def eval_real_epoch(sess, ops, test_writer):
PC_PATH = os.path.join(POINTCLOUD_DIR,
'Dataset/Data/partialface/real_scan/')
INPUT_FOLDER = os.path.join(PC_PATH, 'sampled')
OUTPUT_FOLDER = os.path.join(PC_PATH, 'pred')
from glob import glob
is_training = False
samples = glob(INPUT_FOLDER + "/*.xyz")
samples.sort(reverse=False)
total_num = len(samples)
for i in range(total_num):
filename = samples[i].split('\\')[-1].replace('.xyz', '')
print(filename)
pointclouds_pl = np.loadtxt(samples[i])
pointclouds_pl = np.expand_dims(pointclouds_pl, axis=0)
feed_dict = {
ops['pointclouds_pl']: pointclouds_pl,
ops['is_training_pl']: is_training,
}
# loss_val, pred_angle = sess.run([ops['loss'], ops['pred_angle']], feed_dict=feed_dict)
pred_angle = sess.run([ops['pred_angle']], feed_dict=feed_dict)
pred_angle = np.squeeze(pred_angle)
print(pred_angle.shape)
print(pred_angle)
transform_xyz = Tools3D.quaternion_To_rotation_matrix(
np.squeeze(pred_angle))
transform_xyz = np.array(transform_xyz)
print(transform_xyz)
np.savetxt(os.path.join(INPUT_FOLDER, filename + '.txt'),
np.expand_dims(pred_angle, axis=0),
fmt='%0.6f')
point_cloud_transformed = np.matmul(pointcloud_gt_s, transform_xyz)
# _point_cloud_transformed = sess.run(point_cloud_transformed, feed_dict=feed_dict)
img_filename = '%d_coarse.png' % (
i) # , datetime.now().strftime('%Y-%m-%d %H:%M:%S'))
img_filename = os.path.join(OUTPUT_FOLDER, img_filename)
point_input = np.squeeze(pointclouds_pl)
points_gt = np.squeeze(pointcloud_gt_s)
points_rotated = np.squeeze(point_cloud_transformed)
print(points_gt.shape, points_rotated.shape,
point_cloud_transformed.shape)
info = 'Nothing'
pc_util.point_cloud_three_points(point_input, point_cloud_transformed,
point_cloud_transformed, img_filename,
info)
def eval_one_epoch(sess, ops, test_writer):
is_training = False
# Make sure batch data is of same size
cur_batch_data = np.zeros(
(BATCH_SIZE, NUM_POINT, TEST_DATASET.num_channel()))
cur_batch_angle = np.zeros((BATCH_SIZE, 4))
cur_batch_label = np.zeros((BATCH_SIZE))
loss_sum = 0
batch_idx = 0
while TEST_DATASET.has_next_batch():
batch_data, batch_angel, batch_data_label = TEST_DATASET.next_batch(
augment=True, sess=sess)
bsize = batch_data.shape[0]
print('Batch: %03d, batch size: %d' % (batch_idx, bsize))
# for the last batch in the epoch, the bsize:end are from last batch
cur_batch_label[0:bsize] = batch_data_label
# for the last batch in the epoch, the bsize:end are from last batch
cur_batch_data[0:bsize, ...] = batch_data
cur_batch_angle[0:bsize, ...] = batch_angel[:, :4]
feed_dict = {
ops['pointclouds_pl']: cur_batch_data,
ops['pointclouds_gt']: pointcloud_gt_val,
ops['pointclouds_gt_big']: pointcloud_gt_big_val,
ops['pointclouds_angle']: cur_batch_angle,
ops['is_training_pl']: is_training,
}
# loss_val, pred_angle = sess.run([ops['loss'], ops['pred_angle']], feed_dict=feed_dict)
summary, step, loss_val, pred_angle, cd_dists, knn_dists = sess.run(
[
ops['merged'], ops['step'], ops['loss'], ops['pred_angle'],
ops['cd_dists'], ops['knn_dists']
],
feed_dict=feed_dict)
test_writer.add_summary(summary, step)
loss_sum += loss_val
batch_idx += 1
transform_xyz_input = Tools3D.batch_quaternion2mat(cur_batch_angle)
transform_xyz = Tools3D.batch_quaternion2mat(pred_angle)
point_cloud_transformed = np.matmul(pointcloud_gt_val, transform_xyz)
point_cloud_gt_transformed = np.matmul(pointcloud_gt_val,
transform_xyz_input)
# _point_cloud_transformed = sess.run(point_cloud_transformed, feed_dict=feed_dict)
for i in range(bsize):
index = cur_batch_label[i]
img_filename = '%d.png' % (
index) # , datetime.now().strftime('%Y-%m-%d %H:%M:%S'))
img_filename = os.path.join(DUMP_DIR, img_filename)
points_gt = np.squeeze(point_cloud_gt_transformed[i, :, :])
points_rotated = np.squeeze(cur_batch_data[i, :, :])
points_align = np.squeeze(point_cloud_transformed[i, :, :])
# print(points_rotated.shape)
# print(point_cloud_transformed.shape)
# print(points_align.shape)
info_input = pc_util.log_visu_vec('Input Data %d' % (index),
cur_batch_angle[i, :])
pre_angle = pc_util.log_visu_vec('Pred Data', pred_angle[i, :])
matrix_input = pc_util.log_visu_matrix(
'Input Matrix', np.squeeze(transform_xyz_input[i, :, :]))
matrix_pred = pc_util.log_visu_matrix(
'Pred Matrix', np.squeeze((transform_xyz[i, :, :])))
# print(point_cloud_transformed[i,:,:].shape)
cd_loss = cd_dists[i]
knn_loss = knn_dists[i]
matloss = np.sum(
np.square(transform_xyz_input[i, :, :] -
