def on_epoch_end(self, val_loss=None, logs=None):
# save model
global num_epoch
global dataset_name
global img_rows
global img_cols
global mbllen
num_epoch += 1
modelname = './models/' + str(num_epoch) + '_' + dataset_name + '_base.h5'
mbllen.save_weights(modelname)
# test val data
path = glob('../dataset/test/*.jpg')
number = 0
psnr_ave = 0
for i in range(len(path)):
if i>15:
break
img_B_path = path[i]
img_B = utls.imread_color(img_B_path)
path_mid = os.path.split(img_B_path)
path_A_1 = path_mid[0] + '_' + dataset_name
path_A = os.path.join(path_A_1, path_mid[1])
img_A = utls.imread_color(path_A)
nw = random.randint(0, img_A.shape[0] - img_rows)
nh = random.randint(0, img_A.shape[1] - img_cols)
crop_img_A = img_A[nw:nw + img_rows, nh:nh + img_cols, :]
crop_img_B = img_B[nw:nw + img_rows, nh:nh + img_cols, :]
crop_img_A = crop_img_A[np.newaxis, :]
crop_img_B = crop_img_B[np.newaxis, :]
fake_B = mbllen.predict(crop_img_A)
identy_B = mbllen.predict(crop_img_B)
out_img = np.concatenate([crop_img_A, fake_B, crop_img_B, identy_B], axis=2)
out_img = out_img[0, :, :, :]
fake_B = fake_B[0, :, :, :]
img_B = crop_img_B[0, :, :, :]
clean_psnr = utls.psnr_cau(fake_B, img_B)
L_psnr = ("%.4f" % clean_psnr)
number += 1
psnr_ave += clean_psnr
filename = os.path.basename(path[i])
img_name = './val_images/' + str(num_epoch) + '_' + L_psnr + '_' + filename
utls.imwrite(img_name, out_img)
psnr_ave /= number
print('------------------------------------------------')
print("[Epoch %d] [PSNR_AVE :%f]" % (num_epoch, psnr_ave))
print('------------------------------------------------')
path = glob(input_folder+'/*.*')
model_name = arg.model
mbllen = Network.build_mbllen((None, None, 3))
mbllen.load_weights('../models/'+model_name+'.h5')
opt = keras.optimizers.Adam(lr=2 * 1e-04, beta_1=0.9, beta_2=0.999, epsilon=1e-08)
mbllen.compile(loss='mse', optimizer=opt)
flag = arg.com
lowpercent = arg.lowpercent
highpercent = arg.highpercent
maxrange = arg.maxrange/10.
hsvgamma = arg.gamma/10.
for i in range(len(path)):
img_A_path = path[i]
img_A = utls.imread_color(img_A_path)
img_A = img_A[np.newaxis, :]
starttime = time.clock()
out_pred = mbllen.predict(img_A)
endtime = time.clock()
print('The ' + str(i+1)+'th image\'s Time:' +str(endtime-starttime)+'s.')
fake_B = out_pred[0, :, :, :3]
fake_B_o = fake_B
gray_fake_B = fake_B[:, :, 0] * 0.299 + fake_B[:, :, 1] * 0.587 + fake_B[:, :, 1] * 0.114
percent_max = sum(sum(gray_fake_B >= maxrange))/sum(sum(gray_fake_B <= 1.0))
# print(percent_max)
max_value = np.percentile(gray_fake_B[:], highpercent)
if percent_max < (100-highpercent)/100.:
scale = maxrange / max_value