def patch_prediction(self, patch_point):
# normalize the point clouds
patch_point, centroid, furthest_distance = pc_util.normalize_point_cloud(patch_point)
patch_point = np.expand_dims(patch_point, axis=0)
pred = self.sess.run([self.pred_pc], feed_dict={self.inputs: patch_point})
pred = np.squeeze(centroid + pred * furthest_distance, axis=0)
return pred
def test(self):
self.inputs = tf.placeholder(tf.float32,
shape=[1, self.opts.patch_num_point, 3])
is_training = tf.placeholder_with_default(False,
shape=[],
name='is_training')
Gen = Generator(self.opts, is_training, name='generator')
self.pred_pc = Gen(self.inputs)
for i in range(round(math.pow(self.opts.up_ratio, 1 / 4)) - 1):
self.pred_pc = Gen(self.pred_pc)
saver = tf.train.Saver()
print("****** phrase test ******")
##restore_epoch, checkpoint_path = model_utils.pre_load_checkpoint(self.opts.log_dir)
##to use pretrained model comment the line above
checkpoint_path = "/home/alitokur/Softwares/PU-GAN/model/model-100"
print(checkpoint_path)
saver.restore(self.sess, checkpoint_path)
samples = glob(self.opts.test_data)
point = pc_util.load(samples[0])
self.opts.num_point = point.shape[0]
out_point_num = int(self.opts.num_point * self.opts.up_ratio)
for point_path in samples:
logging.info(point_path)
start = time()
pc = pc_util.load(point_path)[:, :3]
pc, centroid, furthest_distance = pc_util.normalize_point_cloud(pc)
if self.opts.jitter:
pc = pc_util.jitter_perturbation_point_cloud(
pc[np.newaxis, ...],
sigma=self.opts.jitter_sigma,
clip=self.opts.jitter_max)
pc = pc[0, ...]
input_list, pred_list = self.pc_prediction(pc)
end = time()
print("total time: ", end - start)
pred_pc = np.concatenate(pred_list, axis=0)
pred_pc = (pred_pc * furthest_distance) + centroid
pred_pc = np.reshape(pred_pc, [-1, 3])
path = os.path.join(self.opts.out_folder,
point_path.split('/')[-1][:-4] + '.ply')
idx = farthest_point_sample(out_point_num, pred_pc[np.newaxis,
...]).eval()[0]
pred_pc = pred_pc[idx, 0:3]
np.savetxt(path[:-4] + '.xyz', pred_pc, fmt='%.6f')
def test(self):
self.opts.batch_size = 1
final_ratio = self.opts.final_ratio
step_ratio = 4
self.opts.up_ratio = step_ratio
self.build_model_test(final_ratio=self.opts.final_ratio,
step_ratio=step_ratio)
saver = tf.train.Saver()
restore_epoch, checkpoint_path = model_utils.pre_load_checkpoint(
self.opts.log_dir)
print(checkpoint_path)
self.saver.restore(self.sess, checkpoint_path)
#self.restore_model(self.opts.log_dir, epoch=self.opts.restore_epoch, verbose=True)
samples = glob(self.opts.test_data)
point = pc_util.load(samples[0])
self.opts.num_point = point.shape[0]
out_point_num = int(self.opts.num_point * final_ratio)
for point_path in samples:
logging.info(point_path)
start = time()
pc = pc_util.load(point_path)[:, :3]
pc, centroid, furthest_distance = pc_util.normalize_point_cloud(pc)
input_list, pred_list, coarse_list = self.pc_prediction(pc)
end = time()
print("total time: ", end - start)
pred_pc = np.concatenate(pred_list, axis=0)
pred_pc = (pred_pc * furthest_distance) + centroid
pred_pc = np.reshape(pred_pc, [-1, 3])
idx = farthest_point_sample(
out_point_num, pred_pc[np.newaxis,
...]).eval(session=self.sess)[0]
pred_pc = pred_pc[idx, 0:3]
# path = os.path.join(self.opts.out_folder, point_path.split('/')[-1][:-4] + '.ply')
# np.savetxt(path[:-4] + '.xyz',pred_pc,fmt='%.6f')
in_folder = os.path.dirname(self.opts.test_data)
path = os.path.join(
self.opts.out_folder,
point_path.split('/')[-1][:-4] + '_X%d.xyz' % final_ratio)
np.savetxt(path, pred_pc, fmt='%.6f')