Merge pull request #394 from jabooth/memfixes

weakref to break cyclic references

menpo/base.py

@@ -1,7 +1,7 @@
 import abc
 from copy import deepcopy
 import os.path
-from pathlib import Path
+from warnings import warn
 
 
 class Vectorizable(object):
@@ -81,7 +81,12 @@ def from_vector(self, vector):
         object : ``type(self)``
             An new instance of this class.
         """
-        self_copy = deepcopy(self)
+        try:
+            self_copy = self.copy()
+        except AttributeError:
+            warn('{}.from_vector() - no copy '
+                 'method, using deepcopy'.format(type(self).__name__))
+            self_copy = deepcopy(self)
         self_copy.from_vector_inplace(vector)
         return self_copy
 

menpo/fit/fittingresult.py

@@ -1,6 +1,5 @@
 from __future__ import division
 import abc
-from copy import deepcopy
 import numpy as np
 
 from menpo.shape.pointcloud import PointCloud
@@ -29,7 +28,7 @@ class FittingResult(Viewable):
 
     def __init__(self, image, fitter, gt_shape=None, error_type='me_norm'):
         # Initialize the error internal properties
-        self.image = deepcopy(image)
+        self.image = image.copy()
         self._error_type, self._error_text = None, None
         self.fitter = fitter
         self.error_type = error_type
@@ -182,7 +181,7 @@ def view_final_fitting(self, figure_id=None, new_figure=False, **kwargs):
         r"""
         Displays the final fitting result.
         """
-        image = deepcopy(self.image)
+        image = self.image.copy()
         image.landmarks['fitting'] = self.final_shape
         return image.landmarks['fitting'].view(
             figure_id=figure_id, new_figure=new_figure).render(**kwargs)
@@ -191,7 +190,7 @@ def view_initialization(self, figure_id=None, new_figure=False, **kwargs):
         r"""
         Displays the initialization from which the fitting started.
         """
-        image = deepcopy(self.image)
+        image = self.image.copy()
         image.landmarks['fitting'] = self.initial_shape
         return image.landmarks['fitting'].view(
             figure_id=figure_id, new_figure=new_figure).render(**kwargs)
@@ -201,7 +200,7 @@ def view_ground_truth(self, figure_id=None, new_figure=False, **kwargs):
         Displays the ground truth annotation.
         """
         if self.gt_shape is not None:
-            image = deepcopy(self.image)
+            image = self.image.copy()
             image.landmarks['gt_shape'] = self.gt_shape
             return image.landmarks['gt_shape'].view(
                 figure_id=figure_id, new_figure=new_figure, **kwargs)
@@ -250,18 +249,18 @@ def n_iters(self):
 
     def shapes(self, as_points=False):
         if as_points:
-            return [deepcopy(s.points) for s in self.parameters]
+            return [s.points.copy() for s in self.parameters]
 
         else:
-            return deepcopy(self.parameters)
+            return [s.copy() for s in self.parameters]
 
     @property
     def final_shape(self):
-        return deepcopy(self.parameters[-1])
+        return self.parameters[-1].copy()
 
     @property
     def initial_shape(self):
-        return deepcopy(self.parameters[0])
+        return self.parameters[0].copy()
 
     @FittingResult.gt_shape.setter
     def gt_shape(self, value):

menpo/fit/test/fittingresult_test.py

@@ -6,9 +6,14 @@
 from mock import Mock
 from menpo.shape import PointCloud
 from menpo.testing import is_same_array
+from menpo.image import MaskedImage
 
 
 class MockedFittingResult(FittingResult):
+
+    def __init__(self, image, fitter, **kwargs):
+        FittingResult.__init__(self, MaskedImage.blank((10, 10)), fitter,
+                               **kwargs)
     @property
     def n_iters(self):
         return 1

menpo/fitmultilevel/base.py

@@ -1,7 +1,6 @@
 from __future__ import division
 import abc
 import numpy as np
-from copy import deepcopy
 
 from menpo.fit.base import Fitter
 from menpo.transform import AlignmentAffine, Scale
@@ -107,7 +106,7 @@ def fit(self, image, initial_shape, max_iters=50, gt_shape=None,
             A fitting result object.
         """
         # copy image
-        image = deepcopy(image)
+        image = image.copy()
 
         # generate image pyramid
         images = self._prepare_image(image, initial_shape, gt_shape=gt_shape)

menpo/image/base.py

@@ -1,6 +1,5 @@
 from __future__ import division
 import abc
-from copy import deepcopy
 
 import numpy as np
 from scipy.misc import imrotate
@@ -634,7 +633,7 @@ def crop(self, min_indices, max_indices,
             Raised if `constrain_to_boundary` is `False`, and an attempt is made
             to crop the image in a way that violates the image bounds.
         """
-        cropped_image = deepcopy(self)
+        cropped_image = self.copy()
         return cropped_image.crop_inplace(
             min_indices, max_indices,
             constrain_to_boundary=constrain_to_boundary)
@@ -1208,7 +1207,7 @@ def as_greyscale(self, mode='luminosity', channel=None):
         greyscale_image: :class:`MaskedImage`
             A copy of this image in greyscale.
         """
-        greyscale = deepcopy(self)
+        greyscale = self.copy()
         if mode == 'luminosity':
             if self.n_dims != 2:
                 raise ValueError("The 'luminosity' mode only works on 2D RGB"

menpo/image/boolean.py

@@ -1,5 +1,3 @@
-from copy import deepcopy
-
 import numpy as np
 
 from .base import Image

menpo/image/feature/base.py

@@ -1,3 +1,4 @@
+from weakref import proxy
 import features as fc
 
 
@@ -14,7 +15,8 @@ class ImageFeatures(object):
     """
 
     def __init__(self, image):
-        self._image = image
+        # hold a weak ref to the image (to prevent cyclic references)
+        self._image = proxy(image)
 
     def hog(self, mode='dense', algorithm='dalaltriggs', num_bins=9,
             cell_size=8, block_size=2, signed_gradient=True, l2_norm_clip=0.2,

menpo/image/masked.py

@@ -1,6 +1,4 @@
 from __future__ import division
-from copy import deepcopy
-
 import numpy as np
 from scipy.ndimage import binary_erosion
 
