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data_loader.py
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397 lines (363 loc) · 18.8 KB
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'''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''
\copyright Copyright (c) 2022 Visual Computing group of Ulm University,
Germany. See the LICENSE file at the top-level directory of
this distribution.
'''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''
import numpy as np
import imageio
DATA_PATH = 'data/'
S_train = {'path': DATA_PATH,
'list': 'train_motion.txt',
'frequencies': ['20', '50', '70'],
'shape': [600, 600, 4],
'num_frames': 50,
'num_scenes': 116}
S_val = {'path': DATA_PATH,
'list': 'val_motion.txt',
'frequencies': ['20', '50', '70'],
'shape': [600, 600, 4],
'num_frames': 50,
'num_scenes': 13}
S_test = {'path': DATA_PATH,
'list': 'test_motion.txt',
'frequencies': ['20', '50', '70'],
'shape': [600, 600, 4],
'num_frames': 50,
'num_scenes': 13}
def load_filenames(data_set):
""" Loads data of the datasets from publications of Agresti et al.
Args:
data_set: `str`, can be `'train', 'val', 'test'`.
Returns:
filenames: `float`, shape `[S, 50]`.
"""
if 'train' in data_set:
Set = S_train
elif 'val' in data_set:
Set = S_val
elif 'test' in data_set:
Set = S_test
scenes = []
with open(Set['path'] + Set['list'], 'r') as inFile:
for line in inFile:
scenes.append(line.replace('\n', ''))
frames = []
for scene in scenes:
frames.append([Set['path'] + scene + '/' + str(i).zfill(3) + '_render_' for i in range(50)])
return np.array(frames, dtype=str)
def load_batch(files, frequencies=None, slice_id=None):
""" Loads data of the datasets from Cornell dataset
Args:
files: `list`of `str`.
frequencies: `list` of `str`, must be subset of the frequencies in the
respective dataset.
If `None` loads all.
slice_ids: an `int` [0,1,2], to slice the color channel for different materials.
Returns:
depths: `float`, shape `[S, H, W]`.
tof_depths: `float`, shape `[S, F, H, W]`.
correlations: `float`, shape `[S, F, 4, H, W]`.
amplitudes: `float`, shape `[S, F, H, W]`.
intensities: `float`, shape `[S, F, H, W]`.
"""
depths = []
tof_depths = []
correlations = []
amplitudes = []
intensities = []
if frequencies is None:
frequencies = ['20', '50', '70']
for frame in files:
if slice_id is None:
depths.append(imageio.imread(frame + 'depth.hdr', format='HDR-FI'))
tof_depths.append(
[imageio.imread(frame + str(freq) + 'MHz_ToF.hdr', format='HDR-FI') for freq in frequencies]
)
correlations.append(
[[imageio.imread(frame + str(freq) + 'MHz_phase0.hdr', format='HDR-FI'),
imageio.imread(frame + str(freq) + 'MHz_phase1.hdr', format='HDR-FI'),
imageio.imread(frame + str(freq) + 'MHz_phase2.hdr', format='HDR-FI'),
imageio.imread(frame + str(freq) + 'MHz_phase3.hdr', format='HDR-FI')]
for freq in frequencies]
)
else:
depths.append(imageio.imread(frame + 'depth.hdr', format='HDR-FI')[:, :, slice_id])
tof_depths.append(
[imageio.imread(frame + str(freq) + 'MHz_ToF.hdr', format='HDR-FI')[:, :, slice_id] for freq in frequencies]
)
correlations.append(
[[imageio.imread(frame + str(freq) + 'MHz_phase0.hdr', format='HDR-FI')[:, :, slice_id],
imageio.imread(frame + str(freq) + 'MHz_phase1.hdr', format='HDR-FI')[:, :, slice_id],
imageio.imread(frame + str(freq) + 'MHz_phase2.hdr', format='HDR-FI')[:, :, slice_id],
imageio.imread(frame + str(freq) + 'MHz_phase3.hdr', format='HDR-FI')[:, :, slice_id]]
for freq in frequencies]
)
depths = np.array(depths, dtype=np.float32)
tof_depths = np.array(tof_depths, dtype=np.float32)
correlations = np.array(correlations, dtype=np.float32)
amplitudes = 0.5 * np.sqrt((correlations[:, :, 0] - correlations[:, :, 2])**2 + \
(correlations[:, :, 3] - correlations[:, :, 1])**2)
intensities = np.mean(correlations, axis=2)
return depths, tof_depths, correlations, amplitudes, intensities
def load_batch_correlation(files, frequencies=None, slice_id=None):
""" Loads data of the datasets from Cornell dataset
Args:
files: `list`of `str`.
frequencies: `list` of `str`, must be subset of the frequencies in the
respective dataset.
