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test_video.py
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from torch import save
from model import MRCF_test as MRCF
from model import LTE
from utils import flow_to_color
from dataset import dataloader
from utils import calc_psnr_and_ssim_cuda, bgr2ycbcr
import os
import numpy as np
import PIL
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data import DataLoader
import visdom
from imageio import imread, imsave, get_writer
from PIL import Image
import cv2
# from ptflops import get_model_complexity_info
import warnings
warnings.filterwarnings("ignore", category=UserWarning)
def foveated_metric(LR, LR_fv, HR, mn, hw, crop, kernel_size, stride_size, eval_mode=False):
m, n = mn
h, w = hw
crop_h, crop_w = crop
HR_fold = F.unfold(HR.unsqueeze(0), kernel_size=(kernel_size, kernel_size), stride=stride_size) # [N, 3*11*11, Hr*Wr]
LR_fv_fold = F.unfold(LR_fv.unsqueeze(0), kernel_size=(kernel_size, kernel_size), stride=stride_size) # [N, 3*11*11, Hr*Wr]
B, C, N = HR_fold.size()
HR_fold = HR_fold.permute(0, 2, 1).view(B*N , 3, kernel_size, kernel_size) # [N, 3*11*11, Hr*Wr]
LR_fv_fold = LR_fv_fold.permute(0, 2, 1).view(B*N , 3, kernel_size, kernel_size)
Hr = (h - kernel_size) // stride_size + 1
Wr = (w - kernel_size) // stride_size + 1
B, C, H, W = HR_fold.size()
mask = torch.ones((B, 1, H, W)).float()
psnr_score, ssim_score = calc_psnr_and_ssim_cuda(HR_fold, LR_fv_fold, mask, is_tensor=False, batch_avg=True)
psnr_score = psnr_score.view(Hr, Wr)
ssim_score = ssim_score.view(Hr, Wr)
# psnr_y_idx = (torch.argmax(psnr_score) // Wr) * stride_size
# psnr_x_idx = (torch.argmax(psnr_score) % Wr) * stride_size
# ssim_y_idx = (torch.argmax(ssim_score) // Wr) * stride_size
# ssim_x_idx = (torch.argmax(ssim_score) % Wr) * stride_size
if not eval_mode:
HR[:, m:m+crop_h, n] = torch.tensor([0., 0., 255.]).unsqueeze(1).repeat((1,crop_h))
HR[:, m:m+crop_h, n+crop_w-1] = torch.tensor([0., 0., 255.]).unsqueeze(1).repeat((1,crop_h))
HR[:, m, n:n+crop_w] = torch.tensor([0., 0., 255.]).unsqueeze(1).repeat((1,crop_w))
HR[:, m+crop_h-1, n:n+crop_w] = torch.tensor([0., 0., 255.]).unsqueeze(1).repeat((1,crop_w))
LR_fv[:, m:m+crop_h, n] = torch.tensor([0., 0., 255.]).unsqueeze(1).repeat((1,crop_h))
LR_fv[:, m:m+crop_h, n+crop_w-1] = torch.tensor([0., 0., 255.]).unsqueeze(1).repeat((1,crop_h))
LR_fv[:, m, n:n+crop_w] = torch.tensor([0., 0., 255.]).unsqueeze(1).repeat((1,crop_w))
LR_fv[:, m+crop_h-1, n:n+crop_w] = torch.tensor([0., 0., 255.]).unsqueeze(1).repeat((1,crop_w))
psnr_min = psnr_score.min()
psnr_max = psnr_score.max()
ssim_min = ssim_score.min()
ssim_max = ssim_score.max()
# psnr_score = (psnr_score - psnr_min) / (psnr_max - psnr_min)
# ssim_score = (ssim_score - ssim_min) / (ssim_max - ssim_min)
psnr_score = psnr_score / 100
ssim_score = (ssim_score.clip(0, 1) - 0.7) / 0.3
