def test(self):
sml_x = tf.placeholder(tf.float32,
[None, self.idims[0], self.idims[1], 3])
odims = tf.placeholder(tf.int32, [2])
gener_x = generator(sml_x, odims, is_training=False, reuse=False)
init = tf.global_variables_initializer()
with tf.Session() as sess:
sess.run(init)
saver = tf.train.Saver()
try:
saver.restore(sess,
'/'.join(['models', self.model, self.model]))
except Exception as e:
print('Model could not be restored. Exiting.\nError: ' + e)
exit()
makedirs(self.out_path)
print('Performing super resolution ...')
for idx in range(0, self.dataset_size, self.batch_size):
start, end = idx, min(idx + self.batch_size, self.dataset_size)
batch = range(start, end)
batch_big = self.dataset[batch] / 255.0
batch_sml = np.array([
imresize(img, size=(self.idims[0], self.idims[1], 3))
for img in batch_big
])
superres_imgs = sess.run(gener_x,
feed_dict={
sml_x: batch_sml,
odims: self.odims
})
superres_imgs = np.array(superres_imgs * 255.0, dtype=np.uint8)
nearest = np.array([
imresize(
img, size=superres_imgs.shape[1:], interp='nearest')
for img in batch_sml
],
dtype=np.uint8)
bilinear = np.array([
imresize(
img, size=superres_imgs.shape[1:], interp='bilinear')
for img in batch_sml
],
dtype=np.uint8)
bicubic = np.array([
imresize(
img, size=superres_imgs.shape[1:], interp='bicubic')
for img in batch_sml
],
dtype=np.uint8)
lanczos = np.array([
imresize(
img, size=superres_imgs.shape[1:], interp='lanczos')
for img in batch_sml
],
dtype=np.uint8)
original = np.array([
imresize(img, size=(self.odims[0], self.odims[1], 3))
for img in batch_big
],
dtype=np.uint8)
images = np.concatenate((nearest, bilinear, bicubic, lanczos,
superres_imgs, original), 2)
def display(im_data):
dpi = 80
height, width, depth = im_data.shape
figsize = width / float(dpi), height / float(dpi)
fig = plt.figure(figsize=figsize)
ax = fig.add_axes([0, 0, 1, 1])
ax.axis('off')
ax.imshow(im_data, cmap='gray')
plt.show()
for (i, og, nn, bl, bc, la, sr,
image) in zip(range(100), original, nearest, bilinear,
bicubic, lanczos, superres_imgs, images):
nn, _ = compare_ssim(og, og, nn)
bl, _ = compare_ssim(og, og, bl)
bc, _ = compare_ssim(og, og, bc)
la, _ = compare_ssim(og, og, la)
sr, _ = compare_ssim(og, og, sr)
# display(image)
plt.subplot(111)
title = 'Nearest Bilinear Bicubic Lanczos SRGAN Original'.format(
nn, bl, bc, la, sr)
plt.title(title)
title = '{0:.4f} {1:.4f} {2:.4f} {3:.4f} {4:.4f} {5:.4f}'.format(
nn, bl, bc, la, sr, 1.000)
plt.xlabel(title)
plt.xticks([])
plt.yticks([])
plt.imshow(image)
plt.show()
# imsave('%s/%d.png' % (self.out_path, start+i), image)
print('%d/%d saved successfully.' % (end, self.dataset_size))
def test(self):
sml_x = tf.placeholder(tf.float32,
[None, self.idims[0], self.idims[1], 3])
odims = tf.placeholder(tf.int32, [2])
gener_x = generator(sml_x, odims, is_training=False, reuse=False)
init = tf.global_variables_initializer()
with tf.Session() as sess:
sess.run(init)
saver = tf.train.Saver()
try:
saver.restore(sess,
'/'.join(['models', self.model, self.model]))
except Exception as e:
print('Model could not be restored. Exiting.\nError: ' + e)
exit()
succ, total = 0, 0
avg_1, avg_2 = 0, 0
print('Performing super resolution ...')
for idx in range(0, self.dataset_size, self.batch_size):
start, end = idx, min(idx + self.batch_size, self.dataset_size)
batch = range(start, end)
batch_big = self.dataset[batch] / 255.0
batch_sml = np.array([
imresize(img, size=(self.idims[0], self.idims[1], 3))
for img in batch_big
])
superres_imgs = sess.run(gener_x,
feed_dict={
sml_x: batch_sml,
odims: self.odims
})
interpolated_imgs = np.array([
imresize(img, size=superres_imgs.shape[1:]) / 255.0
for img in batch_sml
])
for i in range(len(batch_sml)):
original = np.array(imresize(batch_big[i],
size=(self.odims[0],
self.odims[1])),
dtype=np.uint8)
superres = np.array(superres_imgs[i] * 255.0,
dtype=np.uint8)
interpolated = np.array(imresize(batch_sml[i],
size=(self.odims[0],
self.odims[1]),
interp=args.interpol),
dtype=np.uint8)
ssim_1, ssim_2 = compare_ssim(original, superres,
interpolated)
if ssim_1 <= ssim_2:
succ += 1
total += 1
avg_1 += ssim_1
avg_2 += ssim_2
print('%d/%d completed.' % (end, self.dataset_size))
print('Average SSIM: {0:.4f}, {1:.4f}'.format(
avg_1 / total, avg_2 / total))
print('{}/{} images have better SSIM'.format(succ, total))
print('Average SSIM: {0:.4f}, {1:.4f}'.format(
avg_1 / total, avg_2 / total))