 1080:
pad = 4
else:
pad = 16
pader = torch.nn.ReplicationPad2d([0, 0, pad, pad])
I0 = pader(I0)
I1 = pader(I1)
with torch.no_grad():
pred = inference(I0, I1, pad)
for i in range(4 - 1):
out = (np.round(pred[i].detach().cpu().numpy().transpose(1, 2, 0) * 255)).astype('uint8')
if 'yuv' in name:
diff_rgb = 128.0 + rgb2yuv(gt[i] / 255.)[:, :, 0] * 255 - rgb2yuv(out / 255.)[:, :, 0] * 255
mse = np.mean((diff_rgb - 128.0) ** 2)
PIXEL_MAX = 255.0
psnr = 20 * math.log10(PIXEL_MAX / math.sqrt(mse))
else:
print('Not Implement')
psnr_list.append(psnr)
print(np.mean(psnr_list))
tot.append(np.mean(psnr_list))
print('PSNR: {}(544*1280), {}(720p), {}(1080p)'.format(np.mean(tot[7:11]), np.mean(tot[:3]), np.mean(tot[3:7])))
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