def single_segment_rotations_reconstruction_as_gif(segment, vae, confidence=0.3,
directory="/tmp/online_matcher/visuals/animated/",
frames=120, black_and_white=False):
import os
if not os.path.exists(directory):
os.makedirs(directory)
import voxelize
rotations = voxelize.create_rotations([segment], n_angles=frames)
import autoencoder.model
VOXEL_SIDE = vae.MP.INPUT_SHAPE[0]
rotations_vox, features_voxel_scale = voxelize.voxelize(rotations, VOXEL_SIDE)
reconstruction_vox = vae.batch_encode_decode([np.reshape(sample, vae.MP.INPUT_SHAPE) for sample in rotations_vox], batch_size=120)
reconstruction_vox = [np.reshape(vox, [VOXEL_SIDE, VOXEL_SIDE, VOXEL_SIDE]) for vox in reconstruction_vox]
from voxelize import unvoxelize
reconstruction = [unvoxelize(vox > confidence) for vox in reconstruction_vox]
reconstruction = [voxelize.recenter_segment(segment*scale) for (segment, scale) in zip(reconstruction, features_voxel_scale)]
segments_as_gif(reconstruction, rotate_YP=(2*np.pi/frames), filename='reconstruction_rot',
directory=directory, black_and_white=black_and_white)
def single_segment_rotations_reconstruction_as_gif(segment, vae, confidence=0.3,
directory="/tmp/online_matcher/visuals/animated/",
frames=120, black_and_white=False):
import os
if not os.path.exists(directory):
os.makedirs(directory)
import voxelize
rotations = voxelize.create_rotations([segment], n_angles=frames)
import autoencoder.model
VOXEL_SIDE = vae.MP.INPUT_SHAPE[0]
rotations_vox, features_voxel_scale = voxelize.voxelize(rotations, VOXEL_SIDE)
reconstruction_vox = vae.batch_encode_decode([np.reshape(sample, vae.MP.INPUT_SHAPE) for sample in rotations_vox], batch_size=120)
reconstruction_vox = [np.reshape(vox, [VOXEL_SIDE, VOXEL_SIDE, VOXEL_SIDE]) for vox in reconstruction_vox]
from voxelize import unvoxelize
reconstruction = [unvoxelize(vox > confidence) for vox in reconstruction_vox]
reconstruction = [voxelize.recenter_segment(segment*scale) for (segment, scale) in zip(reconstruction, features_voxel_scale)]
segments_as_gif(reconstruction, rotate_YP=(2*np.pi/frames), filename='reconstruction_rot',
directory=directory, black_and_white=black_and_white)
def single_segment_confidence_as_gif(
segment,
vae,
directory="/tmp/online_matcher/visuals/animated/",
frames=60,
black_and_white=False):
import os
if not os.path.exists(directory):
os.makedirs(directory)
import voxelize
import autoencoder.model
VOXEL_SIDE = vae.MP.INPUT_SHAPE[0]
segment_vox, features_voxel_scale = voxelize.voxelize([segment],
VOXEL_SIDE)
segment_vox = [
np.reshape(sample, vae.MP.INPUT_SHAPE) for sample in segment_vox
]
reconstruction_vox = vae.batch_encode_decode(segment_vox)
reconstruction_vox = [
np.reshape(vox, [VOXEL_SIDE, VOXEL_SIDE, VOXEL_SIDE])
for vox in reconstruction_vox
]
from voxelize import unvoxelize
cmin = 0.1
cmax = np.amax(reconstruction_vox)
confidences = list(np.linspace(cmin, cmax, frames / 2)) + list(
np.linspace(cmax, cmin, frames / 2))
reconstruction = [
unvoxelize(reconstruction_vox[0] > confidence)
for confidence in confidences
]
reconstruction = [
segment * features_voxel_scale[0] for segment in reconstruction
]
segments_as_gif(reconstruction,
rotate_YP=0,
filename='confidence',
directory=directory,
black_and_white=black_and_white)
def single_segment_degeneration_as_gif(
segment,
vae,
confidence=0.3,
directory="/tmp/online_matcher/visuals/animated/",
frames=60,
black_and_white=False):
import os
if not os.path.exists(directory):
os.makedirs(directory)
import voxelize
import autoencoder.model
VOXEL_SIDE = vae.MP.INPUT_SHAPE[0]
segment_vox, features_voxel_scale = voxelize.voxelize([segment],
VOXEL_SIDE)
segment_vox = [
np.reshape(sample, vae.MP.INPUT_SHAPE) for sample in segment_vox
]
for i in range(frames):
