Add files via upload

initial_cell_estimate.py

@@ -0,0 +1,91 @@
+import numpy as np
+import torch
+import matplotlib.pyplot as plt
+import skimage
+
+filename: str = "example_data_crop"
+threshold: float = 0.8
+tolerance: float = 1.0
+minimum_area: int = 100
+
+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.mean(dim=0, keepdim=True)
+data /= data.std(dim=0, keepdim=True)
+
+master_image = (data.max(dim=0)[0] - data.min(dim=0)[0]).nan_to_num(nan=0.0).clone()
+temp_image = master_image.clone()
+master_mask = torch.ones_like(temp_image)
+
+stored_contours: list = []
+counter: int = 0
+contours_found: int = 0
+while int(master_mask.sum()) > 0:
+    if counter % 100 == 0:
+        print(
+            f"number of pixel tested: {counter} remaining pixels: {int(master_mask.sum())} cells found: {contours_found}"
+        )
+    counter += 1
+    mask: np.ndarray | None = None
+
+    temp_image *= master_mask
+
+    # Convert index to 2D
+    temp_idx = temp_image.argmax()
+    x = int(temp_idx // int(temp_image.shape[1]))
+    y = int(temp_idx - x * int(temp_image.shape[1]))
+    if bool(master_mask[x, y]) is False:
+        break
+
+    test_data = data[:, x, y].clone()
+
+    # Calculate the correlation
+    scale = (data * test_data.unsqueeze(-1).unsqueeze(-1)).mean(dim=0)
+    scale = scale.nan_to_num(nan=0.0)
+    scale *= master_mask
+
+    # Check for areas with high correlation
+    image = (scale > threshold).type(torch.uint8).cpu().numpy()
+
+    found_something: bool = False
+    # Find the coutours
+    for contour in skimage.measure.find_contours(image, 0):
+        # soften outline
+        coords = skimage.measure.approximate_polygon(
+            contour, tolerance=tolerance
+        ).astype(dtype=np.float32)
+        # Make a mask out of the polygon
+        mask = skimage.draw.polygon2mask(scale.shape, coords)
+        assert mask is not None
+
+        # check if this is the contour in which the original point was
+        if mask[x, y]:
+            found_something = True
+
+            if mask.sum() > minimum_area:
+                stored_contours.append(coords)
+                contours_found += 1
+            idx_set_mask = torch.where(torch.tensor(mask, device=torch_device) > 0)
+
+            master_mask[idx_set_mask] = 0.0
+            break
+
+    if found_something is False:
+        master_mask[x, y] = 0.0
+print("-==- DONE -==-")
+np.save("cells.npy", np.array(stored_contours, dtype=object))
+
+plt.imshow(master_image.cpu(), cmap="hot")
+for i in range(0, len(stored_contours)):
+    plt.plot(stored_contours[i][:, 1], stored_contours[i][:, 0], "-g", linewidth=2)
+plt.colorbar()
+plt.show()