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pnp_sci_video_orig_simuexp_test.py
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pnp_sci_video_orig_simuexp_test.py
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# %% [markdown]
# ## PnP-TV-FastDVDNet for simulated data experiment
# ### Code credit
# [Xin Yuan](https://www.bell-labs.com/usr/x.yuan "Dr. Xin Yuan, Bell Labs"), [Bell Labs](https://www.bell-labs.com/), xyuan@bell-labs.com, created Aug 7, 2018.
# [Yang Liu](https://liuyang12.github.io "Yang Liu, MIT"), [MIT](https://www.mit.edu/), yliu12@mit.edu, updated Jan 20, 2019.
# [Zhihong Zhang](https://zhihongz.github.io/ "Zhihong Zhang, Tsinghua University"), [Tsinghua University](http://www.tsinghua.edu.cn/publish/thu2018en/index.html), zhangzh19@mails.tsinghua.edu.cn, updated Mar 19, 2021.
# %%
import os
import time
import math
import h5py
import numpy as np
import scipy.io as sio
from statistics import mean
from dvp_linear_inv import admmdenoise_cacti
from joint_dvp_linear_inv import joint_admmdenoise_cacti
from utils import (A_, At_, show_n_save_res)
import matplotlib.pyplot as plt
from scipy.io.matlab.mio import _open_file
from scipy.io.matlab.miobase import get_matfile_version
# import deep models
import torch
from packages.ffdnet.models import FFDNet
from packages.fastdvdnet.models import FastDVDnet
# %%
# [0] environment configuration
## [0.1] path and data name
orig_dir = './dataset/simu_data/orig'
mask_dir = './dataset/simu_data/mask' # mask dataset
resultsdir = './results' # results dir
orig_name = 'football_256'
mask_name = 'shift_binary_mask_256_10f' # name of 'mask'
origpath = orig_dir + '/' + orig_name + '.mat' # path of the .mat orig file
maskpath = mask_dir + '/' + mask_name + '.mat' # path of the .mat mask file
## [0.2] flags and params
show_res_flag = 1 # show results
save_res_flag = 1 # save results
# choose algorithms:
# 'all', 'gaptv', 'gapffdnet', 'gaptv+fastdvdnet'
# test_algo_flag = ['all']
test_algo_flag = ['gaptv+fastdvdnet']
# noise
gaussian_noise_level = 5
poisson_noise = False
# %%
# [1] load data
if get_matfile_version(_open_file(maskpath, appendmat=True)[0])[0] < 2: # MATLAB .mat v7.2 or lower versions
origfile = sio.loadmat(origpath) # for '-v7.2' and below .mat file (MATLAB)
maskfile = sio.loadmat(maskpath)
orig = np.array(origfile['orig'])
mask = np.array(maskfile['mask'])
mask = np.float32(mask)
orig = np.float32(orig)
else: # MATLAB .mat v7.3
with h5py.File(origpath, 'r') as origfile: # for '-v7.3' .mat file (MATLAB)
orig = np.array(origfile['orig'])
orig = np.float32(orig).transpose((2,1,0))
with h5py.File(maskpath, 'r') as maskfile: # for '-v7.3' .mat file (MATLAB)
# print(list(file.keys()))
mask = np.array(maskfile['mask'])
mask = np.float32(mask).transpose((2,1,0))
# calc meas
nmask = mask.shape[2]
norig = orig.shape[2]
meas = np.zeros([orig.shape[0], orig.shape[1], norig//nmask])
for i in range(norig//nmask):
tmp_orig = orig[:,:,i*nmask:(i+1)*nmask]
meas[:,:,i] = np.sum(tmp_orig*mask, 2)
# zzh: expand dim for a single 'meas'
if meas.ndim<3:
meas = np.expand_dims(meas,2)
# print(meas.shape)
# print('meas, mask, orig:', meas.shape, mask.shape, orig.shape)
# add nosie
# print('* before add noise: orig {} mask {} meas {}'.format(np.mean(orig), np.mean(mask), np.mean(meas)))
gaussian_noise = np.random.randn(meas.shape[0], meas.shape[1], meas.shape[2])*gaussian_noise_level
meas = meas + gaussian_noise
if poisson_noise:
meas = np.random.poisson(meas)
# normalize data
mask_max = np.max(mask)
mask = mask/mask_max
meas = meas/mask_max
iframe = 0
nframe = 1
MAXB = 255.