If `None` loads all.
slice_ids: an `int` [0,1,2], to slice the color channel for different materials.
Returns:
correlations: `float`, shape `[S, F, 4, H, W]`.
"""
correlations = []
if frequencies is None:
frequencies = ['20', '50', '70']
for frame in files:
if slice_id is None:
correlations.append(
[[imageio.imread(frame + str(freq) + 'MHz_phase0.hdr', format='HDR-FI'),
imageio.imread(frame + str(freq) + 'MHz_phase1.hdr', format='HDR-FI'),
imageio.imread(frame + str(freq) + 'MHz_phase2.hdr', format='HDR-FI'),
imageio.imread(frame + str(freq) + 'MHz_phase3.hdr', format='HDR-FI')]
for freq in frequencies]
)
else:
correlations.append(
[[imageio.imread(frame + str(freq) + 'MHz_phase0.hdr', format='HDR-FI')[:, :, slice_id],
imageio.imread(frame + str(freq) + 'MHz_phase1.hdr', format='HDR-FI')[:, :, slice_id],
imageio.imread(frame + str(freq) + 'MHz_phase2.hdr', format='HDR-FI')[:, :, slice_id],
imageio.imread(frame + str(freq) + 'MHz_phase3.hdr', format='HDR-FI')[:, :, slice_id]]
for freq in frequencies]
)
correlations = np.array(correlations, dtype=np.float32)
return correlations
def load_batch_correlation_motion(files, frequencies=None, slice_id=None):
""" Loads data of the datasets from Cornell dataset
Args:
files: `list`of `str`.
frequencies: `list` of `str`, must be subset of the frequencies in the
respective dataset.
If `None` loads all.
slice_ids: an `int` [0,1,2], to slice the color channel for different materials.
Returns:
correlations: `float`, shape `[S, F, 4, H, W]`.
"""
correlations = []
if frequencies is None:
frequencies = ['20', '50', '70']
offsets = range(0, len(frequencies) * 4, 4)[::-1]
for frame in files:
curr_timestamp = int(frame.split('/')[-1][:3])
cut = len(frame.split('/')[-1])
if slice_id is None:
correlations.append(
[[imageio.imread(frame[:-cut] + str(max(curr_timestamp - offset - 3, 0)).zfill(3) + '_render_' + str(freq) + 'MHz_phase0.hdr', format='HDR-FI'),
imageio.imread(frame[:-cut] + str(max(curr_timestamp - offset - 2, 0)).zfill(3) + '_render_' + str(freq) + 'MHz_phase1.hdr', format='HDR-FI'),
imageio.imread(frame[:-cut] + str(max(curr_timestamp - offset - 1, 0)).zfill(3) + '_render_' + str(freq) + 'MHz_phase2.hdr', format='HDR-FI'),
imageio.imread(frame[:-cut] + str(max(curr_timestamp - offset, 0)).zfill(3) + '_render_' + str(freq) + 'MHz_phase3.hdr', format='HDR-FI')]
for freq, offset in zip(frequencies, offsets)]
)
else:
correlations.append(
[[imageio.imread(frame[:-cut] + str(max(curr_timestamp - offset - 3, 0)).zfill(3) + '_render_' + str(freq) + 'MHz_phase0.hdr', format='HDR-FI')[:, :, slice_id],
imageio.imread(frame[:-cut] + str(max(curr_timestamp - offset - 2, 0)).zfill(3) + '_render_' + str(freq) + 'MHz_phase1.hdr', format='HDR-FI')[:, :, slice_id],
imageio.imread(frame[:-cut] + str(max(curr_timestamp - offset - 1, 0)).zfill(3) + '_render_' + str(freq) + 'MHz_phase2.hdr', format='HDR-FI')[:, :, slice_id],
imageio.imread(frame[:-cut] + str(max(curr_timestamp - offset, 0)).zfill(3) + '_render_' + str(freq) + 'MHz_phase3.hdr', format='HDR-FI')[:, :, slice_id]]
for freq, offset in zip(frequencies, offsets)]
)
correlations = np.array(correlations, dtype=np.float32)
return correlations
def load_batch_motion(files, frequencies=None, slice_id=None, taps=1):
""" Loads data of the datasets from Cornell dataset with motion simlated as n tap sensor.
