Fix some remaining TODO items

TODO.txt

@@ -41,14 +41,5 @@ Version 0.12
 ------------
 * Change `label` to mark background as 0, not -1, which is consistent with
   SciPy's labelling.
-* Remove deprecated `reverse_map` parameter of `skimage.transform.warp`
-* Change deprecated `enforce_connectivity=False` on skimage.segmentation.slic
-  and set it to True as default
-* Remove deprecated `skimage.measure.fit.BaseModel._params` attribute
-* Remove deprecated `skimage.measure.fit.BaseModel._params`,
-  `skimage.transform.ProjectiveTransform._matrix`,
-  `skimage.transform.PolynomialTransform._params`,
-  `skimage.transform.PiecewiseAffineTransform.affines_*` attributes
-* Remove deprecated functions `skimage.filters.denoise_*`
 * Add 3D phantom in `skimage.data`
 * Add 3D test case of `skimage.feature.phase_correlate`

skimage/filters/__init__.py

@@ -15,12 +15,7 @@ from .rank import median
 
 from .._shared.utils import deprecated, copy_func
 from .. import restoration
-denoise_bilateral = deprecated('skimage.restoration.denoise_bilateral')\
-    (restoration.denoise_bilateral)
-denoise_tv_bregman = deprecated('skimage.restoration.denoise_tv_bregman')\
-    (restoration.denoise_tv_bregman)
-denoise_tv_chambolle = deprecated('skimage.restoration.denoise_tv_chambolle')\
-    (restoration.denoise_tv_chambolle)
+
 gaussian_filter = copy_func(gaussian, name='gaussian_filter')
 gaussian_filter = deprecated('skimage.filters.gaussian')(gaussian_filter)
 gabor_filter = copy_func(gabor, name='gabor_filter')

skimage/measure/fit.py

@@ -24,11 +24,6 @@ class BaseModel(object):
     def __init__(self):
         self.params = None
 
-    @property
-    def _params(self):
-        warn('`_params` attribute is deprecated, use `params` instead.')
-        return self.params
-
 
 class LineModel(BaseModel):
 

skimage/segmentation/slic_superpixels.py

@@ -13,7 +13,7 @@ from ..color import rgb2lab
 
 def slic(image, n_segments=100, compactness=10., max_iter=10, sigma=0,
          spacing=None, multichannel=True, convert2lab=None,
-         enforce_connectivity=False, min_size_factor=0.5, max_size_factor=3,
+         enforce_connectivity=True, min_size_factor=0.5, max_size_factor=3,
          slic_zero=False):
     """Segments image using k-means clustering in Color-(x,y,z) space.
 
@@ -53,7 +53,7 @@ def slic(image, n_segments=100, compactness=10., max_iter=10, sigma=0,
         segmentation. The input image *must* be RGB. Highly recommended.
         This option defaults to ``True`` when ``multichannel=True`` *and*
         ``image.shape[-1] == 3``.
-    enforce_connectivity: bool, optional (default False)
+    enforce_connectivity: bool, optional
         Whether the generated segments are connected or not
     min_size_factor: float, optional
         Proportion of the minimum segment size to be removed with respect
@@ -110,10 +110,6 @@ def slic(image, n_segments=100, compactness=10., max_iter=10, sigma=0,
     >>> segments = slic(img, n_segments=100, compactness=20)
 
     """
-    if enforce_connectivity is None:
-        warn('Deprecation: enforce_connectivity will default to'
-             ' True in future versions.')
-        enforce_connectivity = False
 
     image = img_as_float(image)
     is_2d = False

skimage/transform/_geometric.py

@@ -181,11 +181,6 @@ class ProjectiveTransform(GeometricTransform):
             raise ValueError("invalid shape of transformation matrix")
         self.params = matrix
 
-    @property
-    def _matrix(self):
-        warn('`_matrix` attribute is deprecated, use `params` instead.')
-        return self.params
-
     @property
     def _inv_matrix(self):
         return np.linalg.inv(self.params)
@@ -778,11 +773,6 @@ class PolynomialTransform(GeometricTransform):
             raise ValueError("invalid shape of transformation parameters")
         self.params = params
 
-    @property
-    def _params(self):
-        warn('`_params` attribute is deprecated, use `params` instead.')
-        return self.params
-
     def estimate(self, src, dst, order=2):
         """Set the transformation matrix with the explicit transformation
         parameters.