def main(argv=None):
"""Run a Tensorflow model on the Criteo dataset."""
env = json.loads(os.environ.get('TF_CONFIG', '{}'))
# First find out if there's a task value on the environment variable.
# If there is none or it is empty define a default one.
task_data = env.get('task') or {'type': 'master', 'index': 0}
argv = sys.argv if argv is None else argv
args = create_parser().parse_args(args=argv[1:])
trial = task_data.get('trial')
if trial is not None:
output_dir = os.path.join(args.output_path, trial)
else:
output_dir = args.output_path
# Do only evaluation if instructed so, or call Experiment's run.
if args.eval_only_summary_filename:
experiment = get_experiment_fn(args)(output_dir)
# Note that evaluation here will appear as 'one_pass' in tensorboard.
results = experiment.evaluate(delay_secs=0)
# Converts numpy types to native types for json dumps.
json_out = json.dumps(
{key: value.tolist() for key, value in results.iteritems()})
with tf.Session():
tf.write_file(args.eval_only_summary_filename, json_out).run()
else:
learn_runner.run(experiment_fn=get_experiment_fn(args),
output_dir=output_dir)
def main(_, run_eval_loop=True):
# Fetch and generate images to run through Inception.
with tf.name_scope('inputs'):
real_data, num_classes = _get_real_data(
FLAGS.num_images_generated, FLAGS.dataset_dir)
generated_data = _get_generated_data(
FLAGS.num_images_generated, FLAGS.conditional_eval, num_classes)
# Compute Frechet Inception Distance.
if FLAGS.eval_frechet_inception_distance:
fid = util.get_frechet_inception_distance(
real_data, generated_data, FLAGS.num_images_generated,
FLAGS.num_inception_images)
tf.summary.scalar('frechet_inception_distance', fid)
# Compute normal Inception scores.
if FLAGS.eval_real_images:
inc_score = util.get_inception_scores(
real_data, FLAGS.num_images_generated, FLAGS.num_inception_images)
else:
inc_score = util.get_inception_scores(
generated_data, FLAGS.num_images_generated, FLAGS.num_inception_images)
tf.summary.scalar('inception_score', inc_score)
# If conditional, display an image grid of difference classes.
if FLAGS.conditional_eval and not FLAGS.eval_real_images:
reshaped_imgs = util.get_image_grid(
generated_data, FLAGS.num_images_generated, num_classes,
FLAGS.num_images_per_class)
tf.summary.image('generated_data', reshaped_imgs, max_outputs=1)
# Create ops that write images to disk.
image_write_ops = None
if FLAGS.conditional_eval and FLAGS.write_to_disk:
reshaped_imgs = util.get_image_grid(
generated_data, FLAGS.num_images_generated, num_classes,
FLAGS.num_images_per_class)
uint8_images = data_provider.float_image_to_uint8(reshaped_imgs)
image_write_ops = tf.write_file(
'%s/%s'% (FLAGS.eval_dir, 'conditional_cifar10.png'),
tf.image.encode_png(uint8_images[0]))
else:
if FLAGS.num_images_generated >= 100 and FLAGS.write_to_disk:
reshaped_imgs = tfgan.eval.image_reshaper(
generated_data[:100], num_cols=FLAGS.num_images_per_class)
uint8_images = data_provider.float_image_to_uint8(reshaped_imgs)
image_write_ops = tf.write_file(
'%s/%s'% (FLAGS.eval_dir, 'unconditional_cifar10.png'),
tf.image.encode_png(uint8_images[0]))
# For unit testing, use `run_eval_loop=False`.
if not run_eval_loop: return
tf.contrib.training.evaluate_repeatedly(
FLAGS.checkpoint_dir,
master=FLAGS.master,
hooks=[tf.contrib.training.SummaryAtEndHook(FLAGS.eval_dir),
tf.contrib.training.StopAfterNEvalsHook(1)],
eval_ops=image_write_ops,
max_number_of_evaluations=FLAGS.max_number_of_evaluations)
def main(_, run_eval_loop=True):
with tf.name_scope('inputs'):
noise, one_hot_labels = _get_generator_inputs(
FLAGS.num_images_per_class, NUM_CLASSES, FLAGS.noise_dims)
# Generate images.
with tf.variable_scope('Generator'): # Same scope as in train job.
images = networks.conditional_generator((noise, one_hot_labels))
# Visualize images.
reshaped_img = tfgan.eval.image_reshaper(
images, num_cols=FLAGS.num_images_per_class)
tf.summary.image('generated_images', reshaped_img, max_outputs=1)
# Calculate evaluation metrics.
tf.summary.scalar('MNIST_Classifier_score',
util.mnist_score(images, FLAGS.classifier_filename))
tf.summary.scalar('MNIST_Cross_entropy',
util.mnist_cross_entropy(
images, one_hot_labels, FLAGS.classifier_filename))
# Write images to disk.
image_write_ops = tf.write_file(
'%s/%s'% (FLAGS.eval_dir, 'conditional_gan.png'),
tf.image.encode_png(data_provider.float_image_to_uint8(reshaped_img[0])))
# For unit testing, use `run_eval_loop=False`.
if not run_eval_loop: return
tf.contrib.training.evaluate_repeatedly(
FLAGS.checkpoint_dir,
hooks=[tf.contrib.training.SummaryAtEndHook(FLAGS.eval_dir),
tf.contrib.training.StopAfterNEvalsHook(1)],
eval_ops=image_write_ops,
max_number_of_evaluations=FLAGS.max_number_of_evaluations)
def decode(self, ids):
"""Transform a sequence of int ids into an image file.
Args:
ids: list of integers to be converted.
Returns:
Path to the temporary file where the image was saved.
Raises:
ValueError: if the ids are not of the appropriate size.
"""
_, tmp_file_path = tempfile.mkstemp()
length = self._height * self._width * self._channels
if len(ids) != length:
raise ValueError("Length of ids (%d) must be height (%d) x width (%d) x "
"channels (%d); %d != %d.\n Ids: %s"
% (len(ids), self._height, self._width, self._channels,
len(ids), length, " ".join([str(i) for i in ids])))
with tf.Graph().as_default():
raw = tf.constant(ids, dtype=tf.uint8)
img = tf.reshape(raw, [self._height, self._width, self._channels])
png = tf.image.encode_png(img)
op = tf.write_file(tmp_file_path, png)
with tf.Session() as sess:
sess.run(op)
return tmp_file_path
def testWriteFile(self):
cases = ["", "Some contents"]
for contents in cases:
contents = tf.compat.as_bytes(contents)
temp = tempfile.NamedTemporaryFile(prefix="WriteFileTest", dir=self.get_temp_dir())
with self.test_session() as sess:
w = tf.write_file(temp.name, contents)
sess.run(w)
file_contents = open(temp.name, "rb").read()
self.assertEqual(file_contents, contents)
def testWriteFile(self):
cases = ['', 'Some contents']
for contents in cases:
contents = tf.compat.as_bytes(contents)
with tempfile.NamedTemporaryFile(prefix='WriteFileTest',
dir=self.get_temp_dir(),
delete=False) as temp:
pass
with self.test_session() as sess:
w = tf.write_file(temp.name, contents)
sess.run(w)
with open(temp.name, 'rb') as f:
file_contents = f.read()
self.assertEqual(file_contents, contents)
os.remove(temp.name)
def decode(self, ids, strip_extraneous=False):
"""Transform a sequence of int ids into an image file.
Args:
ids: list of integers to be converted.
strip_extraneous: unused
Returns:
Path to the temporary file where the image was saved.
Raises:
ValueError: if the ids are not of the appropriate size.