transform_xyz[i, :, :])) / 2
vecloss = np.sum(
np.square(pred_angle[i, :] - cur_batch_angle[i, :])) / 2
loss_cd = pc_util.log_visu_loss('CD Loss', cd_loss)
loss_knn = pc_util.log_visu_loss('KNN Loss', knn_loss)
loss_mat = pc_util.log_visu_loss('MAT Loss', matloss)
vec_mat = pc_util.log_visu_loss('VEC Loss', vecloss)
info = info_input + pre_angle + matrix_input + matrix_pred + vec_mat + loss_mat + loss_cd + loss_knn
pc_util.point_cloud_three_points(points_rotated, points_gt,
points_align, img_filename, info)
# scipy.misc.imsave(img_filename, output_img)
log_string('eval mean loss: %f' % (loss_sum / float(batch_idx)))
TEST_DATASET.reset()
def eval_one_frame(self,
points,
isSampled=False,
isNormalized=False,
isSaving=False):
is_training = False
PC_PATH = os.path.join(
POINTCLOUD_DIR,
'Dataset/%s/partialface/real_scan/' % (self.model_name))
OUTPUT_FOLDER = os.path.join(PC_PATH, 'pred')
#PointCloudOperator.get_pairwise_distance(points)
if isSampled:
if self.predict_trans is not None:
point_temp = np.dot(points.copy(),
np.eye(3)) #self.predict_trans.T)
else:
point_temp = np.dot(points.copy(), np.eye(3))
point_temp_max = np.max(point_temp, axis=0)
point_temp_min = np.min(point_temp, axis=0)
#print('point_temp_max:',point_temp_max)
_clip_ = np.where((point_temp[:, 1] < point_temp_max[1] * 0.9))
points = points[_clip_]
# interv = int(points.shape[0]/1024)
# print(points.shape[0],interv)
# idx = np.arange(points.shape[0])
#
# idx_n = [i for i in idx if i % interv == 0]
#
# idx_n = np.arange(points.shape[0])
# np.random.shuffle(idx_n)
#
# if points.shape[0]<POINT_NUM:
# offset = POINT_NUM - points.shape[0]
# idx_n = np.concatenate([np.arange(points.shape[0]), np.random.randint(0, points.shape[0], size=offset)], axis=0)
# np.random.shuffle(idx_n)
#
# #idx_n = np.random.randint(0,points.shape[0],size=POINT_NUM)
#
#
# idx_n = idx_n[:POINT_NUM]
#
# np.random.shuffle(idx_n)
#points = points[idx_n,...]
if isNormalized:
centroid = np.mean(points, axis=0, keepdims=True)
print('------------centroid:', centroid)
furthest_distance = np.amax(np.sqrt(
np.sum((points - centroid)**2, axis=-1)),
keepdims=True)
points = (points - centroid) / furthest_distance
distance = np.sqrt(np.sum(points**2, axis=-1))
med_distance = np.median(distance)
max_distance = np.max(distance)
scale = max_distance / med_distance * 0.8
print('med_distance:', med_distance, '--max_distance:',
max_distance)
_clip_ = np.where(distance < scale * med_distance)
points = points[_clip_]
centroid = np.mean(points, axis=0, keepdims=True)
print('------------centroid:', centroid)
furthest_distance = np.amax(np.sqrt(
np.sum((points - centroid)**2, axis=-1)),
keepdims=True) * 0.89
points = (points - centroid) / furthest_distance
pointclouds_pl = np.expand_dims(points, axis=0)
# pointclouds_pl[:,:,1:3] = -pointclouds_pl[:,:,1:3]
#pointclouds_pl[:,:,2] = -pointclouds_pl[:,:,2]
pointcloud_gt_big = np.expand_dims(self.pointcloud_gt_big, axis=0)
pointcloud_gt_small = np.expand_dims(self.pointcloud_gt_small, axis=0)
feed_dict = {
self.ops['pointclouds_pl']: pointclouds_pl,
self.ops['pointclouds_pl_big']: pointcloud_gt_big,
self.ops['pointclouds_gt_small']: pointcloud_gt_small,
self.ops['is_training_pl']: is_training,
}
# loss_val, pred_angle = sess.run([ops['loss'], ops['pred_angle']], feed_dict=feed_dict)
pred_angle = self.sess.run([self.ops['pred_angle']],
feed_dict=feed_dict)
pred_angle = np.squeeze(pred_angle)
pred_angle = pred_angle[:4]
print(pred_angle)
transform_xyz = Tools3D.quaternion_To_rotation_matrix(pred_angle)
transform_xyz = np.array(transform_xyz)
if isSaving and (time() - self.startime > 2.5):
self.startime = time()
point_cloud_transformed = np.matmul(self.pointcloud_gt,
transform_xyz)
point_input = np.squeeze(pointclouds_pl)
points_gt = np.squeeze(self.pointcloud_gt)
points_aligned = np.squeeze(point_cloud_transformed)
info = 'Nothing'
filename = datetime.now().strftime('%Y_%m_%d_%H_%M_%S')
pc_filename = os.path.join(OUTPUT_FOLDER, '%s.xyz' % (filename))
np.savetxt(pc_filename, points, fmt='%0.6f')
img_filename = os.path.join(OUTPUT_FOLDER, '%s.png' % (filename))
#img_filename = os.path.join(OUTPUT_FOLDER, '1.png')
pc_util.point_cloud_three_points(point_input, points_aligned,
points_aligned, img_filename,
info)
# if self.predict_trans is not None:
# transform_xyz = 0.2*transform_xyz + 0.8*self.predict_trans
self.predict_trans = transform_xyz
return transform_xyz, points