Args:
files: `list`of `str`.
frequencies: `list` of `str`, must be subset of the frequencies in the
respective dataset.
If `None` loads all.
slice_ids: an `int` can be `[0,1,2]`, to slice the color channel for different materials.
taps: `int`, can be `[1, 2, 4]`.
Returns:
depths: `float`, shape `[S, H, W]`.
tof_depths: `float`, shape `[S, F, H, W]`, without motion.
correlations: `float`, shape `[S, F, 4, H, W]`.
amplitudes: `float`, shape `[S, F, H, W]`.
intensities: `float`, shape `[S, F, H, W]`.
"""
if taps == 1:
return load_batch_motion_single_tap(files, frequencies, slice_id)
elif taps == 2:
return load_batch_motion_2tap(files, frequencies, slice_id)
elif taps == 4:
return load_batch_motion_4tap(files, frequencies, slice_id)
def load_batch_motion_single_tap(files, frequencies=None, slice_id=None):
""" Loads data of the datasets from Cornell dataset with motion simlated as single tap sensor.
Args:
files: `list`of `str`.
frequencies: `list` of `str`, must be subset of the frequencies in the
respective dataset.
If `None` loads all.
slice_ids: an `int` [0,1,2], to slice the color channel for different materials.
Returns:
depths: `float`, shape `[S, H, W]`.
tof_depths: `float`, shape `[S, F, H, W]`, without motion.
correlations: `float`, shape `[S, F, 4, H, W]`.
amplitudes: `float`, shape `[S, F, H, W]`.
intensities: `float`, shape `[S, F, H, W]`.
"""
depths = []
tof_depths = []
correlations = []
amplitudes = []
intensities = []
if frequencies is None:
frequencies = ['20', '50', '70']
offsets = range(0, len(frequencies) * 4, 4)[::-1]
for frame in files:
curr_timestamp = int(frame.split('/')[-1][:3])
cut = len(frame.split('/')[-1])
if slice_id is None:
depths.append(imageio.imread(frame + 'depth.hdr', format='HDR-FI'))
tof_depths.append(
[imageio.imread(frame + str(freq) + 'MHz_ToF.hdr', format='HDR-FI') for freq in frequencies]
)
correlations.append(
[[imageio.imread(frame[:-cut] + str(max(curr_timestamp - offset - 3, 0)).zfill(3) + '_render_' + str(freq) + 'MHz_phase0.hdr', format='HDR-FI'),
imageio.imread(frame[:-cut] + str(max(curr_timestamp - offset - 2, 0)).zfill(3) + '_render_' + str(freq) + 'MHz_phase1.hdr', format='HDR-FI'),
imageio.imread(frame[:-cut] + str(max(curr_timestamp - offset - 1, 0)).zfill(3) + '_render_' + str(freq) + 'MHz_phase2.hdr', format='HDR-FI'),
imageio.imread(frame[:-cut] + str(max(curr_timestamp - offset, 0)).zfill(3) + '_render_' + str(freq) + 'MHz_phase3.hdr', format='HDR-FI')]
for freq, offset in zip(frequencies, offsets)]
)
else:
depths.append(imageio.imread(frame + 'depth.hdr', format='HDR-FI')[:, :, slice_id])
tof_depths.append(
[imageio.imread(frame + str(freq) + 'MHz_ToF.hdr', format='HDR-FI')[:, :, slice_id] for freq in frequencies]
)
correlations.append(
[[imageio.imread(frame[:-cut] + str(max(curr_timestamp - offset - 3, 0)).zfill(3) + '_render_' + str(freq) + 'MHz_phase0.hdr', format='HDR-FI')[:, :, slice_id],
imageio.imread(frame[:-cut] + str(max(curr_timestamp - offset - 2, 0)).zfill(3) + '_render_' + str(freq) + 'MHz_phase1.hdr', format='HDR-FI')[:, :, slice_id],
imageio.imread(frame[:-cut] + str(max(curr_timestamp - offset - 1, 0)).zfill(3) + '_render_' + str(freq) + 'MHz_phase2.hdr', format='HDR-FI')[:, :, slice_id],
imageio.imread(frame[:-cut] + str(max(curr_timestamp - offset, 0)).zfill(3) + '_render_' + str(freq) + 'MHz_phase3.hdr', format='HDR-FI')[:, :, slice_id]]
for freq, offset in zip(frequencies, offsets)]
)
depths = np.array(depths, dtype=np.float32)
tof_depths = np.array(tof_depths, dtype=np.float32)
correlations = np.array(correlations, dtype=np.float32)
amplitudes = 0.5 * np.sqrt((correlations[:, :, 0] - correlations[:, :, 2])**2 + \
(correlations[:, :, 3] - correlations[:, :, 1])**2)
intensities = np.mean(correlations, axis=2)
return depths, tof_depths, correlations, amplitudes, intensities
def load_batch_motion_2tap(files, frequencies=None, slice_id=None):
""" Loads data of the datasets from Cornell dataset with motion simulated as 2-tap sensor.
