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David Rotermund
GitHub
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7f332a6fa9
commit
040460c590
2 changed files with 66 additions and 48 deletions
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@ -9,6 +9,7 @@ def adjust_factor(
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upper_frequency_heartbeat: float,
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sample_frequency: float,
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mask: torch.Tensor,
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power_factors: None | list[float],
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) -> tuple[float, float]:
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number_of_active_pixel: torch.Tensor = mask.type(dtype=torch.float32).sum()
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@ -23,60 +24,67 @@ def adjust_factor(
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signal_acceptor_offset = signal_acceptor.mean()
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signal_donor_offset = signal_donor.mean()
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signal_acceptor = signal_acceptor - signal_acceptor_offset
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signal_donor = signal_donor - signal_donor_offset
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if power_factors is None:
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signal_acceptor = signal_acceptor - signal_acceptor_offset
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signal_donor = signal_donor - signal_donor_offset
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blackman_window = torch.blackman_window(
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window_length=signal_acceptor.shape[0],
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periodic=True,
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dtype=signal_acceptor.dtype,
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device=signal_acceptor.device,
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)
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blackman_window = torch.blackman_window(
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window_length=signal_acceptor.shape[0],
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periodic=True,
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dtype=signal_acceptor.dtype,
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device=signal_acceptor.device,
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)
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signal_acceptor *= blackman_window
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signal_donor *= blackman_window
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nfft: int = int(2 ** math.ceil(math.log2(signal_donor.shape[0])))
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nfft = max([256, nfft])
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signal_acceptor *= blackman_window
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signal_donor *= blackman_window
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nfft: int = int(2 ** math.ceil(math.log2(signal_donor.shape[0])))
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nfft = max([256, nfft])
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signal_acceptor_fft: torch.Tensor = torch.fft.rfft(signal_acceptor, n=nfft)
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signal_donor_fft: torch.Tensor = torch.fft.rfft(signal_donor, n=nfft)
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signal_acceptor_fft: torch.Tensor = torch.fft.rfft(signal_acceptor, n=nfft)
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signal_donor_fft: torch.Tensor = torch.fft.rfft(signal_donor, n=nfft)
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frequency_axis: torch.Tensor = (
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torch.fft.rfftfreq(nfft, device=signal_acceptor_fft.device) * sample_frequency
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)
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frequency_axis: torch.Tensor = (
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torch.fft.rfftfreq(nfft, device=signal_acceptor_fft.device)
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* sample_frequency
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)
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signal_acceptor_power: torch.Tensor = torch.abs(signal_acceptor_fft) ** 2
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signal_acceptor_power[1:-1] *= 2
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signal_acceptor_power: torch.Tensor = torch.abs(signal_acceptor_fft) ** 2
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signal_acceptor_power[1:-1] *= 2
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signal_donor_power: torch.Tensor = torch.abs(signal_donor_fft) ** 2
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signal_donor_power[1:-1] *= 2
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signal_donor_power: torch.Tensor = torch.abs(signal_donor_fft) ** 2
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signal_donor_power[1:-1] *= 2
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if frequency_axis[-1] != (sample_frequency / 2.0):
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signal_acceptor_power[-1] *= 2
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signal_donor_power[-1] *= 2
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if frequency_axis[-1] != (sample_frequency / 2.0):
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signal_acceptor_power[-1] *= 2
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signal_donor_power[-1] *= 2
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signal_acceptor_power /= blackman_window.sum() ** 2
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signal_donor_power /= blackman_window.sum() ** 2
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signal_acceptor_power /= blackman_window.sum() ** 2
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signal_donor_power /= blackman_window.sum() ** 2
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idx = torch.where(
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(frequency_axis >= lower_frequency_heartbeat)
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* (frequency_axis <= upper_frequency_heartbeat)
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)[0]
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idx = torch.where(
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(frequency_axis >= lower_frequency_heartbeat)
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* (frequency_axis <= upper_frequency_heartbeat)
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)[0]
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frequency_axis = frequency_axis[idx]
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signal_acceptor_power = signal_acceptor_power[idx]
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signal_donor_power = signal_donor_power[idx]
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frequency_axis = frequency_axis[idx]
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signal_acceptor_power = signal_acceptor_power[idx]
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signal_donor_power = signal_donor_power[idx]
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acceptor_range = signal_acceptor_power.max() - signal_acceptor_power.min()
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acceptor_range: float = float(
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signal_acceptor_power.max() - signal_acceptor_power.min()
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)
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donor_range = signal_donor_power.max() - signal_donor_power.min()
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donor_range: float = float(signal_donor_power.max() - signal_donor_power.min())
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else:
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donor_range = float(power_factors[0])
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acceptor_range = float(power_factors[1])
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acceptor_correction_factor: float = float(
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0.5
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* (
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1
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+ (signal_acceptor_offset * torch.sqrt(donor_range))
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/ (signal_donor_offset * torch.sqrt(acceptor_range))
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+ (signal_acceptor_offset * math.sqrt(donor_range))
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/ (signal_donor_offset * math.sqrt(acceptor_range))
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)
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)
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@ -25,6 +25,7 @@ def preprocessing(
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upper_frequency_heartbeat: float,
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sample_frequency: float,
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dtype: torch.dtype = torch.float32,
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power_factors: None | list[float] = None,
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) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
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mask: torch.Tensor = make_mask(
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@ -44,20 +45,24 @@ def preprocessing(
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)
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# Interpolate in-between images
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interpolate_along_time(camera_sequence)
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if power_factors is None:
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interpolate_along_time(camera_sequence)
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camera_sequence_filtered: list[torch.Tensor] = []
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for id in range(0, len(camera_sequence)):
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camera_sequence_filtered.append(camera_sequence[id].clone())
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idx_volume: int = cameras.index("volume")
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heart_rate: float = heart_beat_frequency(
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input=camera_sequence_filtered[idx_volume],
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lower_frequency_heartbeat=lower_frequency_heartbeat,
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upper_frequency_heartbeat=upper_frequency_heartbeat,
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sample_frequency=sample_frequency,
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mask=mask,
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)
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if power_factors is None:
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idx_volume: int = cameras.index("volume")
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heart_rate: None | float = heart_beat_frequency(
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input=camera_sequence_filtered[idx_volume],
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lower_frequency_heartbeat=lower_frequency_heartbeat,
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upper_frequency_heartbeat=upper_frequency_heartbeat,
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sample_frequency=sample_frequency,
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mask=mask,
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)
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else:
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heart_rate = None
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camera_sequence_filtered = gauss_smear(
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camera_sequence_filtered,
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@ -88,8 +93,12 @@ def preprocessing(
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)
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results.append(output)
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lower_frequency_heartbeat_selection: float = heart_rate - 3
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upper_frequency_heartbeat_selection: float = heart_rate + 3
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if heart_rate is not None:
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lower_frequency_heartbeat_selection: float = heart_rate - 3
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upper_frequency_heartbeat_selection: float = heart_rate + 3
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else:
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lower_frequency_heartbeat_selection = 0
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upper_frequency_heartbeat_selection = 0
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donor_correction_factor, acceptor_correction_factor = adjust_factor(
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input_acceptor=results[0],
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@ -98,6 +107,7 @@ def preprocessing(
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upper_frequency_heartbeat=upper_frequency_heartbeat_selection,
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sample_frequency=sample_frequency,
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mask=mask,
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power_factors=power_factors,
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)
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results[0] = acceptor_correction_factor * (
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