# psnr_score_discrete = torch.zeros_like(psnr_score)
# ssim_score_discrete = torch.zeros_like(ssim_score)
# psnr_score_discrete[psnr_score <= 1.0] = 1.0
# psnr_score_discrete[psnr_score <= 0.9] = 0.9
# psnr_score_discrete[psnr_score <= 0.8] = 0.8
# psnr_score_discrete[psnr_score <= 0.7] = 0.7
# psnr_score_discrete[psnr_score <= 0.6] = 0.6
# psnr_score_discrete[psnr_score <= 0.5] = 0.5
# psnr_score_discrete[psnr_score <= 0.4] = 0.4
# psnr_score_discrete[psnr_score <= 0.3] = 0.3
# psnr_score_discrete[psnr_score <= 0.2] = 0.2
# psnr_score_discrete[psnr_score <= 0.1] = 0.1
# ssim_score_discrete[ssim_score <= 1.0] = 1.0
# ssim_score_discrete[ssim_score <= 0.9] = 0.9
# ssim_score_discrete[ssim_score <= 0.8] = 0.8
# ssim_score_discrete[ssim_score <= 0.7] = 0.7
# ssim_score_discrete[ssim_score <= 0.6] = 0.6
# ssim_score_discrete[ssim_score <= 0.5] = 0.5
# ssim_score_discrete[ssim_score <= 0.4] = 0.4
# ssim_score_discrete[ssim_score <= 0.3] = 0.3
# ssim_score_discrete[ssim_score <= 0.2] = 0.2
# ssim_score_discrete[ssim_score <= 0.1] = 0.1
# self.viz.viz.image(HR.cpu().numpy(), win='{}'.format('HR'), opts=dict(title='{}, Image size : {}'.format('HR', HR.size())))
# self.viz.viz.image(LR.cpu().numpy(), win='{}'.format('LR'), opts=dict(title='{}, Image size : {}'.format('LR', LR.size())))
# self.viz.viz.image(LR_fv.cpu().numpy(), win='{}'.format('FV'), opts=dict(title='{}, Image size : {}'.format('FV', LR_fv.size())))
# self.viz.viz.image(psnr_score.cpu().numpy(), win='{}'.format('PSNR_score'), opts=dict(title='{}, Image size : {}'.format('PSNR_score', psnr_score.size())))
# self.viz.viz.image(ssim_score.cpu().numpy(), win='{}'.format('SSIM_score'), opts=dict(title='{}, Image size : {}'.format('SSIM_score', ssim_score.size())))
# self.viz.viz.image(psnr_score_discrete.cpu().numpy(), win='{}'.format('PSNR_score_discrete'), opts=dict(title='{}, Image size : {}'.format('PSNR_score_discrete', psnr_score_discrete.size())))
# self.viz.viz.image(ssim_score_discrete.cpu().numpy(), win='{}'.format('SSIM_score_discrete'), opts=dict(title='{}, Image size : {}'.format('SSIM_score_discrete', ssim_score_discrete.size())))
return psnr_score, ssim_score, (psnr_min, psnr_max), (ssim_min, ssim_max)
def rgb2yuv(rgb, y_only=True):
# rgb_ = rgb.permute(0,2,3,1)
# A = torch.tensor([[0.299, -0.14714119,0.61497538],
# [0.587, -0.28886916, -0.51496512],
# [0.114, 0.43601035, -0.10001026]])
# yuv = torch.tensordot(rgb_,A,1).transpose(0,2)
r = rgb[:, 0, :, :]
g = rgb[:, 1, :, :]
b = rgb[:, 2, :, :]
y = 0.299 * r + 0.587 * g + 0.114 * b
u = -0.147 * r - 0.289 * g + 0.436 * b
v = 0.615 * r - 0.515 * g - 0.100 * b
yuv = torch.stack([y,u,v], dim=1)
if y_only:
return y.unsqueeze(1)
else:
return yuv
def yuv2rgb(yuv):
y = yuv[:, 0, :, :]
u = yuv[:, 1, :, :]
v = yuv[:, 2, :, :]
r = y + 1.14 * v # coefficient for g is 0
g = y + -0.396 * u - 0.581 * v
b = y + 2.029 * u # coefficient for b is 0
rgb = torch.stack([r,g,b], 1)
return rgb
if __name__ == '__main__':
device = torch.device('cuda')
viz = visdom.Visdom(server='140.113.212.214', port=8803, env='Gen_video')
# fourcc = cv2.VideoWriter_fourcc('m','p','4','v')
# out = cv2.VideoWriter('test_arcane_simple.mp4', fourcc, 30.0, (1080, 1920))
dataset_name = 'REDS'
# dataset_name = 'old_tree'
# dataset_name = 'arcane'
regional_dcn = False
eval_mode = True
model_code = 15
model_epoch = 99
y_only = False
hr_dcn = True
offset_prop = True
split_ratio = 3
sigma = 50
dcn_size = 720
model_name = 'FVSR_x8_simple_v{}_hrdcn_{}_offsetprop_{}_fnet{}'.format(model_code, 'y' if hr_dcn else 'n',
'y' if offset_prop else 'n',
