def save(artist, model_path, num_save):
sample_save_dir = c.get_dir('../save/samples/')
sess = tf.Session()
print artist
data_reader = DataReader(artist)
vocab = data_reader.get_vocab()
print 'Init model...'
model = LSTMModel(sess,
vocab,
c.BATCH_SIZE,
c.SEQ_LEN,
c.CELL_SIZE,
c.NUM_LAYERS,
test=True)
saver = tf.train.Saver()
sess.run(tf.initialize_all_variables())
saver.restore(sess, model_path)
print 'Model restored from ' + model_path
artist_save_dir = c.get_dir(join(sample_save_dir, artist))
for i in xrange(num_save):
print i
path = join(artist_save_dir, str(i) + '.txt')
sample = model.generate()
processed_sample = process_sample(sample)
with open(path, 'w') as f:
f.write(processed_sample)
def test_batch(self, batch, global_step, save_imgs=True):
"""
Runs a training step using the global loss on each of the scale networks.
@param batch: An array of shape
[batch_size x self.height x self.width x (3 * (c.HIST_LEN + 1))].
A batch of the input and output frames, concatenated along the channel axis
(index 3).
@param global_step: The global step.
@param save_imgs: Whether or not to save the input/output images to file. Default = True.
@return: A tuple of (psnr error, sharpdiff error) for the batch.
"""
print('-' * 30)
print('Testing:')
##
# Split into inputs and outputs
##
input_frames = batch[:, :, :, :3 * c.HIST_LEN]
gt_frames = batch[:, :, :, 3 * c.HIST_LEN:3 * (c.HIST_LEN + 1)]
##
# Run the network
##
feed_dict = {self.input_frames_test: input_frames,
self.gt_frames_test: gt_frames}
gen_imgs, psnr, sharpdiff, summaries = self.sess.run([self.scale_preds_test[-1],
self.psnr_error_test,
self.sharpdiff_error_test,
self.summaries_test],
feed_dict=feed_dict)
print('PSNR Error : ', psnr)
print('Sharpdiff Error: ', sharpdiff)
##
# Save images
##
if save_imgs:
for pred_num in range(len(input_frames)):
pred_dir = c.get_dir(os.path.join(
c.IMG_SAVE_DIR, 'Tests/Step_' + str(global_step), str(pred_num)))
# save input images
for frame_num in range(c.HIST_LEN):
img = input_frames[pred_num, :, :, (frame_num * 3):((frame_num + 1) * 3)]
imsave(os.path.join(pred_dir, 'input_' + str(frame_num) + '.png'), img)
# save output and gt images
gen_img = gen_imgs[pred_num]
gt_img = gt_frames[pred_num, :, :, :]
imsave(os.path.join(pred_dir, 'gen.png'), gen_img)
imsave(os.path.join(pred_dir, 'gt.png'), gt_img)
print('-' * 30)
def generator_train_step(self, batch):
feed_dict = {self.input_image: batch, self.is_train: True}
if c.ADVERSARIAL:
_, prediction, generator_loss, loss_adv_g, loss_recon, loss_entropy, precision, summaries = \
self.sess.run([self.train_op_G,
self.prediction,
self.generator_loss,
self.loss_adv_G,
self.loss_recon,
self.loss_entropy,
self.precision,
self.summaries],
feed_dict=feed_dict)
self.update_discrim_accur_MA(precision)
else:
_, prediction, generator_loss, loss_recon, loss_entropy, summaries = \
self.sess.run([self.train_op_G,
self.prediction,
self.generator_loss,
self.loss_recon,
self.loss_entropy,
self.summaries],
feed_dict=feed_dict)
global_step = self.get_global_step()
self.most_recent_summary = summaries
if global_step % c.STATS_FREQ == 0:
print('GeneratorModel : Step ', global_step)
print(' Generator Loss : ', generator_loss)
print(' ')
#Need to add more user output for train time
if global_step % c.IMG_SAVE_FREQ == 0:
print('Saving images ..')
for image in range(len(batch)):
pred_dir = c.get_dir(
os.path.join(c.IMG_SAVE_DIR, 'Step_' + str(global_step),
str(image)))
#save the input image
img = batch[image, :, :, :]
img = denormalize_frames_mine(img)
imsave(os.path.join(pred_dir, 'input.png'), img)
#save the compressed image
compressed_image = denormalize_frames_mine(
prediction[image, :, :, :])
imsave(os.path.join(pred_dir, 'compressed.png'),
compressed_image)
print('Saved images!')
