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pipeline_denoising_withaug_post_square_input.py
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#!/usr/bin/env python
# coding: utf-8
from __future__ import print_function
import matplotlib.pyplot as plt
import os
import numpy as np
from models import *
import torch
import torch.optim
from skimage.measure import compare_psnr
from skimage.measure import compare_ssim
from skimage import color
# from sklearn.metrics import mean_squared_error
# mse = mean_squared_error(A, B)
from utils.denoising_utils import *
from PIL import Image
from tensorboardX import SummaryWriter
import scipy.io
from SomeISP_operator_python.ISP_implement_fuction import ISP
np.random.seed(30)
import pickle
torch.backends.cudnn.enabled = True
torch.backends.cudnn.benchmark =True
dtype = torch.cuda.FloatTensor
def generate_reallike_noise_from_rgb(img_np_rgb, sigma_s='RAN', sigma_c='RAN'):
# follow the pipline, adjust the noise params in ./SomeISP_operator_python/ISP_implement_fuction.py
isp = ISP()
gt, noise, sigma_s_r, sigma_c_r = isp.cbdnet_noise_generate_srgb(img_np_rgb.transpose(1,2,0), sigma_s, sigma_c)
return gt.transpose(2, 0, 1), noise.transpose(2, 0, 1), sigma_s_r, sigma_c_r
def add_PG_noise(img, sigma_s='RAN', sigma_c='RAN'):
min_log = np.log([0.0001])
if sigma_s == 'RAN':
sigma_s = min_log + np.random.rand(1) * (np.log([0.16]) - min_log)
sigma_s = np.exp(sigma_s)
if sigma_c == 'RAN':
sigma_c = min_log + np.random.rand(1) * (np.log([0.06]) - min_log)
sigma_c = np.exp(sigma_c)
# add noise
print('Adding Noise: sigma_s='+str(sigma_s*255)+' sigma_c='+str(sigma_c*255))
noisy_img = img + \
np.random.normal(0.0, 1.0, img.shape) * (sigma_s * img) + \
np.random.normal(0.0, 1.0, img.shape) * sigma_c
# noisy_img = np.clip(noisy_img, 0, 1)
return noisy_img
def create_augmentations(np_image):
"""
convention: original, left, upside-down, right, rot1, rot2, rot3
:param np_image:
:return:
"""
dtype = torch.cuda.FloatTensor
aug = [np_image.copy(), np.rot90(np_image, 1, (1, 2)).copy(),
np.rot90(np_image, 2, (1, 2)).copy(), np.rot90(np_image, 3, (1, 2)).copy()]
flipped = np_image[:,::-1, :].copy()
aug += [flipped.copy(), np.rot90(flipped, 1, (1, 2)).copy(), np.rot90(flipped, 2, (1, 2)).copy(), np.rot90(flipped, 3, (1, 2)).copy()]
aug_torch = [np_to_torch(np_image.copy()).type(dtype), np_to_torch(np.rot90(np_image, 1, (1, 2)).copy()).type(dtype),
np_to_torch(np.rot90(np_image, 2, (1, 2)).copy()).type(dtype), np_to_torch(np.rot90(np_image, 3, (1, 2)).copy()).type(dtype)]
aug_torch += [np_to_torch(flipped.copy()).type(dtype), np_to_torch(np.rot90(flipped, 1, (1, 2)).copy()).type(dtype),
np_to_torch(np.rot90(flipped, 2, (1, 2)).copy()).type(dtype), np_to_torch(np.rot90(flipped, 3, (1, 2)).copy()).type(dtype)]
return aug, aug_torch
def MSE(x, y):
return np.square(x - y).mean()
def save_image(name, image_np, output_path="E:/JunXu/NAC_TPAMI/results/"):
p = np_to_pil(image_np)
p.save(output_path + "{}.png".format(name))
def closure():
global i, net_input, psnr_1_max, psnr_noisy_max, files_name, psnr_2_max, C_TRAIN_PLAN, gt_np, output_dir1
global S_TRAIN_PLAN, current_sigma_s, current_sigma_c, final_ssim, final_ssim_max, files_name, ssim_1_max
global psnr_2, psnr_1, ssim_1, gt_aug_np, gt_aug_torch
out_effect_np = []
for aug in range(len(img_noisy_torch)):
out = net(net_input[aug])
total_loss = mse(out, img_noisy_torch[aug])