# common parameters and pre-calculation for PnP
# define forward model and its transpose
A = lambda x : A_(x, mask) # forward model function handle
At = lambda y : At_(y, mask) # transpose of forward model
# %%
## [2.1] GAP-TV
if ('all' in test_algo_flag) or ('gaptv' in test_algo_flag):
projmeth = 'gap' # projection method
_lambda = 1 # regularization factor, [original set]
accelerate = True # enable accelerated version of GAP
denoiser = 'tv' # total variation (TV)
iter_max = 100 # maximum number of iterations
tv_weight = 0.25 # TV denoising weight (larger for smoother but slower) [kobe:0.25; ]
tv_iter_max = 5 # TV denoising maximum number of iterations each
vgaptv,tgaptv,psnr_gaptv,ssim_gaptv,psnrall_gaptv = admmdenoise_cacti(meas, mask, A, At,
projmeth=projmeth, v0=None, orig=orig,
iframe=iframe, nframe=nframe,
MAXB=MAXB, maskdirection='plain',
_lambda=_lambda, accelerate=accelerate,
denoiser=denoiser, iter_max=iter_max,
tv_weight=tv_weight,
tv_iter_max=tv_iter_max)
print('-'*20+'\n{}-{} PSNR {:2.3f} dB, SSIM {:.4f}, running time {:.1f} seconds.\n'.format(
projmeth.upper(), denoiser.upper(), mean(psnr_gaptv), mean(ssim_gaptv), tgaptv)+'-'*20)
show_n_save_res(vgaptv,tgaptv,psnr_gaptv,ssim_gaptv,psnrall_gaptv, orig, nmask, resultsdir,
projmeth+denoiser+'_'+orig_name, iframe=iframe,nframe=nframe, MAXB=MAXB,
show_res_flag=show_res_flag, save_res_flag=save_res_flag,
tv_weight=tv_weight, iter_max = iter_max)
# %%
## [2.2] GAP-FFDNet (FFDNet-based frame-wise video denoising)
if ('all' in test_algo_flag) or ('gapffdnet' in test_algo_flag):
projmeth = 'gap' # projection method
_lambda = 1 # regularization factor, [original set]
# _lambda = 1.5
accelerate = True # enable accelerated version of GAP
denoiser = 'ffdnet' # video non-local network
noise_estimate = False # disable noise estimation for GAP
sigma = [50/255, 25/255, 12/255, 6/255] # pre-set noise standard deviation
iter_max = [10, 10, 10, 10] # maximum number of iterations
# sigma = [12/255, 6/255] # pre-set noise standard deviation
# iter_max = [10,10] # maximum number of iterations
useGPU = True # use GPU
# pre-load the model for FFDNet image denoising
in_ch = 1
model_fn = 'packages/ffdnet/models/net_gray.pth'
# Absolute path to model file
# model_fn = os.path.join(os.path.abspath(os.path.dirname(__file__)), model_fn)
# Create model
net = FFDNet(num_input_channels=in_ch)
# Load saved weights
if useGPU:
state_dict = torch.load(model_fn)
device_ids = [0]
model = torch.nn.DataParallel(net, device_ids=device_ids).cuda()
else:
state_dict = torch.load(model_fn, map_location='cpu')
# CPU mode: remove the DataParallel wrapper
state_dict = remove_dataparallel_wrapper(state_dict)
model = net
model.load_state_dict(state_dict)
model.eval() # evaluation mode
vgapffdnet,tgapffdnet,psnr_gapffdnet,ssim_gapffdnet,psnrall_gapffdnet = admmdenoise_cacti(meas, mask, A, At,
projmeth=projmeth, v0=None, orig=orig,
iframe=iframe, nframe=nframe,
MAXB=MAXB, maskdirection='plain',
_lambda=_lambda, accelerate=accelerate,
denoiser=denoiser, model=model,
iter_max=iter_max, sigma=sigma)
print('-'*20+'\n{}-{} PSNR {:2.3f} dB, SSIM {:.4f}, running time {:.1f} seconds.\n'.format(
