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main_eval.py
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main_eval.py
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#! /usr/bin/python
# -*- coding: utf8 -*-
import matplotlib.pyplot as plt
import os, time, pickle, random
import numpy as np
import cv2
import tensorflow as tf
import tensorlayer as tl
from model import *
from utils import *
from config import config
def modcrop(imgs, modulo):
tmpsz = imgs.shape
sz = tmpsz[0:2]
h = sz[0] - sz[0]%modulo
w = sz[1] - sz[1]%modulo
imgs = imgs[0:h, 0:w,:]
return imgs
def read_all_imgs(img_list, path='', n_threads=32):
""" Returns all images in array by given path and name of each image file. """
imgs = []
for idx in range(0, len(img_list), n_threads):
b_imgs_list = img_list[idx : idx + n_threads]
b_imgs = tl.prepro.threading_data(b_imgs_list, fn=get_imgs_fn, path=path)
# print(b_imgs.shape)
imgs.extend(b_imgs)
print('read %d from %s' % (len(imgs), path))
return imgs
def scale_image(image,do_scale):
if(do_scale):
return np.maximum(0,np.minimum((image+1)*(255./2.),255))
# return np.maximum(0,np.minimum((image+128.),255))
else:
return np.maximum(0,np.minimum(image,255))
def evaluate():
## create folders to save result images
# save_dir = 'Evaluation7_up_new1_2'
save_dir = 'result/'
tl.files.exists_or_mkdir(save_dir)
# checkpoint_dir = 'checkpoint7_up_new1_2'
checkpoint_dir = 'checkpoint'
im_path_lr = 'input/'
###====================== PRE-LOAD DATA ===========================###
valid_img_list = sorted(tl.files.load_file_list(path=im_path_lr, regx='.*.png', printable=False))
sess = tf.Session(config=tf.ConfigProto(log_device_placement=False))
tl.layers.initialize_global_variables(sess)
t_image = tf.placeholder('float32', [1, 128, 128, 3], name='input_image') # set an arbitrary size of tensor for initialization
net_g_init= EnhanceNet(t_image, is_train=False, reuse=False, hrg=128, wrg=128)
###========================== RESTORE G =============================###
tl.files.load_and_assign_npz(sess=sess, name=checkpoint_dir+'/model.npz', network=net_g_init)
for imid in range(len(valid_img_list)):
valid_img = modcrop(get_imgs_fn(valid_img_list[imid],im_path_lr),4)
print(valid_img.shape)
t_image = tf.placeholder('float32', [1, valid_img.shape[0], valid_img.shape[1], 3], name='input_image')
net_g = EnhanceNet(t_image, is_train=False, reuse=True, hrg=valid_img.shape[0], wrg=valid_img.shape[1])
in_ori = valid_img
valid_img = (valid_img / 127.5) - 1 # rescale to [-1, 1]
###======================= EVALUATION =============================###
start_time = time.time()
out= sess.run(net_g.outputs, {t_image: [valid_img]})
print("took: %4.4fs" % (time.time() - start_time))
print("[*] save images")
scipy.misc.toimage((out[0]+1)/2.*255.,cmin=0,cmax=255).save(save_dir+'/' + valid_img_list[imid][0:-4] +'.png')
# tl.vis.save_image(out[0], save_dir+'/' + valid_lr_img_list[imid][0:-4] +'.png')
if __name__ == '__main__':
evaluate()