'_{}outof4'.format(4-split_ratio) if model_code == 18 else '')
print('Current model name: {}, Epoch: {}'.format(model_name, model_epoch))
video_num = [ 0, 11, 15, 20]
# video_num = [ 0, 1, 6, 17]
if eval_mode:
fv_st_idx = [0, 0, 0, 0]
else:
fv_st_idx = [66, 30, 31, 0]
# fv_st_idx = [100, 100, 100, 100]
video_set = 'train'
# video_set = 'val'
model_path = 'train/REDS/{}/model/'.format(model_name)
model_saves = os.listdir(model_path)
model_save = [v for v in model_saves if '{:05d}'.format(model_epoch) in v]
assert len(model_save) == 1
model_save = model_save[0]
model_name += '_gaussian'
if eval_mode:
save_dir = 'test_png/eval_video/'
else:
save_dir = 'test_png/'
if not os.path.exists(save_dir):
os.makedirs(save_dir)
if not os.path.exists(os.path.join(save_dir, model_name)):
os.makedirs(os.path.join(save_dir, model_name))
# model = MRCF.MRCF_CRA_x8(mid_channels=64, num_blocks=1, spynet_pretrained='pretrained_models/spynet_20210409-c6c1bd09.pth', device=device).to(device)
if model_code == 13:
model = MRCF.MRCF_simple_v13(mid_channels=32, y_only=y_only, hr_dcn=hr_dcn, offset_prop=offset_prop, spynet_pretrained='pretrained_models/fnet.pth', device=device).to(device)
# model = MRCF.MRCF_simple_v13_nodcn(mid_channels=32, y_only=y_only, hr_dcn=hr_dcn, offset_prop=offset_prop, spynet_pretrained='pretrained_models/fnet.pth', device=device).to(device)
elif model_code == 15:
model = MRCF.MRCF_simple_v15(mid_channels=32, y_only=y_only, hr_dcn=hr_dcn, offset_prop=offset_prop, spynet_pretrained='pretrained_models/fnet.pth', device=device).to(device)
elif model_code == 18:
model = MRCF.MRCF_simple_v18(mid_channels=32, y_only=y_only, hr_dcn=hr_dcn, offset_prop=offset_prop, split_ratio=split_ratio, spynet_pretrained='pretrained_models/fnet.pth', device=device).to(device)
# model = MRCF.MRCF_simple_v18_cra(mid_channels=32, y_only=y_only, hr_dcn=hr_dcn, offset_prop=offset_prop, split_ratio=split_ratio, spynet_pretrained='pretrained_models/fnet.pth', device=device).to(device)
elif model_code == 0:
model = MRCF.MRCF_simple_v0(mid_channels=32, y_only=y_only, hr_dcn=hr_dcn, offset_prop=offset_prop, spynet_pretrained='pretrained_models/fnet.pth', device=device).to(device)
model_state_dict = model.state_dict()
model_state_dict_save = {k.replace('basic_', 'basic_module.'):v for k,v in torch.load(os.path.join(model_path, model_save)).items() if k.replace('basic_', 'basic_module.') in model_state_dict}
# model_state_dict_save = {k.replace('module.',''):v for k,v in torch.load(model_path).items() if k.replace('module.','') in model_state_dict}
# for k in model_state_dict.keys():
# print(k)
# print('-----')
# for k in model_state_dict_save.keys():
# print(k)
# print(model_save)
# for k,v in torch.load(model_path).items():
# print(k)
model_state_dict.update(model_state_dict_save)
model.load_state_dict(model_state_dict, strict=True)
psnr_whole_list = []
ssim_whole_list = []
psnr_outskirt_list = []
ssim_outskirt_list = []
psnr_past_list = []
ssim_past_list = []
psnr_fovea_list = []
ssim_fovea_list = []
for v_idx, v in enumerate(video_num):
if dataset_name == 'REDS':
GT_img_dir = '/DATA/REDS_sharp/{}/{}/{}_sharp/{:03d}/'.format(video_set, video_set, video_set, v)
LR_img_dir = '/DATA/REDS_sharp_BI_x8/{}/{}/{}_sharp/{:03d}/'.format(video_set, video_set, video_set, v)
