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David Rotermund 2024-02-14 22:44:15 +01:00 committed by GitHub
parent 7a44748316
commit 7f332a6fa9
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2 changed files with 37 additions and 5 deletions
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21
reproduction_effort/aligned.py
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@ -2,6 +2,7 @@ import scipy.io as sio # type: ignore
import torch
import numpy as np
import matplotlib.pyplot as plt
import json
from functions.align_cameras import align_cameras
@ -16,11 +17,23 @@ if __name__ == "__main__":
dtype: torch.dtype = torch.float32
filename_raw_json: str = "raw/Exp001_Trial001_Part001_meta.txt"
filename_bin_mat: str = "bin_old/Exp001_Trial001_Part001.mat"
filename_data_binning_replace: str = "bin_old/Exp001_Trial001_Part001.mat"
batch_size: int = 200
filename_aligned_mat: str = "aligned_old/Exp001_Trial001_Part001.mat"
with open(filename_raw_json, "r") as file_handle:
metadata: dict = json.load(file_handle)
channels: list[str] = metadata["channelKey"]
data = torch.tensor(
sio.loadmat(filename_data_binning_replace)["nparray"].astype(np.float32),
dtype=dtype,
device=device,
)
ref_image = data[:, :, data.shape[-2] // 2, :].clone()
(
acceptor,
donor,
@ -31,13 +44,15 @@ if __name__ == "__main__":
angle_refref,
tvec_refref,
) = align_cameras(
filename_raw_json=filename_raw_json,
filename_bin_mat=filename_bin_mat,
channels=channels,
data=data,
ref_image=ref_image,
device=device,
dtype=dtype,
batch_size=batch_size,
fill_value=-1,
)
del data
mat_data = torch.tensor(
sio.loadmat(filename_aligned_mat)["data"].astype(dtype=np.float32),

21
reproduction_effort/make_test_data_aligned.py
View file

@ -3,6 +3,7 @@ import torchvision as tv # type: ignore
import numpy as np
import matplotlib.pyplot as plt
import scipy.io as sio # type: ignore
import json
from functions.align_cameras import align_cameras
@ -143,6 +144,19 @@ sio.savemat(filename_bin_mat_fake, mdic)
batch_size: int = 200
filename_raw_json: str = "raw/Exp001_Trial001_Part001_meta.txt"
with open(filename_raw_json, "r") as file_handle:
metadata: dict = json.load(file_handle)
channels: list[str] = metadata["channelKey"]
data = torch.tensor(
sio.loadmat(filename_bin_mat_fake)["nparray"].astype(np.float32),
dtype=dtype,
device=device,
)
ref_image = data[:, :, data.shape[-2] // 2, :].clone()
(
acceptor,
donor,
@ -153,13 +167,16 @@ filename_raw_json: str = "raw/Exp001_Trial001_Part001_meta.txt"
angle_refref,
tvec_refref,
) = align_cameras(
filename_raw_json=filename_raw_json,
filename_bin_mat=filename_bin_mat_fake,
channels=channels,
data=data,
ref_image=ref_image,
device=device,
dtype=dtype,
batch_size=batch_size,
fill_value=-1,
)
del data
print("References Acceptor <-> Donor:")
print("Rotation:")