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run_svd.py

@@ -1,6 +1,20 @@
 import torch
 import numpy as np
-from svd import calculate_svd, to_remove, temporal_filter, svd_denoise
+import os
+
+import torchvision as tv
+
+from svd import (
+    calculate_svd,
+    to_remove,
+    temporal_filter,
+    svd_denoise,
+    convert_avi_to_npy,
+    calculate_translation,
+)
+
+from ImageAlignment import ImageAlignment
+
 
 if __name__ == "__main__":
     filename: str = "example_data_crop"
@@ -12,46 +26,86 @@ if __name__ == "__main__":
     bp_low_frequency: float = 0.1
     bp_high_frequency: float = 1.0
 
+    convert_overwrite: bool | None = None
+
+    fill_value: float = 0.0
+
     torch_device: torch.device = torch.device(
         "cuda:0" if torch.cuda.is_available() else "cpu"
     )
 
-    print("Load data")
-    input = np.load(filename + str(".npy"))
-    data = torch.tensor(input, device=torch_device)
+    if (
+        (convert_overwrite is None)
+        and (os.path.isfile("example_data_crop" + ".npy") is False)
+    ) or (convert_overwrite):
+        print("Convert AVI file to npy file.")
+        convert_avi_to_npy(filename)
+        print("--==-- DONE --==--")
 
-    print("Movement compensation [MISSING!!!!]")
-    print("(include ImageAlignment.py into processing chain)")
+    with torch.no_grad():
+        print("Load data")
+        input = np.load(filename + str(".npy"))
+        data = torch.tensor(input, device=torch_device)
 
-    print("SVD")
-    whiten_mean, whiten_k, eigenvalues = calculate_svd(data)
+        print("Movement compensation [BROKEN!!!!]")
+        print("During development, information about what could move was missing.")
+        print("Thus the preprocessing before shift determination may not work.")
+        data -= data.min(dim=0)[0]
+        data /= data.std(dim=0, keepdim=True) + 1e-20
 
-    print("Calculate to_remove")
-    data = torch.tensor(input, device=torch_device)
-    to_remove_data = to_remove(data, whiten_k, whiten_mean)
+        image_alignment = ImageAlignment(
+            default_dtype=torch.float32, device=torch_device
+        )
 
-    data -= to_remove_data
-    del to_remove_data
+        tvec = calculate_translation(
+            input=data,
+            reference_image=data[0, ...].clone(),
+            image_alignment=image_alignment,
+        )
+        tvec_media = tvec.median(dim=0)[0]
+        print(f"Median of movement: {tvec_media[0]}, {tvec_media[1]}")
 
-    print("apply temporal filter")
-    data = temporal_filter(
-        data,
-        device=torch_device,
-        orig_freq=orig_freq,
-        new_freq=new_freq,
-        filtfilt_chuck_size=filtfilt_chuck_size,
-        bp_low_frequency=bp_low_frequency,
-        bp_high_frequency=bp_high_frequency,
-    )
+        data = torch.tensor(input, device=torch_device)
 
-    print("SVD Denosing")
-    data_out = svd_denoise(data, window_size=window_size)
+        for id in range(0, data.shape[0]):
+            data[id, ...] = tv.transforms.functional.affine(
+                img=data[id, ...].unsqueeze(0),
+                angle=0,
+                translate=[tvec[id, 1], tvec[id, 0]],
+                scale=1.0,
+                shear=0,
+                fill=fill_value,
+            ).squeeze(0)
 
-    print("Pooling")
-    avage_pooling = torch.nn.AvgPool2d(
-        kernel_size=(kernel_size_pooling, kernel_size_pooling),
-        stride=(kernel_size_pooling, kernel_size_pooling),
-    )
-    data_out = avage_pooling(data_out)
+        print("SVD")
+        whiten_mean, whiten_k, eigenvalues = calculate_svd(data)
 