projmeth.upper(), denoiser.upper(), mean(psnr_gapffdnet), mean(ssim_gapffdnet), tgapffdnet)+'-'*20)
show_n_save_res(vgapffdnet,tgapffdnet,psnr_gapffdnet,ssim_gapffdnet,psnrall_gapffdnet, orig, nmask, resultsdir,
projmeth+denoiser+'_'+orig_name, iframe=iframe,nframe=nframe, MAXB=MAXB,
show_res_flag=show_res_flag, save_res_flag=save_res_flag,
iter_max = iter_max, sigma=sigma)
# %%
## [2.3] GAP-TV+FASTDVDNET
import torch
from packages.fastdvdnet.models import FastDVDnet
if ('all' in test_algo_flag) or ('gaptv+fastdvdnet' in test_algo_flag):
projmeth = 'gap' # projection method
_lambda = 1 # regularization factor, [original set]
accelerate = True # enable accelerated version of GAP
denoiser = 'tv+fastdvdnet' # video non-local network
noise_estimate = False # disable noise estimation for GAP
sigma1 = [0] # pre-set noise standard deviation for 1st period denoise
iter_max1 = 10 # maximum number of iterations for 1st period denoise
sigma2 = [100/255, 50/255, 25/255] # pre-set noise standard deviation for 2nd period denoise
iter_max2 = [60, 100, 150] # maximum number of iterations for 2nd period denoise
# sigma2 = [50/255, 25/255] # pre-set noise standard deviation for 2nd period denoise
# iter_max2 = [20, 20] # maximum number of iterations for 2nd period denoise
tv_iter_max = 5 # TV denoising maximum number of iterations each
tv_weight = 0.5 # TV denoising weight (larger for smoother but slower) [kobe:0.25]
tvm = 'tv_chambolle'
# sigma = [12/255] # pre-set noise standard deviation
# iter_max = [20] # maximum number of iterations
useGPU = True # use GPU
# pre-load the model for fastdvdnet image denoising
NUM_IN_FR_EXT = 5 # temporal size of patch
model = FastDVDnet(num_input_frames=NUM_IN_FR_EXT,num_color_channels=1)
# Load saved weights
state_temp_dict = torch.load('./packages/fastdvdnet/model_gray.pth')
if useGPU:
device_ids = [0]
# model = torch.nn.DataParallel(model, device_ids=device_ids).cuda()
model = model.cuda()
# else:
# # CPU mode: remove the DataParallel wrapper
# state_temp_dict = remove_dataparallel_wrapper(state_temp_dict)
model.load_state_dict(state_temp_dict)
# Sets the model in evaluation mode (e.g. it removes BN)
model.eval()
vgaptvfastdvdnet,tgaptvfastdvdnet,psnr_gaptvfastdvdnet,ssim_gaptvfastdvdnet,psnrall_gaptvfastdvdnet = joint_admmdenoise_cacti(meas, mask, A, At,
projmeth=projmeth, v0=None, orig=orig,
iframe=iframe, nframe=nframe,
MAXB=MAXB, maskdirection='plain',
_lambda=_lambda, accelerate=accelerate,
denoiser=denoiser, iter_max1=iter_max1, iter_max2=iter_max2,
tv_weight=tv_weight, tv_iter_max=tv_iter_max,
model=model, sigma1=sigma1, sigma2=sigma2, tvm=tvm)
print('-'*20+'\n{}-{} PSNR {:2.3f} dB, SSIM {:.4f}, running time {:.1f} seconds.\n'.format(
projmeth.upper(), denoiser.upper(), mean(psnr_gaptvfastdvdnet), mean(ssim_gaptvfastdvdnet), tgaptvfastdvdnet)+'-'*20)
show_n_save_res(vgaptvfastdvdnet,tgaptvfastdvdnet,psnr_gaptvfastdvdnet,ssim_gaptvfastdvdnet,psnrall_gaptvfastdvdnet, orig, nmask, resultsdir,
projmeth+denoiser+'_'+orig_name, iframe=iframe,nframe=nframe, MAXB=MAXB,
show_res_flag=show_res_flag, save_res_flag=save_res_flag,
tv_weight=tv_weight, iter_max1=iter_max1, iter_max2=iter_max2, sigma1=sigma1, sigma2=sigma2)
# %%
# [4] show res
# if show_res_flag:
# plt.show()