"""
del strip_extraneous
_, tmp_file_path = tempfile.mkstemp("_decode.png")
if self._height is None or self._width is None:
size = int(math.sqrt(len(ids) / self._channels))
length = size * size * self._channels
else:
size = None
length = self._height * self._width * self._channels
if len(ids) != length:
raise ValueError(
"Length of ids (%d) must be height (%d) x width (%d) x "
"channels (%d); %d != %d.\n Ids: %s" %
(len(ids), self._height, self._width, self._channels, len(ids),
length, " ".join([str(i) for i in ids])))
with tf.Graph().as_default():
raw = tf.constant(ids, dtype=tf.uint8)
if size is None:
img = tf.reshape(raw,
[self._height, self._width, self._channels])
else:
img = tf.reshape(raw, [size, size, self._channels])
png = tf.image.encode_png(img)
op = tf.write_file(tmp_file_path, png)
with tf.Session() as sess:
sess.run(op)
return tmp_file_path
def test(self, saver, files_list, batch_size, path):
self.load_chkp(saver, path)
for step in range(1000):
print("Epoch:\n", step)
p = 1
covers, secrets = get_img_batch(files_list, batch_size, p)
self.sess.run(
[self.hiding_output_op, self.reveal_output_op, self.summary_op, self.loss_op, self.secret_loss_op,
self.cover_loss_op],
feed_dict={"input_prep:0": secrets, "input_hide:0": covers})
hiding_output_op, reveal_output_op, summary, total_loss, cover_loss, secret_loss = self.sess.run(
[self.hiding_output_op, self.reveal_output_op, self.summary_op, self.loss_op, self.cover_loss_op,
self.secret_loss_op],
feed_dict={"input_prep:0": secrets, "input_hide:0": covers})
self.writer.add_summary(summary)
# hiding_output_op = self.get_tensor_to_img_op(hiding_output_op)
for k in range(batch_size):
im = tf.reshape(tf.cast(hiding_output_op[k], tf.uint8), [224, 224, 3])
images_encode = tf.image.encode_jpeg(im)
fname = tf.constant('%s.jpeg' % self.i)
self.i += 1
fwrite = tf.write_file(fname, images_encode)
for k in range(batch_size):
im = Image.fromarray(np.uint8((hiding_output_op[k]) * 255))
im.save('./Wnet/container/%s.jpg' % self.i)
self.i += 1
for k in range(batch_size):
im = Image.fromarray(np.uint8((reveal_output_op[k]) * 255))
im.save('./Wnet/secret/%s.jpg' % self.j)
self.j += 1
print("total loss at step %s: %s" % (step, total_loss))
print("cover loss at step %s: %s" % (step, cover_loss))
print("secret loss at step %s: %s" % (step, secret_loss))
def main(_, run_eval_loop=True):
# Fetch real images.
with tf.name_scope('inputs'):
real_images, _, _ = data_provider.provide_data(
'train', FLAGS.num_images_generated, FLAGS.dataset_dir)
image_write_ops = None
if FLAGS.eval_real_images:
tf.summary.scalar('MNIST_Classifier_score',
util.mnist_score(real_images, FLAGS.classifier_filename))
else:
# In order for variables to load, use the same variable scope as in the
# train job.
with tf.variable_scope('Generator'):
images = networks.unconditional_generator(
tf.random_normal([FLAGS.num_images_generated, FLAGS.noise_dims]),
is_training=False)
tf.summary.scalar('MNIST_Frechet_distance',
util.mnist_frechet_distance(
real_images, images, FLAGS.classifier_filename))
tf.summary.scalar('MNIST_Classifier_score',
util.mnist_score(images, FLAGS.classifier_filename))
if FLAGS.num_images_generated >= 100 and FLAGS.write_to_disk:
reshaped_images = tfgan.eval.image_reshaper(
images[:100, ...], num_cols=10)
uint8_images = data_provider.float_image_to_uint8(reshaped_images)
image_write_ops = tf.write_file(
'%s/%s'% (FLAGS.eval_dir, 'unconditional_gan.png'),
tf.image.encode_png(uint8_images[0]))
# For unit testing, use `run_eval_loop=False`.
if not run_eval_loop: return
tf.contrib.training.evaluate_repeatedly(
FLAGS.checkpoint_dir,
hooks=[tf.contrib.training.SummaryAtEndHook(FLAGS.eval_dir),
tf.contrib.training.StopAfterNEvalsHook(1)],
eval_ops=image_write_ops,
max_number_of_evaluations=FLAGS.max_number_of_evaluations)
def main(_, run_eval_loop=True):
with tf.name_scope('inputs'):
noise, one_hot_labels = _get_generator_inputs(
FLAGS.num_images_per_class, NUM_CLASSES, FLAGS.noise_dims)
# Generate images.
with tf.variable_scope('Generator'): # Same scope as in train job.
images = networks.conditional_generator(
(noise, one_hot_labels), is_training=False)
# Visualize images.
reshaped_img = tfgan.eval.image_reshaper(
images, num_cols=FLAGS.num_images_per_class)
tf.summary.image('generated_images', reshaped_img, max_outputs=1)
# Calculate evaluation metrics.
tf.summary.scalar('MNIST_Classifier_score',
util.mnist_score(images, FLAGS.classifier_filename))
tf.summary.scalar('MNIST_Cross_entropy',
util.mnist_cross_entropy(
images, one_hot_labels, FLAGS.classifier_filename))
# Write images to disk.
image_write_ops = None
if FLAGS.write_to_disk:
image_write_ops = tf.write_file(
'%s/%s' % (FLAGS.eval_dir, 'conditional_gan.png'),
tf.image.encode_png(data_provider.float_image_to_uint8(
reshaped_img[0])))
# For unit testing, use `run_eval_loop=False`.
if not run_eval_loop: return
tf.contrib.training.evaluate_repeatedly(
FLAGS.checkpoint_dir,
hooks=[tf.contrib.training.SummaryAtEndHook(FLAGS.eval_dir),
tf.contrib.training.StopAfterNEvalsHook(1)],
eval_ops=image_write_ops,
max_number_of_evaluations=FLAGS.max_number_of_evaluations)
def log_batch_images(img_batch, log_dir, step, sess):
if step == 1:
step = 100
if step % 100 == 0:
step = int(step / 100) * 100
train_pic_dir = os.path.join(log_dir, 'training_pics')
if not os.path.exists(train_pic_dir):
os.makedirs(train_pic_dir)
train_pic_dir = os.path.join(train_pic_dir,
'batches_in_step_' + str(step))
if not os.path.exists(train_pic_dir):
os.makedirs(train_pic_dir)
for idx, item in enumerate(img_batch[0]):
write = tf.image.encode_png(item)
write = tf.write_file(
os.path.join(
train_pic_dir, 'trainee_' + str(step) + '_' + str(idx) +
'_' + str(img_batch[1][idx])) + '.png', write)
sess.run(write)
def write_image(image, file_name):
'''
Writes the given image to file.
Arguments:
image -- The image to write to file.
file_name -- The full file name for the file to write.
Returns:
The write file output node.