return global_step
def main():
##
# Handle command line input
##
num_clips = 5000000
try:
opts, _ = getopt.getopt(sys.argv[1:], 'w:n:t:c:oH', [
'num_workers=', 'num_clips=', 'train_dir=', 'clips_dir=',
'overwrite', 'help'
])
except getopt.GetoptError:
usage()
sys.exit(2)
for opt, arg in opts:
if opt in ('-w', '--num_workers'):
c.NUM_WORKERS = int(arg)
if opt in ('-n', '--num_clips'):
num_clips = int(arg)
if opt in ('-t', '--train_dir'):
c.TRAIN_DIR = c.get_dir(arg)
if opt in ('-c', '--clips_dir'):
c.TRAIN_DIR_CLIPS = c.get_dir(arg)
if opt in ('-o', '--overwrite'):
c.clear_dir(c.TRAIN_DIR_CLIPS)
if opt in ('-H', '--help'):
usage()
sys.exit(2)
# set train frame dimensions
assert os.path.exists(c.TRAIN_DIR)
c.FULL_HEIGHT, c.FULL_WIDTH = c.get_train_frame_dims()
##
# Process data for training
##
process_training_data(num_clips)
def main():
##
# Handle command line input, generate the video for the selected directory containing a video file.
##
try:
opts, _ = getopt.getopt(sys.argv[1:], 'v:H', ['video_dir=', 'help'])
except getopt.GetoptError:
usage()
sys.exit(2)
for opt, arg in opts:
if opt in ('-v', '--video_dir'):
source = c.get_dir(arg)
if opt in ('-H', '--help'):
usage()
sys.exit(2)
for root, dirs, filenames in os.walk(source):
for f in filenames:
print('Reading video file:', f)
name = str(f).split(".")
fullpath = os.path.join(source, f)
vidcap = cv2.VideoCapture(fullpath)
success, image = vidcap.read()
count = 0
success = True
while success:
success, image = vidcap.read()
#print ('Read a new frame: ', success)
newDir = os.path.join(source, name[0])
newFileName = name[0] + "_frame" + str(count) + ".jpg"
#print('file name:', newFileName)
finalFilePath = os.path.join(newDir, newFileName)
#print('final path:', finalFilePath)
if not os.path.exists(newDir):
print('creating dir:', newDir)
os.makedirs(newDir)
cv2.imwrite(finalFilePath, image) # save frame as JPEG file
count += 1
def test_batch(self, batch, global_step, num_rec_out=1, save_imgs=True):
"""
Runs a training step using the global loss on each of the scale networks.
@param batch: An array of shape
[batch_size x self.height x self.width x (3 * (c.HIST_LEN+ num_rec_out))].
A batch of the input and output frames, concatenated along the channel axis
(index 3).
@param global_step: The global step.
@param num_rec_out: The number of outputs to predict. Outputs > 1 are computed recursively,
using previously-generated frames as input. Default = 1.
@param save_imgs: Whether or not to save the input/output images to file. Default = True.
@return: A tuple of (psnr error, sharpdiff error) for the batch.
"""
if num_rec_out < 1:
raise ValueError('num_rec_out must be >= 1')
print('-' * 30)
print('Testing:')
##
# Split into inputs and outputs
##
input_frames = batch[:, :, :, :3 * c.HIST_LEN]
gt_frames = batch[:, :, :, 3 * c.HIST_LEN:]
##
# Generate num_rec_out recursive predictions
##
working_input_frames = deepcopy(input_frames) # input frames that will shift w/ recursion
rec_preds = []
rec_summaries = []
for rec_num in range(num_rec_out):
working_gt_frames = gt_frames[:, :, :, 3 * rec_num:3 * (rec_num + 1)]
feed_dict = {self.input_frames_test: working_input_frames,
self.gt_frames_test: working_gt_frames}
preds, psnr, sharpdiff, summaries = self.sess.run([self.scale_preds_test[-1],
self.psnr_error_test,
self.sharpdiff_error_test,
self.summaries_test],
feed_dict=feed_dict)
# remove first input and add new pred as last input
working_input_frames = np.concatenate(
[working_input_frames[:, :, :, 3:], preds], axis=3)
# add predictions and summaries
rec_preds.append(preds)
rec_summaries.append(summaries)
print('Recursion ', rec_num)
print('PSNR Error : ', psnr)
print('Sharpdiff Error: ', sharpdiff)
