random.shuffle(r)
###############################################################################
# Create Database
print 'Creating PoseNet Dataset.'
SO3_GROUP = SpecialOrthogonalGroup(3)
writer = tf.python_io.TFRecordWriter(out_file)
for i in tqdm(r):
pose_q = np.array(poses[i][3:7])
pose_x = np.array(poses[i][0:3])
rot_vec = SO3_GROUP.rotation_vector_from_quaternion(pose_q)[0]
pose = np.concatenate((rot_vec, pose_x), axis=0)
X = imageio.imread(images[i])
X = X[::4, ::4, :]
#X = exposure.equalize_hist(X)
img_raw = X.tostring() #.astype('float32').tostring()
pose_raw = pose.astype('float32').tostring()
pose_q_raw = pose_q.astype('float32').tostring()
pose_x_raw = pose_x.astype('float32').tostring()
example = tf.train.Example(features=tf.train.Features(
feature={
'height': _int64_feature(X.shape[0]),
'width': _int64_feature(X.shape[1]),
def main(args):
poses = []
images = []
# Processing Image Lables
logger.info('Processing Image Lables')
with open(FLAGS.root_dir + '/' + FLAGS.dataset) as f:
next(f) # skip the 3 header lines
next(f)
next(f)
for line in f:
fname, p0, p1, p2, p3, p4, p5, p6 = line.split()
p0 = float(p0)
p1 = float(p1)
p2 = float(p2)
p3 = float(p3)
p4 = float(p4)
p5 = float(p5)
p6 = float(p6)
poses.append((p0, p1, p2, p3, p4, p5, p6))
images.append(FLAGS.root_dir + '/' + fname)
r = list(range(len(images)))
random.shuffle(r)
random.shuffle(r)
random.shuffle(r)
# Writing TFRecords
logger.info('Writing TFRecords')
SO3_GROUP = SpecialOrthogonalGroup(3)
writer = tf.python_io.TFRecordWriter(FLAGS.out_file)
for i in tqdm(r):
pose_q = np.array(poses[i][3:7])
pose_x = np.array(poses[i][0:3])
rot_vec = SO3_GROUP.rotation_vector_from_quaternion(pose_q)[0]
pose = np.concatenate((rot_vec, pose_x), axis=0)
logger.info('Processing Image: ' + images[i])
X = imageio.imread(images[i])
X = X[::4, ::4, :]
if FLAGS.hist_norm:
X = exposure.equalize_hist(X)
img_raw = X.tostring()
pose_raw = pose.astype('float32').tostring()
pose_q_raw = pose_q.astype('float32').tostring()
pose_x_raw = pose_x.astype('float32').tostring()
example = tf.train.Example(features=tf.train.Features(feature={
'height': _int64_feature(X.shape[0]),
'width': _int64_feature(X.shape[1]),
'channel': _int64_feature(X.shape[2]),
'image': _bytes_feature(img_raw),
'pose': _bytes_feature(pose_raw),
'pose_q': _bytes_feature(pose_q_raw),
'pose_x': _bytes_feature(pose_x_raw)}))
writer.write(example.SerializeToString())
writer.close()
logger.info('\n', 'Creating Dataset Success.')
def main(argv):
# TF Record
datafiles = FLAGS.data_dir + '/test/' + FLAGS.subject_id + '.tfrecord'
dataset = tf.data.TFRecordDataset(datafiles)
dataset = dataset.map(_parse_function_ifind)
# dataset = dataset.repeat()
# dataset = dataset.shuffle(FLAGS.queue_buffer)
dataset = dataset.batch(1)
image, vec, qt, AP1, AP2, AP3 = dataset.make_one_shot_iterator().get_next()
# Nifti Volume
subject_path = FLAGS.scan_dir + '/test/' + FLAGS.subject_id + '.nii.gz'
fixed_image_sitk_tmp = sitk.ReadImage(subject_path, sitk.sitkFloat32)
fixed_image_sitk = sitk.GetImageFromArray(
sitk.GetArrayFromImage(fixed_image_sitk_tmp))
fixed_image_sitk = sitk.RescaleIntensity(fixed_image_sitk, 0, 1) # * 255.
# Network Definition
image_input = tf.placeholder(shape=[1, 224, 224, 1], dtype=tf.float32)
image_resized = tf.image.resize_images(image, size=[224, 224])
if FLAGS.loss == 'PoseNet':
y_pred, _ = inception.inception_v3(image_input, num_classes=7)
quaternion_pred, translation_pred = tf.split(y_pred, [4, 3], axis=1)
sess = tf.Session()
ckpt_file = tf.train.latest_checkpoint(FLAGS.model_dir)
tf.train.Saver().restore(sess, ckpt_file)
print('restoring parameters from', ckpt_file)
SO3_GROUP = SpecialOrthogonalGroup(3)
for i in range(FLAGS.n_iter):
_image, _image_resized, _quaternion_true, _translation_true = \
sess.run([image, image_resized, qt, AP2], )
_quaternion_pred_sample = []
_translation_pred_sample = []
for j in range(FLAGS.n_samples):
_quaternion_pred_i, _translation_pred_i = \
sess.run([quaternion_pred, translation_pred],
feed_dict={image_input: _image_resized})
_quaternion_pred_sample.append(_quaternion_pred_i)
_translation_pred_sample.append(_translation_pred_i)
print(_quaternion_pred_i, _translation_pred_i)
_quaternion_pred_sample = np.vstack(_quaternion_pred_sample)
_rotvec_pred_sample = SO3_GROUP.rotation_vector_from_quaternion(
_quaternion_pred_sample)
_rotvec_pred = SO3_GROUP.left_canonical_metric.mean(
_rotvec_pred_sample)
_quaternion_pred = SO3_GROUP.quaternion_from_rotation_vector(
_rotvec_pred)
_translation_pred = np.mean(np.vstack(_translation_pred_sample),
axis=0)
# _quaternion_pred_variance = SO3_GROUP.left_canonical_metric.variance(_rotvec_pred_sample)
_translation_pred_variance = np.var(
np.vstack(_translation_pred_sample), axis=0)
rx = SO3_GROUP.matrix_from_quaternion(_quaternion_pred)[0]
tx = _translation_pred[0] * 60.