'''
target_shape = [
tf.shape(image)[1],
tf.shape(image)[2],
int(image.shape[3])
]
target_shape[BATCH_STACK_AXIS] = -1
converted_image = tf.reshape(convert_image(image), target_shape)
raw_image = ENCODER_FUNCTION(converted_image)
return tf.write_file(file_name, raw_image)
def test(sess, test_references, run_keys=[], feed_dict_keys=[]):
global IMAGE_HEIGHT, IMAGE_WIDTH
model_loss, prediction = run_keys
input_layer, ground_truth = feed_dict_keys
total_loss = 0
for test_reference in test_references:
depth_filename = "/Users/georgestoica/Desktop/Research/Mini-Project/ds_depth/{}.png".format(
test_reference)
input_filename = "/Users/georgestoica/Desktop/Research/Mini-Project/ds_image/{}.png".format(
test_reference)
depth = np.reshape(
cv2.imread(depth_filename,
cv2.IMREAD_ANYCOLOR | cv2.IMREAD_ANYDEPTH),
(1, IMAGE_HEIGHT, IMAGE_WIDTH, 1))
input_img = np.reshape(
cv2.imread(input_filename,
cv2.IMREAD_ANYCOLOR | cv2.IMREAD_ANYDEPTH),
(1, IMAGE_HEIGHT, IMAGE_WIDTH, 3))
loss, pred = sess.run([model_loss, prediction],
feed_dict={
input_layer: input_img,
ground_truth: depth
})
# Encode Image
pred_rs = tf.reshape(pred, [94, 311, 1])
pred_u16 = tf.image.convert_image_dtype(pred_rs, tf.uint16)
pred_enc = tf.image.encode_png(pred_u16)
depth_fpath = tf.constant(
"/Users/georgestoica/Desktop/Research/Mini-Project/pred_depth/{}.png"
.format(test_reference))
depth_write = tf.write_file(depth_fpath, pred_enc)
dummy_ = sess.run(depth_write)
total_loss += loss
avg_loss = float(total_loss) / float(len(test_references))
print("The average test loss is: " + str(avg_loss))
def make_image(name, var, image_dims):
prod = np.prod(image_dims)
grid = form_image_grid(tf.reshape(var, [BATCH_SIZE, prod]), [GRID_ROWS,
GRID_COLS], image_dims, 1)
s_grid = tf.squeeze(grid, axis=0)
# This reproduces the code in: tensorflow/core/kernels/summary_image_op.cc
im_min = tf.reduce_min(s_grid)
im_max = tf.reduce_max(s_grid)
kZeroThreshold = tf.constant(1e-6)
max_val = tf.maximum(tf.abs(im_min), tf.abs(im_max))
offset = tf.cond(
im_min < tf.constant(0.0),
lambda: tf.constant(128.0),
lambda: tf.constant(0.0)
)
scale = tf.cond(
im_min < tf.constant(0.0),
lambda: tf.cond(
max_val < kZeroThreshold,
lambda: tf.constant(0.0),
lambda: tf.div(127.0, max_val)
),
lambda: tf.cond(
im_max < kZeroThreshold,
lambda: tf.constant(0.0),
lambda: tf.div(255.0, im_max)
)
)
s_grid = tf.cast(tf.add(tf.multiply(s_grid, scale), offset), tf.uint8)
enc = tf.image.encode_jpeg(s_grid)
fwrite = tf.write_file(name, enc)
return fwrite
#-----------------------------------------------------------------------------------
#creating augmented images----------------------------------------------------------
types = ('newImagesForAugment\*jpg', 'newImagesForAugment\*jpeg')
for files in types:
IMAGES_NAME.extend(glob.glob(files))
# Path to image
sess1 = tf.Session()
for singleImage in IMAGES_NAME:
img = pt.imread(singleImage)
tf_img = tf.convert_to_tensor(img)
brght_img = tf.image.flip_left_right(tf_img)
fileToSave = tf.image.encode_jpeg(brght_img)
fname = tf.constant(singleImage.split('.')[0] + 'output2.jpg')
fwrite = tf.write_file(fname, fileToSave)
result = sess1.run(fwrite)
brght_img = tf.image.transpose_image(tf_img)
fileToSave = tf.image.encode_jpeg(brght_img)
fname = tf.constant(singleImage.split('.')[0] + 'transposed.jpg')
fwrite = tf.write_file(fname, fileToSave)
result = sess1.run(fwrite)
brght_img = tf.image.rot90(tf_img)
fileToSave = tf.image.encode_jpeg(brght_img)
fname = tf.constant(singleImage.split('.')[0] + 'rot90.jpg')
fwrite = tf.write_file(fname, fileToSave)
result = sess1.run(fwrite)
brght_img = tf.image.rot90(tf_img, k=3)
fileToSave = tf.image.encode_jpeg(brght_img)
fname = tf.constant(singleImage.split('.')[0] + 'rot90.jpg')
fwrite = tf.write_file(fname, fileToSave)
def run_batch_colornorm(filenames,
nstains,
lamb,
output_direc,
img_level,
background_correction=True,
config=None):
if config is None:
config = tf.ConfigProto(log_device_placement=False)
g_1 = tf.Graph()
with g_1.as_default():
Wis1 = tf.placeholder(tf.float32)
Img1 = tf.placeholder(tf.float32, shape=(None, None, 3))
src_i_0 = tf.placeholder(tf.float32)
s = tf.shape(Img1)
Img_vecd = tf.reshape(tf.minimum(Img1, src_i_0), [s[0] * s[1], s[2]])
V = tf.log(src_i_0 + 1.0) - tf.log(Img_vecd + 1.0)
Wi_inv = tf.transpose(tf.py_func(np.linalg.pinv, [Wis1], tf.float32))
Hiv1 = tf.nn.relu(tf.matmul(V, Wi_inv))
Wit1 = tf.placeholder(tf.float32)
Hiv2 = tf.placeholder(tf.float32)
sav_name = tf.placeholder(tf.string)
tar_i_0 = tf.placeholder(tf.float32)
normfac = tf.placeholder(tf.float32)
shape = tf.placeholder(tf.int32)
Hsonorm = Hiv2 * normfac
source_norm = tf.cast(
tar_i_0 * tf.exp(
(-1) * tf.reshape(tf.matmul(Hsonorm, Wit1), shape)), tf.uint8)
enc = tf.image.encode_png(source_norm)
fwrite = tf.write_file(sav_name, enc)
session1 = tf.Session(graph=g_1, config=config)
file_no = 0
print "To be normalized:", filenames[1:], "using", filenames[0]
for filename in filenames:
display_separator()
if background_correction:
correc = "back-correc"
else:
correc = "no-back-correc"
base_t = os.path.basename(filenames[0]) #target.svs
fname_t = os.path.splitext(base_t)[0] #target
base_s = os.path.basename(filename) #source.svs
fname_s = os.path.splitext(base_s)[0] #source
f_form = os.path.splitext(base_s)[1] #.svs
s = output_direc + base_s.replace(
".", "_") + " (using " + base_t.replace(
".", "_") + " " + correc + ").png"
# s=output_direc+base_s.replace(".", "_")+" (no-norm using "+base_t.replace(".", "_")+").png"
#s=output_direc+fname_s+"_normalized.png"
tic = time.time()
print
I = openslide.open_slide(filename)
if img_level >= I.level_count:
print "Level", img_level, "unavailable for image, proceeding with level 0"
level = 0
else:
level = img_level
xdim, ydim = I.level_dimensions[level]
ds = I.level_downsamples[level]
if file_no == 0:
print "Target Stain Separation in progress:", filename, str(
xdim) + str("x") + str(ydim)
else:
print "Source Stain Separation in progress:", filename, str(
xdim) + str("x") + str(ydim)
print "\t \t \t \t \t \t \t \t \t \t Time: 0"
#parameters for W estimation
num_patches = 20
patchsize = 1000 #length of side of square
i0_default = np.array([255., 255., 255.], dtype=np.float32)
Wi, i0 = Wfast(I, nstains, lamb, num_patches, patchsize, level,
background_correction)
if i0 is None:
print "No white background detected"
i0 = i0_default
if not background_correction:
print "Background correction disabled, default background intensity assumed"
i0 = i0_default
if Wi is None:
print "Color Basis Matrix Estimation failed...image normalization skipped"
continue
print "W estimated",
print "\t \t \t \t \t \t Time since processing started:", round(
time.time() - tic, 3)
Wi = Wi.astype(np.float32)
if file_no == 0:
print "Target Color Basis Matrix:"
print Wi
Wi_target = np.transpose(Wi)
tar_i0 = i0
print "Target Image Background white intensity:", i0
else:
print "Source Color Basis Matrix:"
print Wi
print "Source Image Background white intensity:", i0
_max = 2000
print
if (xdim * ydim) <= (_max * _max):
print "Small image processing..."
img = np.asarray(I.read_region((0, 0), level, (xdim, ydim)),
dtype=np.float32)[:, :, :3]
Hiv = session1.run(Hiv1,
feed_dict={
Img1: img,
Wis1: Wi,
src_i_0: i0
})
# Hta_Rmax = np.percentile(Hiv,q=99.,axis=0)
H_Rmax = np.ones((nstains, ), dtype=np.float32)
for i in range(nstains):
t = Hiv[:, i]
H_Rmax[i] = np.percentile(t[t > 0], q=99., axis=0)
if file_no == 0:
file_no += 1
Hta_Rmax = np.copy(H_Rmax)
print "Target H calculated",
print "\t \t \t \t \t \t \t Total Time:", round(
time.time() - tic, 3)
display_separator()
continue
print "Color Normalization in progress..."
norm_fac = np.divide(Hta_Rmax, H_Rmax).astype(np.float32)
session1.run(fwrite,
feed_dict={
shape: np.array(img.shape),
Wit1: Wi_target,
Hiv2: Hiv,
sav_name: s,
tar_i_0: tar_i0,
normfac: norm_fac
})
print "File written to:", s
print "\t \t \t \t \t \t \t \t \t \t Total Time:", round(
time.time() - tic, 3)
display_separator()
else:
_maxtf = 3000
x_max = xdim
y_max = min(max(int(_maxtf * _maxtf / x_max), 1), ydim)
print "Large image processing..."
if file_no == 0:
Hivt = np.memmap('H_target',
dtype='float32',
mode='w+',
shape=(xdim * ydim, 2))
else:
Hivs = np.memmap('H_source',
dtype='float32',
mode='w+',
shape=(xdim * ydim, 2))
sourcenorm = np.memmap('wsi',
dtype='uint8',
mode='w+',
shape=(ydim, xdim, 3))
x_tl = range(0, xdim, x_max)
y_tl = range(0, ydim, y_max)
print "WSI divided into", str(len(x_tl)) + "x" + str(len(y_tl))
count = 0
print "Patch-wise H calculation in progress..."