# total_loss = mse(out, img_noisy_torch[aug])+ gd_loss(out, img_noisy_torch[aug])
total_loss.backward()
# psrn_noisy = compare_psnr(img_np, out.detach().cpu().numpy()[0])
psrn_noisy = compare_psnr(np.clip(img_noisy_np[aug], 0, 1), out.detach().cpu().numpy()[0])
if psnr_noisy_max == 0:
psnr_noisy_max = psrn_noisy
elif psnr_noisy_max < psrn_noisy:
psnr_noisy_max = psrn_noisy
if SAVE_DURING_TRAINING and i % save_every == 0:
# output_dir
# out_test_np = torch_to_np(out) # I +N1
# out_test_name = f'{i}_test'
# save_image(out_test_name, np.clip(out_test_np, 0, 1), output_path=output_dir)
net.eval()
with torch.no_grad():
out_effect_np_ = torch_to_np(net(img_noisy_torch[aug]))
out_effect_np.append(out_effect_np_)
psnr_1 = compare_psnr(gt_aug_np[aug], np.clip(out_effect_np_, 0, 1))
ssim_1 = compare_ssim(gt_aug_np[aug].transpose(1, 2, 0), np.clip(out_effect_np_.transpose(1, 2, 0), 0, 1), multichannel=True)
# out_effect_name = f'{i}_effect_{psnr_1:.2f}'
# save_image(out_effect_name, np.clip(out_effect_np_, 0,1), output_path=output_dir)
writer.add_scalar('scalar_noisy_psnr', psrn_noisy, i)
writer.add_scalar('scalar_test_psnr', psnr_1, i)
if psnr_1_max == 0:
psnr_1_max = psnr_1
elif psnr_1_max < psnr_1:
psnr_1_max = psnr_1
tmp_name_p = f'{files_name[:-4]}_{psnr_1:.2f}_test_{ssim_1:.4f}'
save_image(tmp_name_p, np.clip(out_effect_np_, 0, 1), output_path=output_dir1)
if ssim_1_max == 0:
ssim_1_max = ssim_1
elif ssim_1_max<ssim_1:
ssim_1_max = ssim_1
tmp_name_p = f'{files_name[:-4]}_{ssim_1:.4f}_test_{psnr_1:.2f}'
save_image(tmp_name_p, np.clip(out_effect_np_, 0, 1), output_path=output_dir1)
print('%s Iteration %05d lr: %f, Loss %f , PSNR_noisy: %f, PSNR_noisy_max: %f, '
'test psnr: %f , test max psnr: %f , current sigma s: %f, current sigma c: %f ' %
(files_name, i, LR, total_loss.item(), psrn_noisy, psnr_noisy_max,
psnr_1, psnr_1_max, current_sigma_s*255, current_sigma_c*255))
if i % 10 == 0:
out_effect_np[0] = out_effect_np[0].transpose(1, 2, 0)
for aug in range(1, 8):
if aug < 4:
out_effect_np[aug] = np.rot90(out_effect_np[aug].transpose(1, 2, 0), 4-aug)
else:
out_effect_np[aug] = np.flipud(np.rot90(out_effect_np[aug].transpose(1, 2, 0), 8-aug))
# final_reuslt = np.median(out_effect_np, 0)
final_reuslt = np.mean(out_effect_np, 0)
psnr_2 = compare_psnr(gt_np.transpose(1, 2, 0), np.clip(final_reuslt, 0, 1))
final_ssim = compare_ssim(gt_np.transpose(1, 2, 0), np.clip(final_reuslt, 0, 1), multichannel=True)
# out_test_name = f'{i}_test'
# save_image(out_test_name, np.clip(final_reuslt, 0, 1), output_path=output_dir)
if psnr_2_max==0:
psnr_2_max = psnr_2
elif psnr_2_max< psnr_2:
psnr_2_max = psnr_2
tmp_name_p = f'{files_name[:-4]}_{psnr_2:.2f}_final_{final_ssim:.4f}'
save_image(tmp_name_p, np.clip(final_reuslt.transpose(2, 0, 1), 0, 1), output_path=output_dir)
if final_ssim_max==0:
final_ssim_max = final_ssim
elif final_ssim_max<final_ssim:
final_ssim_max = final_ssim
tmp_name = f'{files_name[:-4]}_{final_ssim:.4f}_final_{psnr_2:.2f}'
save_image(tmp_name, np.clip(final_reuslt.transpose(2, 0, 1), 0, 1), output_path=output_dir)
print('psnr 2: %f, psnr 2 max: %f, final ssim : %f, final ssim max: %f'
%(psnr_2, psnr_2_max, final_ssim, final_ssim_max))
writer.add_scalar('final_test_psnr', psnr_2, i)
writer.add_scalar('final_max_test_psnr', psnr_2_max, i)
i += 1
return total_loss
imsize = -1
SAVE_DURING_TRAINING = True
save_every = 1
sigma = 25
sigma_ = sigma/255.
sigma1 = 25/255.
sigma2 = 25/255.