Args:
files: `list`of `str`.
frequencies: `list` of `str`, must be subset of the frequencies in the
respective dataset.
If `None` loads all.
slice_ids: an `int` [0,1,2], to slice the color channel for different materials.
Returns:
depths: `float`, shape `[S, H, W]`.
tof_depths: `float`, shape `[S, F, H, W]`, without motion.
correlations: `float`, shape `[S, F, 4, H, W]`.
amplitudes: `float`, shape `[S, F, H, W]`.
intensities: `float`, shape `[S, F, H, W]`.
"""
depths = []
tof_depths = []
correlations = []
amplitudes = []
intensities = []
if frequencies is None:
frequencies = ['20', '50', '70']
offsets = range(0, len(frequencies) * 2, 2)[::-1]
for frame in files:
curr_timestamp = int(frame.split('/')[-1][:3])
cut = len(frame.split('/')[-1])
if slice_id is None:
depths.append(imageio.imread(frame + 'depth.hdr', format='HDR-FI'))
tof_depths.append(
[imageio.imread(frame + str(freq) + 'MHz_ToF.hdr', format='HDR-FI') for freq in frequencies]
)
correlations.append(
[[imageio.imread(frame[:-cut] + str(max(curr_timestamp - offset - 1, 0)).zfill(3) + '_render_' + str(freq) + 'MHz_phase0.hdr', format='HDR-FI'),
imageio.imread(frame[:-cut] + str(max(curr_timestamp - offset, 0)).zfill(3) + '_render_' + str(freq) + 'MHz_phase1.hdr', format='HDR-FI'),
imageio.imread(frame[:-cut] + str(max(curr_timestamp - offset - 1, 0)).zfill(3) + '_render_' + str(freq) + 'MHz_phase2.hdr', format='HDR-FI'),
imageio.imread(frame[:-cut] + str(max(curr_timestamp - offset, 0)).zfill(3) + '_render_' + str(freq) + 'MHz_phase3.hdr', format='HDR-FI')]
for freq, offset in zip(frequencies, offsets)]
)
else:
depths.append(imageio.imread(frame + 'depth.hdr', format='HDR-FI')[:, :, slice_id])
tof_depths.append(
[imageio.imread(frame + str(freq) + 'MHz_ToF.hdr', format='HDR-FI')[:, :, slice_id] for freq in frequencies]
)
correlations.append(
[[imageio.imread(frame[:-cut] + str(max(curr_timestamp - offset - 1, 0)).zfill(3) + '_render_' + str(freq) + 'MHz_phase0.hdr', format='HDR-FI')[:, :, slice_id],
imageio.imread(frame[:-cut] + str(max(curr_timestamp - offset, 0)).zfill(3) + '_render_' + str(freq) + 'MHz_phase1.hdr', format='HDR-FI')[:, :, slice_id],
imageio.imread(frame[:-cut] + str(max(curr_timestamp - offset - 1, 0)).zfill(3) + '_render_' + str(freq) + 'MHz_phase2.hdr', format='HDR-FI')[:, :, slice_id],
imageio.imread(frame[:-cut] + str(max(curr_timestamp - offset, 0)).zfill(3) + '_render_' + str(freq) + 'MHz_phase3.hdr', format='HDR-FI')[:, :, slice_id]]
for freq, offset in zip(frequencies, offsets)]
)
depths = np.array(depths, dtype=np.float32)
tof_depths = np.array(tof_depths, dtype=np.float32)
correlations = np.array(correlations, dtype=np.float32)
amplitudes = 0.5 * np.sqrt((correlations[:, :, 0] - correlations[:, :, 2])**2 + \
(correlations[:, :, 3] - correlations[:, :, 1])**2)
intensities = np.mean(correlations, axis=2)
return depths, tof_depths, correlations, amplitudes, intensities
def load_batch_motion_4tap(files, frequencies=None, slice_id=None):
""" Loads data of the datasets from Cornell dataset with motion simulated as 4-tap sensor.
