Development robert

TPTBox/core/nii_wrapper.py

@@ -799,9 +799,7 @@ def apply_pad(
         mode: MODES = "constant",
         inplace=False,
         verbose: logging = True
-        ):
-        #TODO add other modes
-        #TODO add testcases and options for modes
+    ):
         if padd is None or padd == 0:
             return self if inplace else self.copy()
 
@@ -824,13 +822,36 @@ def apply_pad(
 
         affine = self.affine @ transform
 
+        arr = self.get_array()
+
+        # ---- 1. CROPPING (negative padding) ----
+        slices = []
+
+        for i, (before, after) in enumerate(padd[:self.dims]):
+            start = max(0, -before)
+            end = arr.shape[i] - max(0, -after)
+            slices.append(slice(start, end))
+
+        # keep non-spatial dims unchanged
+        slices += [slice(None)] * (arr.ndim - self.dims)
+
+        arr = arr[tuple(slices)]
+
+        # ---- 2. PADDING (positive only) ----
+        padd_positive = tuple(
+            (max(0, b), max(0, a)) for b, a in padd
+        )
+
         args = {}
         if mode == "constant":
             args["constant_values"] = self.get_c_val()
 
+        if mode == "nearest":
+            mode = "edge"
+
         log.print(f"Padd {padd}; {mode=}, {args}", verbose=verbose)
 
-        arr = np.pad(self.get_array(), padd, mode=mode, **args)
+        arr = np.pad(arr, padd_positive, mode=mode, **args)
 
         nii = (arr, affine, self.header)
 
@@ -935,35 +956,38 @@ def resample_from_to(self, to_vox_map:Image_Reference|Has_Grid|tuple[SHAPE,AFFIN
             mapping = to_vox_map.to_gird()
         else:
             mapping = to_vox_map if isinstance(to_vox_map, tuple) else to_nii_optional(to_vox_map, seg=self.seg, default=to_vox_map)
-        if isinstance(mapping,Has_Grid) and mapping.assert_affine(self,raise_error=False,origin_tolerance=0.000001,error_tolerance=0.000001,shape_tolerance=0):
-            log.print(f"resample_from_to skipped; already in space: {self}",verbose=verbose)
-            return self if inplace else self.copy()
-
-            #m1 = mapping.make_empty_POI().reorient(self.orientation)
-            #if m1.assert_affine(self,raise_error=False,origin_tolerance=0.000001,error_tolerance=0.000001,shape_tolerance=0):
-            #    log.print(f"resample_from_to only need reorientation; {self.orientation}",verbose=verbose)
-            #    return self.reorient(mapping.orientation,inplace=inplace)
-            #if self.orientation == mapping.orientation and self.zoom == mapping.zoom:
-            #    shift = (np.array(self.origin) - np.array(m1.origin)) / np.array(m1.zoom)
-            #    if np.allclose(shift, np.round(shift), atol=1e-6):
-            #        self = self.reorient(mapping.orientation,inplace=inplace)  # noqa: PLW0642
-            #        shift = (np.array(self.origin) - np.array(mapping.origin)) / np.array(mapping.zoom)
-            #        shift = np.round(shift).astype(int)
-            #        src_shape = np.array(mapping.shape)
-            #        dst_shape = np.array(self.shape)
-            #        # padding before = how much dst starts before src
-            #        pad_before = np.maximum(-shift, 0)
-            #
-            #        # where src ends inside dst
-            #        src_end_in_dst = shift + src_shape
-            #        # padding after = remaining dst size after src
-            #        pad_after = np.maximum(dst_shape - src_end_in_dst, 0)
-            #        pad = tuple((int(b), int(a)) for b, a in zip(pad_before, pad_after))
-            #        ret = self.apply_pad(pad, mode=mode)
-            #
-            #        log.print(f"resample_from_to only needs padding/cropping {pad}, ",verbose=verbose,)
-            #        ret.assert_affine(mapping,raise_error=False,origin_tolerance=0.000001,error_tolerance=0.000001,shape_tolerance=0)
-            #        return ret
+        if isinstance(mapping,Has_Grid):
+            if mapping.assert_affine(self,raise_error=False,origin_tolerance=0.000001,error_tolerance=0.000001,shape_tolerance=0):
+                log.print(f"resample_from_to skipped; already in space: {self}",verbose=verbose)
+                return self if inplace else self.copy()
+
+            m1 = mapping if mapping.orientation == self.orientation else mapping.make_empty_POI().reorient(self.orientation)
+            if m1.assert_affine(self,raise_error=False,origin_tolerance=0.00001,error_tolerance=0.00001,shape_tolerance=0):
+                log.print(f"resample_from_to only need reorientation; {self.orientation}",verbose=verbose)
+                ret = self.reorient(mapping.orientation,inplace=inplace)
+                ret.affine = mapping.affine #remove floating point error
+                return ret
+            if self.orientation == mapping.orientation and np.allclose(self.zoom , mapping.zoom, atol=1e-6):
+                shift = (np.array(self.origin) - np.array(m1.origin)) / np.array(m1.zoom)
+                if np.allclose(shift, np.round(shift), atol=1e-6):
+                    s = self.reorient(mapping.orientation,inplace=inplace)  # noqa: PLW0642
+                    shift = (np.array(self.origin) - np.array(mapping.origin)) / np.array(mapping.zoom)
+                    shift = np.round(shift).astype(int)
+                    dst_shape = np.array(mapping.shape)
+                    src_shape = np.array(s.shape)
+                    # padding before = how much dst starts before src
+                    pad_before = shift
+                    # padding after = remaining dst size after src
+                    pad_after = dst_shape-shift-src_shape
+                    pad = tuple((int(b), int(a)) for b, a in zip(pad_before, pad_after))
+                    ret = s.apply_pad(pad, mode=mode,)
+
+                    #TODO SET raise_error=False before committing
+                    valid = ret.assert_affine(mapping,raise_error=True,origin_tolerance=0.0001,error_tolerance=0.0001,shape_tolerance=0)
+                    if valid:
+                        log.print(f"resample_from_to only needs padding/cropping {pad}",verbose=verbose)
+                        ret.affine = mapping.affine #remove floating point error
+                        return ret
 