ind = 0
perc = []
for x in x_tl:
for y in y_tl:
count += 1
xx = min(x_max, xdim - x)
yy = min(y_max, ydim - y)
print "Processing:", count, " patch size", str(
xx) + "x" + str(yy),
print "\t \t Time since processing started:", round(
time.time() - tic, 3)
img = np.asarray(I.read_region((int(ds * x), int(ds * y)),
level, (xx, yy)),
dtype=np.float32)[:, :, :3]
Hiv = session1.run(Hiv1,
feed_dict={
Img1: img,
Wis1: Wi,
src_i_0: i0
})
if file_no == 0:
Hivt[ind:ind + len(Hiv), :] = Hiv
_Hta_Rmax = np.ones((nstains, ), dtype=np.float32)
for i in range(nstains):
t = Hiv[:, i]
_Hta_Rmax[i] = np.percentile(t[t > 0],
q=99.,
axis=0)
perc.append([_Hta_Rmax[0], _Hta_Rmax[1]])
ind += len(Hiv)
continue
else:
Hivs[ind:ind + len(Hiv), :] = Hiv
_Hso_Rmax = np.ones((nstains, ), dtype=np.float32)
for i in range(nstains):
t = Hiv[:, i]
_Hso_Rmax[i] = np.percentile(t[t > 0],
q=99.,
axis=0)
perc.append([_Hso_Rmax[0], _Hso_Rmax[1]])
ind += len(Hiv)
if file_no == 0:
print "Target H calculated",
Hta_Rmax = np.percentile(np.array(perc), 50, axis=0)
file_no += 1
del Hivt
print "\t \t \t \t \t Time since processing started:", round(
time.time() - tic, 3)
ind = 0
continue
print "Source H calculated",
print "\t \t \t \t \t Time since processing started:", round(
time.time() - tic, 3)
Hso_Rmax = np.percentile(np.array(perc), 50, axis=0)
print "H Percentile calculated",
print "\t \t \t \t Time since processing started:", round(
time.time() - tic, 3)
_normfac = np.divide(Hta_Rmax, Hso_Rmax).astype(np.float32)
print "Color Normalization in progress..."
count = 0
ind = 0
np_max = 1000
x_max = xdim
y_max = min(max(int(np_max * np_max / x_max), 1), ydim)
x_tl = range(0, xdim, x_max)
y_tl = range(0, ydim, y_max)
print "Patch-wise color normalization in progress..."
total = len(x_tl) * len(y_tl)
prev_progress = 0
for x in x_tl:
for y in y_tl:
count += 1
xx = min(x_max, xdim - x)
yy = min(y_max, ydim - y)
pix = xx * yy
sh = np.array([yy, xx, 3])
#Back projection into spatial intensity space (Inverse Beer-Lambert space)
sourcenorm[y:y + yy, x:x + xx, :3] = session1.run(
source_norm,
feed_dict={
Hiv2: np.array(Hivs[ind:ind + pix, :]),
Wit1: Wi_target,
normfac: _normfac,
shape: sh,
tar_i_0: tar_i0
})
ind += pix
percent = 5 * int(count * 20 / total) #nearest 5 percent
if percent > prev_progress and percent < 100:
print str(percent) + " percent complete...",
print "\t \t \t \t \t Time since processing started:", round(
time.time() - tic, 3)
prev_progress = percent
print "Color Normalization complete!",
print "\t \t \t \t Time since processing started:", round(
time.time() - tic, 3)
p = time.time() - tic
s = output_direc + base_s.replace(
".", "_") + " (using " + base_t.replace(
".", "_") + " " + correc + ").png"
print "Saving normalized image..."
cv2.imwrite(s, cv2.cvtColor(sourcenorm, cv2.COLOR_RGB2BGR))
del sourcenorm
print "File written to:", s
print "\t \t \t \t \t \t \t \t \t Total Time:", round(
time.time() - tic, 3)
display_separator()
file_no += 1
if os.path.exists("H_target"):
os.remove("H_target")
if os.path.exists("H_source"):
os.remove("H_source")
if os.path.exists("wsi"):
os.remove("wsi")
session1.close()
def save_image(filename, image):
image = tf.image.encode_png(image)
return tf.write_file(filename, image)
def save_image(filename, image):
"""Saves an image to a PNG file."""
image = quantize_image(image)
string = tf.image.encode_png(image)
return tf.write_file(filename, string)
#AutoA_eval = tf.split(AutoA_eval,1)
#AutoB_eval = tf.split(AutoB_eval,1)
#AutoA_eval.set_shape([288,512,3])
#AutoB_eval.set_shape([288,512,3])
encode_imA = tf.image.encode_jpeg(tf.cast(255 * AutoA_eval[0], tf.uint8))
encode_imB = tf.image.encode_jpeg(tf.cast(255 * AutoB_eval[0], tf.uint8))
encode_imAA = tf.image.encode_jpeg(tf.cast(255 * AutoA[0], tf.uint8))
encode_imBB = tf.image.encode_jpeg(tf.cast(255 * AutoB[0], tf.uint8))
name_A = tf.placeholder(tf.string)
name_B = tf.placeholder(tf.string)
name_AA = tf.placeholder(tf.string)
name_BB = tf.placeholder(tf.string)
fwriteA = tf.write_file(name_A, encode_imA)
fwriteB = tf.write_file(name_B, encode_imB)
fwriteAA = tf.write_file(name_AA, encode_imAA)
fwriteBB = tf.write_file(name_BB, encode_imBB)
diffA = tf.reduce_mean(tf.square(imA - AutoA))
diffB = tf.reduce_mean(tf.square(imB - AutoB))
kf = tf.placeholder(tf.float32)
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
with tf.control_dependencies(update_ops):
optimizeA = tf.train.AdamOptimizer(kf).minimize(diffA)
optimizeB = tf.train.AdamOptimizer(kf).minimize(diffB)
#Option for GTX 970 on home PC. Design flaw in that card makes last bit of VRAM Slow
gpu_opt = tf.GPUOptions(per_process_gpu_memory_fraction=0.7)
def _get_write_image_ops(eval_dir, filename, images):
"""Create Ops that write images to disk."""
return tf.write_file(
'%s/%s' % (eval_dir, filename),
tf.image.encode_png(data_provider.float_image_to_uint8(images)))
#test -> zdjecie powinno być zgodne z nazwa klasy
print(all_image_paths[2])
print(classNames[imagesClassIdList[2]])
#wczytywanie zdjecia do tensora (macierzy) -> jak na JA :P
img_raw = tf.read_file(all_image_paths[2])
img_tensor = tf.image.decode_jpeg(img_raw,channels=3)
#przycianie obrazka -> kazdy opis naszego obrazu ma "bounding boxy" dp ktorych nalezy obciac
#obrazy
cropped_image = tf.image.crop_to_bounding_box(img_tensor,2,2,100,100)
#testowy zapis
saveImage = tf.image.encode_jpeg(cropped_image)
writer = tf.write_file('test.jpg', saveImage)
print(img_tensor.shape)
#wszystko fajnie ale sie nie wykonalo
#tu jest takie ala lazy loading dla tensorowych operacji, read_file oraz
#decode_jpeg sie nie wykonaly, dopiero jak zadeklaruje i uruchomie sesje to sie to wykona
with tf.Session() as sess:
img = sess.run(img_tensor)
img2 = sess.run(cropped_image)
sess.run(writer)
print(img.shape)
print(img2.shape)
imsp = tf.shape(im)
impng = tf.reshape(im, [imsp[0], imsp[1] * imsp[2], 1])
impng = tf.cast(
tf.clip_by_value(impng + 0.5, 0.0, 2.0) / 2.0 * (2**16 - 1), tf.uint16)
impng = tf.image.encode_png(impng)
nzpng = tf.reshape(nz, [imsp[0], imsp[1] * imsp[2], 1])
nzpng = tf.cast(
tf.clip_by_value(nzpng + 0.5, 0.0, 2.0) / 2.0 * (2**16 - 1), tf.uint16)
nzpng = tf.image.encode_png(nzpng)
dzpng = tf.reshape(dz, [imsp[0], imsp[1] * imsp[2], 1])
dzpng = tf.cast(
tf.clip_by_value(dzpng + 0.5, 0.0, 2.0) / 2.0 * (2**16 - 1), tf.uint16)
dzpng = tf.image.encode_png(dzpng)
encoded = [impng, nzpng, dzpng]
fwrites = [tf.write_file(fnms[i], encoded[i]) for i in range(len(fnms))]
#########################################################################
# Start TF session (respecting OMP_NUM_THREADS) & load model
nthr = os.getenv('OMP_NUM_THREADS')
if nthr is None:
sess = tf.Session()
else:
sess = tf.Session(config=tf.ConfigProto(
intra_op_parallelism_threads=int(nthr)))
sess.run(tf.global_variables_initializer())
# Load model
wts = np.load(mfile)
ph = tf.placeholder(tf.float32)
for k in wts.keys():
def _get_write_image_ops(eval_dir, filename, images):
"""Create Ops that write images to disk."""