else:
GT_img_dir = '{}_x1'.format(dataset_name)
LR_img_dir = '{}_x8'.format(dataset_name)
print('Data location: {}'.format(GT_img_dir))
lr_frames = []
hr_frames = []
GT_imgs = []
LR_imgs = []
LRSR_imgs = []
GT_files = os.listdir(GT_img_dir)
LR_files = os.listdir(LR_img_dir)
GT_files = sorted(GT_files)
LR_files = sorted(LR_files)
for file in GT_files:
img = cv2.imread(os.path.join(GT_img_dir, file))
GT_imgs.append(img[:1072, :1920, :])
# img = cv2.cvtColor(img.copy(), cv2.COLOR_RGB2BGR)
# hr_frames.append(img)
H_, W_, _ = GT_imgs[0].shape
for file in LR_files:
img = cv2.imread(os.path.join(LR_img_dir, file))
LR_imgs.append(img[:134, :240, :])
LRSR_imgs.append(np.array(PIL.Image.fromarray(img).resize((W_, H_), PIL.Image.BICUBIC)))
# img = cv2.cvtColor(img.copy(), cv2.COLOR_RGB2BGR)
# lr_frames.append(img)
gen_frames = []
gt_frames = []
lr_frames = []
psnr_score_list = []
ssim_score_list = []
psnr_score_bicubic_list = []
ssim_score_bicubic_list = []
# fv_size = 144
fv_size = 96
dx = 0
dy = 0
psnr_min = 1000
psnr_max = 0
ssim_min = 1000
ssim_max = 0
if dataset_name == 'arcane':
st_x = 760
st_y = 300
ed_x = 1160
ed_y = 500
else:
st_x = 360 + dx
st_y = 300 + dy
ed_x = 720 + dx
ed_y = 500 + dy
cur_x = ed_x
cur_y = ed_y
step_x = 20
step_y = 0
n_frames = 100
bd_length = 10
rg_w = dcn_size
rg_h = dcn_size
GT_imgs = GT_imgs[:n_frames]
LR_imgs = LR_imgs[:n_frames]
LRSR_imgs = LRSR_imgs[:n_frames]
# with get_writer(os.path.join(save_dir,'test_{}_{}_{:03}_{}_bicubic.gif'.format(model_name, dataset_name, model_epoch, int(y_only))), mode="I", fps=7) as writer:
# for n in range(n_frames):
# writer.append_data(LRSR_imgs[n][:,:,::-1])
# with get_writer(os.path.join(save_dir,'test_{}_{}_{:03}_{}_gt.gif'.format(model_name, dataset_name, model_epoch, int(y_only))), mode="I", fps=7) as writer:
# for n in range(n_frames):
# writer.append_data(GT_imgs[n][:,:,::-1])
GT_imgs = np.stack(GT_imgs, axis=0)
LR_imgs = np.stack(LR_imgs, axis=0)
LRSR_imgs = np.stack(LRSR_imgs, axis=0)
GT_imgs = GT_imgs.astype(np.float32) / 255.
LR_imgs = LR_imgs.astype(np.float32) / 255.
LRSR_imgs = LRSR_imgs.astype(np.float32) / 255.
GT_imgs = torch.from_numpy(np.ascontiguousarray(np.transpose(GT_imgs, (0, 3, 1, 2)))).float()
LR_imgs = torch.from_numpy(np.ascontiguousarray(np.transpose(LR_imgs, (0, 3, 1, 2)))).float()
LRSR_imgs = torch.from_numpy(np.ascontiguousarray(np.transpose(LRSR_imgs, (0, 3, 1, 2)))).float()
N, C, H, W = GT_imgs.size()
kernel = np.array([ [1, 1, 1],
[1, 1, 1],
[1, 1, 1] ], dtype=np.float32)
kernel_tensor = torch.Tensor(np.expand_dims(np.expand_dims(kernel, 0), 0)) # size: (1, 1, 3, 3)
mk_list = []
mk_one = torch.ones((1, 1, H, W)).to(device)
x_array = sigma * np.random.randn(N) + (W / 2)
y_array = sigma * np.random.randn(N) + (H / 2)
white_paper = np.ones((H,W,3), np.uint8) * 255
traj_list = []
model.eval()
with torch.no_grad():
for n in range(N):
print(n, '\r', end='')
lr = LR_imgs[n:n+1].unsqueeze(0).to(device)
lrsr = LRSR_imgs[n:n+1].unsqueeze(0).to(device).clone()
gt = GT_imgs[n:n+1].unsqueeze(0).to(device).clone()
fv = torch.zeros_like(gt).to(device)
mk = torch.zeros((1, 1, 1, H, W)).to(device)
fg = torch.zeros((1, 1, 1, H, W)).to(device)
