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main.py
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# -*- coding: utf-8 -*-
import argparse
import logging
import os, time, glob
import PIL.Image as Image
import numpy as np
import pandas as pd
#from keras import backend as K
#import tensorflow as tf
from keras.callbacks import CSVLogger, ModelCheckpoint, LearningRateScheduler
from keras.models import load_model
from keras.optimizers import Adam
from skimage.measure import compare_psnr, compare_ssim
import models
#from util import *
## Params
parser = argparse.ArgumentParser()
parser.add_argument('--model', default='DnCNN', type=str, help='choose a type of model')
parser.add_argument('--batch_size', default=128, type=int, help='batch size')
parser.add_argument('--train_data', default='./data/npy_data/clean_patches.npy', type=str, help='path of train data')
parser.add_argument('--test_dir', default='./data/Test/Set68', type=str, help='directory of test dataset')
parser.add_argument('--sigma', default=25, type=int, help='noise level')
parser.add_argument('--epoch', default=50, type=int, help='number of train epoches')
parser.add_argument('--lr', default=1e-3, type=float, help='initial learning rate for Adam')
parser.add_argument('--save_every', default=5, type=int, help='save model at every x epoches')
parser.add_argument('--pretrain', default=None, type=str, help='path of pre-trained model')
parser.add_argument('--only_test', default=False, type=bool, help='train and test or only test')
args = parser.parse_args()
if not args.only_test:
save_dir = './snapshot/save_'+ args.model + '_' + 'sigma' + str(args.sigma) + '_' + time.strftime("%Y-%m-%d-%H-%M-%S", time.localtime()) + '/'
if not os.path.exists(save_dir):
os.mkdir(save_dir)
# log
logging.basicConfig(level=logging.INFO,format='%(asctime)s %(filename)s[line:%(lineno)d] %(levelname)s %(message)s',
datefmt='%Y %H:%M:%S',
filename=save_dir+'info.log',
filemode='w')
console = logging.StreamHandler()
console.setLevel(logging.INFO)
formatter = logging.Formatter('%(name)-6s: %(levelname)-6s %(message)s')
console.setFormatter(formatter)
logging.getLogger('').addHandler(console)
logging.info(args)
else:
save_dir = '/'.join(args.pretrain.split('/')[:-1]) + '/'
def load_train_data():
logging.info('loading train data...')
data = np.load(args.train_data)
logging.info('Size of train data: ({}, {}, {})'.format(data.shape[0],data.shape[1],data.shape[2]))
return data
def step_decay(epoch):
initial_lr = args.lr
if epoch<50:
lr = initial_lr
else:
lr = initial_lr/10
return lr
def train_datagen(y_, batch_size=8):
# y_ is the tensor of clean patches
indices = list(range(y_.shape[0]))
while(True):
np.random.shuffle(indices) # shuffle
for i in range(0, len(indices), batch_size):
ge_batch_y = y_[indices[i:i+batch_size]]
noise = np.random.normal(0, args.sigma/255.0, ge_batch_y.shape) # noise
#noise = K.random_normal(ge_batch_y.shape, mean=0, stddev=args.sigma/255.0)
ge_batch_x = ge_batch_y + noise # input image = clean image + noise
yield ge_batch_x, ge_batch_y
def train():
data = load_train_data()
data = data.reshape((data.shape[0],data.shape[1],data.shape[2],1))
data = data.astype('float32')/255.0
# model selection
if args.pretrain: model = load_model(args.pretrain, compile=False)
else:
if args.model == 'DnCNN': model = models.DnCNN()
# compile the model
model.compile(optimizer=Adam(), loss=['mse'])
# use call back functions
ckpt = ModelCheckpoint(save_dir+'/model_{epoch:02d}.h5', monitor='val_loss',
verbose=0, period=args.save_every)
csv_logger = CSVLogger(save_dir+'/log.csv', append=True, separator=',')
lr = LearningRateScheduler(step_decay)
# train
history = model.fit_generator(train_datagen(data, batch_size=args.batch_size),
steps_per_epoch=len(data)//args.batch_size, epochs=args.epoch, verbose=1,
callbacks=[ckpt, csv_logger, lr])
return model
def test(model):
print('Start to test on {}'.format(args.test_dir))
out_dir = save_dir + args.test_dir.split('/')[-1] + '/'
if not os.path.exists(out_dir):
os.mkdir(out_dir)
name = []
psnr = []
ssim = []
file_list = glob.glob('{}/*.png'.format(args.test_dir))
for file in file_list:
# read image
img_clean = np.array(Image.open(file), dtype='float32') / 255.0
img_test = img_clean + np.random.normal(0, args.sigma/255.0, img_clean.shape)
img_test = img_test.astype('float32')
# predict
x_test = img_test.reshape(1, img_test.shape[0], img_test.shape[1], 1)
y_predict = model.predict(x_test)
# calculate numeric metrics
img_out = y_predict.reshape(img_clean.shape)
img_out = np.clip(img_out, 0, 1)
psnr_noise, psnr_denoised = compare_psnr(img_clean, img_test), compare_psnr(img_clean, img_out)
ssim_noise, ssim_denoised = compare_ssim(img_clean, img_test), compare_ssim(img_clean, img_out)
psnr.append(psnr_denoised)
ssim.append(ssim_denoised)
# save images
filename = file.split('/')[-1].split('.')[0] # get the name of image file
name.append(filename)
img_test = Image.fromarray((img_test*255).astype('uint8'))
img_test.save(out_dir+filename+'_sigma'+'{}_psnr{:.2f}.png'.format(args.sigma, psnr_noise))
img_out = Image.fromarray((img_out*255).astype('uint8'))
img_out.save(out_dir+filename+'_psnr{:.2f}.png'.format(psnr_denoised))
psnr_avg = sum(psnr)/len(psnr)
ssim_avg = sum(ssim)/len(ssim)
name.append('Average')
psnr.append(psnr_avg)
ssim.append(ssim_avg)
print('Average PSNR = {0:.2f}, SSIM = {1:.2f}'.format(psnr_avg, ssim_avg))
pd.DataFrame({'name':np.array(name), 'psnr':np.array(psnr), 'ssim':np.array(ssim)}).to_csv(out_dir+'/metrics.csv', index=True)
if __name__ == '__main__':
if args.only_test:
model = load_model(args.pretrain, compile=False)
test(model)
else:
model = train()
test(model)