return tf.write_file(
'%s/%s'% (eval_dir, filename),
tf.image.encode_png(data_provider.float_image_to_uint8(images)))
def save_image(filename, image): """Saves an image to a PNG file.""" image = quantize_image(image) string = tf.image.encode_png(image) return tf.write_file(filename, string)
import tensorflow as tf
import cv2
imageObj = cv2.imread('input.jpg')
imageObj = imageObj[:, :, ::-1]
image = tf.convert_to_tensor(imageObj, dtype=tf.float32)
input_layer = tf.reshape(image, [-1, 720, 1280, 3])
pool1 = tf.layers.average_pooling2d(inputs=input_layer,
pool_size=[2, 2],
strides=2)
pool2 = tf.layers.average_pooling2d(inputs=pool1, pool_size=[2, 2], strides=2)
pool3 = tf.layers.average_pooling2d(inputs=pool2, pool_size=[2, 2], strides=2)
pool4 = tf.layers.average_pooling2d(inputs=pool3, pool_size=[2, 2], strides=2)
resized = tf.image.resize_images(pool4, [720, 1280])
output_image = tf.image.encode_jpeg(tf.cast(resized[0], tf.uint8))
file_name = tf.constant('./Ouput_image.jpeg')
with tf.Session():
tf.write_file(file_name, output_image).run()
def main():
if (not args.use_gpu):
os.environ['CUDA_VISIBLE_DEVICES'] = '-1'
# load and build graph
with tf.Graph().as_default():
model_input_path = tf.placeholder(tf.string, [])
model_output_path = tf.placeholder(tf.string, [])
image = tf.read_file(model_input_path)
image = [tf.image.decode_png(image, channels=3, dtype=tf.uint8)]
image = tf.cast(image, tf.float32)
with tf.gfile.GFile(args.model_name, 'rb') as f:
model_graph_def = tf.GraphDef()
model_graph_def.ParseFromString(f.read())
# add a workaround to support frozen models having input scale as a placeholder
model_output = None
if (model_output is None):
try:
model_input_scale = tf.constant(4, dtype=tf.float32)
model_output = tf.import_graph_def(
model_graph_def,
name='model',
input_map={
'sr_input:0': image,
'sr_input_scale:0': model_input_scale
},
return_elements=['sr_output:0'])[0]
except:
model_output = None
if (model_output is None):
try:
model_output = tf.import_graph_def(
model_graph_def,
name='model',
input_map={'sr_input:0': image},
return_elements=['sr_output:0'])[0]
except:
model_output = None
model_output = model_output[0, :, :, :]
model_output = tf.round(model_output)
model_output = tf.clip_by_value(model_output, 0, 255)
model_output = tf.cast(model_output, tf.uint8)
image = tf.image.encode_png(model_output)
write_op = tf.write_file(model_output_path, image)
init = tf.global_variables_initializer()
sess = tf.Session(config=tf.ConfigProto(log_device_placement=False,
allow_soft_placement=True))
sess.run(init)
# get image path list
image_path_list = []
for root, subdirs, files in os.walk(args.input_path):
for filename in files:
if (filename.lower().endswith('.png')):
input_path = os.path.join(args.input_path, filename)
output_path = os.path.join(args.output_path, filename)
image_path_list.append([input_path, output_path])
print('Found %d images' % (len(image_path_list)))
# iterate
for input_path, output_path in image_path_list:
print('- %s -> %s' % (input_path, output_path))
sess.run([write_op],
feed_dict={
model_input_path: input_path,
model_output_path: output_path
})
print('Done')
def main():
# Build the scene geometry, which is just an axis-aligned cube centred at the origin in world space
cube_vertices_object = []
cube_uvs = []
cube_faces = []
def add_quad(vertices, uvs):
index = len(cube_vertices_object)
cube_faces.extend([[index + 2, index + 1, index],
[index, index + 3, index + 2]])
cube_vertices_object.extend(vertices)
cube_uvs.extend(uvs)
add_quad(vertices=[[-1, -1, 1], [1, -1, 1], [1, 1, 1], [-1, 1, 1]],
uvs=[[0.1, 0.9], [0.9, 0.9], [0.9, 0.1], [0.1, 0.1]]) # front
add_quad(vertices=[[-1, -1, -1], [1, -1, -1], [1, 1, -1], [-1, 1, -1]],
uvs=[[1, 1], [0, 1], [0, 0], [1, 0]]) # back
add_quad(vertices=[[1, 1, 1], [1, 1, -1], [1, -1, -1], [1, -1, 1]],
uvs=[[0.3, 0.25], [0.6, 0.25], [0.6, 0.55], [0.3, 0.55]]) # right
add_quad(vertices=[[-1, 1, 1], [-1, 1, -1], [-1, -1, -1], [-1, -1, 1]],
uvs=[[0.4, 0.4], [0.5, 0.4], [0.5, 0.5], [0.4, 0.5]]) # left
add_quad(vertices=[[-1, 1, -1], [1, 1, -1], [1, 1, 1], [-1, 1, 1]],
uvs=[[0, 0], [2, 0], [2, 2], [0, 2]]) # top
add_quad(vertices=[[-1, -1, -1], [1, -1, -1], [1, -1, 1], [-1, -1, 1]],
uvs=[[0, 0], [2, 0], [2, 2], [0, 2]]) # bottom
cube_vertices_object = np.asarray(cube_vertices_object, np.float32)
cube_uvs = np.asarray(cube_uvs, np.float32)
# Load the texture image
texture = tf.cast(
tf.image.decode_jpeg(
tf.read_file(os.path.dirname(__file__) + '/cat.jpg')),
tf.float32) / 255.
# Convert vertices to homogeneous coordinates
cube_vertices_object = tf.concat(
[cube_vertices_object,
tf.ones_like(cube_vertices_object[:, -1:])],
axis=1)
# Transform vertices from object to world space, by rotating around the vertical axis
cube_vertices_world = tf.matmul(cube_vertices_object,
matrices.rodrigues([0., 0.6, 0.]))
# Calculate face normals
cube_normals_world = lighting.vertex_normals(cube_vertices_world,
cube_faces)
# Transform vertices from world to camera space; note that the camera points along the negative-z axis in camera space
view_matrix = matrices.compose(
matrices.translation([0., -2.,
-3.2]), # translate it away from the camera
matrices.rodrigues([-0.5, 0., 0.]) # tilt the view downwards
)
cube_vertices_camera = tf.matmul(cube_vertices_world, view_matrix)
# Transform vertices from camera to clip space
projection_matrix = matrices.perspective_projection(
near=0.1, far=20., right=0.1, aspect=float(frame_height) / frame_width)
cube_vertices_clip = tf.matmul(cube_vertices_camera, projection_matrix)
# The following function is applied to the G-buffer, which is a multi-channel image containing all the vertex attributes. It
# uses this to calculate the shading (texture and lighting) at each pixel, hence their final intensities
def shader_fn(gbuffer, texture, light_direction):
# Unpack the different attributes from the G-buffer
mask = gbuffer[:, :, :1]
uvs = gbuffer[:, :, 1:3]
normals = gbuffer[:, :, 3:]
# Sample the texture at locations corresponding to each pixel; this defines the unlit material color at each point
unlit_colors = sample_texture(
texture, uvs_to_pixel_indices(uvs,
tf.shape(texture)[:2]))
# Calculate a simple grey ambient lighting component
ambient_contribution = unlit_colors * [0.4, 0.4, 0.4]
# Calculate a diffuse (Lambertian) lighting component
diffuse_contribution = lighting.diffuse_directional(
tf.reshape(normals, [-1, 3]),
tf.reshape(unlit_colors, [-1, 3]),
light_direction,
light_color=[0.6, 0.6, 0.6],
double_sided=True)
diffuse_contribution = tf.reshape(diffuse_contribution,
[frame_height, frame_width, 3])
# The final pixel intensities inside the shape are given by combining the ambient and diffuse components;
# outside the shape, they are set to a uniform background color
pixels = (diffuse_contribution +
ambient_contribution) * mask + [0., 0., 0.3] * (1. - mask)
return pixels
# Render the G-buffer channels (mask, UVs, and normals at each pixel), then perform the deferred shading calculation
# In general, any tensor required by shader_fn and wrt which we need derivatives should be included in shader_additional_inputs;
# although in this example they are constant, we pass the texture and lighting direction through this route as an illustration
light_direction = tf.linalg.l2_normalize([1., -0.3, -0.5])
pixels = dirt.rasterise_deferred(
vertices=cube_vertices_clip,
vertex_attributes=tf.concat(
[
tf.ones_like(cube_vertices_object[:, :1]), # mask
cube_uvs, # texture coordinates
cube_normals_world # normals
],
axis=1),
faces=cube_faces,
background_attributes=tf.zeros([frame_height, frame_width, 6]),
shader_fn=shader_fn,
shader_additional_inputs=[texture, light_direction])
save_pixels = tf.write_file(
'textured.jpg', tf.image.encode_jpeg(tf.cast(pixels * 255, tf.uint8)))
session = tf.Session(config=tf.ConfigProto(gpu_options=tf.GPUOptions(
allow_growth=True)))
with session.as_default():
save_pixels.run()
def val(args):
## set hyparameter
img_mean = np.array((104.00698793, 116.66876762, 122.67891434),
dtype=np.float32)
tf.set_random_seed(args.random_seed)
## load data
image_list, label_list, png_list = read_labeled_image_list(
args.data_dir,
is_val=True,
valid_image_store_path=args.valid_image_store_path)
num_val = len(image_list)
image_name = tf.placeholder(dtype=tf.string)
label_name = tf.placeholder(dtype=tf.string)
png_name = tf.placeholder(dtype=tf.string)
image_batch, label_batch = get_validate_data(image_name, label_name,
img_mean)
print("data load completed!")