Args:
files: `list`of `str`.
frequencies: `list` of `str`, must be subset of the frequencies in the
respective dataset.
If `None` loads all.
slice_ids: an `int` [0,1,2], to slice the color channel for different materials.
Returns:
depths: `float`, shape `[S, H, W]`.
tof_depths: `float`, shape `[S, F, H, W]`, without motion.
correlations: `float`, shape `[S, F, 4, H, W]`.
amplitudes: `float`, shape `[S, F, H, W]`.
intensities: `float`, shape `[S, F, H, W]`.
"""
depths = []
tof_depths = []
correlations = []
amplitudes = []
intensities = []
if frequencies is None:
frequencies = ['20', '50', '70']
offsets = range(0, len(frequencies), 1)[::-1]
for frame in files:
curr_timestamp = int(frame.split('/')[-1][:3])
cut = len(frame.split('/')[-1])
if slice_id is None:
depths.append(imageio.imread(frame + 'depth.hdr', format='HDR-FI'))
tof_depths.append(
[imageio.imread(frame + str(freq) + 'MHz_ToF.hdr', format='HDR-FI') for freq in frequencies]
)
correlations.append(
[[imageio.imread(frame[:-cut] + str(max(curr_timestamp - offset, 0)).zfill(3) + '_render_' + str(freq) + 'MHz_phase0.hdr', format='HDR-FI'),
imageio.imread(frame[:-cut] + str(max(curr_timestamp - offset, 0)).zfill(3) + '_render_' + str(freq) + 'MHz_phase1.hdr', format='HDR-FI'),
imageio.imread(frame[:-cut] + str(max(curr_timestamp - offset, 0)).zfill(3) + '_render_' + str(freq) + 'MHz_phase2.hdr', format='HDR-FI'),
imageio.imread(frame[:-cut] + str(max(curr_timestamp - offset, 0)).zfill(3) + '_render_' + str(freq) + 'MHz_phase3.hdr', format='HDR-FI')]
for freq, offset in zip(frequencies, offsets)]
)
else:
depths.append(imageio.imread(frame + 'depth.hdr', format='HDR-FI')[:, :, slice_id])
tof_depths.append(
[imageio.imread(frame + str(freq) + 'MHz_ToF.hdr', format='HDR-FI')[:, :, slice_id] for freq in frequencies]
)
correlations.append(
[[imageio.imread(frame[:-cut] + str(max(curr_timestamp - offset, 0)).zfill(3) + '_render_' + str(freq) + 'MHz_phase0.hdr', format='HDR-FI')[:, :, slice_id],
imageio.imread(frame[:-cut] + str(max(curr_timestamp - offset, 0)).zfill(3) + '_render_' + str(freq) + 'MHz_phase1.hdr', format='HDR-FI')[:, :, slice_id],
imageio.imread(frame[:-cut] + str(max(curr_timestamp - offset, 0)).zfill(3) + '_render_' + str(freq) + 'MHz_phase2.hdr', format='HDR-FI')[:, :, slice_id],
imageio.imread(frame[:-cut] + str(max(curr_timestamp - offset, 0)).zfill(3) + '_render_' + str(freq) + 'MHz_phase3.hdr', format='HDR-FI')[:, :, slice_id]]
for freq, offset in zip(frequencies, offsets)]
)
depths = np.array(depths, dtype=np.float32)
tof_depths = np.array(tof_depths, dtype=np.float32)
correlations = np.array(correlations, dtype=np.float32)
amplitudes = 0.5 * np.sqrt((correlations[:, :, 0] - correlations[:, :, 2])**2 + \
(correlations[:, :, 3] - correlations[:, :, 1])**2)
intensities = np.mean(correlations, axis=2)
return depths, tof_depths, correlations, amplitudes, intensities