#### Raster scan
# N_H = H // fv_size
# N_W = W // fv_size
# SP_H = H / N_H
# SP_W = W / N_W
# fv_sp = []
# x_i = n % N_W
# y_i = (n // N_W) % N_H
# cur_y = int((1+y_i)*SP_H - (SP_H + fv_size)//2)
# cur_x = int((1+x_i)*SP_W - (SP_W + fv_size)//2)
#### Gaussian span
cur_y = int(y_array[n]) - fv_size//2
cur_x = int(x_array[n]) - fv_size//2
traj_list.append((cur_y, cur_x))
if n >= fv_st_idx[v_idx]:
fv[:, :, :, cur_y:cur_y+fv_size, cur_x:cur_x+fv_size] = gt[:, :, :, cur_y:cur_y+fv_size, cur_x:cur_x+fv_size]
mk[:, :, :, cur_y:cur_y+fv_size, cur_x:cur_x+fv_size] = 1
mk_fv = mk.clone()
mk_fv[:, :, :, cur_y:cur_y+fv_size, cur_x:cur_x+fv_size] = 1
mk_out = mk_fv.clone().squeeze(0)
for _ in range(10):
mk_out = torch.clamp(F.conv2d(mk_out, kernel_tensor.to(mk_out.device), padding=(1, 1)), 0, 1)
mk_out = torch.logical_and(torch.logical_not(mk), mk_out)
st_rg_x = max(cur_x+(fv_size//2)-(rg_w//2), 0)
ed_rg_x = min(cur_x+(fv_size//2)+(rg_w//2), 1920)
st_rg_y = max(cur_y+(fv_size//2)-(rg_h//2), 0)
ed_rg_y = min(cur_y+(fv_size//2)+(rg_h//2), 1080)
if regional_dcn:
fg[:, :, :, st_rg_y:ed_rg_y, st_rg_x:ed_rg_x] = 1
else:
fg = torch.ones((1, 1, 1, H, W)).to(device)
sr = model(lrs=lr, fvs=fv, mks=mk, fgs=fg)
psnr, ssim = calc_psnr_and_ssim_cuda(sr.squeeze(0), gt.squeeze(0), mk_one)
psnr_whole_list.append(psnr)
ssim_whole_list.append(ssim)
psnr, ssim = calc_psnr_and_ssim_cuda(sr.squeeze(0), gt.squeeze(0), mk_fv)
psnr_fovea_list.append(psnr)
ssim_fovea_list.append(ssim)
psnr, ssim = calc_psnr_and_ssim_cuda(sr.squeeze(0), gt.squeeze(0), mk_out)
psnr_outskirt_list.append(psnr)
ssim_outskirt_list.append(ssim)
if n > 0:
psnr, ssim = calc_psnr_and_ssim_cuda(sr.squeeze(0), gt.squeeze(0), mk_past)
psnr_past_list.append(psnr)
ssim_past_list.append(ssim)
mk_list.append(mk_out.squeeze(0))
if len(mk_list) > 3:
mk_list.pop(0)
mk_past = torch.sum(torch.cat(mk_list, dim=1), dim=1, keepdim=True).clip(0, 1)
psnr_score, ssim_score, psnr, ssim = foveated_metric(lr[0,0], sr[0,0], gt[0,0].clone(), (cur_y, cur_x), (H, W), (fv_size, fv_size), kernel_size=10, stride_size=5, eval_mode=eval_mode)
psnr_score_list.append((psnr_score.unsqueeze(2).repeat(1, 1, 3) * 255).round().cpu().detach().numpy().astype(np.uint8))
ssim_score_list.append((ssim_score.unsqueeze(2).repeat(1, 1, 3) * 255).round().cpu().detach().numpy().astype(np.uint8))
psnr_score, ssim_score, psnr, ssim = foveated_metric(lr[0,0], lrsr[0,0], gt[0,0], (cur_y, cur_x), (H, W), (fv_size, fv_size), kernel_size=10, stride_size=5, eval_mode=eval_mode)
if psnr[0] < psnr_min:
psnr_min = psnr[0]
if psnr[1] > psnr_max:
psnr_max = psnr[1]
if ssim[0] < ssim_min:
ssim_min = ssim[0]
if ssim[1] > ssim_max:
ssim_max = ssim[1]
psnr_score_bicubic_list.append((psnr_score.unsqueeze(2).repeat(1, 1, 3) * 255).round().cpu().detach().numpy().astype(np.uint8))
ssim_score_bicubic_list.append((ssim_score.unsqueeze(2).repeat(1, 1, 3) * 255).round().cpu().detach().numpy().astype(np.uint8))
if y_only:
B, N, C, H, W = lrsr.size()
lrsr = lrsr.view(B*N, C, H, W)
B, N, C, H, W = sr.size()
sr = sr.view(B*N, C, H, W)
lrsr = rgb2yuv(lrsr, y_only=False)
sr = yuv2rgb(torch.cat((sr[:,0:1,:,:], lrsr[:,1:3,:,:]), dim=1))
sr = (sr * 255.).clip(0., 255.)
lr = (lrsr * 255.).clip(0., 255.)
gt = (gt * 255.).clip(0., 255.)