-    np.save(filename + str("_decorrelated.npy"), data_out.cpu())
+        print("Calculate to_remove")
+        data = torch.tensor(input, device=torch_device)
+        to_remove_data = to_remove(data, whiten_k, whiten_mean)
+
+        data -= to_remove_data
+        del to_remove_data
+
+        print("apply temporal filter")
+        data = temporal_filter(
+            data,
+            device=torch_device,
+            orig_freq=orig_freq,
+            new_freq=new_freq,
+            filtfilt_chuck_size=filtfilt_chuck_size,
+            bp_low_frequency=bp_low_frequency,
+            bp_high_frequency=bp_high_frequency,
+        )
+
+        print("SVD Denosing")
+        data_out = svd_denoise(data, window_size=window_size)
+
+        print("Pooling")
+        avage_pooling = torch.nn.AvgPool2d(
+            kernel_size=(kernel_size_pooling, kernel_size_pooling),
+            stride=(kernel_size_pooling, kernel_size_pooling),
+        )
+        data_out = avage_pooling(data_out)
+
+        np.save(filename + str("_decorrelated.npy"), data_out.cpu())

show.py

@@ -0,0 +1,24 @@
+import numpy as np
+import torch
+from Anime import Anime
+
+# Convert from avi to npy
+filename: str = "example_data_crop"
+
+
+torch_device: torch.device = torch.device(
+    "cuda:0" if torch.cuda.is_available() else "cpu"
+)
+
+print("Load data")
+input = np.load(filename + str("_decorrelated.npy"))
+data = torch.tensor(input, device=torch_device)
+del input
+print("loading done")
+
+data = data.nan_to_num(nan=0.0)
+#data -= data.min(dim=0, keepdim=True)[0]
+
+
+ani = Anime()
+ani.show(data, vmin=0.0)

show_b.py

@@ -0,0 +1,24 @@
+import numpy as np
+import torch
+from Anime import Anime
+
+# Convert from avi to npy
+filename: str = "example_data_crop"
+
+
+torch_device: torch.device = torch.device(
+    "cuda:0" if torch.cuda.is_available() else "cpu"
+)
+
+print("Load data")
+input = np.load(filename + str("_decorrelated.npy"))
+data = torch.tensor(input, device=torch_device)
+del input
+print("loading done")
+
+data = data.nan_to_num(nan=0.0)
+data -= data.min(dim=0, keepdim=True)[0]
+data *= data.std(dim=0, keepdim=True)
+
+ani = Anime()
+ani.show(data)

svd.py

@@ -96,6 +96,7 @@ def filtfilt(
     output = ta.functional.filtfilt(
         process_data_padded.unsqueeze(0), butter_a, butter_b, clamp=False
     ).squeeze(0)
+
     output = output[..., padding_length:-padding_length].movedim(-1, 0)
     return output
 
@@ -124,7 +125,7 @@ def chunk_iterator(array: torch.Tensor, chunk_size: int):
 
 
 @torch.no_grad()
-def lowpass(
+def bandpass(
     data: torch.Tensor,
     device: torch.device,
     low_frequency: float = 0.1,
@@ -168,7 +169,7 @@ def temporal_filter(
         data.movedim(0, -1), orig_freq=orig_freq, new_freq=new_freq
     ).movedim(-1, 0)
 
-    data = lowpass(
+    data = bandpass(
         data,
         device=device,
         low_frequency=bp_low_frequency,
@@ -202,3 +203,39 @@ def svd_denoise(data: torch.Tensor, window_size: int) -> torch.Tensor:
                 to_remove_data = to_remove(data_sel, whiten_k, whiten_mean)
                 data_out[:, x, y] = to_remove_data[:, window_size, window_size]
     return data_out
+
+
+@torch.no_grad()
+def calculate_translation(
+    input: torch.Tensor,
+    reference_image: torch.Tensor,
+    image_alignment,
+    start_position_coefficients: int = 0,
+    batch_size: int = 100,
+) -> torch.Tensor:
+    tvec = torch.zeros((input.shape[0], 2))
+
+    data_loader = torch.utils.data.DataLoader(
+        torch.utils.data.TensorDataset(input[start_position_coefficients:, ...]),
+        batch_size=batch_size,
+        shuffle=False,
+    )
+    start_position: int = 0
+    for input_batch in data_loader:
+        assert len(input_batch) == 1
+
+        end_position = start_position + input_batch[0].shape[0]
+
+        tvec_temp = image_alignment.dry_run_translation(
+            input=input_batch[0],
+            new_reference_image=reference_image,
+        )
+
+        assert tvec_temp is not None
+
+        tvec[start_position:end_position, :] = tvec_temp
+
+        start_position += input_batch[0].shape[0]
+
+    tvec = torch.round(tvec)
+    return tvec