image_true = np.squeeze(_image)
image_pred = resample_sitk(fixed_image_sitk, rx, tx)
imageio.imsave('imgdump/image_{}_true.png'.format(i), _image[0,
...])
imageio.imsave('imgdump/image_{}_pred.png'.format(i), image_pred)
calc_psnr(image_pred, image_true)
calc_mse(image_pred, image_true)
calc_ssim(image_pred, image_true)
calc_correlation(image_pred, image_true)
elif FLAGS.loss == 'AP':
y_pred, _ = inception.inception_v3(image_input, num_classes=9)
AP1_pred, AP2_pred, AP3_pred = tf.split(y_pred, 3, axis=1)
sess = tf.Session()
ckpt_file = tf.train.latest_checkpoint(FLAGS.model_dir)
tf.train.Saver().restore(sess, ckpt_file)
print('restoring parameters from', ckpt_file)
for i in range(FLAGS.n_iter):
_image, _image_resized, _AP1, _AP2, _AP3 = \
sess.run([image, image_resized, AP1, AP2, AP3])
_AP1_sample = []
_AP2_sample = []
_AP3_sample = []
for j in range(FLAGS.n_samples):
_AP1_pred_i, _AP2_pred_i, _AP3_pred_i = \
sess.run([AP1_pred, AP2_pred, AP3_pred],
feed_dict={image_input: _image_resized})
_AP1_sample.append(_AP1_pred_i)
_AP2_sample.append(_AP2_pred_i)
_AP3_sample.append(_AP3_pred_i)
_AP1_pred = np.mean(np.vstack(_AP1_sample), axis=0)
_AP2_pred = np.mean(np.vstack(_AP2_sample), axis=0)
_AP3_pred = np.mean(np.vstack(_AP3_sample), axis=0)
_AP1_pred_variance = np.var(np.vstack(_AP1_sample), axis=0)
_AP2_pred_variance = np.var(np.vstack(_AP2_sample), axis=0)
_AP3_pred_variance = np.var(np.vstack(_AP3_sample), axis=0)
dist_ap1 = np.linalg.norm(_AP1 - _AP1_pred)
dist_ap2 = np.linalg.norm(_AP2 - _AP2_pred)
dist_ap3 = np.linalg.norm(_AP3 - _AP3_pred)
rx = matrix_from_anchor_points(_AP1_pred[0], _AP2_pred[0],
_AP3_pred[0])
tx = _AP2_pred[0] * 60.
image_true = np.squeeze(_image)
image_pred = resample_sitk(fixed_image_sitk, rx, tx)
imageio.imsave('imgdump/image_{}_true.png'.format(i), _image[0,
...])
imageio.imsave('imgdump/image_{}_pred.png'.format(i), image_pred)
calc_psnr(image_pred, image_true)
calc_mse(image_pred, image_true)
calc_ssim(image_pred, image_true)
calc_correlation(image_pred, image_true)
elif FLAGS.loss == 'SE3':
y_pred, _ = inception.inception_v3(image_input, num_classes=6)
sess = tf.Session()
ckpt_file = tf.train.latest_checkpoint(FLAGS.model_dir)
tf.train.Saver().restore(sess, ckpt_file)
print('restoring parameters from', ckpt_file)
SO3_GROUP = SpecialOrthogonalGroup(3)
SE3_GROUP = SpecialEuclideanGroup(3)
for i in range(FLAGS.n_iter):
print(i)
_image, _image_resized, _rvec, _tvec = \
sess.run([image, image_resized, vec, AP2])
_y_pred_sample = []
for j in range(FLAGS.n_samples):
_y_pred_i = sess.run([y_pred],
feed_dict={image_input: _image_resized})
_y_pred_sample.append(_y_pred_i[0])
_y_pred_sample = np.vstack(_y_pred_sample)
_y_pred = SE3_GROUP.left_canonical_metric.mean(_y_pred_sample)
_y_pred_variance = SE3_GROUP.left_canonical_metric.variance(
_y_pred_sample)
rx = SO3_GROUP.matrix_from_rotation_vector(_y_pred[0, :3])[0]
tx = _y_pred[0, 3:] * 60.
image_true = np.squeeze(_image)
image_pred = resample_sitk(fixed_image_sitk, rx, tx)
imageio.imsave('imgdump/image_{}_true.png'.format(i), _image[0,
...])
imageio.imsave('imgdump/image_{}_pred.png'.format(i), image_pred)
calc_psnr(image_pred, image_true)
calc_mse(image_pred, image_true)
calc_ssim(image_pred, image_true)
calc_correlation(image_pred, image_true)
else:
print('Invalid Option:', FLAGS.loss)
raise SystemExit