reconstruction_vox = vae.batch_encode_decode(
reconstruction_vox) if i > 0 else segment_vox
degen_vox = degen_vox + list(reconstruction_vox) if i > 0 else list(
reconstruction_vox)
degen_vox = [
np.reshape(vox, [VOXEL_SIDE, VOXEL_SIDE, VOXEL_SIDE])
for vox in degen_vox
]
from voxelize import unvoxelize
reconstruction = [unvoxelize(vox > confidence) for vox in degen_vox]
reconstruction = [
voxelize.recenter_segment(segment * features_voxel_scale[0])
for segment in reconstruction
]
print(len(reconstruction))
segments_as_gif(reconstruction,
rotate_YP=0,
filename='degeneration',
directory=directory,
black_and_white=black_and_white)
def single_segment_confidence_as_gif(segment, vae,
directory="/tmp/online_matcher/visuals/animated/",
frames=60, black_and_white=False):
import os
if not os.path.exists(directory):
os.makedirs(directory)
import voxelize
import autoencoder.model
VOXEL_SIDE = vae.MP.INPUT_SHAPE[0]
segment_vox, features_voxel_scale = voxelize.voxelize([segment], VOXEL_SIDE)
segment_vox = [np.reshape(sample, vae.MP.INPUT_SHAPE) for sample in segment_vox]
reconstruction_vox = vae.batch_encode_decode(segment_vox)
reconstruction_vox = [np.reshape(vox, [VOXEL_SIDE, VOXEL_SIDE, VOXEL_SIDE]) for vox in reconstruction_vox]
from voxelize import unvoxelize
cmin=0.1; cmax=np.amax(reconstruction_vox);
confidences = list(np.linspace(cmin,cmax,frames/2))+list(np.linspace(cmax,cmin,frames/2))
reconstruction = [unvoxelize(reconstruction_vox[0] > confidence) for confidence in confidences]
reconstruction = [segment*features_voxel_scale[0] for segment in reconstruction]
segments_as_gif(reconstruction, rotate_YP=0, filename='confidence',
directory=directory, black_and_white=black_and_white)
def single_segment_degeneration_as_gif(segment, vae, confidence=0.3,
directory="/tmp/online_matcher/visuals/animated/",
frames=60, black_and_white=False):
import os
if not os.path.exists(directory):
os.makedirs(directory)
import voxelize
import autoencoder.model
VOXEL_SIDE = vae.MP.INPUT_SHAPE[0]
segment_vox, features_voxel_scale = voxelize.voxelize([segment], VOXEL_SIDE)
segment_vox = [np.reshape(sample, vae.MP.INPUT_SHAPE) for sample in segment_vox]
for i in range(frames):
reconstruction_vox = vae.batch_encode_decode(reconstruction_vox) if i > 0 else segment_vox
degen_vox = degen_vox + list(reconstruction_vox) if i > 0 else list(reconstruction_vox)
degen_vox = [np.reshape(vox, [VOXEL_SIDE, VOXEL_SIDE, VOXEL_SIDE]) for vox in degen_vox]
from voxelize import unvoxelize
reconstruction = [unvoxelize(vox > confidence) for vox in degen_vox]
reconstruction = [voxelize.recenter_segment(segment*features_voxel_scale[0]) for segment in reconstruction]
print(len(reconstruction))
segments_as_gif(reconstruction, rotate_YP=0, filename='degeneration',
directory=directory, black_and_white=black_and_white)
###############
PROFILING = True
if PROFILING:
from timeit import default_timer as timer
total_start = timer()
reconstr_start = timer()
## RECONSTRUCT SEGMENTS ##
##########################
segments_vox = vae.batch_decode(ae_features)
segments_vox = [
np.reshape(vox, [voxel_side, voxel_side, voxel_side])
for vox in segments_vox
]
from voxelize import unvoxelize
segments = [unvoxelize(vox > RC_CONFIDENCE) for vox in segments_vox]
segments = [
segment * scale for (segment, scale) in zip(segments, xyz_scales)
]
if PROFILING:
reconstr_end = timer()
overhead_out_start = timer()
print("__RCST_COMPLETE__")
## OUTPUT DATA TO FILES ##
##########################
print("Writing segments")
from import_export import write_segments
write_segments(ids, segments, folder="", filename=segments_fifo_path)
# Reconstructions
if not RUN_AS_PY_SCRIPT:
N = 100
SV_ = segments_vox[:N]
S_ = segments[:N]
I_ = ids[:N]
reconstruction_vox = vae.batch_encode_decode(