S_TRAIN_PLAN = [1, 2, 3, 4, 5]
C_TRAIN_PLAN = [0]
## denoising
# img_root = 'data/denoising/BM3D_images'
img_root = 'data/denoising/cc_real/'
gt_root = 'data/denoising/cc_mean/'
files = os.listdir(img_root)
psnr_record_img_name = []
psnr_record_max_test_psnr = []
ssim_record_max_test_psnr = []
psnr_2_max_record = []
final_ssim_max_record = []
current_sigma_s_record = []
current_sigma_c_record = []
output_dir = 'E:/JunXu/NAC_TPAMI/results/multi_real_noise_estimate/'
output_dir1 = 'E:/JunXu/NAC_TPAMI/results/multi_real_noise_estimate1/'
os.makedirs(output_dir, exist_ok=True)
os.makedirs(output_dir1, exist_ok=True)
DATA_AUG = True
for index in range(0, len(files)):
files_name = str(files[index])
if files_name.endswith('.png'):
fname = os.path.join(img_root, files_name)
gt_dir = os.path.join(gt_root, files_name[:-8]+'mean.png')
writer = SummaryWriter()
img_pil = Image.open(fname)
gt_pil = Image.open(gt_dir)
img_np = pil_to_np(img_pil)
gt_np = pil_to_np(gt_pil)
# noisy_np_norm = np.random.normal(scale=1 / 255., size=img_np.shape)
input_depth = img_np.shape[0]
pad = 'replication' # ['zero', 'replication', 'none']
OPT_OVER = 'net' # 'net,input'
LR = 0.001
OPTIMIZER = 'myadam' # 'LBFGS'
show_every = 1
exp_weight = 0.99
num_iter_plan = 1001
figsize = 4
sigma_c_record = []
sigma_s_record = []
for sigma_s_idx, current_sigma_s in enumerate(S_TRAIN_PLAN):
for sigma_c_idx, current_sigma_c in enumerate(C_TRAIN_PLAN):
num_iter = num_iter_plan
psnr_2_max = 0
psnr_noisy_max = 0
ssim_1_max = 0
psnr_1_max = 0
final_ssim_max = 0
if DATA_AUG:
gt_aug_np, gt_aug_torch = create_augmentations(gt_np)
img_aug_np, img_aug_torch = create_augmentations(img_np)
img_noisy_np = img_aug_np
img_noisy_torch = img_aug_torch
img_noisy_noisy_np = []
img_noisy_noisy_torch = []
for idx in range(len(img_aug_np)):
img_noisy_gt, img_noisy_noisy_np_, sigma_s_r, sigma_c_r = generate_reallike_noise_from_rgb(img_aug_np[idx])
sigma_c_record.append(sigma_c_r)
sigma_s_record.append(sigma_s_r)
img_noisy_noisy_torch_ = np_to_torch(img_noisy_noisy_np_).type(dtype)
img_noisy_noisy_np.append(img_noisy_noisy_np_)
img_noisy_noisy_torch.append(img_noisy_noisy_torch_)
# del net
net = ResNet(input_depth, img_np.shape[0], 10, 64, 1).type(dtype)
# Compute number of parameters
s = sum([np.prod(list(p.size())) for p in net.parameters()]);
print('Number of params: %d' % s)
# Loss
mse = torch.nn.MSELoss().type(dtype)
# Optimize
net_input = img_noisy_noisy_torch
i = 0
p = get_params(OPT_OVER, net, net_input)
optimize(OPTIMIZER, p, closure, LR, num_iter)
psnr_record_img_name.append(files_name)
psnr_record_max_test_psnr.append(psnr_1_max)
ssim_record_max_test_psnr.append(ssim_1_max)
psnr_2_max_record.append(psnr_2_max)
final_ssim_max_record.append(final_ssim_max)
current_sigma_s_record.extend(sigma_s_record*255)
current_sigma_c_record.extend(sigma_c_record*255)
with open('resnet_realnoise_l2.pickle','wb') as f:
resnet_aug = {'psnr_record_img_name': psnr_record_img_name,
'psnr_record_max_test_psnr': psnr_record_max_test_psnr,
'ssim_record_max_test_psnr': ssim_record_max_test_psnr,
'psnr_2_max_record': psnr_2_max_record,
'final_ssim_max_record':final_ssim_max_record,
'current_sigma_s_record':current_sigma_s_record,
'current_sigma_c_record':current_sigma_c_record}
pickle.dump(resnet_aug, f)
print('I am finish training now!')
# psnr_record_max_test_psnr.append(psnr_max)
# out_np = torch_to_np(net(net_input))
# q = plot_image_grid([np.clip(out_np, 0, 1), img_np], factor=13)