## load model
g_net = choose_generator(args.g_name, image_batch)
raw_output = g_net.terminals[-1]
predict_batch = g_net.topredict(raw_output, tf.shape(label_batch)[1:3])
predict_img = tf.write_file(
png_name,
tf.image.encode_png(
tf.cast(tf.squeeze(predict_batch, axis=0), dtype=tf.uint8)))
labels, logits = convert_to_calculateloss(raw_output, label_batch,
args.num_classes)
pre_labels = tf.argmax(logits, 1)
print("Model load completed!")
iou, iou_op = tf.metrics.mean_iou(labels,
pre_labels,
args.num_classes,
name='iou')
acc, acc_op = tf.metrics.accuracy(labels, pre_labels)
m_op = tf.group(iou_op, acc_op)
image = tf.py_func(inv_preprocess,
[image_batch, args.save_num_images, img_mean], tf.uint8)
label = tf.py_func(decode_labels, [
label_batch,
], tf.uint8)
pred = tf.py_func(decode_labels, [
predict_batch,
], tf.uint8)
tf.summary.image(name='img_collection_val',
tensor=tf.concat([image, label, pred], 2))
tf.summary.scalar(name='iou_val', tensor=iou)
tf.summary.scalar(name='acc_val', tensor=acc)
sum_op = tf.summary.merge_all()
sum_writer = tf.summary.FileWriter(args.log_dir, max_queue=5)
sess = tf.Session()
global_init = tf.global_variables_initializer()
local_init = tf.local_variables_initializer()
sess.run(global_init)
sess.run(local_init)
saver = tf.train.Saver(var_list=tf.global_variables())
_ = load_weight(args.restore_from, saver, sess)
if not os.path.exists(args.valid_image_store_path):
os.makedirs(args.valid_image_store_path)
print("validation begining")
for step in range(num_val):
it = time.time()
dict = {
image_name: image_list[step],
label_name: label_list[step],
png_name: png_list[step]
}
_, _, iou_val = sess.run([m_op, predict_img, iou], dict)
if step % 50 == 0 or step == num_val - 1:
summ = sess.run(sum_op, dict)
sum_writer.add_summary(summ, step)
print("step:{},time:{},iou:{}".format(step,
time.time() - it, iou_val))
print("end......")
if prev == None:
lines, depth = generator(
tf.concat([image, tf.zeros((1, IMG_HEIGHT, IMG_WIDTH, 6))],
axis=-1))
else:
warped = tf.contrib.image.dense_image_warp(prev, flow)
warped_depth = tf.contrib.image.dense_image_warp(prev_depth, flow)
lines, depth = generator(
tf.concat([image, warped, warped_depth], axis=-1))
prev = lines
prev_depth = depth
def prepare(img):
img = tf.image.resize_images(img,
size=[IMG_HEIGHT, IMG_WIDTH],
align_corners=True,
method=tf.image.ResizeMethod.BICUBIC)
img = img * 0.5 + 0.5
img = tf.squeeze(img)
img = tf.image.convert_image_dtype(img, dtype=tf.uint8, saturate=True)
img = tf.image.encode_png(img)
return img
lines = prepare(lines)
depth = prepare(depth)
tf.write_file(os.path.join(output_dir, "lines", file), lines)
tf.write_file(os.path.join(output_dir, "depth", file), depth)
D_loss_summary = tf.summary.histogram("D_loss", D_loss)
G_loss_summary = tf.summary.histogram("G_loss", G_loss)
D_train = tf.train.AdamOptimizer(learning_rate).minimize(-D_loss,
var_list=D_var_list)
G_train = tf.train.AdamOptimizer(learning_rate).minimize(-G_loss,
var_list=G_var_list)
samples = Generator(Y)
samples_img = tf.reshape(tf.cast(samples * 128, tf.uint8), [-1, 28, 28, 1])
img = tf.image.encode_jpeg(tf.reshape(samples_img, [28, 28, 1]),
format='grayscale')
temp_name = tf.constant("./testimages/") + E + tf.constant(
"_") + J + tf.constant(".jpeg")
fsave = tf.write_file(temp_name, img)
sess = tf.Session()
summary_merge = tf.summary.merge_all()
writer = tf.summary.FileWriter("./logs")
writer.add_graph(sess.graph)
sess.run(tf.global_variables_initializer())
print('Learing Started')
total_batch = int(mnist.train.num_examples / batch_size)
for epoch in range(training_epochs):
if True: # (epoch + 1) % 10 == 0 or epoch == 0:
flag = 1
def save_dataset(dataset, shape, subdir_name):
#dataset = dataset['train']
#valid_data = dataset['valid']
#test_data = dataset['test']
print(dataset)
#print(valid_data)
#sys.exit(0)
# create TensorFlow Iterator object
iterator = Iterator.from_structure(dataset.output_types,
dataset.output_shapes)
next_element = iterator.get_next() #features, labels = iterator.get_next()
# create two initialization ops to switch between the datasets
train_init_op = iterator.make_initializer(dataset)
#valid_init_op = iterator.make_initializer(valid_data)
# 3) Calculate bottleneck in TF
height, width, color = shape
x = tf.placeholder(tf.float32, [height, width, 3], name='Placeholder-x')
#x = x * tf.constant(255.0)
#resized_input_tensor = tf.reshape(x, [-1, height, width, 3])
# num_features = 2048, height x width = 224 x 224 pixels
#assert height, width == hub.get_expected_image_size(module)
#bottleneck_tensor = module(resized_input_tensor) # Features with shape [batch_size, num_features]
#print('bottleneck_tensor:', bottleneck_tensor)
#bottleneck_data = dict()
#bottleneck_data['train'] = {'images':[], 'labels':[]}
#bottleneck_data['valid'] = {'images':[], 'labels':[]}
#bottleneck_data['test'] = {'images':[], 'labels':[]}
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
# initialize the iterator on the training data
sess.run(train_init_op) # switch to train dataset
i = 0
# get each element of the training dataset until the end is reached
while True:
i += 1
try:
print('i = ', i)
item = sess.run(next_element)
#jpeg_image = tf.image.encode_jpeg(item)
#op = tf.image.encode_jpeg(item, format='rgb', quality=100)
op = tf.image.encode_jpeg(item)
#data_np = sess.run(op, feed_dict={ x: item })
#print(data_np)
image = op.eval()
#print(image)
fname = tf.constant('{0}/augment_{1}.jpg'.format(
subdir_name, i))
wr = tf.write_file(fname, image)
sess.run(wr)
#with open('01.jpg', 'wb') as fp:
# fp.write(image)
#tf.write_file(fname, data_np * tf.constant(255.0))
#print("tf.write_file('1.jpg', jpeg_image)")
#img, label = elem
#feature_vectors = bottleneck_tensor.eval(feed_dict={ x : batch[0] })
#label = elem[1]
#images = list(map(list, feature_vectors))
#labels = list(map(list, batch[1]))
#bottleneck_data['train']['images'] += images
#bottleneck_data['train']['labels'] += labels
#print(labels)
except tf.errors.OutOfRangeError:
print("End of training dataset.")
break
return 0
"""
def main():
if (not args.use_gpu):
os.environ['CUDA_VISIBLE_DEVICES'] = '-1'
# load and build graph
with tf.Graph().as_default():
model_input_path = tf.placeholder(tf.string, [])
model_output_path = tf.placeholder(tf.string, [])
data = tf.placeholder(tf.string, shape=[])
image = tf.read_file(model_input_path)
image = [tf.image.decode_png(image, channels=3, dtype=tf.uint8)]
image = tf.cast(image, tf.float32)
with tf.gfile.GFile("4pp_eusr_pirm.pb", 'rb') as f:
model_graph_def = tf.GraphDef()
model_graph_def.ParseFromString(f.read())
model_output = tf.import_graph_def(model_graph_def,
name='model',
input_map={'sr_input:0': image},
return_elements=['sr_output:0'])[0]
print(model_output)
model_output = model_output[0, :, :, :]
model_output = tf.round(model_output)
model_output = tf.clip_by_value(model_output, 0, 255)
model_output = tf.cast(model_output, tf.uint8)
print(model_output)
image = tf.image.encode_png(model_output) #RIGHT. HERE.
