def test_only(args, model, sess, saver):
saver.restore(sess, args.pre_trained_model)
graph = sess.graph
print("saved model is loaded for test only!")
print("model path is %s" % args.pre_trained_model)
inp_img_name = sorted(os.listdir(args.test_inp_path))
input_imgs = util.image_loader_new(args.test_inp_path,
args.test_focus_path, args.load_X,
args.load_Y)
for i, ele in enumerate(input_imgs):
inp = np.expand_dims(ele, axis=0)
if args.chop_forward:
output = util.recursive_forwarding(inp, args.chop_size, sess,
model, args.chop_shave)
output = Image.fromarray(output[0])
else:
output = sess.run(model.output, feed_dict={model.inp: inp})
output = Image.fromarray(output[0])
split_name = inp_img_name[i].split('.')
output.save(
os.path.join(
args.result_path,
'%s_radiance.png' % (''.join(map(str, split_name[:-1])))))
def test_only(args, model, sess, saver):
saver.restore(sess,args.pre_trained_model)
print("saved model is loaded for test only!")
print("model path is %s"%args.pre_trained_model)
blur_img_name = sorted(os.listdir(args.test_Blur_path))
if args.in_memory :
blur_imgs = util.image_loader(args.test_Blur_path, args.load_X, args.load_Y, is_train = False)
for i, ele in enumerate(blur_imgs):
blur = np.expand_dims(ele, axis = 0)
if args.chop_forward:
output = util.recursive_forwarding(blur, args.chop_size, sess, model, args.chop_shave)
output = Image.fromarray(output[0])
else:
output = sess.run(model.output, feed_dict = {model.blur : blur})
output = Image.fromarray(output[0])
split_name = blur_img_name[i].split('.')
output.save(os.path.join(args.result_path, '%s_sharp.png'%(''.join(map(str, split_name[:-1])))))
else:
sess.run(model.data_loader.init_op['te_init'])
for i in range(len(blur_img_name)):
output = sess.run(model.output)
output = Image.fromarray(output[0])
split_name = blur_img_name[i].split('.')
output.save(os.path.join(args.result_path, '%s_sharp.png'%(''.join(map(str, split_name[:-1])))))
def test_only(args, model, sess):
loader = tf.train.Saver(max_to_keep=None)
loader.restore(sess, args.pre_trained_model)
print("saved model is loaded for test only!")
print("model path is %s" % args.pre_trained_model)
val_LR = sorted(os.listdir(args.test_LR_path))
val_LR_imgs = util.image_loader(args.test_LR_path)
if args.in_memory:
for i, img_LR in enumerate(val_LR_imgs):
batch_img_LR = np.expand_dims(img_LR, axis=0)
if args.self_ensemble:
output = util.self_ensemble(args,
model,
sess,
batch_img_LR,
is_recursive=args.chop_forward)
else:
if args.chop_forward:
output = util.recursive_forwarding(batch_img_LR,
args.scale,
args.chop_size, sess,
model, args.chop_shave)
output = output[0]
else:
output = sess.run(model.output,
feed_dict={model.LR: batch_img_LR})
output = output[0]
im = Image.fromarray(output)
split_name = val_LR[i].split(".")
im.save(
os.path.join(
args.result_path,
"%sX%d.%s" % (''.join(map(
str, split_name[:-1])), args.scale, split_name[-1])))
else:
sess.run(model.data_loader.init_op['val_init'])
for i in range(len(val_LR)):
output = sess.run([model.output])
output = output[0]
im = Image.fromarray(output)
split_name = val_LR[i].split(".")
im.save(
os.path.join(
args.result_path,
"%sX%d.%s" % (''.join(map(
str, split_name[:-1])), args.scale, split_name[-1])))
def test(args, model, sess):
loader = tf.train.Saver(max_to_keep=None)
loader.restore(sess, args.pre_trained_model)
print("saved model is loaded for test!")
print("model path is %s" % args.pre_trained_model)
val_LR = sorted(os.listdir(args.test_LR_path))
val_HR = sorted(os.listdir(args.test_GT_path))
val_LR_imgs = util.image_loader(args.test_LR_path)
val_GT_imgs = util.image_loader(args.test_GT_path)
Y_PSNR_list = []
Y_SSIM_list = []
file = open('./ARDN_X%d_%s_result.txt' % (args.scale, args.test_set), 'w')
if args.in_memory:
for i, img_LR in enumerate(val_LR_imgs):
batch_img_LR = np.expand_dims(img_LR, axis=0)
img_HR = val_GT_imgs[i]
if args.self_ensemble:
output = util.self_ensemble(args,
model,
sess,
batch_img_LR,
is_recursive=args.chop_forward)
else:
if args.chop_forward:
output = util.recursive_forwarding(batch_img_LR,
args.scale,
args.chop_size, sess,
model, args.chop_shave)
output = output[0]
else:
output = sess.run(model.output,
feed_dict={model.LR: batch_img_LR})
output = output[0]
h, w, c = output.shape
val_gt = img_HR[:h, :w]
y_psnr, y_ssim = util.compare_measure(val_gt, output, args)
Y_PSNR_list.append(y_psnr)
Y_SSIM_list.append(y_ssim)
file.write('file name : %s PSNR : %04f SSIM : %04f \n' %
(val_LR[i], y_psnr, y_ssim))
if args.save_test_result:
im = Image.fromarray(output)
split_name = val_LR[i].split(".")
im.save(
os.path.join(
args.result_path,
"%sX%d.%s" % (''.join(map(str, split_name[:-1])),
args.scale, split_name[-1])))
else:
sess.run(model.data_loader.init_op['val_init'])
for i in range(len(val_LR)):
output, val_gt = sess.run([model.output, model.label])
output = output[0]
val_gt = val_gt[0]
h, w, c = output.shape
val_gt = val_gt[:h, :w]
val_gt = val_gt.astype(np.uint8)
y_psnr, y_ssim = util.compare_measure(val_gt, output, args)
Y_PSNR_list.append(y_psnr)
Y_SSIM_list.append(y_ssim)
file.write('file name : %s PSNR : %04f SSIM : %04f \n' %
(val_LR[i], y_psnr, y_ssim))
if args.save_test_result:
im = Image.fromarray(output)
split_name = val_LR[i].split(".")
im.save(
os.path.join(
args.result_path,
"%sX%d.%s" % (''.join(map(str, split_name[:-1])),
args.scale, split_name[-1])))
length = len(val_LR)
mean_Y_PSNR = sum(Y_PSNR_list) / length
mean_SSIM = sum(Y_SSIM_list) / length
file.write("Y_PSNR : %0.4f SSIM : %0.4f \n" % (mean_Y_PSNR, mean_SSIM))
file.close()
print('############Test completed！############')