style: minor style edits

Author: okunator

cellseg_models_pytorch/postproc/functional/cellpose/cellpose.py

@@ -270,153 +270,3 @@ def post_proc_cellpose(
         return inst_map, hsv_flows
 
     return inst_map
-
-
-# def get_masks_cellpose(
-#     p: np.ndarray,
-#     mask: np.ndarray,
-#     rpad: int = 20,
-# ) -> np.ndarray:
-#     """Create masks using pixel convergence after running dynamics.
-
-#     Makes a histogram of final pixel locations p, initializes masks
-#     at peaks of histogram and extends the masks from the peaks so that
-#     they include all pixels with more than 2 final pixels p. Discards
-#     masks with flow errors greater than the threshold.
-
-#     Parameters
-#     ----------
-#         p : np.ndarray
-#             Final locations of each pixel after dynamics. Shape (2, H, W).
-#         mask : np.ndarray
-#             The binary mask of the cells. Shape (H, W).
-#         rpad : int, default=20
-#             Histogram edge padding.
-
-#     Returns
-#     -------
-#         np.ndarray:
-#             Instance labelled mask. Shape (H, W).
-#     """
-#     shape0 = p.shape[1:]
-#     dims = len(p)
-
-#     inds = np.meshgrid(np.arange(shape0[0]), np.arange(shape0[1]), indexing="ij")
-
-#     for i in range(dims):
-#         p[i, ~mask] = inds[i][~mask]
-
-#     pflows = []
-#     edges = []
-#     for i in range(dims):
-#         pflows.append(p[i].flatten().astype("int32"))
-#         edges.append(np.arange(-0.5 - rpad, shape0[i] + 0.5 + rpad, 1))
-
-#     h, _ = np.histogramdd(tuple(pflows), bins=edges)
-#     hmax = h.copy()
-#     for i in range(dims):
-#         hmax = maximum_filter1d(hmax, 5, axis=i)
-
-#     seeds = np.nonzero(np.logical_and(h - hmax > -1e-6, h > 10))
-#     Nmax = h[seeds]
-#     isort = np.argsort(Nmax)[::-1]
-#     for s in seeds:
-#         s = s[isort]
-
-#     pix = list(np.array(seeds).T)
-#     shape = h.shape
-#     expand = np.nonzero(np.ones((3, 3)))
-#     for e in expand:
-#         e = np.expand_dims(e, 1)
-
-#     for iter in range(5):
-#         for k in range(len(pix)):
-#             if iter == 0:
-#                 pix[k] = list(pix[k])
-#             newpix = []
-#             iin = []
-#             for i, e in enumerate(expand):
-#                 epix = e[:, None] + np.expand_dims(pix[k][i], 0) - 1
-#                 epix = epix.flatten()
-#                 iin.append(np.logical_and(epix >= 0, epix < shape[i]))
-#                 newpix.append(epix)
-#             iin = np.all(tuple(iin), axis=0)
-#             for p in newpix:
-#                 p = p[iin]
-#             newpix = tuple(newpix)
-#             igood = h[newpix] > 2
-#             for i in range(dims):
-#                 pix[k][i] = newpix[i][igood]
-#             if iter == 4:
-#                 pix[k] = tuple(pix[k])
-
-#     M = np.zeros(h.shape, np.int32)
-#     for k in range(len(pix)):
-#         M[pix[k]] = 1 + k
-
-#     for i in range(dims):
-#         pflows[i] = pflows[i] + rpad
-
-#     # remove big masks
-#     M0 = M[tuple(pflows)]
-#     _, counts = np.unique(M0, return_counts=True)
-#     big = np.prod(shape0) * 1.0
-#     for i in np.nonzero(counts > big)[0]:
-#         M0[M0 == i] = 0
-
-#     _, M0 = np.unique(M0, return_inverse=True)
-#     M0 = np.reshape(M0, shape0)
-
-#     return M0
-
-
-# def post_proc_cellpose(
-#     inst_map: np.ndarray,
-#     flow_map: np.ndarray,
-#     dist_map: np.ndarray = None,
-#     return_flows: bool = False,
-#     min_size: int = 30,
-#     **kwargs
-# ) -> np.ndarray:
-#     """Run the cellpose post-processing pipeline.
-
-#     https://www.nature.com/articles/s41592-020-01018-x
-
-#     Parameters
-#     ----------
-#         inst_map : np.ndarray
-#             Instance labelled or binary mask. Shape (H, W).
-#         flow_map : np.ndarray
-#             Y- and x-flows. Shape: (2, H, W)
-#         dist_map : np.ndarray, default=None
-#             Regressed distance transform. Shape: (H, W).
-#         return_flows : bool, default=False
-#             If True, returns the HSV converted flows. They are just not
-#             needed for anything relevant.
-#         min_size : int
-#             The minimum size for the objects that will not be removed.
-
-#     Returns
-#     -------
-#         np.ndarray:
-#             The instance labelled segmentation mask. Shape (H, W)
-#     """
-#     #  convert channels to CHW
-#     if dist_map is not None:
-#         binary_mask = apply_hysteresis_threshold(dist_map, 0.5, 0.5)
-#     else:
-#         binary_mask = binarize(inst_map).astype(bool)
-
-#     dP = flow_map * binary_mask  # /5.
-#     # dP = normalize_field(dP)
-
-#     pixel_loc, _ = follow_flows(dP, niter=200, mask=binary_mask, suppress_euler=False)
-
-#     mask = get_masks_cellpose(p=pixel_loc, mask=binary_mask)
-#     inst_map = fill_holes_and_remove_small_masks(mask, min_size=min_size).astype("i4")
-
-#     if return_flows:
-#         hsv_flows = gen_flows(dP)
-#         return inst_map, hsv_flows
-
-#     return inst_map

cellseg_models_pytorch/utils/multiproc.py

@@ -46,7 +46,7 @@ def run_pool(
     Parameters
     ----------
         func : Callable
-            The function that will be copied to existing core and run in parallel.
+            The function that will be copied to existing cores and run in parallel.
         args : List[Any]
             A list of arguments for each of the parallelly executed functions.
         ret : bool, default=True