#image = tf.image.random_brightness(image)
#image = tf.image.encode_png(image)
write_op = tf.write_file(model_output_path,
image) #it's literally right here smartass
image = tf.image.adjust_saturation(tf.io.decode_png(image), float(100))
#experiment time
print(image)
#image = tf.io.decode_png(image)
print(image)
#ttt = image.eval()
init = tf.global_variables_initializer()
tf.print(image)
sess = tf.Session(config=tf.ConfigProto(log_device_placement=False,
allow_soft_placement=True))
print("post sess declare")
print(data)
sess.run(init)
#print(sess.run([toot],feed_dict={data:image}))
#with sess.as_default(): # or `with sess:` to close on exit
#assert sess is tf.get_default_session()
#assert image.eval() == sess.run(image)
# get image path list
image_path_list = []
image_byte_list = []
for root, subdirs, files in os.walk(args.input_path):
for filename in files:
if (filename.lower().endswith('.png')):
input_path = os.path.join(args.input_path, filename)
output_path = os.path.join(args.output_path, filename)
image_path_list.append([input_path, output_path])
print('Found %d images' % (len(image_path_list)))
#global data_format
# iterate
for input_path, output_path in image_path_list:
print('- %s -> %s' % (input_path, output_path))
sess.run([write_op],
feed_dict={
model_input_path: input_path,
model_output_path: output_path
})
#file = Image.open(output_path,'r')
#imgbytes = save_image_in_memory(test)
# image_byte_list.append(imgbytes)
#print(imgbytes)
#test=sess.run()
#print(test)
#sess.run([toot],feed_dict={data:image})
#with sess.as_default():
#print(image.eval())
print('Done')
def autoencoder_run(_):
g1 = tf.Graph()
with g1.as_default():
with tf.device("/cpu:0"):
ds_dict, init_dict, nxt_dict = read_images()
with tf.device("/gpu:0"):
#Define config
tfconfig = tf.ConfigProto(allow_soft_placement=True)
tfconfig.gpu_options.per_process_gpu_memory_fraction = 0.5
#PLACEHOLDERS
lr = tf.placeholder_with_default(tf.cast(FLAGS.learning_rate,tf.float32), shape=[], name="learning_rate")
mom = tf.placeholder_with_default(tf.cast(FLAGS.mom1,tf.float32), shape=[], name="momentum")
IMG = tf.placeholder(dtype=tf.float32,shape= (None,32,32,3),name="placeholder/IMG")
ZCA = tf.placeholder(dtype=tf.float32,shape= (None,32,32,3),name="placeholder/ZCA")
#BUILD GRAPH
with tf.variable_scope("CNN") as scope:
loss, train_op = build_training_graph(IMG,ZCA,lr, mom)
scope.reuse_variables()
# Build eval graph
losses_eval_train, sample = build_eval_graph(IMG,ZCA)
losses_eval_test, _ = build_eval_graph(IMG,ZCA)
latent_space = build_emb_graph(IMG, ZCA)
X = tf.placeholder(tf.float32, shape=[None, 128, 128, 3], name="X")
latent_space = tf.reshape(tensor=latent_space,shape=[FLAGS.eval_batch_size,-1])
#Create FileWriter
if not FLAGS.log_dir:
writer_train = None
writer_test = None
else:
writer_train = tf.summary.FileWriter(FLAGS.log_dir + "/train", g1)
writer_test = tf.summary.FileWriter(FLAGS.log_dir + "/test", g1)
#FileWriter for embedding
if FLAGS.autoencoder_mode=="embedding":
writer_emb = tf.summary.FileWriter(FLAGS.log_dir + "emb", g1)
else:
writer_emb = None
with tf.train.MonitoredTrainingSession(\
checkpoint_dir=FLAGS.train_dir,
hooks=[],
config=tfconfig) as mon_sess:
if FLAGS.autoencoder_mode == "embedding":
#Get embedded rep
mon_sess.run(init_dict["AE_emb"].initializer)
bs = FLAGS.eval_batch_size
labels = np.zeros((NUM_EXAMPLES_TRAIN))
zca = np.zeros((NUM_EXAMPLES_TRAIN,32*32*3))
image = np.zeros((NUM_EXAMPLES_TRAIN,32*32*3))
for i in range(NUM_EXAMPLES_TRAIN//bs):
nxt = mon_sess.run(nxt_dict["AE_emb"])
assert (nxt["id"][0] == i * bs)
labels[i * bs : (i+1)*bs] = np.reshape(np.argmax(nxt["label"],axis=1),(-1))
zca[i * bs : (i+1)*bs,:] = np.reshape(nxt["zca"],(bs,-1))
image[i * bs : (i+1)*bs,:] = np.reshape(nxt["image"],(bs,-1))
feed_dict = { IMG:nxt["image"],
ZCA:nxt["zca"]}
emb = mon_sess.run(latent_space,feed_dict=feed_dict)
if i == 0:
all_emb = np.zeros((NUM_EXAMPLES_TRAIN,emb.shape[1]))
all_emb[i * bs : (i+1)*bs,:] = emb
convert_images_and_labels_and_emb(image, labels, zca, all_emb, os.path.join(FLAGS.data_dir, FLAGS.emb_name+'.tfrecords'))
return(True)
for ep in range(FLAGS.num_epochs):
print("EPOCH:{}".format(ep))
#Adjust decay if necessary
if ep < FLAGS.epoch_decay_start:
feed_dict = {lr: FLAGS.learning_rate, mom: FLAGS.mom1}
print("MOMENTUM:{},lr:".format(FLAGS.mom1,FLAGS.learning_rate))
else:
decayed_lr = ((FLAGS.num_epochs - ep) / float(
FLAGS.num_epochs - FLAGS.epoch_decay_start)) * FLAGS.learning_rate
feed_dict = {lr: decayed_lr, mom: FLAGS.mom2}
#Initialize loss,time and iterator
sum_loss = 0
start = time.time()
mon_sess.run(init_dict["AE_train"].initializer)
#Run training examples
for i in range(FLAGS.num_iter_per_epoch):
nxt = mon_sess.run(nxt_dict["AE_train"])
feed_dict[IMG] = nxt["image"]
feed_dict[ZCA] = nxt["zca"]
_, batch_loss = mon_sess.run([train_op, loss],
feed_dict=feed_dict)
sum_loss += batch_loss
#Print elapsed time
end = time.time()
print("Epoch:", ep, "CE_loss_train:", sum_loss / FLAGS.num_iter_per_epoch,
"elapsed_time:", end - start)
''' EVAL Procedure '''
if (ep + 1) % FLAGS.eval_freq == 0 or ep + 1 == FLAGS.num_epochs:
#BEGIN: Get Sample
sample_img = mon_sess.run([sample],
feed_dict=feed_dict)
with tf.Graph().as_default():
tf.Session().run([tf.write_file("sample.png",tf.image.encode_png(sample_img[0]))])
print("saved sample")
#END: Get Sample
#EVAL TRAIN
classifier_eval(initializer = init_dict["AE_eval_train"].initializer,
nxt_op = nxt_dict["AE_eval_train"],
losses_eval = losses_eval_train,
writer=writer_train,
mon_sess = mon_sess,
IMG=IMG,ZCA=ZCA,description="train")
#EVAL TEST
classifier_eval(initializer = init_dict["AE_eval_test"].initializer,
nxt_op = nxt_dict["AE_eval_test"],
losses_eval = losses_eval_test,
writer=writer_test,
mon_sess = mon_sess,
IMG=IMG,ZCA=ZCA,description="test")
#END Eval Test data
''' END EVAL'''
def save_image(filename, image):
image = quantize_image(image)
string = tf.image.encode_png(image)
return tf.write_file(filename, string)
def main(unused_argv):
# initialize
FLAGS.vmnet_intermediate_outputs = True
os.environ['CUDA_VISIBLE_DEVICES'] = FLAGS.cuda_device
scale_list = list(map(lambda x: int(x), FLAGS.scales.split(',')))
tf.logging.set_verbosity(tf.logging.INFO)
# image reading session
tf_image_read_graph = tf.Graph()
with tf_image_read_graph.as_default():
tf_image_read_path = tf.placeholder(tf.string, [])
tf_image = tf.read_file(tf_image_read_path)
tf_image = tf.image.decode_png(tf_image, channels=3, dtype=tf.uint8)
tf_image_read = tf_image
tf_image_read_init = tf.global_variables_initializer()