# write summaries
# TODO: Think of a good way to write rec output summaries - rn, just using first output.
self.summary_writer.add_summary(rec_summaries[0], global_step)
##
# Save images
##
if save_imgs:
for pred_num in range(len(input_frames)):
pred_dir = c.get_dir(os.path.join(
c.IMG_SAVE_DIR, 'Tests/Step_' + str(global_step), str(pred_num)))
# save input images
for frame_num in range(c.HIST_LEN):
img = input_frames[pred_num, :, :, (frame_num * 3):((frame_num + 1) * 3)]
imsave(os.path.join(pred_dir, 'input_' + str(frame_num) + '.png'), img)
# save recursive outputs
for rec_num in range(num_rec_out):
gen_img = rec_preds[rec_num][pred_num]
gt_img = gt_frames[pred_num, :, :, 3 * rec_num:3 * (rec_num + 1)]
imsave(os.path.join(pred_dir, 'gen_' + str(rec_num) + '.png'), gen_img)
imsave(os.path.join(pred_dir, 'gt_' + str(rec_num) + '.png'), gt_img)
print('-' * 30)
def train_step(self, batch, discriminator=None):
"""
Runs a training step using the global loss on each of the scale networks.
@param batch: An array of shape
[c.BATCH_SIZE x self.height x self.width x (3 * (c.HIST_LEN + 1))].
The input and output frames, concatenated along the channel axis (index 3).
@param discriminator: The discriminator model. Default = None, if not adversarial.
@return: The global step.
"""
##
# Split into inputs and outputs
##
input_frames = batch[:, :, :, :-3]
gt_frames = batch[:, :, :, -3:]
##
# Train
##
feed_dict = {self.input_frames_train: input_frames, self.gt_frames_train: gt_frames}
if c.ADVERSARIAL:
# Run the generator first to get generated frames
scale_preds = self.sess.run(self.scale_preds_train, feed_dict=feed_dict)
# Run the discriminator nets on those frames to get predictions
d_feed_dict = {}
for scale_num, gen_frames in enumerate(scale_preds):
d_feed_dict[discriminator.scale_nets[scale_num].input_frames] = gen_frames
d_scale_preds = self.sess.run(discriminator.scale_preds, feed_dict=d_feed_dict)
# Add discriminator predictions to the
for i, preds in enumerate(d_scale_preds):
feed_dict[self.d_scale_preds[i]] = preds
_, global_loss, global_psnr_error, global_sharpdiff_error, global_step, summaries = \
self.sess.run([self.train_op,
self.global_loss,
self.psnr_error_train,
self.sharpdiff_error_train,
self.global_step,
self.summaries_train],
feed_dict=feed_dict)
##
# User output
##
if global_step % c.STATS_FREQ == 0:
print('GeneratorModel : Step ', global_step)
print(' Global Loss : ', global_loss)
print(' PSNR Error : ', global_psnr_error)
print(' Sharpdiff Error: ', global_sharpdiff_error)
if global_step % c.SUMMARY_FREQ == 0:
self.summary_writer.add_summary(summaries, global_step)
print('GeneratorModel: saved summaries')
if global_step % c.IMG_SAVE_FREQ == 0:
print('-' * 30)
print('Saving images...')
# if not adversarial, we didn't get the preds for each scale net before for the
# discriminator prediction, so do it now
if not c.ADVERSARIAL:
scale_preds = self.sess.run(self.scale_preds_train, feed_dict=feed_dict)
