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## -*- coding: utf-8 -*-
import tensorflow as tf
import numpy as np
import time
import glob
import scipy
import argparse
import os
import subprocess
from random import choice
from PIL import Image
from utils import LoadImage, DownSample, AVG_PSNR, depth_to_space_3D, DynFilter3D, LoadParams
from nets import FR_16L, FR_28L, FR_52L
parser = argparse.ArgumentParser()
parser.add_argument('--L', metavar='L', type=int, default=choice(16, 28, 52), help='Network depth: One of 16, 28, 52')
parser.add_argument('--T', metavar='T', default='L', help='Input type: L(Low-resolution) or G(Ground-truth)')
parser.add_argument('--in_dir', metavar='in_dir', default=None, help='Directory to process')
parser.add_argument('--out_dir', metavar='out_dir', default='/media/blue/uprez', help='Directory to output to')
parser.add_argument('--network_dir', default='.', help='Path to networks')
parser.add_argument('--no_mov', action='store_true')
args = parser.parse_args()
# Size of input temporal radius
T_in = 7
# Upscaling factor
R = 4
# Selecting filters and residual generating network
if args.L == 16:
FR = FR_16L
elif args.L == 28:
FR = FR_28L
elif args.L == 52:
FR = FR_52L
else:
print('Invalid network depth: {} (Must be one of 16, 28, 52)'.format(args.L))
exit(1)
if not(args.T == 'L' or args.T =='G'):
print('Invalid input type: {} (Must be L(Low-resolution) or G(Ground-truth))'.format(args.T))
exit(1)
def process_dir(dir):
dir_partz = dir.split('/')
dataset = dir_partz[-2]
part = dir_partz[-1]
tag = '_'.join([dataset, str(args.L) + 'L', part])
out_path = os.path.join(args.out_dir, 'results', dataset, str(args.L) + 'L', part)
render_path = os.path.join(args.out_dir, 'renders')
os.makedirs(out_path, exist_ok=True)
os.makedirs(render_path, exist_ok=True)
dir_frames = sorted(glob.glob(os.path.join(dir, '*.png')))
print(dir_frames)
frames = []
for f in dir_frames:
frames.append(LoadImage(f))
frames = np.asarray(frames)
frames_padded = np.lib.pad(frames, pad_width=((T_in//2,T_in//2),(0,0),(0,0),(0,0)), mode='constant')
for i in range(frames.shape[0]):
print('Scene {}: Frame {}/{} processing'.format(tag, i+1, frames.shape[0]))
in_L = frames_padded[i:i+T_in] # select T_in frames
in_L = in_L[np.newaxis,:,:,:,:]
out_H = sess.run(GH, feed_dict={L: in_L, is_train: False})
out_H = np.clip(out_H, 0, 1)
Image.fromarray(np.around(out_H[0,0]*255).astype(np.uint8)).save('{}/frame_{:05d}.png'.format(out_path, i+1))
if not args.no_mov:
subprocess.call([
'ffmpeg',
'-hide_banner',
'-i', os.path.join(out_path, 'frame_%05d.png'),
'-y',
'-c:v', 'libx264',
'-preset', 'slow',
'-crf', '19',
'-vf', 'fps=25',
'-pix_fmt', 'yuv420p',
os.path.join(render_path, tag + '.mp4')
])
def G(x, is_train):
# shape of x: [B,T_in,H,W,C]
# Generate filters and residual
# Fx: [B,1,H,W,1*5*5,R*R]
# Rx: [B,1,H,W,3*R*R]
Fx, Rx = FR(x, is_train)
x_c = []
for c in range(3):
t = DynFilter3D(x[:,T_in//2:T_in//2+1,:,:,c], Fx[:,0,:,:,:,:], [1,5,5]) # [B,H,W,R*R]
t = tf.depth_to_space(t, R) # [B,H*R,W*R,1]
x_c += [t]
x = tf.concat(x_c, axis=3) # [B,H*R,W*R,3]
x = tf.expand_dims(x, axis=1)
Rx = depth_to_space_3D(Rx, R) # [B,1,H*R,W*R,3]
x += Rx
return x
# Gaussian kernel for downsampling
def gkern(kernlen=13, nsig=1.6):
import scipy.ndimage.filters as fi
# create nxn zeros
inp = np.zeros((kernlen, kernlen))
# set element at the middle to one, a dirac delta
inp[kernlen//2, kernlen//2] = 1
# gaussian-smooth the dirac, resulting in a gaussian filter mask
return fi.gaussian_filter(inp, nsig)
h = gkern(13, 1.6) # 13 and 1.6 for x4
h = h[:,:,np.newaxis,np.newaxis].astype(np.float32)
# Network
H = tf.placeholder(tf.float32, shape=[None, T_in, None, None, 3])
L_ = DownSample(H, h, R)
L = L_[:,:,2:-2,2:-2,:] # To minimize boundary artifact
is_train = tf.placeholder(tf.bool, shape=[]) # Phase ,scalar
with tf.variable_scope('G') as scope:
GH = G(L, is_train)
params_G = [v for v in tf.global_variables() if v.name.startswith('G/')]
# Session
config = tf.ConfigProto()
config.gpu_options.allow_growth=True
with tf.Session(config=config) as sess:
tf.global_variables_initializer().run()
# Load parameters
LoadParams(sess, [params_G], in_file=os.path.join(args.network_dir, 'params_{}L_x{}.h5'.format(args.L, R)))
if args.T == 'L':
# Test using Low-resolution videos
if args.in_dir:
for dir in sorted(glob.glob(os.path.join(args.in_dir, '*'))):
process_dir(dir)
else:
for dir in sorted(glob.glob('./inputs/L/*')):
process_dir(dir)
elif args.T == 'G':
# Test using GT videos
avg_psnrs = []
dir_inputs = glob.glob('./inputs/G/*')
for v in dir_inputs:
scene_name = v.split('/')[-1]
os.makedirs('./results/{}L/G/{}/'.format(args.L, scene_name), exist_ok=True)
dir_frames = glob.glob(v + '/*.png')
dir_frames.sort()
frames = []
for f in dir_frames:
frames.append(LoadImage(f))
frames = np.asarray(frames)
frames_padded = np.lib.pad(frames, pad_width=((T_in//2,T_in//2),(0,0),(0,0),(0,0)), mode='constant')
frames_padded = np.lib.pad(frames_padded, pad_width=((0,0),(8,8),(8,8),(0,0)), mode='reflect')
out_Hs = []
for i in range(frames.shape[0]):
print('Scene {}: Frame {}/{} processing'.format(scene_name, i+1, frames.shape[0]))
in_H = frames_padded[i:i+T_in] # select T_in frames
in_H = in_H[np.newaxis,:,:,:,:]
out_H = sess.run(GH, feed_dict={H: in_H, is_train: False})
out_H = np.clip(out_H, 0, 1)
Image.fromarray(np.around(out_H[0,0]*255).astype(np.uint8)).save('./results/{}L/G/{}/frame_{:05d}.png'.format(args.L, scene_name, i+1))
out_Hs.append(out_H[0, 0])
out_Hs = np.asarray(out_Hs)
avg_psnr = AVG_PSNR(((frames)*255).astype(np.uint8)/255.0, ((out_Hs)*255).astype(np.uint8)/255.0, vmin=0, vmax=1, t_border=2, sp_border=8)
avg_psnrs.append(avg_psnr)
print('Scene {}: PSNR {}'.format(scene_name, avg_psnr))
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