tf_image_read_session = tf.Session(config=tf.ConfigProto(
device_count={'GPU': 0}
))
tf_image_read_session.run(tf_image_read_init)
# image saving session
tf_image_save_graph = tf.Graph()
with tf_image_save_graph.as_default():
tf_image_save_path = tf.placeholder(tf.string, [])
tf_image_save_image = tf.placeholder(tf.float32, [None, None, 3])
tf_image = tf_image_save_image
tf_image = tf.round(tf_image)
tf_image = tf.clip_by_value(tf_image, 0, 255)
tf_image = tf.cast(tf_image, tf.uint8)
tf_image_png = tf.image.encode_png(tf_image)
tf_image_save_op = tf.write_file(tf_image_save_path, tf_image_png)
tf_image_save_init = tf.global_variables_initializer()
tf_image_save_session = tf.Session(config=tf.ConfigProto(
device_count={'GPU': 0}
))
tf_image_save_session.run(tf_image_save_init)
# model
model = MODEL_MODULE.create_model()
model.prepare(is_training=False, global_step=FLAGS.restore_global_step)
# model > restore
model.restore(ckpt_path=FLAGS.restore_path, target=FLAGS.restore_target)
tf.logging.info('restored the model')
# get image path list
image_list = [f for f in os.listdir(FLAGS.input_path) if f.lower().endswith('.png')]
tf.logging.info('found %d images' % (len(image_list)))
# iterate
running_time_list = []
num_total_outputs = FLAGS.vmnet_recursions // FLAGS.vmnet_recursion_frequency
for scale in scale_list:
for image_name in image_list:
tf.logging.info('- x%d: %s' % (scale, image_name))
input_image_path = os.path.join(FLAGS.input_path, image_name)
input_image = tf_image_read_session.run(tf_image_read, feed_dict={tf_image_read_path: input_image_path})
t1 = time.perf_counter()
output_images = model.upscale(input_list=[input_image], scale=scale)
t2 = time.perf_counter()
running_time = (t2 - t1)
output_image_ensemble = np.zeros_like(output_images[0][0])
ensemble_factor_total = 0.0
for i in range(num_total_outputs):
num_recursions = (i + 1) * FLAGS.vmnet_recursion_frequency
output_image = output_images[i][0]
ensemble_factor = 1.0 / (2.0 ** (num_total_outputs-num_recursions))
output_image_ensemble = output_image_ensemble + (output_image * ensemble_factor)
ensemble_factor_total += ensemble_factor
output_image = output_image_ensemble / ensemble_factor_total
output_image_path = os.path.join(FLAGS.output_path, 'x%d' % (scale), os.path.splitext(image_name)[0]+'.png')
tf_image_save_session.run(tf_image_save_op, feed_dict={tf_image_save_path:output_image_path, tf_image_save_image:output_image})
running_time_list.append(running_time)
# finalize
tf.logging.info('finished')
tf.logging.info('%.6f sec' % (np.mean(running_time_list)))
cur_number = 0
for file in lst:
cur_class = file.split('_')[0]
cur_light = file.split('_')[1]
cur_source_file = file.split('_')[2]
cur_frame = file.split('_')[7]
filename = path_to_augment + "aug_" + cur_class + "_" + cur_light + "_" + video_number + "_" + str(
cur_number
) + "_" + "from_" + cur_source_file + "_" + cur_frame + ".jpg"
print("Create file:", filename)
image = cv2.imread(str(path_to_source) + str(file))
rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
tf_img = tf.convert_to_tensor(rgb)
sat_image = tf.image.random_saturation(tf_img, lower=0.5, upper=1)
contrast_image = tf.image.random_contrast(sat_image, lower=0.5, upper=1)
brght_img = tf.image.random_brightness(contrast_image, max_delta=0.2)
jpg = tf.image.random_jpeg_quality(brght_img,
min_jpeg_quality=50,
max_jpeg_quality=100)
enc = tf.image.encode_jpeg(brght_img)
fwrite = tf.write_file(filename, enc)
with tf.Session() as sess:
sess.run(fwrite)
cur_number += 1
os.system("rm -rf {0}".format(path_to_source))
def main(_, run_eval_loop=True):
# Fetch and generate images to run through Inception.
with tf.name_scope('inputs'):
real_data, num_classes = _get_real_data(FLAGS.num_images_generated,
FLAGS.dataset_dir)
generated_data = _get_generated_data(FLAGS.num_images_generated,
FLAGS.conditional_eval,
num_classes)
# Compute Frechet Inception Distance.
if FLAGS.eval_frechet_inception_distance:
fid = util.get_frechet_inception_distance(real_data, generated_data,
FLAGS.num_images_generated,
FLAGS.num_inception_images)
tf.summary.scalar('frechet_inception_distance', fid)
# Compute normal Inception scores.
if FLAGS.eval_real_images:
inc_score = util.get_inception_scores(real_data,
FLAGS.num_images_generated,
FLAGS.num_inception_images)
else:
inc_score = util.get_inception_scores(generated_data,
FLAGS.num_images_generated,
FLAGS.num_inception_images)
tf.summary.scalar('inception_score', inc_score)
# If conditional, display an image grid of difference classes.
if FLAGS.conditional_eval and not FLAGS.eval_real_images:
reshaped_imgs = util.get_image_grid(generated_data,
FLAGS.num_images_generated,
num_classes,
FLAGS.num_images_per_class)
tf.summary.image('generated_data', reshaped_imgs, max_outputs=1)
# Create ops that write images to disk.
image_write_ops = None
if FLAGS.conditional_eval and FLAGS.write_to_disk:
reshaped_imgs = util.get_image_grid(generated_data,
FLAGS.num_images_generated,
num_classes,
FLAGS.num_images_per_class)
uint8_images = data_provider.float_image_to_uint8(reshaped_imgs)
image_write_ops = tf.write_file(
'%s/%s' % (FLAGS.eval_dir, 'conditional_cifar10.png'),
tf.image.encode_png(uint8_images[0]))
else:
if FLAGS.num_images_generated >= 100 and FLAGS.write_to_disk:
reshaped_imgs = tfgan.eval.image_reshaper(
generated_data[:100], num_cols=FLAGS.num_images_per_class)
uint8_images = data_provider.float_image_to_uint8(reshaped_imgs)
image_write_ops = tf.write_file(
'%s/%s' % (FLAGS.eval_dir, 'unconditional_cifar10.png'),
tf.image.encode_png(uint8_images[0]))
# For unit testing, use `run_eval_loop=False`.
if not run_eval_loop: return
tf.contrib.training.evaluate_repeatedly(
FLAGS.checkpoint_dir,
master=FLAGS.master,
hooks=[
tf.contrib.training.SummaryAtEndHook(FLAGS.eval_dir),
tf.contrib.training.StopAfterNEvalsHook(1)
],
eval_ops=image_write_ops,
max_number_of_evaluations=FLAGS.max_number_of_evaluations)
swap_memory=True)
#
out_coarse = out_ta_coarse.stack()
out_fine = out_ta_fine.stack()
out = combine_signal(out_coarse, out_fine)
out = tf.squeeze(out, axis=-2)
aud = tf.cast(out, dtype=tf.float32)
aud = aud / 2**15
encoded_audio_data = tf.contrib.ffmpeg.encode_audio(
aud, file_format="wav", samples_per_second=sample_rate)
write_file_op = tf.write_file("sample.wav", encoded_audio_data)
#
saver = tf.train.Saver()
#
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
#
ckpt = tf.train.get_checkpoint_state(
os.path.dirname('checkpoints_wavernn/wavernn'))
if ckpt and ckpt.model_checkpoint_path:
saver.restore(sess, ckpt.model_checkpoint_path)
#