sr = np.transpose(sr.squeeze().clone().detach().round().cpu().numpy().astype(np.uint8), (1, 2, 0))
lr = np.transpose(lr.squeeze().clone().detach().round().cpu().numpy().astype(np.uint8), (1, 2, 0))
gt = np.transpose(gt.squeeze().clone().detach().round().cpu().numpy().astype(np.uint8), (1, 2, 0))
sr = cv2.cvtColor(sr, cv2.COLOR_RGB2BGR)
H, W, C = sr.shape
# lr = cv2.resize(cv2.cvtColor(lr, cv2.COLOR_RGB2BGR), (W, H), cv2.INTER_CUBIC)
lr = cv2.cvtColor(lr, cv2.COLOR_RGB2BGR)
gt = cv2.cvtColor(gt, cv2.COLOR_RGB2BGR)
sr_copy = sr.copy()
# cv2.rectangle(sr, (cur_x, cur_y), (cur_x+fv_size, cur_y+fv_size), (51, 51, 255), 3)
# cv2.rectangle(sr, (st_rg_x, st_rg_y), (ed_rg_x, ed_rg_y), (255, 51, 51), 3)
# cv2.line(sr, (cur_x+fv_size//2-5, cur_y+fv_size//2), (cur_x+fv_size//2+5, cur_y+fv_size//2), (51, 51, 255), 3)
# cv2.line(sr, (cur_x+fv_size//2, cur_y+fv_size//2-5), (cur_x+fv_size//2, cur_y+fv_size//2+5), (51, 51, 255), 3)
# sr[cur_y+bd_length:cur_y+fv_size-bd_length, cur_x, :] = sr_copy[cur_y+bd_length:cur_y+fv_size-bd_length, cur_x, :]
# sr[cur_y+bd_length:cur_y+fv_size-bd_length, cur_x-1, :] = sr_copy[cur_y+bd_length:cur_y+fv_size-bd_length, cur_x-1, :]
# sr[cur_y+bd_length:cur_y+fv_size-bd_length, cur_x+1, :] = sr_copy[cur_y+bd_length:cur_y+fv_size-bd_length, cur_x+1, :]
# sr[cur_y+bd_length:cur_y+fv_size-bd_length, cur_x-2, :] = sr_copy[cur_y+bd_length:cur_y+fv_size-bd_length, cur_x-2, :]
# sr[cur_y+bd_length:cur_y+fv_size-bd_length, cur_x+2, :] = sr_copy[cur_y+bd_length:cur_y+fv_size-bd_length, cur_x+2, :]
# sr[cur_y+bd_length:cur_y+fv_size-bd_length, cur_x+fv_size, :] = sr_copy[cur_y+bd_length:cur_y+fv_size-bd_length, cur_x+fv_size, :]
# sr[cur_y+bd_length:cur_y+fv_size-bd_length, cur_x+fv_size-1, :] = sr_copy[cur_y+bd_length:cur_y+fv_size-bd_length, cur_x+fv_size-1, :]
# sr[cur_y+bd_length:cur_y+fv_size-bd_length, cur_x+fv_size+1, :] = sr_copy[cur_y+bd_length:cur_y+fv_size-bd_length, cur_x+fv_size+1, :]
# sr[cur_y+bd_length:cur_y+fv_size-bd_length, cur_x+fv_size-2, :] = sr_copy[cur_y+bd_length:cur_y+fv_size-bd_length, cur_x+fv_size-2, :]
# sr[cur_y+bd_length:cur_y+fv_size-bd_length, cur_x+fv_size+2, :] = sr_copy[cur_y+bd_length:cur_y+fv_size-bd_length, cur_x+fv_size+2, :]
# sr[cur_y, cur_x+bd_length:cur_x+fv_size-bd_length, :] = sr_copy[cur_y, cur_x+bd_length:cur_x+fv_size-bd_length, :]
# sr[cur_y-1, cur_x+bd_length:cur_x+fv_size-bd_length, :] = sr_copy[cur_y-1, cur_x+bd_length:cur_x+fv_size-bd_length, :]
# sr[cur_y+1, cur_x+bd_length:cur_x+fv_size-bd_length, :] = sr_copy[cur_y+1, cur_x+bd_length:cur_x+fv_size-bd_length, :]
# sr[cur_y-2, cur_x+bd_length:cur_x+fv_size-bd_length, :] = sr_copy[cur_y-2, cur_x+bd_length:cur_x+fv_size-bd_length, :]
# sr[cur_y+2, cur_x+bd_length:cur_x+fv_size-bd_length, :] = sr_copy[cur_y+2, cur_x+bd_length:cur_x+fv_size-bd_length, :]
# sr[cur_y+fv_size, cur_x+bd_length:cur_x+fv_size-bd_length, :] = sr_copy[cur_y+fv_size, cur_x+bd_length:cur_x+fv_size-bd_length, :]
# sr[cur_y+fv_size-1, cur_x+bd_length:cur_x+fv_size-bd_length, :] = sr_copy[cur_y+fv_size-1, cur_x+bd_length:cur_x+fv_size-bd_length, :]
# sr[cur_y+fv_size+1, cur_x+bd_length:cur_x+fv_size-bd_length, :] = sr_copy[cur_y+fv_size+1, cur_x+bd_length:cur_x+fv_size-bd_length, :]
