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David Rotermund 2024-02-16 09:59:49 +01:00 committed by GitHub
parent 7f332a6fa9
commit 040460c590
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GPG key ID: B5690EEEBB952194
2 changed files with 66 additions and 48 deletions
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18
reproduction_effort/functions/adjust_factor.py
View file

@ -9,6 +9,7 @@ def adjust_factor(
upper_frequency_heartbeat: float,
sample_frequency: float,
mask: torch.Tensor,
power_factors: None | list[float],
) -> tuple[float, float]:
number_of_active_pixel: torch.Tensor = mask.type(dtype=torch.float32).sum()
@ -23,6 +24,7 @@ def adjust_factor(
signal_acceptor_offset = signal_acceptor.mean()
signal_donor_offset = signal_donor.mean()
if power_factors is None:
signal_acceptor = signal_acceptor - signal_acceptor_offset
signal_donor = signal_donor - signal_donor_offset
@ -42,7 +44,8 @@ def adjust_factor(
signal_donor_fft: torch.Tensor = torch.fft.rfft(signal_donor, n=nfft)
frequency_axis: torch.Tensor = (
torch.fft.rfftfreq(nfft, device=signal_acceptor_fft.device) * sample_frequency
torch.fft.rfftfreq(nfft, device=signal_acceptor_fft.device)
* sample_frequency
)
signal_acceptor_power: torch.Tensor = torch.abs(signal_acceptor_fft) ** 2
@ -67,16 +70,21 @@ def adjust_factor(
signal_acceptor_power = signal_acceptor_power[idx]
signal_donor_power = signal_donor_power[idx]
acceptor_range = signal_acceptor_power.max() - signal_acceptor_power.min()
acceptor_range: float = float(
signal_acceptor_power.max() - signal_acceptor_power.min()
)
donor_range = signal_donor_power.max() - signal_donor_power.min()
donor_range: float = float(signal_donor_power.max() - signal_donor_power.min())
else:
donor_range = float(power_factors[0])
acceptor_range = float(power_factors[1])
acceptor_correction_factor: float = float(
0.5
* (
1
+ (signal_acceptor_offset * torch.sqrt(donor_range))
/ (signal_donor_offset * torch.sqrt(acceptor_range))
+ (signal_acceptor_offset * math.sqrt(donor_range))
/ (signal_donor_offset * math.sqrt(acceptor_range))
)
)

12
reproduction_effort/functions/preprocessing.py
View file

@ -25,6 +25,7 @@ def preprocessing(
upper_frequency_heartbeat: float,
sample_frequency: float,
dtype: torch.dtype = torch.float32,
power_factors: None | list[float] = None,
) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
mask: torch.Tensor = make_mask(
@ -44,20 +45,24 @@ def preprocessing(
)
# Interpolate in-between images
if power_factors is None:
interpolate_along_time(camera_sequence)
camera_sequence_filtered: list[torch.Tensor] = []
for id in range(0, len(camera_sequence)):
camera_sequence_filtered.append(camera_sequence[id].clone())
if power_factors is None:
idx_volume: int = cameras.index("volume")
heart_rate: float = heart_beat_frequency(
heart_rate: None | float = heart_beat_frequency(
input=camera_sequence_filtered[idx_volume],
lower_frequency_heartbeat=lower_frequency_heartbeat,
upper_frequency_heartbeat=upper_frequency_heartbeat,
sample_frequency=sample_frequency,
mask=mask,
)
else:
heart_rate = None
camera_sequence_filtered = gauss_smear(
camera_sequence_filtered,
@ -88,8 +93,12 @@ def preprocessing(
)
results.append(output)
if heart_rate is not None:
lower_frequency_heartbeat_selection: float = heart_rate - 3
upper_frequency_heartbeat_selection: float = heart_rate + 3
else:
lower_frequency_heartbeat_selection = 0
upper_frequency_heartbeat_selection = 0
donor_correction_factor, acceptor_correction_factor = adjust_factor(
input_acceptor=results[0],
@ -98,6 +107,7 @@ def preprocessing(
upper_frequency_heartbeat=upper_frequency_heartbeat_selection,
sample_frequency=sample_frequency,
mask=mask,
power_factors=power_factors,
)
results[0] = acceptor_correction_factor * (