# re-generate scale gt_frames to avoid having to run through TensorFlow.
scale_gts = []
for scale_num in range(self.num_scale_nets):
scale_factor = 1. / 2 ** ((self.num_scale_nets - 1) - scale_num)
scale_height = int(self.height_train * scale_factor)
scale_width = int(self.width_train * scale_factor)
# resize gt_output_frames for scale and append to scale_gts_train
scaled_gt_frames = np.empty([c.BATCH_SIZE, scale_height, scale_width, 3])
for i, img in enumerate(gt_frames):
# for skimage.transform.resize, images need to be in range [0, 1], so normalize
# to [0, 1] before resize and back to [-1, 1] after
sknorm_img = (img / 2) + 0.5
resized_frame = resize(sknorm_img, [scale_height, scale_width, 3])
scaled_gt_frames[i] = (resized_frame - 0.5) * 2
scale_gts.append(scaled_gt_frames)
# for every clip in the batch, save the inputs, scale preds and scale gts
for pred_num in range(len(input_frames)):
pred_dir = c.get_dir(os.path.join(c.IMG_SAVE_DIR, 'Step_' + str(global_step),
str(pred_num)))
# save input images
for frame_num in range(c.HIST_LEN):
img = input_frames[pred_num, :, :, (frame_num * 3):((frame_num + 1) * 3)]
imsave(os.path.join(pred_dir, 'input_' + str(frame_num) + '.png'), img)
# save preds and gts at each scale
# noinspection PyUnboundLocalVariable
for scale_num, scale_pred in enumerate(scale_preds):
gen_img = scale_pred[pred_num]
path = os.path.join(pred_dir, 'scale' + str(scale_num))
gt_img = scale_gts[scale_num][pred_num]
imsave(path + '_gen.png', gen_img)
imsave(path + '_gt.png', gt_img)
print('Saved images!')
print('-' * 30)
return global_step
def test_batch(self, batch, global_step, save_feature_maps=True,save_imgs=True):
"""
Runs a training step using the global loss on each of the scale networks.
@param batch: An array of shape
[batch_size x self.height x self.width x (3 * (c.HIST_LEN+ num_rec_out))].
A batch of the input and output frames, concatenated along the channel axis
(index 3).
@param global_step: The global step.
@param save_imgs: Whether or not to save the input/output images to file. Default = True.
@return: A tuple of (psnr error, sharpdiff error) for the batch.
"""
print('-' * 30)
print('Testing:')
##
# Split into inputs and outputs
##
ref_image = batch['ref_image']
multi_plane = batch['multi_plane']
gt = batch['gt']
batch_size = ref_image.shape[0]
feed_dict = {self.ref_image: ref_image,
self.multi_plane: multi_plane,
self.gt: gt}
if save_feature_maps:
preds, psnr, sharpdiff, ssim, feature_maps, blending_weights, alpha_images = self.sess.run([self.preds,
self.psnr,
self.sharpdiff,
self.ssim,
self.feature_maps,
self.blending_weights,
self.alpha_images],
feed_dict=feed_dict)
else:
# Run the generator first to get generated images
preds, psnr, sharpdiff, ssim = self.sess.run([self.preds,
self.psnr,
self.sharpdiff,
self.ssim],
feed_dict=feed_dict)
##
# User output
##
print('PSNR : ', psnr)
print('Sharpdiff : ', sharpdiff)
print('SSIM : ', ssim)
print('-' * 30)
print('Saving images...')
for pred_num in range(batch_size):
pred_dir = c.get_dir(os.path.join(c.IMG_SAVE_DIR, 'Test/Step_' + str(global_step),
str(pred_num)))
# save input images
ref_img = ref_image[pred_num, :, :, :]
imsave(os.path.join(pred_dir, 'ref_image.png'), imresize(ref_img,[128,128]))
hr_img = multi_plane_img = multi_plane[pred_num, :, :, 0:3]
imsave(os.path.join(pred_dir, 'hr_image.png'), hr_img)
plane_dir = c.get_dir(os.path.join(pred_dir,'planes'))
for i in range(1,c.NUM_PLANE):
multi_plane_img = multi_plane[pred_num, :, :, 3*i:3*(i+1)]
imsave(os.path.join(plane_dir, 'plane_%d.png'%(i+1)), multi_plane_img)
gt_img = gt[pred_num, :, :, :]
imsave(os.path.join(pred_dir, 'gt.png'), gt_img)
gen_img = preds[pred_num,:,:,:]
imsave(os.path.join(pred_dir, 'pred.png'), gen_img)
if save_feature_maps:
feature_dir = c.get_dir(os.path.join(pred_dir,'features'))
for layer in feature_maps:
layer_dir = c.get_dir(os.path.join(feature_dir,layer))
layer_img = feature_maps[layer][pred_num,:,:,:]
num_feature = (layer_img.shape)[2]
for k in range(num_feature):
imsave(os.path.join(layer_dir, '%d.bmp'%k), layer_img[:,:,k])
blending_weights_dir = c.get_dir(os.path.join(feature_dir,'blending_weights'))
alpha_dir = c.get_dir(os.path.join(feature_dir,'alpha'))
for k in range(c.NUM_PLANE):
imsave(os.path.join(blending_weights_dir, '%d.bmp'%k), blending_weights[pred_num,:,:,k])
imsave(os.path.join(alpha_dir, '%d.bmp'%k), alpha_images[pred_num,:,:,k])
print('Saved images!')
print('-' * 30)
def train_step(self, batch, discriminator=None):
"""
Runs a training step using the global loss on each of the scale networks.