# sr[cur_y+fv_size-2, cur_x+bd_length:cur_x+fv_size-bd_length, :] = sr_copy[cur_y+fv_size-2, cur_x+bd_length:cur_x+fv_size-bd_length, :]
# sr[cur_y+fv_size+2, cur_x+bd_length:cur_x+fv_size-bd_length, :] = sr_copy[cur_y+fv_size+2, cur_x+bd_length:cur_x+fv_size-bd_length, :]
# sr[st_rg_y+bd_length:ed_rg_y-bd_length, st_rg_x-2:st_rg_x+3, :] = sr_copy[st_rg_y+bd_length:ed_rg_y-bd_length, st_rg_x-2:st_rg_x+3, :]
# sr[st_rg_y+bd_length:ed_rg_y-bd_length, ed_rg_x-2:ed_rg_x+3, :] = sr_copy[st_rg_y+bd_length:ed_rg_y-bd_length, ed_rg_x-2:ed_rg_x+3, :]
# sr[st_rg_y-2:st_rg_y+3, st_rg_x+bd_length:ed_rg_x-bd_length, :] = sr_copy[st_rg_y-2:st_rg_y+3, st_rg_x+bd_length:ed_rg_x-bd_length, :]
# sr[ed_rg_y-2:ed_rg_y+3, st_rg_x+bd_length:ed_rg_x-bd_length, :] = sr_copy[ed_rg_y-2:ed_rg_y+3, st_rg_x+bd_length:ed_rg_x-bd_length, :]
# cv2.rectangle(sr, (0, 100), (0+fv_size, 100+fv_size), (51, 51, 255), 3)
# sr = cv2.cvtColor(sr, cv2.COLOR_BGR2RGB)
gen_frames.append(sr.copy())
lr_frames.append(lr.copy())
gt_frames.append(gt.copy())
cur_x += step_x
cur_y += step_y
# viz.image(sr.transpose(2, 0, 1), win='{}'.format('sr'), opts=dict(title='{}, Image size : {}'.format('sr', sr.shape)))
# viz.image(lr.transpose(2, 0, 1), win='{}'.format('lr'), opts=dict(title='{}, Image size : {}'.format('lr', lr.shape)))
# viz.image(gt.transpose(2, 0, 1), win='{}'.format('gt'), opts=dict(title='{}, Image size : {}'.format('gt', gt.shape)))
if cur_x >= ed_x and cur_y <= st_y:
step_x = 0
step_y = 20
elif cur_x >= ed_x and cur_y >= ed_y:
step_x = -20
step_y = 0
elif cur_x <= st_x and cur_y >= ed_y:
step_x = 0
step_y = -20
elif cur_x <= st_x and cur_y <= st_y:
step_x = 20
step_y = 0
# for (idy, idx) in traj_list:
# white_paper = cv2.circle(white_paper, (idx,idy), radius=10, color=(0, 0, 255), thickness=-1)
model.clear_states()
if dataset_name == 'REDS':
#### Reconstructed results
if not os.path.exists(os.path.join(save_dir, model_name, str(video_num[v_idx]), 'results')):
os.makedirs(os.path.join(save_dir, model_name, str(video_num[v_idx]), 'results'))
for i in range(len(gen_frames)):
cv2.imwrite(os.path.join(save_dir, model_name, str(video_num[v_idx]), 'results', '{:03d}.png'.format(i)), gen_frames[i][:,:,::-1])
with get_writer(os.path.join(save_dir, model_name, str(video_num[v_idx]), 'results', 'results.gif'), mode="I", fps=7) as writer:
for n in range(len(gen_frames)):
writer.append_data(gen_frames[n])
if not os.path.exists(os.path.join(save_dir, model_name, str(video_num[v_idx]), 'psnr')):
os.makedirs(os.path.join(save_dir, model_name, str(video_num[v_idx]), 'psnr'))
for i in range(len(gen_frames)):
cv2.imwrite(os.path.join(save_dir, model_name, str(video_num[v_idx]), 'psnr', '{:03d}.png'.format(i)), psnr_score_list[i])
if not os.path.exists(os.path.join(save_dir, model_name, str(video_num[v_idx]), 'ssim')):
os.makedirs(os.path.join(save_dir, model_name, str(video_num[v_idx]), 'ssim'))
for i in range(len(gen_frames)):
cv2.imwrite(os.path.join(save_dir, model_name, str(video_num[v_idx]), 'ssim', '{:03d}.png'.format(i)), ssim_score_list[i])
# cv2.imwrite(os.path.join(save_dir, model_name, str(video_num[v_idx]), 'traj.png'), white_paper)
#### Bicubic upsample results
if not os.path.exists(os.path.join(save_dir, 'Bicubic', str(video_num[v_idx]))):