 
         assert mapping is not None

TPTBox/core/np_utils.py

@@ -1050,7 +1050,7 @@ def np_fill_holes(
         else:
             assert 0 <= slice_wise_dim <= arr.ndim - 1, f"slice_wise_dim needs to be in range [0, {arr.ndim - 1}]"
             filled = np.swapaxes(arr_lc.copy(), 0, slice_wise_dim)
-            filled = np.stack([_fill(x) for x in filled])
+            filled = np.stack([_fill(x).astype(arr.dtype) for x in filled])
             filled = np.swapaxes(filled, 0, slice_wise_dim)
         filled[filled != 0] = l
         if use_crop:

TPTBox/mesh3D/snapshot3D.py

@@ -243,7 +243,7 @@ def _set_input(
     return vtk_object
 
 
-def _contour_from_roi_smooth(data, affine=None, color: np.ndarray | list = _red, opacity=1, smoothing=0):
+def _contour_from_roi_smooth(data: np.ndarray, affine=None, color: np.ndarray | list = _red, opacity=1, smoothing=0):
     """Generates surface actor from a binary ROI.
     Code from dipy, but added awesome smoothing!
 
@@ -274,10 +274,8 @@ def _contour_from_roi_smooth(data, affine=None, color: np.ndarray | list = _red,
     else:
         nb_components = 1
 
-    data = (data > 0) * 1
-    vol = np.interp(data, xp=[data.min(), data.max()], fp=[0, 255])
-    vol = vol.astype("uint8")
-
+    vol = data.astype("uint8") * 255
+    assert data.max() <= 1, np.unique(data)
     im = vtk.vtkImageData()
     if major_version <= 5:
         im.SetScalarTypeToUnsignedChar()  # type: ignore
@@ -291,12 +289,10 @@ def _contour_from_roi_smooth(data, affine=None, color: np.ndarray | list = _red,
         im.SetNumberOfScalarComponents(nb_components)  # type: ignore
     else:
         im.AllocateScalars(vtk.VTK_UNSIGNED_CHAR, nb_components)
-
-    # copy data
     vol = np.swapaxes(vol, 0, 2)
-    vol = np.ascontiguousarray(vol)
+    # vol = np.ascontiguousarray(vol) # already is
 
-    vol = vol.ravel() if nb_components == 1 else np.reshape(vol, [np.prod(vol.shape[:3]), vol.shape[3]])
+    vol = vol.reshape(-1) if nb_components == 1 else np.reshape(vol, [np.prod(vol.shape[:3]), vol.shape[3]])
 
     uchar_array = numpy_support.numpy_to_vtk(vol, deep=0)
     im.GetPointData().SetScalars(uchar_array)

pyproject.toml

@@ -15,24 +15,36 @@ packages = [{ include = "TPTBox" }]
 python = "^3.9 || ^3.10 || ^3.11 || ^3.12 || ^3.13 || ^3.14"
 pathlib = "*"
 nibabel = "^5.2.0"
-numpy = "^1.26.3"
 typing-extensions = "^4.9.0"
-scipy = "^1.12.0"
 dataclasses = "*"
 SimpleITK = "^2.3.1"
 matplotlib = "^3.8.2"
 dill = "^0.3.7"
-scikit-image = "^0.22.0"
 fill-voids = "^2.0.6"
 connected-components-3d = "^3.12.3"
 tqdm = "*"
 joblib = "*"
 scikit-learn = "*"
 antspyx = "0.4.2"
 pynrrd = "*"
-#hf-deepali = "*"
 requests = "*"
 
+# --- OLD STACK (Python < 3.11)
+numpy = [
+  { version = ">=1.26.3,<2.0", python = "<3.11" },
+  { version = ">=2.0,<3.0", python = ">=3.11" }
+]
+
+scipy = [
+  { version = ">=1.11,<1.13", python = "<3.11" },
+  { version = ">=1.13", python = ">=3.11" }
+]
+
+scikit-image = [
+  { version = ">=0.22,<0.23", python = "<3.11" },
+  { version = ">=0.24", python = ">=3.11" }
+]
+
 [tool.poetry.group.dev.dependencies]
 pytest = ">=8.1.1"
 vtk = "*"