@param batch: An array of shape
[c.BATCH_SIZE x self.height x self.width x (3 * (c.HIST_LEN + 1))].
The input and output frames, concatenated along the channel axis (index 3).
@param discriminator: The discriminator model. Default = None, if not adversarial.
@return: The global step.
"""
##
# Split into inputs and outputs
##
ref_image = batch['ref_image']
multi_plane = batch['multi_plane']
gt = batch['gt']
batch_size = ref_image.shape[0]
##
# Train
##
feed_dict = {self.ref_image: ref_image,
self.multi_plane: multi_plane,
self.gt: gt}
# Run the generator first to get generated images
preds = self.sess.run(self.preds, feed_dict=feed_dict)
_, loss, global_psnr, global_sharpdiff, global_ssim, global_step, summaries = \
self.sess.run([self.train_op,
self.loss,
self.psnr,
self.sharpdiff,
self.ssim,
self.global_step,
self.summaries],
feed_dict=feed_dict)
##
# User output
##
if global_step % c.STATS_FREQ == 0:
print('EXAMPLE-BASED EDSR FlowModel : Step ', global_step)
print(' Global Loss : ', loss)
print(' PSNR : ', global_psnr)
print(' Sharpdiff : ', global_sharpdiff)
print(' SSIM : ', global_ssim)
if global_step % c.SUMMARY_FREQ == 0:
self.summary_writer.add_summary(summaries, global_step)
print('GeneratorModel: saved summaries')
if global_step % c.IMG_SAVE_FREQ == 0:
print('-' * 30)
print('Saving images...')
for pred_num in range(batch_size):
pred_dir = c.get_dir(os.path.join(c.IMG_SAVE_DIR, 'Step_' + str(global_step),
str(pred_num)))
# save input images
ref_img = ref_image[pred_num, :, :, :]
imsave(os.path.join(pred_dir, 'ref_image.png'), imresize(ref_img,[128,128]))
hr_img = multi_plane_img = multi_plane[pred_num, :, :, 0:3]
imsave(os.path.join(pred_dir, 'hr_image.png'), hr_img)
plane_dir = c.get_dir(os.path.join(pred_dir,'planes'))
for i in range(1,c.NUM_PLANE):
multi_plane_img = multi_plane[pred_num, :, :, 3*i:3*i+3]
imsave(os.path.join(plane_dir, 'plane_%d.png'%(i+1)), multi_plane_img)
gt_img = gt[pred_num, :, :, :]
imsave(os.path.join(pred_dir, 'gt.png'), gt_img)
gen_img = preds[pred_num,:,:,:]
imsave(os.path.join(pred_dir, 'pred.png'), gen_img)
print('Saved images!')