os.makedirs(os.path.join(save_dir, 'Bicubic', str(video_num[v_idx]), 'results'))
os.makedirs(os.path.join(save_dir, 'Bicubic', str(video_num[v_idx]), 'psnr'))
os.makedirs(os.path.join(save_dir, 'Bicubic', str(video_num[v_idx]), 'ssim'))
for i in range(len(lr_frames)):
cv2.imwrite(os.path.join(save_dir, 'Bicubic', str(video_num[v_idx]), 'results', '{:03d}.png'.format(i)), lr_frames[i][:,:,::-1])
for i in range(len(lr_frames)):
cv2.imwrite(os.path.join(save_dir, 'Bicubic', str(video_num[v_idx]), 'psnr', '{:03d}.png'.format(i)), psnr_score_bicubic_list[i])
for i in range(len(lr_frames)):
cv2.imwrite(os.path.join(save_dir, 'Bicubic', str(video_num[v_idx]), 'ssim', '{:03d}.png'.format(i)), ssim_score_bicubic_list[i])
#### GroundTruth
if not os.path.exists(os.path.join(save_dir, 'GroundTruth', str(video_num[v_idx]))):
os.makedirs(os.path.join(save_dir, 'GroundTruth', str(video_num[v_idx])))
for i in range(len(lr_frames)):
cv2.imwrite(os.path.join(save_dir, 'GroundTruth', str(video_num[v_idx]), '{:03d}.png'.format(i)), gt_frames[i][:,:,::-1])
else:
if not os.path.exists(os.path.join(save_dir, model_name, dataset_name, 'results')):
os.makedirs(os.path.join(save_dir, model_name, dataset_name, 'results'))
for i in range(len(gen_frames)):
cv2.imwrite(os.path.join(save_dir, model_name, dataset_name, 'results', '{:03d}.png'.format(i)), gen_frames[i][:,:,::-1])
if not os.path.exists(os.path.join(save_dir, model_name, dataset_name, 'psnr')):
os.makedirs(os.path.join(save_dir, model_name, dataset_name, 'psnr'))
for i in range(len(gen_frames)):
cv2.imwrite(os.path.join(save_dir, model_name, dataset_name, 'psnr', '{:03d}.png'.format(i)), psnr_score_list[i])
if not os.path.exists(os.path.join(save_dir, model_name, dataset_name, 'ssim')):
os.makedirs(os.path.join(save_dir, model_name, dataset_name, 'ssim'))
for i in range(len(gen_frames)):
cv2.imwrite(os.path.join(save_dir, model_name, dataset_name, 'ssim', '{:03d}.png'.format(i)), ssim_score_list[i])
# cv2.imwrite(os.path.join(save_dir, model_name, dataset_name, 'traj.png'), white_paper)
break
print('PSNR_MIN: {}, PSNR_MAX: {}'.format(psnr_min, psnr_max))
print('SSIM_MIN: {}, SSIM_MAX: {}'.format(ssim_min, ssim_max))
# with get_writer(os.path.join(save_dir,'test_{}_{}_{:03}_{}.gif'.format(model_name, dataset_name, model_epoch, int(y_only))), mode="I", fps=7) as writer:
# for n in range(n_frames):
# # out.write(gen_frames[n][:,:,::-1])
# writer.append_data(gen_frames[n])
# with get_writer('test_output_hr.gif', mode="I", fps=10) as writer:
# for n in range(n_frames):
# writer.append_data(hr_frames[n])
# with get_writer('test_output_lr.gif', mode="I", fps=10) as writer:
# for n in range(n_frames):
# writer.append_data(lr_frames[n])
print('PSNR_W: {}, SSIM_W: {}'.format(sum(psnr_whole_list)/len(psnr_whole_list), sum(ssim_whole_list)/len(ssim_whole_list)))
print('PSNR_F: {}, SSIM_F: {}'.format(sum(psnr_fovea_list)/len(psnr_fovea_list), sum(ssim_fovea_list)/len(ssim_fovea_list)))
print('PSNR_P: {}, SSIM_P: {}'.format(sum(psnr_past_list)/len(psnr_past_list), sum(ssim_past_list)/len(ssim_past_list)))
print('PSNR_O: {}, SSIM_O: {}'.format(sum(psnr_outskirt_list)/len(psnr_outskirt_list), sum(ssim_outskirt_list)/len(ssim_outskirt_list)))