[np.reshape(sample, MP.INPUT_SHAPE) for sample in SV_])
reconstruction_vox = [
np.reshape(vox, [VOXEL_SIDE, VOXEL_SIDE, VOXEL_SIDE])
for vox in reconstruction_vox
]
from voxelize import unvoxelize
reconstruction = [
unvoxelize(vox > RC_CONFIDENCE) for vox in reconstruction_vox
]
reconstruction = [
segment * scale
for (segment, scale) in zip(reconstruction, features_voxel_scale)
]
if CREATE_VISUALS:
dir_ = "/tmp/online_matcher/visuals/reconstructions/"
from visuals import visuals_of_matches
reconstruction_ids = [id_ + max(I_) + 1 for id_ in I_]
one_to_one_matches = [[id1, id2]
for id1, id2 in zip(I_, reconstruction_ids)]
visuals_of_matches(one_to_one_matches,
S_ + reconstruction,
I_ + reconstruction_ids,
directory=dir_,
RC_CONFIDENCE = 0.2
ONEVIEW = True
# In[ ]:
# Reconstructions
if not RUN_AS_PY_SCRIPT:
N = 400
SV_ = segments_vox[:N]
S_ = segments[:N]
I_ = ids[:N]
reconstruction_vox = vae.batch_encode_decode([np.reshape(sample, MP.INPUT_SHAPE) for sample in SV_])
reconstruction_vox = [np.reshape(vox, [VOXEL_SIDE, VOXEL_SIDE, VOXEL_SIDE]) for vox in reconstruction_vox]
from voxelize import unvoxelize
reconstruction = [unvoxelize(vox > RC_CONFIDENCE) for vox in reconstruction_vox]
reconstruction = [segment*scale for (segment, scale) in zip(reconstruction, features_voxel_scale)]
if CREATE_VISUALS:
dir_ = "/tmp/online_matcher/visuals/reconstructions/"
from visuals import visuals_of_matches
reconstruction_ids = [id_+max(I_)+1 for id_ in I_]
one_to_one_matches = [[id1, id2] for id1, id2 in zip(I_, reconstruction_ids)]
visuals_of_matches(one_to_one_matches, S_+reconstruction, I_+reconstruction_ids, directory=dir_, oneview=ONEVIEW)
clear_output()
# In[ ]:
# Reconstructions of rotations for one object
if CREATE_VISUALS:
dir_ = "/tmp/online_matcher/visuals/rotations/"
RC_CONFIDENCE = 0.1
ONEVIEW = True
# In[ ]:
# Reconstructions
if not RUN_AS_PY_SCRIPT:
N = 100
SV_ = segments_vox[:N]
S_ = segments[:N]
I_ = ids[:N]
reconstruction_vox = vae.batch_encode_decode([np.reshape(sample, MP.INPUT_SHAPE) for sample in SV_])
reconstruction_vox = [np.reshape(vox, [VOXEL_SIDE, VOXEL_SIDE, VOXEL_SIDE]) for vox in reconstruction_vox]
from voxelize import unvoxelize
reconstruction = [unvoxelize(vox > RC_CONFIDENCE) for vox in reconstruction_vox]
reconstruction = [segment*scale for (segment, scale) in zip(reconstruction, features_voxel_scale)]
if CREATE_VISUALS:
dir_ = "/tmp/online_matcher/visuals/reconstructions/"
from visuals import visuals_of_matches
reconstruction_ids = [id_+max(I_)+1 for id_ in I_]
one_to_one_matches = [[id1, id2] for id1, id2 in zip(I_, reconstruction_ids)]
visuals_of_matches(one_to_one_matches, S_+reconstruction, I_+reconstruction_ids, directory=dir_, oneview=ONEVIEW)
clear_output()
# In[ ]:
# Exploring influence of first dimension on generated segments
dim_ = 0
if CREATE_VISUALS:
xyz_scales = sc_features[:,3:]
## PROFILING ##
###############
PROFILING = True
if PROFILING:
from timeit import default_timer as timer
total_start = timer()
reconstr_start = timer()
## RECONSTRUCT SEGMENTS ##
##########################
segments_vox = vae.batch_decode(ae_features)
segments_vox = [np.reshape(vox, [voxel_side, voxel_side, voxel_side]) for vox in segments_vox]
from voxelize import unvoxelize, unalign
segments = [unvoxelize(vox > RC_CONFIDENCE) for vox in segments_vox]
segments = [segment*scale for (segment, scale) in zip(segments, xyz_scales)]
segments = unalign(segments, al_features)
if PROFILING:
reconstr_end = timer()
overhead_out_start = timer()
print("__RCST_COMPLETE__")
## OUTPUT DATA TO FILES ##
##########################
print("Writing segments")
from import_export import write_segments
write_segments(ids, segments, folder="", filename=segments_fifo_path)