print('-' * 30)
return global_step
def generator_test_batch(self, batch, global_step, save_imgs=True):
print('Compressing test batch')
feed_dict = {self.input_image_test: batch, self.is_train: False}
prediction, code = self.sess.run(
[self.prediction_test, self.code_test], feed_dict=feed_dict)
##
# Everything else in the function is currently rough and needs
# finalising
##
start = timer()
time_test_code = self.sess.run(self.code_test, feed_dict=feed_dict)
end = timer()
print("Time taken for compression ", end - start)
##Reshape the code / quantised coefficents via rasta scan ordering into
#A continuous integer stream to be encoded by the entropy encoder
print("Size of coffiecnt tensor")
print(code.shape)
code = code.astype(np.int32)
code = np.reshape(code,
(-1, code.shape[1] * code.shape[2] * code.shape[3]))
print("Size of reshaped coefficent array")
print(code.shape)
num_bits_total = 0
for i in range(code.shape[0]):
num_bits = cabac.encode(code[i, :])
print("Bytes for image ", i, " : ", num_bits / 8)
print("Bits per pixel image ", i, " : ",
num_bits / (c.FULL_HEIGHT * c.FULL_WIDTH * 3))
print("Effective entropy ", i, " : ", num_bits / code.shape[1])
num_bits_total += num_bits
av_bits_per_pixel = num_bits_total / (code.shape[0] * c.FULL_HEIGHT *
c.FULL_WIDTH * 3)
entropy_total = 0
for i in range(code.shape[0]):
entropy = cabac.estimate_entropy(code[i, :])
print("Entropy image ", i, " : ", entropy)
entropy_total += entropy
entropy = entropy_total / code.shape[0]
print("Average entropy")
print(entropy)
print("Average number of bpp")
print(av_bits_per_pixel)
print("PSNR calcuation")
compressed_images = deepcopy(prediction)
input_images = deepcopy(batch)
compressed_images = denormalize_frames_mine(compressed_images)
input_images = denormalize_frames_mine(input_images)
print(pc.PSNR_error_np(compressed_images, input_images))
if save_imgs:
for image in range(len(batch)):
pred_dir = c.get_dir(
os.path.join(c.IMG_SAVE_DIR,
'Tests/Step_' + str(global_step), str(image)))
#Save the input image
img = batch[image, :, :, :]
img = denormalize_frames_mine(img)
imsave(os.path.join(pred_dir, 'input.png'), img)
#Save the compressed img
compressed_image = denormalize_frames_mine(
prediction[image, :, :, :])
imsave(os.path.join(pred_dir, 'compressed.png'),
compressed_image)
print('Saved test batch')
def test_batch(self, batch, global_step, num_rec_out=1, save_imgs=True):
"""
Runs a training step using the global loss on each of the scale networks.
@param batch: An array of shape
[batch_size x self.height x self.width x (3 * (c.HIST_LEN+ num_rec_out))].
A batch of the input and output frames, concatenated along the channel axis
(index 3).
@param global_step: The global step.
@param num_rec_out: The number of outputs to predict. Outputs > 1 are computed recursively,
using previously-generated frames as input. Default = 1.
@param save_imgs: Whether or not to save the input/output images to file. Default = True.
@return: A tuple of (psnr error, sharpdiff error) for the batch.
"""
if num_rec_out < 1:
raise ValueError('num_rec_out must be >= 1')
print '-' * 30
print 'Testing:'
##
# Split into inputs and outputs
##
input_frames = batch[:, :, :, :3 * c.HIST_LEN]
gt_frames = batch[:, :, :, 3 * c.HIST_LEN:]
##
# Generate num_rec_out recursive predictions
##
feed_dict = {
self.input_frames_test: input_frames,
self.gt_frames_test: gt_frames
}
preds, psnr, sharpdiff, ssim, summaries = self.sess.run(
[
self.scale_preds_test[-1], self.psnr_error_test,
self.sharpdiff_error_test, self.ssim_error_test,
self.summaries_test
],
feed_dict=feed_dict)
print 'SSIM Errors : ', [ssim_error for ssim_error in ssim]
print 'PSNR Errors : ', [psnr_error for psnr_error in psnr]
print 'Sharpdiff Errors: ', [
sharpdiff_error for sharpdiff_error in sharpdiff
]
# write summaries
self.summary_writer.add_summary(summaries, global_step)
##
# Save images
##
if save_imgs:
for pred_num in xrange(len(input_frames)):
pred_dir = c.get_dir(
os.path.join(c.IMG_SAVE_DIR,
'Tests/Step_' + str(global_step),
str(pred_num)))
# save input images
for frame_num in xrange(c.HIST_LEN):
img = input_frames[pred_num, :, :,
(frame_num * 3):((frame_num + 1) * 3)]
imsave(
os.path.join(pred_dir,
'input_' + str(frame_num) + '.jpg'), img)
# save recursive outputs
for rec_num in xrange(num_rec_out):
gen_img = preds[pred_num, :, :,
3 * rec_num:3 * (rec_num + 1)]
gt_img = gt_frames[pred_num, :, :,
3 * rec_num:3 * (rec_num + 1)]
imsave(
os.path.join(pred_dir,
'_gen_' + str(rec_num) + '.jpg'), gen_img)
imsave(
os.path.join(pred_dir, '_gt_' + str(rec_num) + '.jpg'),
gt_img)
print '-' * 30
