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reproduction_effort/aligned.py

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+import scipy.io as sio  # type: ignore
+import torch
+import numpy as np
+import matplotlib.pyplot as plt
+
+from functions.align_cameras import align_cameras
+
+if __name__ == "__main__":
+
+    if torch.cuda.is_available():
+        device_name: str = "cuda:0"
+    else:
+        device_name = "cpu"
+    print(f"Using device: {device_name}")
+    device: torch.device = torch.device(device_name)
+    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"
+    batch_size: int = 200
+
+    filename_aligned_mat: str = "aligned_old/Exp001_Trial001_Part001.mat"
+
+    (
+        acceptor,
+        donor,
+        oxygenation,
+        volume,
+        angle_donor_volume,
+        tvec_donor_volume,
+        angle_refref,
+        tvec_refref,
+    ) = align_cameras(
+        filename_raw_json=filename_raw_json,
+        filename_bin_mat=filename_bin_mat,
+        device=device,
+        dtype=dtype,
+        batch_size=batch_size,
+        fill_value=-1,
+    )
+
+    mat_data = torch.tensor(
+        sio.loadmat(filename_aligned_mat)["data"].astype(dtype=np.float32),
+        dtype=dtype,
+        device=device,
+    )
+
+    old: list = []
+    old.append(mat_data[..., 0].movedim(-1, 0))
+    old.append(mat_data[..., 1].movedim(-1, 0))
+    old.append(mat_data[..., 2].movedim(-1, 0))
+    old.append(mat_data[..., 3].movedim(-1, 0))
+
+    new: list = []
+    new.append(acceptor)
+    new.append(donor)
+    new.append(oxygenation)
+    new.append(volume)
+
+    names: list = []
+    new.append("acceptor")
+    new.append("donor")
+    new.append("oxygenation")
+    new.append("volume")
+
+    mask = torch.zeros(
+        (acceptor.shape[-2], acceptor.shape[-1]),
+        dtype=torch.bool,
+        device=device,
+    )
+
+    mask[torch.any(acceptor < 0, dim=0)] = True
+    mask[torch.any(donor < 0, dim=0)] = True
+    mask[torch.any(oxygenation < 0, dim=0)] = True
+    mask[torch.any(volume < 0, dim=0)] = True
+
+    frame_id: int = 0
+    image: list = []
+    for channel_id in range(0, len(old)):
+        temp = np.zeros((new[channel_id].shape[-2], new[channel_id].shape[-1], 3))
+        temp[:, :, 0] = (
+            old[channel_id][frame_id, ...] / old[channel_id][frame_id, ...].max()
+        ).cpu()
+        temp[:, :, 1] = (
+            new[channel_id][frame_id, ...] / new[channel_id][frame_id, ...].max()
+        ).cpu()
+        temp[:, :, 2] = 0.0
+        image.append(temp)
+
+    subplot_position: int = 1
+    for channel_id in range(0, len(old)):
+        difference = (image[channel_id][..., 0] - image[channel_id][..., 1]) / (
+            image[channel_id][..., 0] + image[channel_id][..., 1]
+        )
+        plt.subplot(4, 2, subplot_position)
+        plt.imshow(difference, cmap="hot")
+        plt.colorbar()
+        subplot_position += 1
+
+        plt.subplot(4, 2, subplot_position)
+        plt.plot(np.sort(difference.flatten()))
+        subplot_position += 1
+
+    plt.show()