@@ -197,9 +195,6 @@ def copy(self):
         Return a new image with copies of the pixels, landmarks, and masks of
         this image.
 
-        This is an efficient copy method. If you need to copy all the state on
-        the object, consider deepcopy instead.
-
         Returns
         -------
 
@@ -593,7 +588,7 @@ def gradient(self, nullify_values_at_mask_boundaries=False):
         """
         grad_image_pixels = features.gradient(self.pixels)
         grad_image = MaskedImage(grad_image_pixels,
-                                 mask=deepcopy(self.mask))
+                                 mask=self.mask.copy(), copy=False)
 
         if nullify_values_at_mask_boundaries:
             # Erode the edge of the mask in by one pixel

menpo/landmark/base.py

@@ -1,4 +1,5 @@
 import abc
+from weakref import ref
 
 import numpy as np
 
@@ -58,8 +59,10 @@ class LandmarkManager(Transformable, Viewable):
 
     def __init__(self, target):
         super(LandmarkManager, self).__init__()
-        self.__target = target
+        self.__target = None
         self._landmark_groups = {}
+        # use the _target setter last one everything else is cleaned up
+        self._target = target
 
     def copy(self):
         r"""
@@ -163,13 +166,19 @@ def __delitem__(self, group_label):
 
     @property
     def _target(self):
-        return self.__target
+        if self.__target is not None:
+            return self.__target()
+        else:
+            return None
 
     @_target.setter
     def _target(self, value):
-        self.__target = value
+        if value is not None:
+            self.__target = ref(value)
+        else:
+            self.__target = None
         for group in self._landmark_groups.itervalues():
-            group._target = self.__target
+            group._target = self._target
 
     @property
     def n_groups(self):
@@ -296,6 +305,7 @@ def __init__(self, target, group_label, pointcloud, labels_to_masks,
         self._verify_all_labels_masked()
 
         self._group_label = group_label
+        self.__target = None
         self._target = target
         if copy:
             self._pointcloud = pointcloud.copy()
@@ -305,6 +315,20 @@ def __init__(self, target, group_label, pointcloud, labels_to_masks,
             self._pointcloud = pointcloud
             self._labels_to_masks = labels_to_masks
 
+    @property
+    def _target(self):
+        if self.__target is not None:
+            return self.__target()
+        else:
+            return None
+
+    @_target.setter
+    def _target(self, value):
+        if value is not None:
+            self.__target = ref(value)
+        else:
+            self.__target = None
+
     def copy(self):
         r"""
         An efficient copy of this landmark group.

menpo/model/modelinstance.py

@@ -1,4 +1,5 @@
 from copy import deepcopy
+from warnings import warn
 import numpy as np
 
 from menpo.base import Targetable, Vectorizable, DP
@@ -297,10 +298,15 @@ def __init__(self, model, global_transform_cls):
         # 1. Construct similarity model from the mean of the model
         self.similarity_model = Similarity2dInstanceModel(model.mean)
         # 2. Orthonormalize model and similarity model
-        model = deepcopy(model)
-        model.orthonormalize_against_inplace(self.similarity_model)
-        self.similarity_weights = self.similarity_model.project(model.mean)
-        super(OrthoPDM, self).__init__(model, global_transform_cls)
+        try:
+            model_cpy = model.copy()
+        except AttributeError:
+            warn('OrthoPDM() - no copy on model {}'
+                 ', using deepcopy'.format(type(model).__name__))
+            model_cpy = deepcopy(model)
+        model_cpy.orthonormalize_against_inplace(self.similarity_model)
+        self.similarity_weights = self.similarity_model.project(model_cpy.mean)
+        super(OrthoPDM, self).__init__(model_cpy, global_transform_cls)
 
     @property
     def global_parameters(self):

menpo/transform/base/__init__.py

@@ -1,5 +1,4 @@
 import abc
-from copy import deepcopy
 
 
 class Transform(object):
@@ -242,6 +241,18 @@ def _transform_inplace(self, transform):
             The transformed object, having been transformed in place.
         """
 
+    @abc.abstractmethod
+    def copy(self):
+        r"""
+        Return an efficient copy of self.
+
+        Returns
+        -------
+        copy : ``type(self)``
+            An efficient copy of self.
+
+        """
+
     def _transform(self, transform):
         r"""
         Apply the :map:`Transform` given in a non destructive manner -
@@ -257,7 +268,7 @@ def _transform(self, transform):
         transformed : ``type(self)``
             A copy of the object, transformed.
         """
-        copy_of_self = deepcopy(self)
+        copy_of_self = self.copy()
         # transform the copy destructively
         copy_of_self._transform_inplace(transform)
         return copy_of_self